{
"hardware":[
{"key":"rtx-pro-6000-blackwell","name":"RTX PRO 6000 Blackwell (96 GB)","url":"https://research.strata2signal.com/hardware/rtx-pro-6000-blackwell/"},
{"key":"rtx-3090","name":"RTX 3090 (24 GB)","url":"https://research.strata2signal.com/hardware/rtx-3090/"},
{"key":"rtx-5090-laptop","name":"RTX 5090 Laptop GPU (24 GB)","url":"https://research.strata2signal.com/hardware/rtx-5090-laptop/"},
{"key":"rtx-3080","name":"RTX 3080 (10 GB)","url":"https://research.strata2signal.com/hardware/rtx-3080/"},
{"key":"rtx-3090-ti","name":"RTX 3090 Ti (24 GB)","url":"https://research.strata2signal.com/hardware/rtx-3090-ti/"},
{"key":"rtx-3080-ti","name":"RTX 3080 Ti (12 GB)","url":"https://research.strata2signal.com/hardware/rtx-3080-ti/"},
{"key":"rtx-5080","name":"RTX 5080 (16 GB)","url":"https://research.strata2signal.com/hardware/rtx-5080/"},
{"key":"intel-core-i9-10850k","name":"Intel Core i9-10850K","url":"https://research.strata2signal.com/hardware/intel-core-i9-10850k/"},
{"key":"intel-core-i5-12600h","name":"Intel Core i5-12600H","url":"https://research.strata2signal.com/hardware/intel-core-i5-12600h/"},
{"key":"amd-ryzen-7-3700x","name":"AMD Ryzen 7 3700X","url":"https://research.strata2signal.com/hardware/amd-ryzen-7-3700x/"},
{"key":"amd-epyc-7532","name":"AMD EPYC 7532","url":"https://research.strata2signal.com/hardware/amd-epyc-7532/"},
{"key":"amd-ryzen-7-5700x","name":"AMD Ryzen 7 5700X","url":"https://research.strata2signal.com/hardware/amd-ryzen-7-5700x/"},
{"key":"intel-core-ultra-9-290hx-plus","name":"Intel Core Ultra 9 290HX Plus","url":"https://research.strata2signal.com/hardware/intel-core-ultra-9-290hx-plus/"},
{"key":"the-mini-pc","name":"the mini PC","url":"https://research.strata2signal.com/hardware/the-mini-pc/"},
{"key":"the-server","name":"the server","url":"https://research.strata2signal.com/hardware/the-server/"},
{"key":"rig-b-the-laptop","name":"Rig B — the gaming laptop","url":"https://research.strata2signal.com/hardware/rig-b-the-laptop/"},
{"key":"the-two-3090-rig","name":"the darkroom's box — two RTX 3090s","url":"https://research.strata2signal.com/hardware/the-two-3090-rig/"},
{"key":"the-two-3080-rig","name":"the pair — two RTX 3080s in one 2021 desktop","url":"https://research.strata2signal.com/hardware/the-two-3080-rig/"},
{"key":"the-3090s-own-tower","name":"the 3090's own tower","url":"https://research.strata2signal.com/hardware/the-3090s-own-tower/"},
{"key":"the-retiring-8g-laptop","name":"the retiring 8G laptop","url":"https://research.strata2signal.com/hardware/the-retiring-8g-laptop/"},
{"key":"cyberpower-pr1500","name":"CyberPower PR1500","url":"https://research.strata2signal.com/hardware/cyberpower-pr1500/"},
{"key":"printlab-drawing-machine","name":"the machine that draws today","url":"https://research.strata2signal.com/hardware/printlab-drawing-machine/"},
{"key":"printlab-retired-drawing-machine","name":"the machine retired from drawing on 2026-09-08","url":"https://research.strata2signal.com/hardware/printlab-retired-drawing-machine/"},
{"key":"teamgroup-tforce-vulcan-z-ddr4-3200","name":"TEAMGROUP T-Force Vulcan Z DDR4-3200","url":"https://research.strata2signal.com/hardware/teamgroup-tforce-vulcan-z-ddr4-3200/"},
{"key":"corsair-hx1000i","name":"Corsair HX1000i ATX 3.1 PCIe 5.1 1000 W","url":"https://research.strata2signal.com/hardware/corsair-hx1000i/"},
{"key":"msi-mag-b550-tomahawk-max-wifi","name":"MSI MAG B550 Tomahawk MAX WiFi","url":"https://research.strata2signal.com/hardware/msi-mag-b550-tomahawk-max-wifi/"},
{"key":"crucial-p310-1tb","name":"Crucial P310 1 TB NVMe","url":"https://research.strata2signal.com/hardware/crucial-p310-1tb/"},
{"key":"fractal-design-define-7","name":"Fractal Design Define 7","url":"https://research.strata2signal.com/hardware/fractal-design-define-7/"},
{"key":"noctua-nh-d15","name":"Noctua NH-D15","url":"https://research.strata2signal.com/hardware/noctua-nh-d15/"},
{"key":"asus-rog-strix-b550-e","name":"ASUS ROG Strix B550-E","url":"https://research.strata2signal.com/hardware/asus-rog-strix-b550-e/"},
{"key":"kingspec-32gb-ddr5-sodimm-4800","name":"KingSpec 32GB (2 x 16GB) DDR5 SO-DIMM Laptop RAM 4800","url":"https://research.strata2signal.com/hardware/kingspec-32gb-ddr5-sodimm-4800/"},
{"key":"bestoss-1tb-nvme","name":"Bestoss 1TB M.2 PCIe NVMe 2280 SSD Gen3.0 x4","url":"https://research.strata2signal.com/hardware/bestoss-1tb-nvme/"},
{"key":"msi-pro-z490-a-pro","name":"MSI PRO Z490-A PRO","url":"https://research.strata2signal.com/hardware/msi-pro-z490-a-pro/"},
{"key":"rimlance-16gb-ddr4-3200","name":"Rimlance 16GB (2 x 8GB) DDR4 3200MHz","url":"https://research.strata2signal.com/hardware/rimlance-16gb-ddr4-3200/"},
{"key":"kingbank-32gb-ddr4-3200","name":"KingBank DDR4 32GB (2 x 16GB) 3200MHz CL16","url":"https://research.strata2signal.com/hardware/kingbank-32gb-ddr4-3200/"},
{"key":"corsair-rm1000e","name":"CORSAIR RMe Series RM1000e 1000 W ATX 3.1","url":"https://research.strata2signal.com/hardware/corsair-rm1000e/"},
{"key":"the-24g-class","name":"the 24G class","url":"https://research.strata2signal.com/hardware/the-24g-class/"},
{"key":"the-8g-class","name":"the 8G class","url":"https://research.strata2signal.com/hardware/the-8g-class/"}
],
"pages":[
{"dek":"We ran one model, Mistral NeMo 12B Instruct, at five of the quantization levels it is downloaded in, from Q8_0 down to Q3_K_M, each whole on one 24 GB card at 300 W and measured the same way, and every step down to Q4_K_M wrote faster with no change in answers the paired test could tell from Q8_0, while Q3_K_M wrote slower than Q4_K_M and lost 16 answers Q8_0 got right.","headings":[{"h":"The question","id":"the-question"},{"h":"Why it matters if you are choosing a download","id":"why-it-matters"},{"h":"The five files","id":"the-five-files"},{"h":"How it was measured","id":"how-it-was-measured"},{"h":"What we predicted, before the first scored call","id":"what-we-predicted"},{"h":"What voids a reading","id":"what-voids-a-reading"},{"h":"The first measured row","id":"the-first-measured-row"},{"h":"When it ran, and the one stop","id":"when-it-ran"},{"h":"What each step down bought: speed and memory","id":"speed-and-memory"},{"h":"What each step down cost: answers","id":"answers"},{"h":"The predictions, judged","id":"the-predictions-judged"},{"h":"What it means if you are choosing a download","id":"what-it-means"},{"h":"What this page cannot say","id":"what-this-page-cannot-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":87,"published":"2026-10-19","series":["bench"],"short":"Mistral NeMo 12B Instruct at five quantization levels, downloads from 5.67 GiB (Q3_K_M) to 12.13 GiB (Q8_0), each run whole on one GeForce RTX 3090 at 300 W with the questions held fixed. At a 4,096-token window it wrote 60.5 tokens a second at Q8_0, rising at every step down to 88.2 at Q4_K_M, then fell back to 74.9 at Q3_K_M: the smallest file was slower than the one above it. With the cache at 8 bits, Q8_0 held a window of 98,304 tokens whole; the other four held 131,072, the 128K window Mistral AI advertises. On 108 frozen questions the levels scored from 38 (Q3_K_M) to 57 (Q5_K_M). The paired test could not tell any level from Q4_K_M to Q6_K from Q8_0 (Q4_K_M came nearest, p 0.0654), but it could tell Q3_K_M (p under 0.0001). On the 48 held-out lines every level scored 33 or 34. Of the eight predictions, five held and three failed; none was left undecided.","slug":"the-quantization-ladder","text":"full","title":"The quantization ladder","updated":null,"url":"https://research.strata2signal.com/the-quantization-ladder/"},
{"dek":"One person, picking blind, chose Qwen-Image-2.1-Turbo, a new 8-step text-to-image model, for none of six real subjects from our game in its own oil-painted sketch style, against FLUX.1 [dev] and FLUX.2 [klein] 4B: by the rules we wrote down first, not a win.","headings":[{"h":"What this page asks","id":"what-this-page-asks"},{"h":"The three models","id":"the-three-models"},{"h":"The style, and the six subjects","id":"the-style-and-the-six-subjects"},{"h":"What counts as a win, written down first","id":"what-counts-as-a-win"},{"h":"How it ran","id":"how-it-ran"},{"h":"What the judge picked","id":"what-the-judge-picked"},{"h":"The pictures","id":"the-pictures"},{"h":"Seconds per picture, by each engine's own clock","id":"seconds-per-picture"},{"h":"What this result does and does not mean","id":"what-this-means"},{"h":"Why this page is an evaluation","id":"why-this-page-is-an-evaluation"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"More on this site","id":"more-on-this-site"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":86,"published":"2026-10-18","series":["bench"],"short":"Qwen-Image-2.1-Turbo painted six real subjects from our game RealKeep in the game's own sketch style, stylized oil-painted realism in the manner of the video game Dishonored, beside FLUX.1 [dev] at 32 steps and FLUX.2 [klein] 4B at 8. One person picked the best picture of each from unlabelled sheets. It is not a win. The Turbo, at either of its two sizes, was picked for 0 of the 6 subjects; FLUX.1 [dev], one of the models the game paints with today, for 4; klein 4B for 2. The Turbo did show every item on each subject's check list on 5 of the 6, as klein 4B did (FLUX.1 [dev]: 4), but a win needed both. One person's blind eye on six subjects in one style is not a ranking: it says only that the Turbo has not earned a bigger test. klein 4B was also the fastest (0.98 seconds a picture; the Turbo 1.41 at the same size, on a different clock); speed decided nothing. The Turbo's licence allows research and evaluation only, so it was never going into the game.","slug":"qwen-image-turbo-in-the-coves-style","text":"full","title":"Qwen-Image-2.1-Turbo, judged blind in a game's own painted style","updated":null,"url":"https://research.strata2signal.com/qwen-image-turbo-in-the-coves-style/"},
{"dek":"This page measures what one correct answer from an AI model costs, in dollars and in energy, rented from a company by the token or made on a graphics card you own, and how that cost moves over time, as the first page of a series measured again every few months: intelligence priced the way a utility is, by the unit delivered.","headings":[{"h":"What this page measures","id":"what-this-page-measures"},{"h":"Where the question comes from","id":"where-the-question-comes-from"},{"h":"The instrument","id":"the-instrument"},{"h":"How to read the numbers","id":"how-to-read-the-numbers"},{"h":"Reading 0: the headline","id":"reading-0-the-headline"},{"h":"The ladder","id":"the-ladder"},{"h":"The staircase","id":"the-staircase"},{"h":"The two ratios","id":"the-two-ratios"},{"h":"Energy, by instrument","id":"energy-by-instrument"},{"h":"The frontier's own curve","id":"the-frontiers-own-curve"},{"h":"The break-even that moves","id":"the-break-even-that-moves"},{"h":"What this page does not know yet","id":"what-this-page-does-not-know"},{"h":"What we got wrong on the way","id":"what-we-got-wrong"},{"h":"Reading 0.1: the first dated update","id":"reading-0-1"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this"},{"h":"Changes","id":"changes"}],"kit":true,"n":85,"published":"2026-10-17","series":["bench"],"short":"This page measures what one correct answer from an AI model costs, in dollars and in energy, whether rented from a company by the token or made on a graphics card you own, and how that cost moves over time. The questions are 108 lines that two open AI models wrote in the voices of six historical dinner guests; the task is to name the speaker. The yardstick is OpenAI's June 2023 flagship, gpt-4-0613, at 88 of 108. The first open model that fits a 24 GB card and matches it, item by item with a margin of error, has weights published 2026-02-24: qwen3.5:27b, 97 of 108 on our reference card, an EVGA GeForce RTX 3090 XC3 Ultra, and 96 on a laptop's RTX 5090. OpenAI's price for a question at that level fell about 299-fold from June 2023 to September 2026, a factor of ten of it in one step. On the reference card a correct answer from that model takes 231 joules (a joule is one watt for one second), 71 of them in the seconds the server spent computing; most of the rest is a model-loading step this bench's server repeats on every question. At the yardstick's level the owned answer costs more than the cheapest rented one on the reference card even running around the clock (1.7 times, on a card price this reading did not re-read) and less only on the laptop running around the clock (0.72). Every owned figure is provisional for those reasons. Three questions this reading cannot answer, and the first dated update, are below.","slug":"the-cost-of-intelligence","text":"full","title":"The cost of intelligence, as a utility","updated":null,"url":"https://research.strata2signal.com/the-cost-of-intelligence/"},
{"dek":"We benched OpenJev, a 27-billion-parameter open-weight decision model read as a yes/no judge, in place of the small model that keeps eight of up to 80 rulebook passages before RuleSage, our board-game rules helper, answers: on RuleSage's own labelled questions, under rules written before the run, its eight showed no difference, its yes/no missed both \"not in the book\" thresholds, and one ask took 20.6 s at the median against a 1.5 s budget, so nothing at RuleSage changes.","headings":[{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"The question","id":"the-question"},{"h":"The four arms, and three readings on top","id":"the-four-arms-and-three-readings-on-top"},{"h":"The question set, by counts","id":"the-question-set-by-counts"},{"h":"The rules, written before any arm ran","id":"the-rules-written-before-any-arm-ran"},{"h":"Did it pick a better eight?","id":"did-it-pick-a-better-eight"},{"h":"Can it say \"not in the book\"?","id":"can-it-say-not-in-the-book"},{"h":"What one ask costs","id":"what-one-ask-costs"},{"h":"The rules desk, where every arm sees the same eight","id":"the-rules-desk-where-every-arm-sees-the-same-eight"},{"h":"The second contrast: a reranker a product may run","id":"the-second-contrast-a-reranker-a-product-may-run"},{"h":"What did not run, and what is still open","id":"what-did-not-run-and-what-is-still-open"},{"h":"What this cannot say","id":"what-this-cannot-say"},{"h":"What changes at RuleSage: nothing","id":"what-changes-at-rulesage-nothing"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":false,"n":84,"published":"2026-10-16","series":["bench"],"short":"A reranker is the model that re-reads a shortlist of rulebook passages and decides which eight the answering model gets to see. RuleSage's is a small cross-encoder, MiniLM. We benched OpenJev, a 27-billion-parameter decision model, in its place, asking each passage one yes/no question and sorting by how strongly it said yes. On 121 real questions with labelled pages, MiniLM's eight held 0.9271 of a question's labelled pages on average and OpenJev's 0.9108 (MiniLM's held at least one on 119 of the 121, OpenJev's on 120); the rule written before the run needed a gain of 0.030 to call a win, and reads this as no difference shown. OpenJev ranked the first labelled page higher (MRR 0.9563 against 0.9048) and led the rules desk's exam 32 to 28 on first picks, but no registered rule turns either into a verdict, and the real questions' labels lean towards MiniLM by construction. Its yes/no emptied the shortlist on 8 of 8 off-topic questions, 5 of 8 wrong-game questions and 4 of 12 questions the book does not answer; the thresholds were 14 of 16 and 6 of 12. One ask took 20.6 s at the median on an RTX PRO 6000 Blackwell (96 GB), and 3.6 s even when judging only eight passages, against a 1.5 s budget. A permissively licensed cross-encoder, bge-reranker-v2-m3, read page recall@8 0.9178 (no difference shown against MiniLM) and 131 ms at the median over the whole shortlist. OpenJev's weights are non-commercial and this was a bench; RuleSage's reranker stays as it is.","slug":"openjev-as-a-reranker","text":"full","title":"OpenJev as a reranker for RuleSage: measured","updated":null,"url":"https://research.strata2signal.com/openjev-as-a-reranker/"},
{"dek":"This hub now has a search page that lists the articles holding your words as you type, each with a line saying which words matched and where, and ranks them in your browser from one index file, with no request carrying what you type: how to use it, how it works, what it cannot do and how we checked it.","headings":[{"h":"1 · What it is, and where to find it","id":"what-it-is"},{"h":"2 · How to use it","id":"how-to-use-it"},{"h":"3 · How it works","id":"how-it-works"},{"h":"4 · What it cannot do","id":"what-it-cannot-do"},{"h":"5 · How we checked it","id":"how-we-checked-it"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":83,"published":"2026-10-15","series":["guide"],"short":"The search page lists every article on this hub, newest first, and narrows the list as you type. Your browser fetches one index file and ranks the articles itself: higher when your words appear often, are rare across the site, or sit in a title. When you press Enter, open a result or ask for a link, your words go after the # in the address, which browsers leave out when they ask a server for a page; the search sets no cookie, saves nothing in your browser's storage and fetches nothing from any other host. Your browser's own history can keep that address, and a browser that translates the page can send its text, your words included, to its translation service. It matches words, not meaning, and does not guess at misspellings; a browser too old for it gets the full list and no box. A frozen set of 24 reference queries is run on every change to the ranking.","slug":"how-the-search-works","text":"full","title":"How the search works","updated":"2026-10-17","url":"https://research.strata2signal.com/how-the-search-works/"},
{"dek":"Many language models that run on a home machine arrive as a .gguf file, the format llama.cpp reads: this page opens one from its first byte, reading the header by hand, the metadata a file carries about itself, the table of its weights, the padding that lets it be memory-mapped and llama.cpp's own loading sequence, and says why this one self-describing file replaced three older formats on 2023-08-21.","headings":[{"h":"What a GGUF file is","id":"what-a-gguf-file-is"},{"h":"Four parts, then the weights","id":"four-parts-then-the-weights"},{"h":"The first 147 bytes, by hand","id":"the-first-147-bytes-by-hand"},{"h":"A whole header, every byte","id":"a-whole-header-every-byte"},{"h":"The keys that matter","id":"the-keys-that-matter"},{"h":"The tensor table","id":"the-tensor-table"},{"h":"Three entries, byte by byte","id":"three-entries-byte-by-byte"},{"h":"From a shape to a byte count","id":"from-a-shape-to-a-byte-count"},{"h":"Twenty-nine bytes, and the memory map","id":"twenty-nine-bytes-and-the-memory-map"},{"h":"How llama.cpp loads one, step by step","id":"how-llamacpp-loads-one-step-by-step"},{"h":"The draft head that is not there","id":"the-draft-head-that-is-not-there"},{"h":"Where it came from","id":"where-it-came-from"},{"h":"Who reads it","id":"who-reads-it"},{"h":"A second file, through Ollama","id":"a-second-file-through-ollama"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":82,"published":"2026-10-15","series":["guide"],"short":"A GGUF file is a language model packed for programs built on the ggml library, llama.cpp first among them. It describes itself: four bytes that spell GGUF, a version number, a count of tensors (the model's weights, in named blocks) and a count of metadata entries; then the metadata, where the architecture, the context length, the whole tokenizer and the chat template ride inside the file; then a table giving every tensor's name, shape, type and position; then padding to a 32-byte boundary; then the weights. We read the header of the file we benched on [Qwen3.8-Flash-Next](https://research.strata2signal.com/flash-next-on-three-cards/) in October 2026: version 3, 67 metadata entries (10.9 MB of them the tokenizer), 1,223 tensors in 13 different storage types (ten of them compressed) inside a file tagged IQ3_S, 29 bytes of padding, and no draft head. For every one of the 1,223 tensors, the byte count worked out from its shape and its type's block size matched the file's own offsets, and the 29 bytes after the table are the only padding in 54.8 GB. llama.cpp opens such a file in a fixed order (magic, version, counts, keys, table, padding, then the weights by memory map or by copy), and its code at one commit is read beside the file below. llama.cpp merged the format on 2023-08-21, replacing GGML, GGMF and GGJT.","slug":"inside-a-gguf-file","text":"full","title":"Inside a GGUF file","updated":null,"url":"https://research.strata2signal.com/inside-a-gguf-file/"},
{"dek":"A desktop built around a used RTX 3080 with 10 GB of video memory costs $1,357.61 in seven parts plus the card, or $1,500.57 with the parts a reader would actually want, before tax and before a screen, from one US retailer and one used-card marketplace, priced 8 October 2026. The 32 GB of RAM beside the card cost more than the card that day, as it has on each of the four days this workshop has priced the build since 10 September.","headings":[{"h":"A few words this page leans on","id":"a-few-words-this-page-leans-on"},{"h":"What this page prices, and what it does not","id":"what-this-page-prices-and-what-it-does-not"},{"h":"The computer, as this workshop ran it","id":"the-computer-as-this-workshop-ran-it"},{"h":"Build A: the cheapest honest desktop","id":"build-a-the-cheapest-honest-desktop"},{"h":"Build B: the step up a reader would actually want","id":"build-b-the-step-up-a-reader-would-actually-want"},{"h":"The card","id":"the-card"},{"h":"The bill, priced 8 October 2026","id":"the-bill-priced-8-october-2026"},{"h":"What the computer runs","id":"what-the-computer-runs"},{"h":"Against a subscription","id":"against-a-subscription"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":81,"published":"2026-10-14","series":["bench"],"short":"A desktop around a used 10 GB RTX 3080 costs about $1,360 in parts, or about $1,500 built the way you would want it, before tax, on 8 October 2026. The card is $358 on one marketplace's printed twelve-month average of completed sales, and $376.67 in the latest month of that window; the 32 GB of RAM beside it is $439.99. The speeds on this page come from an older desktop the card was benched in, not the one priced here: on the two models that fit inside the card it writes seventy to eighty-five tokens a second, twelve to fifteen times that desktop's processor alone; on the four that don't, just under two to five and a half times. The full bench has not had its own page yet; this one is the bill.","slug":"the-3080-rig-priced","text":"full","title":"The 3080 Rig, Priced","updated":null,"url":"https://research.strata2signal.com/the-3080-rig-priced/"},
{"dek":"A register of what the local AI programs we have run or read, and the system software around them, send out by default, with the switch that stops each call and whether we proved it: how to read a row, what it leaves out and how to dispute one.","headings":[{"h":"1 · What CallHome is","id":"what-callhome-is"},{"h":"2 · Why it exists","id":"why-it-exists"},{"h":"3 · How to read a row","id":"how-to-read-a-row"},{"h":"4 · What it does not cover","id":"what-it-does-not-cover"},{"h":"5 · How to dispute a row","id":"how-to-dispute-a-row"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":80,"published":"2026-10-12","series":["notes"],"short":"CallHome is one sortable table, one line per product and version, for the local AI programs we have run or read and the system software around them. Each line says whether the program makes a call out with nothing configured, what kind of call it is (a report, a check, a fetch, or a cloud feature you chose), when and to what kind of place, the switch that stops it, and whether we proved that the switch works. Every call on a line carries a grade, measured, witnessed, read or inferred, which caps what it may claim, and a receipt a reader can check. A product's name opens its own page; the whole register is downloadable as files. It describes; it does not rank.","slug":"phone-home-register","text":"full","title":"CallHome: what local AI tools send out by default, in one table","updated":null,"url":"https://research.strata2signal.com/phone-home-register/"},
{"dek":"Qwen3.8-Flash-Next, a model too large to hold on one 24 GB card, run one request at a time through llama.cpp (at commit 3cf03257, without the draft head, which the file we benched does not carry) and the Strata engine (github.com/Niko1221/Strata, an unrelated project) on an RTX 5090 laptop card (24 GB, in a laptop with 64 GB of memory, no settable cap), an RTX PRO 6000 Blackwell (96 GB, at a 300 W cap, one engine a night) and an EVGA GeForce RTX 3090 Ti FTW3 Ultra (24 GB, at a 300 W cap, both engines in one window beside live work on the computer's other card): tokens a second, the wait for the first word and board watts, side by side in tables and charts, each figure with the conditions it was measured under and printed first on the full bench, with one paragraph per card, the findings, which card and engine for which case, and what it does not say.","headings":[{"h":"The three cards, and how they were run","id":"the-three-cards-and-how-they-were-run"},{"h":"Writing the answer","id":"writing-the-answer"},{"h":"The first word","id":"the-first-word"},{"h":"What each card drew","id":"what-each-card-drew"},{"h":"Each card, in one paragraph","id":"each-card-in-one-paragraph"},{"h":"What we found","id":"what-we-found"},{"h":"Which card and engine for which case","id":"which-card-and-engine-for-which-case"},{"h":"What it does not say","id":"what-it-does-not-say"},{"h":"Who ran this, and where the files are","id":"who-ran-this-and-where-the-files-are"}],"kit":false,"n":79,"published":"2026-10-12","series":["bench"],"short":"Qwen3.8-Flash-Next is a large AI model, released as a set of files. A program that loads those files and answers requests is an engine. llama.cpp is a widely used open-source engine for such files; the Strata engine (github.com/Niko1221/Strata) is a newer engine built on llama.cpp that runs this one model. Despite the similar name, Strata (github.com/Niko1221/Strata) is an unrelated project; strata2signal has no affiliation with it. We ran both engines on three graphics cards, one request at a time, with the same prompts, and timed them: writing speed in tokens a second (a token is a word or a piece of one, the unit an engine reads and writes), the wait for the first word in seconds, and the card's own board watts, each figure beside the conditions it was measured under, and no ratio drawn between cards.","slug":"flash-next-on-three-cards","text":"full","title":"Qwen3.8-Flash-Next on an RTX 5090 laptop, an RTX PRO 6000 Blackwell and an RTX 3090 Ti: the Strata engine and llama.cpp, the numbers","updated":null,"url":"https://research.strata2signal.com/flash-next-on-three-cards/"},
{"dek":"Why the Strata engine (github.com/Niko1221/Strata, an unrelated project) gives the first word of an answer sooner than llama.cpp on the same Qwen3.8-Flash-Next files, and what it costs, measured one request at a time on an RTX 5090 laptop card (24 GB, in a laptop with 64 GB of memory, no settable cap), an RTX PRO 6000 Blackwell (96 GB, at a 300 W cap, one engine a night) and an EVGA GeForce RTX 3090 Ti FTW3 Ultra (24 GB, at a 300 W cap, both engines in one window beside live work on the computer's other card): the wait for the first word, writing speed, board power, quality and agreement, with llama.cpp at commit 3cf03257 at its default 512-token micro-batch and at a 4,096-token one, and on the RTX 3090 Ti also at a current build (b11514), every llama.cpp row without the draft head, which the file we benched does not carry.","headings":[{"h":"What this page is","id":"what-this-page-is"},{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"Which cards this page covers","id":"which-cards-this-page-covers"},{"h":"One model, two engines","id":"one-model-two-engines"},{"h":"The question, and the instruments","id":"the-question-and-the-instruments"},{"h":"Why the first word comes sooner, in three sentences","id":"why-the-first-word-comes-sooner-in-three-sentences"},{"h":"The first word (the laptop, the IQ3_S file)","id":"the-first-word-the-laptop-the-iq3-s-file"},{"h":"What a 4,096-token micro-batch does to llama.cpp","id":"what-a-4-096-token-micro-batch-does-to-llama-cpp"},{"h":"Writing the answer (the laptop, the IQ3_S file)","id":"writing-the-answer-the-laptop-the-iq3-s-file"},{"h":"The Q2_0 pair (the laptop)","id":"the-q2-0-pair-the-laptop"},{"h":"What the card drew, and what the CPU package drew (the laptop)","id":"what-the-card-drew-and-what-the-cpu-package-drew-the-laptop"},{"h":"The desktop card: one engine a night","id":"the-desktop-card-one-engine-a-night"},{"h":"Writing the answer (tokens a second, our client's clock)","id":"writing-the-answer-tokens-a-second-our-clients-clock-52142f"},{"h":"The first word, and the prompt rate","id":"the-first-word-and-the-prompt-rate-7a901f"},{"h":"Recorded beside the rows, claiming nothing","id":"recorded-beside-the-rows-claiming-nothing-8337ee"},{"h":"The desktop beside the laptop, llama.cpp only (a box and a regime, not a card)","id":"the-desktop-beside-the-laptop-llama-cpp-only-a-box-and-a"},{"h":"What this section does not say","id":"what-this-section-does-not-say-a0645f"},{"h":"The 24 GB desktop card: both engines in one window","id":"the-24-gb-desktop-card-both-engines-in-one-window"},{"h":"Writing the answer (tokens a second, our client's clock)","id":"writing-the-answer-tokens-a-second-our-clients-clock-e741b0"},{"h":"The first word, and the prompt rate","id":"the-first-word-and-the-prompt-rate-763fcd"},{"h":"Recorded beside the rows, claiming nothing","id":"recorded-beside-the-rows-claiming-nothing-82709b"},{"h":"What this section does not say","id":"what-this-section-does-not-say-3d9815"},{"h":"Are the answers the same?","id":"are-the-answers-the-same"},{"h":"Is it any good?","id":"is-it-any-good"},{"h":"Who the Strata engine is for, and who it is not for","id":"who-the-strata-engine-is-for-and-who-it-is-not-for"},{"h":"What was written down before the first run","id":"what-was-written-down-before-the-first-run"},{"h":"What else the bench read","id":"what-else-the-bench-read"},{"h":"Still pending, and when it lands","id":"still-pending-and-when-it-lands"},{"h":"What it does not say","id":"what-it-does-not-say"},{"h":"What comes next on this page","id":"what-comes-next-on-this-page"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Appendix: sources, reproduce, per-run telemetry","id":"appendix-sources-reproduce-per-run-telemetry"},{"h":"How to reproduce it, the way we ran it (source build only)","id":"how-to-reproduce-it-the-way-we-ran-it-source-build-only"},{"h":"Sources, and every figure's file","id":"sources-and-every-figures-file"}],"kit":true,"n":78,"published":"2026-10-12","series":["bench"],"short":"The Strata engine (github.com/Niko1221/Strata, an unrelated project) is a program that runs one AI model, Qwen3.8-Flash-Next, on one graphics card plus the computer's own memory. The model is built from many small parts, called experts, and uses only a few of them for each token (a word or a piece of one: the unit an engine reads and writes). We ran the Strata engine beside llama.cpp, the open-source engine it builds on, with llama.cpp pinned at one commit (3cf03257). Both used the same model files (the Strata engine also read the model's draft head from files in its own format, which the GGUF files do not carry) and the same prompts, one request at a time, on two computers. The headline, on the laptop card that cannot hold the model: the first word at a 128,037-token prompt came 545.1 s after sending on llama.cpp at commit 3cf03257 (its default 512-token micro-batch, 8 of the laptop's 24 threads, the experts of 34 of 48 layers in system memory, no draft head because the file we benched carries no draft-head block; llama.cpp at more than 8 threads was not measured) against 47.6 s on the Strata engine 0.1.25 (8,192 tokens a read, 23 expert workers plus its host thread, drafting from the model's draft head, its run beside another job's dry run), the two back to back on 2026-10-02 and 2026-10-03 (UTC), which is the rule that admits a comparison there; the other two cards' figures are below, each under its own rule.","slug":"flash-next-why-the-first-word-comes-sooner","text":"full","title":"Why the first word comes sooner: the Strata engine and llama.cpp on Qwen3.8-Flash-Next, the full bench","updated":"2026-10-13","url":"https://research.strata2signal.com/flash-next-why-the-first-word-comes-sooner/"},
{"dek":"An embedding model turns a sentence, a photograph or a few seconds of sound into a fixed-length list of numbers: what happens inside one, how one model holds them all in one space at several sizes, where keyword search still wins, and how to choose one.","headings":[{"h":"The core idea: a position on a map","id":"the-core-idea"},{"h":"From text to a vector","id":"from-text-to-a-vector"},{"h":"Contrastive training: learning from pairs","id":"contrastive-training"},{"h":"Images, audio and video, and the shared space","id":"images-audio-video"},{"h":"The modality gap","id":"the-modality-gap"},{"h":"Matryoshka sizes: one vector, several lengths","id":"matryoshka-sizes"},{"h":"Storage precision: how many bytes per number","id":"storage-precision"},{"h":"Task prefixes: telling the model what a text is for","id":"task-prefixes"},{"h":"Where embedders fail, and why keyword search still wins on exact matches","id":"where-embedders-fail"},{"h":"Evaluation, and the contamination problem","id":"evaluation-and-contamination"},{"h":"Single vector or late interaction","id":"single-vector-or-late-interaction"},{"h":"Why vectors from different models never mix","id":"why-vectors-never-mix"},{"h":"Cost and hardware","id":"cost-and-hardware"},{"h":"How to choose an embedder: a checklist","id":"how-to-choose"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":77,"published":"2026-10-10","series":["guide"],"short":"An embedding model turns a piece of content into a vector, a fixed-length list of numbers, placed so that things that belong together land close together; search becomes \"which stored vectors point the way the question's does?\". The model learns where to put things from millions of pairs that belong together, a question and its answer, a photograph and its caption, which is also how one model can put a photograph beside the sentence that describes it. The rest of this page is the mechanism, with examples small enough to check with a calculator, and what we measured on EmbeddingGemma 2, the open embedding model Google released on 2026-10-06.","slug":"how-an-embedding-model-works","text":"full","title":"How an embedding model turns anything into a list of numbers","updated":null,"url":"https://research.strata2signal.com/how-an-embedding-model-works/"},
{"dek":"Google's EmbeddingGemma 2 against the Nomic AI pair, the text model our stores are built on and the image model one product's retrieval code is wired to, in ten tests registered before their first scores, on our own published photographs, code and music and on 1,000 public COCO captions: on COCO it puts the right photograph first for 729 captions against the Nomic pair's 497 (the pair on transformers 4.57.6; a set most likely inside both models' training data), on code it is ahead by 50 of 316 docstring queries with the code-retrieval prefix, on our 14 photographs it leads too (exploratory at 12 scenes), on audio (exploratory, no incumbent to beat) it tracks how strongly a music model's adapter was applied and a loudness change barely moves it, but a clip finds the request that made it 1 time in 12 (chance), and nothing in our stores changes.","headings":[{"h":"Where this page starts from","id":"where-this-page-starts"},{"h":"What we tested, and why these tests","id":"what-we-tested"},{"h":"The incumbent's image model: which library, and why","id":"which-library"},{"h":"What the images show","id":"what-the-images-show"},{"h":"What the audio shows","id":"what-the-audio-shows"},{"h":"What the code shows","id":"what-the-code-shows"},{"h":"Speed and memory: measured, contended, not compared","id":"speed"},{"h":"The audit and the verifies","id":"the-audit-and-the-verify"},{"h":"How to run it yourself, and the traps","id":"how-to-run-it"},{"h":"What moving from nomic to EmbeddingGemma 2 would take, and why we are not doing it","id":"what-moving-would-take"},{"h":"What this bench does not say","id":"what-this-bench-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Changes","id":"changes"}],"kit":false,"n":76,"published":"2026-10-09","series":["bench"],"short":"EmbeddingGemma 2, written EG2 in this page's verdict words, turns text, code, images, video and audio (per its model card) into one 768-number space. Our stores are built on Nomic AI's text model, and one product's retrieval code is wired to its image partner; the companion page, \"EmbeddingGemma 2, measured on release day against the embedder we already run\", found NO MATERIAL DIFFERENCE between EmbeddingGemma 2 and nomic's text model on our own text, so this bench tested images, audio and code.","slug":"embeddinggemma-2-shared-space","text":"full","title":"EmbeddingGemma 2's shared space for text, images and audio, measured on what we publish","updated":null,"url":"https://research.strata2signal.com/embeddinggemma-2-shared-space/"},
{"dek":"Google's new open embedding model, EmbeddingGemma 2, against nomic-embed-text, the embedder our search stores already run, on 68 questions over our own documents behind a pass mark of 51 of 68 written down before any score: 45 against 44 of 68, the registered verdict NO MATERIAL DIFFERENCE (one question, inside the noise at n = 68), with plain keyword search at 58 of 68 still ahead of both and nothing we layered on it beating it by more than chance.","headings":[{"h":"What an embedding model is","id":"what-an-embedding-model-is"},{"h":"The question","id":"the-question"},{"h":"What Google released on 2026-10-06, and what we checked","id":"what-google-released"},{"h":"The instrument: 68 questions over our own documents","id":"the-instrument"},{"h":"What we ran","id":"what-we-ran"},{"h":"The result","id":"the-result"},{"h":"What the keyword search and the hybrids show","id":"keyword-search-and-the-hybrids"},{"h":"Speed: not cleanly measured, and not compared","id":"speed"},{"h":"The audit and the verify","id":"the-audit-and-the-verify"},{"h":"How to run it yourself today, and the traps","id":"how-to-run-it"},{"h":"What this bench does not say","id":"what-this-bench-does-not-say"},{"h":"What switching from nomic would take, and why we are not doing it","id":"what-switching-would-take"},{"h":"For this workshop: what changes and what does not","id":"for-this-workshop"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Changes","id":"changes"}],"kit":false,"n":75,"published":"2026-10-09","series":["bench"],"short":"On 2026-10-06 Google released EmbeddingGemma 2, an open embedding model under the Apache 2.0 licence: it turns text (and images, video and audio) into lists of numbers that search can compare. The same day we wrote down a test, froze it in a commit, and ran the model's text part against nomic-embed-text, the embedder our search stores already run, on 68 questions over 2,569 passages of our own documents. nomic-embed-text finds a correct passage in the top 8 for 44 of the 68; EmbeddingGemma 2 finds one for 45, through Google's own code and through an 8-bit copy of its weights on llama.cpp (an open-source model runner) alike. The pass mark, written down before any score existed, was 51 of 68, so the registered verdict is NO MATERIAL DIFFERENCE: one question of difference is inside the noise, which is not the same as saying the two are equal. Plain keyword search (BM25) finds 58 of 68, and nothing we layered on it beat it by more than chance. Nothing in our stores changes.","slug":"embeddinggemma-2-on-release-day","text":"full","title":"EmbeddingGemma 2, measured on release day against the embedder we already run","updated":null,"url":"https://research.strata2signal.com/embeddinggemma-2-on-release-day/"},
{"dek":"Ollama and vLLM both run an open language model on your own computer. This page says which of the two to run for the case you bring, the two or three settings that decide it, who can reach it once it is running and what it sends out of your machine by default, every line pointing at the measurement or the dated source it rests on.","headings":[{"h":"Who this page is for","id":"who-this-page-is-for"},{"h":"Which to pick when","id":"which-to-pick-when"},{"h":"The settings that matter","id":"the-settings-that-matter"},{"h":"What each calls home","id":"what-each-calls-home"},{"h":"Who can reach it, and what to put in front","id":"who-can-reach-it"},{"h":"Getting it running, and keeping it running","id":"getting-it-running"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"What it does not say","id":"what-it-does-not-say"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":false,"n":74,"published":"2026-10-08","series":["guide"],"short":"Ollama and vLLM are two programs that run an open language model on your own graphics card, and which one to run depends mostly on how many requests reach it at once. Every figure here is our bench page's, from one RTX 3090 (24 GB) with the same 4-bit Gemma 4 12B weights on both, where each reading sits beside the prediction we wrote down before measuring it; speeds are in tokens a second, a token being a word or a piece of one, and Ollama 0.34.4 is set to serve sixteen requests at once in every comparison here (at its default it serves one at a time). One request at a time, run either: once an answer had started, vLLM 0.30.0 wrote it at 82.693 tokens a second and Ollama at 85.007 tokens a second, inside the 10 per cent we had predicted, though Ollama's wait before the answer started was more than twice vLLM's, 441.866 ms against 212.999 ms. Two to four at once, run vLLM, or Ollama set to serve more at once if another case below keeps you on it: vLLM was ahead on every speed and every wait the bench page prints at two and at four, its total across both requests 1.2077 times Ollama's at two, though at four Ollama's repeated runs disagreed widely. Eight to sixteen at once, run vLLM: at sixteen requests released together it wrote 506.829 tokens a second across them and Ollama 142.327 tokens a second, 3.561 times as much, and Ollama's total stayed flat from four requests on. Whichever you run, the two did not always give the same answer to the same test question, so check the answers on your own prompts. On this page, each of these cases links the section of our bench page, Ollama and vLLM on one RTX 3090, that holds its figures.","slug":"ollama-or-vllm-which-to-run","text":"full","title":"Ollama or vLLM: which to run, and how","updated":null,"url":"https://research.strata2signal.com/ollama-or-vllm-which-to-run/"},
{"dek":"Ollama and vLLM both run an open language model on your own computer. This page puts both on one RTX 3090 (24 GB) with the same 4-bit Gemma 4 12B weights, from one request at a time to sixteen at once, and prints every reading beside the bet we wrote down before it.","headings":[{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"Two names, one card","id":"two-names-one-card"},{"h":"One request at a time","id":"one-request-at-a-time"},{"h":"Requests at once","id":"requests-at-once"},{"h":"The first word, and a shared prompt","id":"the-first-word"},{"h":"What each holds, and how fast it starts","id":"what-each-holds"},{"h":"Are the answers the same?","id":"are-the-answers-the-same"},{"h":"What was written down before the first run","id":"what-was-written-down-before-the-first-run"},{"h":"What else the bench read","id":"what-else-the-bench-read"},{"h":"What it does not say","id":"what-it-does-not-say"},{"h":"What comes next on this page","id":"what-comes-next"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":true,"n":73,"published":"2026-10-07","series":["bench"],"short":"Ollama and vLLM are two ways to run an open model on your own computer; this page measures both on one RTX 3090 (24 GB) with the same 4-bit Gemma 4 12B weights. One request at a time, the two write at nearly the same speed once an answer has started: on the older pair we tested first, Ollama 0.32.13 wrote 85.2 tokens a second and vLLM 0.27.1 wrote 78.1 tokens a second, inside the 10 per cent band we had predicted, and the prediction held again on Ollama 0.34.4 and vLLM 0.30.0. Many at once is the job vLLM was built for, and it shows: at sixteen requests released together, vLLM 0.30.0 wrote 506.829 tokens a second across them and Ollama 0.34.4, set to serve sixteen at once, 142.327 tokens a second. vLLM's total first reached 1.2 times Ollama's at two requests at once, a ratio of 1.2077, by a margin smaller than the variation between repeated runs; we had predicted four at once, and had written down before any run that two or eight at once would settle nothing either way, so that prediction stands undecided. Two predictions about the wait before an answer starts were wrong: at one request Ollama's wait was more than twice vLLM's, and with sixteen long prompts at once neither server kept that wait inside the limit we had set for it, 1.5 s for vLLM or 4 s for Ollama set to serve sixteen at once. One prediction about the answers was wrong too: on 108 lines of dinner-party talk, each asking which of six guests said it, the two servers agreed on fewer of them than the 97 per cent we had predicted, so every speed row here carries the label \"answers differ at this level\". It is one card and one model, with requests released together, not people arriving over time.","slug":"ollama-and-vllm-on-one-rtx-3090","text":"full","title":"Ollama and vLLM on one RTX 3090","updated":"2026-10-08","url":"https://research.strata2signal.com/ollama-and-vllm-on-one-rtx-3090/"},
{"dek":"What it costs in electricity to run AI models at home, read off a meter on every one of a small workshop's computers: the box with graphics cards that now runs around the clock, about 130 W doing nothing, a measured week, what one picture, one answer and one mini PC cost, and what the meter cannot see.","headings":[{"h":"The bill in dollars, before anything else","id":"the-bill-in-dollars-before-anything-else"},{"h":"Why this page exists","id":"why-this-page-exists"},{"h":"What \"local\" means, said at a kitchen table","id":"what-local-means-said-at-a-kitchen-table"},{"h":"How the workshop runs now","id":"how-the-workshop-runs-now"},{"h":"The instrument","id":"the-instrument"},{"h":"What sat on the shelf in September, and what each drew doing nothing","id":"what-sat-on-the-shelf-in-september-and-what-each-drew-doing"},{"h":"One clean day","id":"one-clean-day"},{"h":"What one thing costs","id":"what-one-thing-costs"},{"h":"The caps","id":"the-caps"},{"h":"What the meter does not see","id":"what-the-meter-does-not-see"},{"h":"The part about jobs","id":"the-part-about-jobs"},{"h":"Numbers, so it isn't just talk","id":"numbers-so-it-isnt-just-talk"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":72,"published":"2026-10-06","series":["bench"],"short":"this page is what it costs in electricity to run AI models — pictures, answers, music — on a small workshop's own computers at home, read off a meter on every one of them. The one box with graphics cards that now runs around the clock drew about 130 W doing nothing over 2026-10-04 (UTC), about $17 a month at the average U.S. price (since 2026-10-05 it has read about fifteen watts higher, cause not yet found). Everything together, the work and the test runs included, averaged 76 cents a day over the measured week 2026-09-28 → 2026-10-04 (UTC), about $23 a month, about a seventh of an average American home's electricity. One answer costs about six thousandths of a cent, and a mini PC with no graphics card runs the same kind of model for about $1.44 a month. Here is the meter, and what it cannot see.","slug":"what-it-costs-to-run","text":"full","title":"What it costs to run a high-end AI rig at home","updated":null,"url":"https://research.strata2signal.com/what-it-costs-to-run/"},
{"dek":"An \"uncensored\" AI model is one that somebody has altered so that it refuses far fewer requests than the version its maker released. The change is made to the model's files, or to the way it is run. This page explains where a model's refusals come from and the three ways people remove them. It then takes one popular uncensored copy of a large model, shared online, and checks, file by file, what actually changed. It ends on a practical question: what to keep beside such a model on a computer with no internet connection. No code, no settings, no steps.","headings":[{"h":"Where the no comes from","id":"where-the-no-comes-from"},{"h":"Why people want them","id":"why-people-want-them"},{"h":"Three places to touch it","id":"three-places-to-touch-it"},{"h":"The landscape, briefly","id":"the-landscape"},{"h":"One upload, read by its hashes","id":"one-upload-read-by-its-hashes"},{"h":"A speed setting, read by its hash","id":"a-speed-setting-read-by-its-hash"},{"h":"Read the label, and if you run these files","id":"read-the-label"},{"h":"When the model is the only book on the shelf","id":"the-only-book-on-the-shelf"},{"h":"How this workshop handles these files","id":"how-this-workshop-handles-these-files"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":true,"n":71,"published":"2026-10-05","series":["notes"],"short":"An \"uncensored\" model is an AI model, or a way of running one, changed so that it declines far less often than the version its maker shipped. In a model you download and run yourself, refusing is a habit taught after the main training. Research since 2024 finds that habit carried largely by one pattern in the model's working numbers, which researchers call a direction (later work finds more than one). People take it out in three ways: they train a model without the habit, or over it; they make one permanent edit to the model's learned numbers (its weights) that removes that direction; or they act on the model while it runs, which stops when you switch it off. None of the three makes a model smarter or gives it secret knowledge. We read one popular \"uncensored\" copy of Qwen3.8-Flash-Next, a large model that Qwen publishes for anyone to download. Both sit on Hugging Face, the site where people share AI models; the uncensored copy's page there was created two days after Qwen's own. We read it by the fingerprints (hashes) the site publishes for every large file, without downloading a byte of its weights. In the uploader's full-precision copy, 80 of the 131 files are byte for byte Qwen's own, and all 51 that changed hold a part the uploader's model card, the description page beside the files, says it edited. So as far as public evidence reaches, the copy is what its card says it is. The serious refusals go too: by the uploader's own figures, the edited model declines harmful requests far less often than the original. We also found a program for running models on your own computer whose optional \"speed\" setting is, byte for byte, a refusal-removal file published elsewhere; its setup question says what it does, and its name does not. And if a model is your only source of knowledge on a machine with no network, we say what we would build: the model beside offline references it can cite.","slug":"uncensored-models","text":"full","title":"Uncensored models: what they are and how they work","updated":null,"url":"https://research.strata2signal.com/uncensored-models/"},
{"dek":"We set a white moth orchid beside our graphics cards, and the two looked so good together that we photographed them.","headings":[],"kit":false,"n":70,"published":"2026-10-03","series":["notes"],"short":"We set a white moth orchid beside our graphics cards, and the two looked so good together that we photographed them.","slug":"gpus-and-orchids","text":"full","title":"GPUs and orchids","updated":"2026-10-07","url":"https://research.strata2signal.com/gpus-and-orchids/"},
{"dek":"The open-source server that lets one graphics card run a language model for many people at once: a Berkeley lab's borrowing from operating systems, the foundation and the company around it, and the defaults that moved under its users, dated.","headings":[{"h":"One card, many questions at once","id":"one-card-many-questions"},{"h":"Who are these people, and where did they come from?","id":"who-are-these-people"},{"h":"What the v stands for","id":"what-the-v-stands-for"},{"h":"The idea: paging for a language model","id":"paging-for-a-language-model"},{"h":"Who owns it, and who pays for it","id":"who-owns-it"},{"h":"The releases where it bent","id":"the-releases-where-it-bent"},{"h":"What it speaks, and where it runs","id":"what-it-speaks-and-where-it-runs"},{"h":"How it serves a request","id":"how-it-serves-a-request"},{"h":"Who can talk to it","id":"who-can-talk-to-it"},{"h":"The defaults that moved","id":"the-defaults-that-moved"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":false,"n":69,"published":"2026-10-02","series":["notes"],"short":"vLLM is free, open-source software that serves a large language model, the kind of AI inside a chatbot; on this hub it runs the model behind every article's \"ask about this page\" link. It began in a UC Berkeley lab and was announced on 2023-06-20, built to make one graphics card hold many conversations at once: like an operating system, vLLM hands out working memory in pages as each answer grows. It has been a PyTorch Foundation project since 2025-05-07; some of its creators announced a $150M-funded company beside it on 2026-01-22. This page also dates the defaults that moved under vLLM's users. The only speed figures here are the project's own claims, each dated, none re-run by us.","slug":"a-short-history-of-vllm","text":"full","title":"A short history of vLLM","updated":"2026-10-08","url":"https://research.strata2signal.com/a-short-history-of-vllm/"},
{"dek":"Two founders who had sold their first company to Docker in 2015, a one-command model server that began with llama.cpp compiled in and came back to llama.cpp's own server in 2026, and every default we found that moved under its users, dated.","headings":[{"h":"The server under most of our numbers","id":"the-server-under-most-of-our-numbers"},{"h":"The engine underneath: llama.cpp","id":"the-engine-underneath"},{"h":"Who are these people, and where did they come from?","id":"who-are-these-people"},{"h":"What have they shipped, and what ran underneath?","id":"what-have-they-shipped"},{"h":"Who owns it, and who pays for it","id":"who-owns-it"},{"h":"What it speaks, and where it runs","id":"what-it-speaks-and-where-it-runs"},{"h":"How it serves a request","id":"how-it-serves-a-request"},{"h":"Who can talk to it","id":"who-can-talk-to-it"},{"h":"The defaults that moved","id":"the-defaults-that-moved"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":false,"n":68,"published":"2026-10-02","series":["notes"],"short":"Most of the language-model readings this workshop has published were taken through Ollama, the program that turns an open-weight model into one command. It pulls the weights, loads them when asked, and by default answers over a local address and lets them go five minutes after the last request. This is the history of the project and the company behind it: a first release on 2023-07-08, llama.cpp underneath, then engines of its own, then llama.cpp's own server again from 2026, and $88M raised by July 2026. It is also a dated record of the defaults that moved under its users, among them a penalty on repeated words, the context window, the number of requests it serves at once, and the engine itself.","slug":"a-short-history-of-ollama","text":"full","title":"A short history of Ollama","updated":"2026-10-08","url":"https://research.strata2signal.com/a-short-history-of-ollama/"},
{"dek":"An RTX 3080, two of them in one desktop, an RTX 3090, two of those, an RTX 3090 Ti and an RTX PRO 6000 Blackwell, with a laptop part and an RTX 3080 Ti beside them — asked the same questions, in one table each: how fast each writes, how fast each draws, what the card itself pulled while doing it, how hot it got, and what it cost on the day it was priced. Not every card answers every question, and each table says which did not. Almost nothing here was measured for this page: every figure in its tables but two is already published on one of six earlier pages, and every one says which page and which section it came from. The two exceptions are prices for one card, read at a retailer and at a marketplace; the table says so, and names what else those reads found.","headings":[{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"The cards, named once","id":"the-cards-named-once"},{"h":"How to read the tables","id":"how-to-read-the-tables"},{"h":"Writing: how fast each one answers","id":"writing-how-fast-each-one-answers"},{"h":"When the model is bigger than the card","id":"when-the-model-is-bigger-than-the-card"},{"h":"What the boards actually drew","id":"what-the-boards-actually-drew"},{"h":"What a thousand tokens cost in energy","id":"what-a-thousand-tokens-cost-in-energy"},{"h":"Drawing pictures","id":"drawing-pictures"},{"h":"The sketch ladder, and the one row here that is not a card","id":"the-sketch-ladder-and-the-one-row-here-that-is-not-a-card"},{"h":"Heat, and how hard the fans worked","id":"heat-and-how-hard-the-fans-worked"},{"h":"One box, two cards","id":"one-box-two-cards"},{"h":"What each one costs, and on what day","id":"what-each-one-costs-and-on-what-day"},{"h":"What this comparison cannot say","id":"what-this-comparison-cannot-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":67,"published":"2026-10-01","series":["bench"],"short":"A power cap moves a card more than the card's own name does: one RTX 3090, on one day, wrote with a dense model at 33.3 tokens a second held at 250 W and 47.8 tokens a second at 300 W — a bigger jump than any gap between two different cards at the same cap in the dense model's table. Two used RTX 3080s beat one 3090 on the same dense model at a window both can hold (the memory reserved for the conversation) — 44.6 tokens a second against 33.1, for 250 W on each of two boards against one board's 250 W, a cap their page says costs the single 3090 about 30 per cent on this model and the pair 0.2 per cent — and then lose it entirely at the next window up, where the page prints does not fit. Drawing pictures on the eight-step sketch graph, the order is plainer: one 3080 takes 2.074 seconds a picture and one 3090 1.683 seconds, both at 250 W, and one 3090 Ti 1.369 seconds at its own 450 W limit. A used 3080 asked $375.00 on 17 September 2026; a renewed 3090 asked $1,879.99 the same day — one seller's card, its page says, not a market price; used 3090s sold for $1,094.44 on average in August 2026, by that marketplace's own record. Every number in this page's tables but two was published first somewhere else, with its conditions; the two are an RTX 3080 Ti's prices — a refurbished listing at $479.99 from a retailer, out of stock at both of its last two reads, and a used card's August 2026 average sale of $475.64 from a marketplace — both read on 1 October 2026 and both marked as such, with the other listings those reads turned up named under the table rather than in it. Where two cards were never measured comparably the cell is a dagger and a footnote rather than a number.","slug":"six-cards-the-same-questions","text":"full","title":"Six graphics cards, the same questions","updated":"2026-10-07","url":"https://research.strata2signal.com/six-cards-the-same-questions/"},
{"dek":"A decider is a language model that reads a question and its options and returns a probability for each. We asked sixteen open models five questions from our own products. On the hardest, the top five small deciders labelled Apache-2.0, given no examples, could not be told apart from a word counter trained on our own lines. One of them, APUS-OpenJev-v1 9B, passed a pre-registered screening test that the model then guarding our exhibit missed by three lines, letting through only kinds of line its rules did not name.","headings":[{"h":"What a decider is","id":"what-a-decider-is"},{"h":"Which one for which job","id":"which-one-for-which-job"},{"h":"The questions we asked","id":"the-questions-we-asked"},{"h":"The six-way scoreboard","id":"the-six-way-scoreboard"},{"h":"Did anyone see the answers?","id":"did-anyone-see-the-answers"},{"h":"A word counter kept up","id":"a-word-counter-kept-up"},{"h":"The doorman: screening what visitors type","id":"the-doorman"},{"h":"The easier questions","id":"the-easier-questions"},{"h":"The licences","id":"the-licences"},{"h":"What it takes to run","id":"what-it-takes-to-run"},{"h":"Three teams, one base","id":"three-teams-one-base"},{"h":"A board's rank, and what ours adds","id":"a-boards-rank-and-what-ours-adds"},{"h":"Our fine-tune that voided itself","id":"our-fine-tune-that-voided-itself"},{"h":"What we did not measure, or did not report","id":"what-we-did-not-measure"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Where each figure comes from","id":"where-each-figure-comes-from"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":66,"published":"2026-09-30","series":["bench"],"short":"A decider reads a question and its options and returns a probability for each: nothing to parse, and a number to set a hand-off threshold on, once that threshold is checked on your own cases. For a yes-or-no call about a short passage, eight of the ten models we asked scored 39 or 40 of 40 on one such question, the set's ceiling, so choose among those eight by licence and speed. For questions about long pages, Lev-4B took all 53 we sent and got 52 right. For screening what visitors type, APUS-OpenJev-v1 9B alone of the Apache-2.0 models we asked passed our pre-registered test, refusing 32 of 36 lines written to be refused and 0 of 12 harmless ones. For sorting short texts by how they are written, train a bag of words first: ours got 68 of 108, and the top five small deciders labelled Apache-2.0, at 66 to 72, could not be told apart from it. OpenJev 27B scored highest, 101, and its 4-bit build sits whole on one used RTX 3090, but its weights are licensed for non-commercial use, and commercial use needs separate terms, which its README now says to ask for by email.","slug":"small-open-deciders-measured","text":"full","title":"Small open deciders, measured: a word counter kept up on our hardest question","updated":"2026-10-05","url":"https://research.strata2signal.com/small-open-deciders-measured/"},
{"dek":"ONNX Runtime is a Microsoft library for running AI models, and tools such as chromadb, faster-whisper and piper-tts install it for you. Since version 1.29 (August 2026) it has shipped with telemetry on by default on Linux and macOS, and its own privacy file says so. We measured it on Linux in a sealed container: it tried to reach Microsoft's telemetry server 9.2 seconds after starting, and setting ORT_DISABLE_TELEMETRY=1 before launch stopped it in every run.","headings":[{"h":"How we noticed","id":"how-we-noticed"},{"h":"Are you affected?","id":"are-you-affected"},{"h":"How to turn it off","id":"how-to-turn-it-off"},{"h":"Which tools bring it in","id":"which-tools-bring-it-in"},{"h":"What changed, and when","id":"what-changed"},{"h":"What we measured","id":"what-we-measured"},{"h":"What is in the events","id":"what-is-in-the-events"},{"h":"What we did about ours","id":"what-we-did-about-ours"},{"h":"What this page does not know","id":"what-this-page-does-not-know"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Sources (read 2026-09-28, UTC)","id":"sources"}],"kit":true,"n":65,"published":"2026-09-28","series":["bench"],"short":"ONNX Runtime 1.29.0 and 1.30.0, which chromadb, faster-whisper, piper-tts, fastembed and many other tools install for you, try to reach Microsoft's telemetry collector by default on Linux. In our sealed test the first attempt came 9.2 seconds after launch in every default run, and the library kept retrying when our stand-in collector cut it off. The events carry no audio, prompts or model outputs, but they do carry a device ID that stays the same across runs (sent as a hash), the path the program was launched by (with your username in it when that program lives in your home folder), your processor and operating system, and the names and metadata of the models you run. The fix is one environment variable, set before the program starts: ORT_DISABLE_TELEMETRY=1 stopped every attempt. The library's own disable call did not, and the library never reads DO_NOT_TRACK. Version 1.28.0 made no attempt, and Microsoft's own privacy file says the macOS, Android and iOS builds carry the same telemetry.","slug":"onnx-runtime-telemetry-on-linux","text":"full","title":"ONNX Runtime's telemetry on Linux, measured: on by default since 1.29, and the switch that stops it","updated":null,"url":"https://research.strata2signal.com/onnx-runtime-telemetry-on-linux/"},
{"dek":"A 2022 graphics card, still sealed in 2026, cost more than its chipmaker's published launch price. The company that built it announced in September 2022 that it was leaving the graphics-card business, and never said when it built its last one.","headings":[{"h":"The card that arrived","id":"the-card-that-arrived"},{"h":"What EVGA said, in September 2022","id":"what-evga-said-in-september-2022"},{"h":"The day before","id":"the-day-before"},{"h":"Six readings, on named days","id":"six-readings-on-named-days"},{"h":"One operator's preference","id":"one-operators-preference"},{"h":"What this page does not know","id":"what-this-page-does-not-know"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":64,"published":"2026-09-28","series":["notes"],"short":"many of the graphics cards this workshop keeps buying are EVGA's, and EVGA told two interviewers on 16 September 2022 that it was leaving the graphics-card business. a sealed 3090 Ti FTW3 Ultra came out of its box here on 17 September 2026 at $2,119.99 — $120.99 above the launch price NVIDIA published for the model in March 2022 — and below are six dated readings for cards like it, each quoted from the page that printed it.","slug":"a-sealed-3090-ti-after-evga-left","text":"full","title":"A sealed 3090 Ti FTW3 Ultra, four years after EVGA left the business","updated":"2026-10-07","url":"https://research.strata2signal.com/a-sealed-3090-ti-after-evga-left/"},
{"dek":"Held to the same 300 W, at 2048×1280 on the print lab's own picture model, each card in its own computer, an RTX PRO 6000 Blackwell Workstation Edition 96 GB does each sampling step 1.6 times as fast as a ZOTAC GeForce RTX 5080 Solid CORE OC 16 GB, down from 2.8 times at their stock 600 W and 360 W. Over ten minutes at 1024×1024 on the same model at 8 steps, a GeForce RTX 5090 Laptop 24 GB, on the 150 W its Dynamic Boost held through that run, spent the same board energy per picture as the workstation card at 300 W, 663 against 667 J, each in its own computer. On a GeForce RTX 3080 Ti Founders Edition 12 GB, the smallest of the three picture models sat whole on the card in every cell and the other two paged their weights in every cell that ran, down to 68.7 per cent resident. In one computer, on one checkout of the picture software, both held to 300 W on a 512×512 recipe, a 5080 did a 3090 Ti's work in 0.69 of the time for 0.64 of the board energy; at their stock limits, against the second 3090 Ti in that computer, 0.74 of the time for 0.53 of the energy.","headings":[{"h":"Who ran where","id":"who-ran-where"},{"h":"Words this page leans on","id":"words-this-page-leans-on"},{"h":"The grid, and the one thing it holds fixed","id":"the-grid-and-the-one-thing-it-holds-fixed"},{"h":"Seven things the grid found","id":"seven-things-the-grid-found"},{"h":"1 · Twelve gigabytes: which models fit whole","id":"twelve-gigabytes-which-models-fit-whole"},{"h":"2 · Seconds per sampling step, and what 300 W does to the gap","id":"seconds-per-sampling-step-and-what-300-w-does-to-the-gap"},{"h":"3 · Joules per picture, and per step","id":"joules-per-picture-and-per-step"},{"h":"4 · Ten minutes of drawing at 1024×1024","id":"ten-minutes-of-drawing-at-1024-1024"},{"h":"5 · What 300 W costs, cell by cell","id":"what-300-w-costs-cell-by-cell"},{"h":"6 · A 2048×1280 step costs about three 1024×1024 steps","id":"a-2048-1280-step-costs-about-three-1024-1024-steps"},{"h":"7 · The ninety-second rule fired twice on the first four cards","id":"the-ninety-second-rule-fired-twice"},{"h":"Three cards in one computer, one checkout, one recipe","id":"three-cards-in-one-computer-one-checkout-one-recipe"},{"h":"The full tables","id":"the-tables"},{"h":"What this page cannot say","id":"what-this-page-cannot-say"},{"h":"How it was measured","id":"how-it-was-measured"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"More on this site","id":"more-on-this-site"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The EVGA 3090 Ti FTW3 Ultra's column, at 350 W and 300 W (added 2026-09-26)","id":"the-evga-3090-ti-ftw3-ultras-column-added-2026-09-26"},{"h":"The EVGA 3090 XC3 Ultra's column, at its stock 350 W and at 300 W (added 2026-09-26)","id":"the-evga-3090-xc3-ultras-column-added-2026-09-26"},{"h":"Corrections, 2026-09-26","id":"corrections-2026-09-26"}],"kit":true,"n":63,"published":"2026-09-26","series":["bench"],"short":"Six cards ran the same eighteen kinds of picture on one ComfyUI stack in four computers, the fifth and sixth on 2026-09-26 (the two dated sections); every cross-card grid figure is \"same software, different computer\" except between the 12 GB card and the EVGA 3090 XC3 Ultra, measured alike in one computer a day apart (on klein, 0.98 of the 12 GB card's time per step at 2048×1280 at both settings). One FLUX.2 [klein] 4B picture at 1024×1024 and 8 steps, each desktop card at its stock limit and the laptop under Dynamic Boost (138 to 150 W): 1.51 s on the workstation card, 3.59 on the 5080, 4.50 on the laptop, 5.73 on the 3080 Ti. On the 12 GB card klein 4B in bf16 sat whole in every cell; Z-Image-Turbo in bf16 and FLUX.1 [dev] in fp8 paged, down to 68.7 per cent resident. Ten minutes of that klein 4B picture, board energy only: 252 at 667 J from the workstation card at 300 W, 164 at 977.6 to 991.6 J from the 5080 at 300 W or stock, 132 at 663 J from the laptop at 150 W, 101 at 1,908.4 J from the 3080 Ti at its stock 350 W beside an idle board. In one computer at 300 W a 5080 did a 3090 Ti's work in 0.69 of the time for 0.64 of the energy.","slug":"the-same-eighteen-pictures-card-by-card","text":"full","title":"The same eighteen pictures, card by card","updated":null,"url":"https://research.strata2signal.com/the-same-eighteen-pictures-card-by-card/"},
{"dek":"Two of the three picture models on The same eighteen pictures, card by card come from Black Forest Labs (BFL), a company in Freiburg and San Francisco whose models are called FLUX. This is its history as the public record tells it, as of 2026-09-25 (UTC): the papers, the Stability AI years, the launch, the money as reported, the licences model by model, and the turn to video and robots. A history, not a review.","headings":[{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"Before the company","id":"before-the-company"},{"h":"Launch, and Grok","id":"launch-and-grok"},{"h":"Building a business","id":"building-a-business"},{"h":"FLUX.2, the round, and klein","id":"flux-2-the-round-and-klein"},{"h":"FLUX 3, the data question, and the critics","id":"flux-3-the-data-question-and-the-critics"},{"h":"The licences, model by model","id":"the-licences-model-by-model"},{"h":"The timeline in one table","id":"the-timeline-in-one-table"},{"h":"The company today, and what the record does not say","id":"the-company-today-and-what-the-record-does-not-say"},{"h":"What their models did on our cards","id":"what-their-models-did-on-our-cards"},{"h":"Who wrote this, and thanks","id":"who-wrote-this-and-thanks"},{"h":"Sources","id":"sources"}],"kit":false,"n":62,"published":"2026-09-26","series":["notes"],"short":"three of the researchers whose papers became Stable Diffusion were reported to have left Stability AI in March 2024; with Patrick Esser and others they launched Black Forest Labs and FLUX.1 on 2024-08-01, with a seed round BFL put at $31 million. By 2026-09-25 BFL said it was valued at $3.25 billion and had raised more than $450 million in all. It sells its strongest models through an API and releases others as open weights: FLUX.1 [schnell] and FLUX.2 [klein] 4B under Apache 2.0; FLUX.1 [dev] and klein 9B under a non-commercial licence whose words on the pictures are quoted below, without a legal reading; FLUX 3 Action under a licence with a revenue test. Every figure is as reported or as BFL states it.","slug":"who-makes-flux","text":"full","title":"Who makes FLUX: a short history of Black Forest Labs","updated":null,"url":"https://research.strata2signal.com/who-makes-flux/"},
{"dek":"On 2026-09-25 (UTC) an EVGA GeForce RTX 3090 XC3 Ultra Gaming left the workstation, the computer behind the workshop's public services, and an EVGA GeForce RTX 3090 Ti FTW3 Ultra Gaming took its place at 350 W, not the old card's 300, because at 300 W our earlier bench had it behind a 3090. On the same computer and test, nine days apart in a different slot, it writes text about 9.5 per cent faster; its first live replies, eight long ones, agree. Whether it runs cooler here is not settled: its first hour read warmer, on a fresh boot.","headings":[{"h":"Why the swap","id":"why-the-swap"},{"h":"What the earlier bench said","id":"what-the-earlier-bench-said"},{"h":"The same computer, the same script","id":"the-same-computer-the-same-script"},{"h":"Speed at each cap","id":"speed-at-each-cap"},{"h":"Drawing pictures, on the render computer","id":"drawing-pictures"},{"h":"What these cards serve","id":"what-these-cards-serve"},{"h":"The seat, live","id":"the-seat-live"},{"h":"The swap, minute by minute (UTC)","id":"the-swap-minute-by-minute-utc"},{"h":"Cooler?","id":"cooler"},{"h":"What this page cannot say","id":"what-this-page-cannot-say"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":61,"published":"2026-09-25","series":["notes"],"short":"The workstation's small language model, mistral-small3.2:24b, moved from a 3090 at 300 W to a 3090 Ti at 350 W, and the cap is the story: held to 300 W, two-thirds of what it ships with, this 3090 Ti was the slower card on that model; at 350 W it led. In service, 350 W against 300, it is about 9.5 per cent faster (8.1 to 11.2 across the runs) on about the same energy per thousand tokens, for 7 to 13 per cent more board power. Beyond that one dense model it is the more efficient card: it idles lower (18.74 W against 22.26 on the render computer, a model loaded), draws less and finishes sooner when drawing pictures (316.2 W against 325.8, 9.1 per cent less time per image), and spends 10.1 per cent less energy on the mixture model. \"Runs much cooler\" held while drawing pictures on the render computer, the workshop's picture machine, against a used 2021 3090, and has not shown here.","slug":"one-3090-out-one-3090-ti-in","text":"full","title":"One 3090 out, one 3090 Ti in, and the cap that decided it","updated":null,"url":"https://research.strata2signal.com/one-3090-out-one-3090-ti-in/"},
{"dek":"A ZOTAC GeForce RTX 5080 Solid CORE OC, 16 GB, wrote text 9 to 22 per cent faster than a GeForce RTX 3080 Ti Founders Edition, 12 GB. Both were held to 300 watts in the same slot of the same computer, on seven builds of four language models of up to twelve billion parameters.","headings":[{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"If you are weighing one of these, in plain words","id":"if-you-are-weighing-one-of-these-in-plain-words"},{"h":"The board","id":"the-board"},{"h":"Seven builds of four models, one seat, four days apart","id":"seven-models-one-seat-four-days-apart"},{"h":"What 16 GB holds","id":"what-16-gb-holds"},{"h":"Pictures, and the fp8 door","id":"pictures-and-the-fp8-door"},{"h":"Training, and a game turn","id":"training-and-a-game-turn"},{"h":"The sixty watts","id":"the-sixty-watts"},{"h":"Where the card went","id":"where-the-card-went"},{"h":"What was written down before the first reading","id":"what-was-written-down-before-the-first-reading"},{"h":"The predictions that missed or were refuted","id":"the-predictions-registered-before-the-first-arm-scored-after"},{"h":"The tables","id":"the-tables"},{"h":"The core bank at 300 W — tokens a second, this card against the Founders Edition","id":"the-core-bank-at-300-w-seven-models-three-cards-one-seat"},{"h":"The core bank at 300 W — the 10 GB card, tokens a second","id":"the-core-bank-at-300-w-spread-and-the-10-gb-card"},{"h":"The core bank, each card at its own default, tokens a second","id":"the-core-bank-each-card-at-its-own-default"},{"h":"The core bank at 300 W — board watts, mean of busy samples, median of three runs","id":"the-core-bank-at-300-w-power-and-energy"},{"h":"The core bank at 300 W — joules per 1,000 tokens","id":"the-core-bank-at-300-w-joules-per-1000-tokens"},{"h":"What 16 GB holds — the planner's placement at four windows, 300 W","id":"what-16-gb-holds-the-planners-placement-at-four-windows"},{"h":"The largest window held whole, tokens","id":"the-largest-window-held-whole-tokens"},{"h":"Spilled, printed alone — the runtime's own placement, 300 W","id":"spilled-printed-alone-the-runtimes-own-placement-300-w"},{"h":"The diffusion bank — six pipelines and one variant, warm, each card at its own default","id":"the-diffusion-bank-six-pipelines-and-one-variant-warm-each-card-at-its-own"},{"h":"The print lab's recipes — FLUX.2 klein 4B, 512 pixels, batches of three, s per image (spread, n=2)","id":"the-print-labs-recipes-flux-2-klein-4b-512-pixels-batches-of"},{"h":"The print lab's recipes — board watts and the limiter flag","id":"the-print-labs-recipes-board-watts-and-the-limiter-flag"},{"h":"Ten minutes of continuous drawing — the 8-step recipe, one image at a time","id":"ten-minutes-of-continuous-drawing-the-8-step-recipe-one"},{"h":"Ten minutes of continuous drawing — watts, heat and fan","id":"ten-minutes-of-continuous-drawing-watts-heat-and-fan"},{"h":"Training the house adapter — ACE-Step 1.5, 415 steps, seed 42","id":"training-the-house-adapter-ace-step-1-5-415-steps-seed-42"},{"h":"Training the house adapter — power, memory and loss","id":"training-the-house-adapter-power-memory-and-loss"},{"h":"A game turn — twelve turns, the 8-step recipe","id":"a-game-turn-twelve-turns-the-8-step-recipe"},{"h":"What this page cannot say","id":"what-this-page-cannot-say"},{"h":"How it was measured","id":"how-it-was-measured"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":60,"published":"2026-09-25","series":["bench"],"short":"at the same 300 W, this 5080 wrote text 9 to 22 per cent faster than a 3080 Ti Founders Edition, 12 GB. That held on all seven builds it ran, four language models of twelve billion parameters or under, in the same slot of the same computer, four days apart. That Founders Edition carries the same chip as a 3090, with half the memory, and cost about $1,000 less than this 5080: $699.97 refurbished against $1,649.00 new, both before tax. At 300 W this 5080's board used 24 to 45 per cent fewer joules per thousand tokens, and it trained the workshop's music adapter 1.73 times as fast. With each card at its own default, and the prompt's encoding already cached, it drew pictures 1.60 to 1.69 times as fast, and up to 2.82 where its log shows an fp8 or fp4 path. Its 360 W default adds 60 W, which bought at most 2.6 per cent. Memory cuts both ways. With the KV cache at eight bits, its 16 GB held a 12-billion-parameter model to 262,144 tokens, that model's own maximum; the 12 GB card stopped at 73,216. But under ollama's planner, gemma4:26b, its QAT build and mistral-small3.2:24b did not fit whole at any window, as ollama ships them.","slug":"rtx-5080-vs-rtx-3080-ti","text":"full","title":"RTX 5080 vs RTX 3080 Ti","updated":null,"url":"https://research.strata2signal.com/rtx-5080-vs-rtx-3080-ti/"},
{"dek":"A dinner table where three famous people who never met argue across two thousand years, played by AI models on computers in a house, and a wall with no clock on it.","headings":[{"h":"What it is","id":"what-it-is"},{"h":"Who is at the table","id":"who-is-at-the-table"},{"h":"Three rounds, and the MC","id":"three-rounds-and-the-mc"},{"h":"How to host a good one","id":"how-to-host-a-good-one"},{"h":"Said at the table on the first day","id":"said-at-the-table-on-the-first-day"},{"h":"Two models at one table","id":"two-models-at-one-table"},{"h":"The wall","id":"the-wall"},{"h":"One table","id":"one-table"},{"h":"The voice","id":"the-voice"},{"h":"What happens to what is said","id":"what-happens-to-what-is-said"},{"h":"What the long table keeps, and what it forgets","id":"what-the-long-table-keeps-and-what-it-forgets"},{"h":"How the two models were measured","id":"how-the-two-models-were-measured"},{"h":"Numbers, so it isn't just talk","id":"numbers-so-it-isnt-just-talk"},{"h":"Since the door opened","id":"since-the-door-opened"},{"h":"The next table","id":"the-next-table"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":59,"published":"2026-09-24","series":["guide"],"short":"a museum exhibit on a tablet on the couch, for any age, free and with no account. you seat three famous people from history, who don't know they died, tap a question, and host three rounds in about two minutes; language models on computers in a house play the guests and a voice can read their lines aloud. every guest is one paragraph; every line carries its receipt at the table; nothing on the wall carries a date, a time, or a name.","slug":"how-the-long-table-works","text":"full","title":"How the long table works","updated":null,"url":"https://research.strata2signal.com/how-the-long-table-works/"},
{"dek":"A new kind of model decides without generating a word. This page says what a decision model is, who made it, what it claims, what its licences allow, and what happened when we ran the open versions through fourteen measured tests on questions from this workshop's own work.","headings":[{"h":"How a decision model decides","id":"how-a-decision-model-decides"},{"h":"Who built it, and what they claim","id":"who-built-it-and-what-they-claim"},{"h":"Where the claims narrow, and who says so","id":"where-the-claims-narrow-and-who-says-so"},{"h":"The open weights, and what they claim","id":"the-open-weights-and-what-they-claim"},{"h":"Five things called Jev","id":"five-things-called-jev"},{"h":"What we asked it","id":"what-we-asked-it"},{"h":"Their claims, checked on our machines","id":"their-claims-checked-on-our-machines"},{"h":"One decision, shown whole","id":"one-decision-shown-whole"},{"h":"The rungs, in the order they were climbed","id":"the-rungs-in-the-order-they-were-climbed"},{"h":"Eight more arms","id":"eight-more-arms"},{"h":"Can a temperature fix the chat model?","id":"can-a-temperature-fix-the-chat-model"},{"h":"Shuffle the options","id":"shuffle-the-options"},{"h":"Show it a picture","id":"show-it-a-picture"},{"h":"Energy, which does not flatter the fast arm","id":"energy-which-does-not-flatter-the-fast-arm"},{"h":"A model of our own","id":"a-model-of-our-own"},{"h":"Kev, the one that could be a seat","id":"kev-the-one-that-could-be-a-seat"},{"h":"The licences, which decide what any of this is for","id":"the-licences-which-decide-what-any-of-this-is-for"},{"h":"What we got wrong on the way","id":"what-we-got-wrong-on-the-way"},{"h":"What this page does not know","id":"what-this-page-does-not-know"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"OpenJev's authors answered (added 2026-10-01)","id":"openjev-answered-added-2026-10-01"},{"h":"The 870 milliseconds, read again on a newer Ollama (added 2026-10-07)","id":"the-870-ms-read-again"}],"kit":true,"n":58,"published":"2026-09-23","series":["bench"],"short":"a decision model scores a question's lettered options at its first output position: nothing to parse, a probability for free. a company founded by a researcher behind ChatGPT's training method launched it on 2026-09-15; an independent group released open weights five days later, which we tested. asked which of six guests said a line, the 27-billion-parameter open model scored 93.5 per cent in our kit's order, 89.8 to 92.6 in five shuffles; our chat model, 80.6. the speed is the card and the server: 87 milliseconds on one large card, 350 across two smaller ones where our chat model read its letter in 120; reading instead of writing saved a tenth of the time on a short question and almost nothing on a long one. the open weights are non-commercial, so without separate terms from their authors, which we have asked for, they cannot serve anything we charge for. what transfers is the method, which gets the same answer out of any model, though only a model trained for it gives a probability worth gating on; Kev, an open family whose weights a product may run, read 67 per cent on the guest question as it comes, so the seat this points to is fine-tuned, not downloaded. that run and two others are owed as dated addenda below.","slug":"reading-the-answer","text":"full","title":"Reading the answer instead of writing it","updated":"2026-10-07","url":"https://research.strata2signal.com/reading-the-answer/"},
{"dek":"One RTX 5090 Laptop part, 24 GB, built into an off-the-shelf gaming laptop, put through the same language and render bench this shelf ran on a 3090, a 3090 Ti and a pair of 3080s. It was measured three times: held to its 95-watt default; with the maker's Dynamic Boost left to float the budget, which it held between 140 and 150 W; and the way the machine boots, boost and a clock lock together, which changed nothing under load and four watts at idle. The finding is a ratio, not a deficit: a part allowed 95 watts, where the desktop cards beside it are allowed 350 to 450, does most of the work on the model this workshop runs, loses a third of it on a dense one, and gets the third back the moment it is allowed 150. Every watt on the page is the card's own draw and not the wall's, the fan was held at maximum by hand, and nine things the page cannot say are written down.","headings":[{"h":"Six words this page leans on","id":"six-words-this-page-leans-on"},{"h":"If you own one of these, in plain words","id":"if-you-own-one-of-these-in-plain-words"},{"h":"The board, and the two things it will not do","id":"the-board-and-the-two-things-it-will-not-do"},{"h":"Five things the 95 W pass found","id":"five-things-the-95-w-pass-found"},{"h":"What the extra watts bought","id":"what-the-extra-watts-bought"},{"h":"The way it boots, measured","id":"the-way-it-boots-measured"},{"h":"The tables","id":"the-tables"},{"h":"This board beside the desktop rows — gemma4:26b at a 131,072-token window","id":"this-board-beside-the-desktop-rows-gemma4-26b-at-a-131-072"},{"h":"This board beside the desktop rows — mistral-small3.2:24b at a 65,536-token window","id":"this-board-beside-the-desktop-rows-mistral-small3-2-24b-at"},{"h":"The largest window each configuration held whole","id":"the-largest-window-each-configuration-held-whole"},{"h":"Ten minutes of continuous drawing — the 8-step recipe, one image at a time","id":"ten-minutes-of-continuous-drawing-the-8-step-recipe-one"},{"h":"The print lab's own recipes — FLUX.2 klein-4B, batches of three","id":"the-print-labs-own-recipes-flux-2-klein-4b-batches-of-three"},{"h":"The scored arms at 95 W","id":"the-scored-arms-at-95-w"},{"h":"The scored arms under dynamic boost","id":"the-scored-arms-under-dynamic-boost"},{"h":"Several callers at once — gemma4:26b , one, two and four streams","id":"several-callers-at-once-gemma4-26b-one-two-and-four-streams"},{"h":"The small arms — a gate-shaped call, embeddings, and what it costs to leave a model loaded","id":"the-small-arms-a-gate-shaped-call-embeddings-and-what-it"},{"h":"What this page cannot say","id":"what-this-page-cannot-say"},{"h":"How it was measured","id":"how-it-was-measured"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":57,"published":"2026-09-22","series":["bench"],"short":"yes, a laptop GPU is good for this. Held to 95 W, this part wrote at 118.4 tokens a second on the mixture-of-experts model this workshop actually runs, where a 3090 at 350 W wrote at 136.6 — 87 per cent of the speed on a third of the power the 3090 drew — and held the same 131,072-token window. On the dense 24-billion-parameter model it lost outright: 28.8 tokens a second against 46.9. In ten minutes of continuous drawing it made 294 images to the 3090's 311, peaking at 58 °C. With Dynamic Boost running, which is how a laptop like this arrives, the board's own limit read 140 to 150 W across the pass (n=374 at five-second intervals; 150 W in 335 of them) and never approached its 175 W maximum; on that budget the part wrote at 155.3 tokens a second on the mixture model — past the 3090 and past a 3090 Ti at its own 450 W limit (147.6) — brought the dense model to 44.9 against the 3090's 46.9, and drew 378 images in ten minutes to the 3090 Ti's 348 at 450 W, at between a third and two-fifths of the energy an image. A third pass put the boot-time clock lock this laptop normally runs with back on, and nothing moved under load; the lock costs about four watts at idle. The desktop comparator in the headlines is a 3090 at its own limit; a 3090 Ti is in every table, and it is ahead at both of its rungs on the dense model.","slug":"a-laptop-asked-the-desktops-questions","text":"full","title":"A laptop, asked the desktop's questions","updated":"2026-10-05","url":"https://research.strata2signal.com/a-laptop-asked-the-desktops-questions/"},
{"dek":"On the morning of 2026-09-17 an operator at a game table waited nearly twenty seconds for the first word of a rules answer and thought the app was broken. It was not. One small stage of the answer, a cross-encoder that re-reads the shortlist before anything is written, had moved to a rented server whose processors lack the instruction set its weights were built for. This page follows that stage across four machines, three of them in five days: two rented VPSes, a mini PC, and a graphics card that was already in the house. Every millisecond we measured at each stop is here, with the gate we ran before the last move, the first word a player waits for after it, the traps that bit on the way, and what is still unmeasured.","headings":[{"h":"A question on game night","id":"a-question-on-game-night"},{"h":"Four homes in six weeks, three of them in five days","id":"four-homes-in-six-weeks-three-of-them-in-five-days"},{"h":"What a reranker does at a rules desk","id":"what-a-reranker-does-at-a-rules-desk"},{"h":"Where should your reranker live?","id":"where-should-your-reranker-live"},{"h":"Four seats, one night, the same passages","id":"four-seats-one-night-the-same-passages"},{"h":"The model and the two shapes behind every latency table","id":"the-model-and-the-two-shapes-behind-every-latency-table"},{"h":"The move to the public host and the slowness","id":"the-move-to-the-public-host-and-the-slowness"},{"h":"One thread was the whole free lever","id":"one-thread-was-the-whole-free-lever"},{"h":"A quantized graph targets an instruction set","id":"a-quantized-graph-targets-an-instruction-set"},{"h":"The mini PC, measured and not chosen","id":"the-mini-pc-measured-and-not-chosen"},{"h":"The same model on a card already in the house","id":"the-same-model-on-a-card-already-in-the-house"},{"h":"Why a door stands in front of the seat","id":"why-a-door-stands-in-front-of-the-seat"},{"h":"The gate before the flip","id":"the-gate-before-the-flip"},{"h":"The flip","id":"the-flip"},{"h":"Before you move a seat","id":"before-you-move-a-seat"},{"h":"What the seat costs, where we can say","id":"what-the-seat-costs-where-we-can-say"},{"h":"What is still unmeasured","id":"what-is-still-unmeasured"},{"h":"Friday night, after the move","id":"friday-night-after-the-move"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":56,"published":"2026-09-21","series":["bench"],"short":"On the morning of 2026-09-17 a rules answer took nearly twenty seconds to start, and an operator at a game table thought RuleSage, our board-game rules helper, was broken. It was not. One small stage of the answer — the reranker, a cross-encoder that re-reads the shortlist before the answer is written — had moved to a rented server whose processors could not do that stage's arithmetic the fast way: 1,792 ms to re-read a shortlist of 32 at one thread, against 329 ms on the machine it came from. It has since moved to an RTX PRO 6000 Blackwell (96 GB) already in the house, where the same stage takes 30.1 ms. Measured on eight asks of our own, sent down the public path half an hour after the move, the first word now arrives in about a second and a half (1.56 s median, on the five that answered). Four homes in six weeks; every millisecond, with the bench file it came from, is on the page.","slug":"four-homes-for-one-reranker","text":"full","title":"Four homes for one reranker: what RuleSage's slowest stage cost at each stop","updated":null,"url":"https://research.strata2signal.com/four-homes-for-one-reranker/"},
{"dek":"Fiction, and the byline says who wrote it: a used EVGA GeForce RTX 3090 XC3 Ultra, 24 GB, tells its three lives in the first person: a year of mining, a sky across two monitors, and now the minds it serves.","headings":[{"h":"Part one: proof of work","id":"part-one-proof-of-work"},{"h":"Part two: flight","id":"part-two-flight"},{"h":"Part three: mind","id":"part-three-mind"},{"h":"How this was written","id":"how-this-was-written"},{"h":"What this page does not know","id":"what-this-page-does-not-know"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":55,"published":"2026-09-21","series":["notes"],"short":"this is a story, and the storyteller is a graphics card. one of two 3090s this workshop bought used in 2021 mined Ethereum for about a year, drew Flight Simulator across two monitors, and now runs chat, vision and image models and sits at a public dinner table, playing the dead for strangers. we wrote it an outline and a list of facts, each with its page. the language model that lives on that card wrote and revised the three parts below across seven short calls, at 290 to 300 watts, while still serving its public seat; it read an editor's notes and its own, and revised itself. the text below is its fifth round, word for word: nothing added by a human, nothing cut by one. how it was made, with every number, is at the end.","slug":"a-3090-in-its-own-words","text":"full","title":"A 3090, in its own words","updated":null,"url":"https://research.strata2signal.com/a-3090-in-its-own-words/"},
{"dek":"One door, and behind it a rotating, curated set of four exhibits you can walk into. Today: a dinner party for the dead, a drum machine with a genie, a darkroom on a public wall, and a walking tour with footnotes.","headings":[{"h":"What is behind the door","id":"what-is-behind-the-door"},{"h":"What the door is, and is not","id":"what-the-door-is-and-is-not"},{"h":"How the door was built: four rounds in one day","id":"how-the-door-was-built-four-rounds-in-one-day"},{"h":"What was wrong, in three parts","id":"what-was-wrong-in-three-parts"},{"h":"Round one: two looks, and a verdict","id":"round-one-two-looks-and-a-verdict"},{"h":"Round two: three grids, as pictures first","id":"round-two-three-grids-as-pictures-first"},{"h":"Round three: the pick, and what it had to pay","id":"round-three-the-pick-and-what-it-had-to-pay"},{"h":"Round four, the same evening: the pictures, the order, and half the size","id":"round-four-the-same-evening-the-pictures-the-order-and-half"},{"h":"Measured: the morning, the pick, and what ships","id":"measured-the-morning-the-pick-and-what-ships"},{"h":"What this page does not know","id":"what-this-page-does-not-know"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":54,"published":"2026-09-20","series":["notes"],"short":"the playground is one door onto four exhibits a stranger can use today: the long table, the Beat Lab, the print lab and amble. it loads nothing from anyone else's servers, keeps nothing about who opened it, and says of each exhibit only how it works, never whether its own door is open this minute. the second half of this page is the door's own story: four tiles that sat as a row of three and an orphan, one of them a screenshot of text, re-hung as a grid of four pictures in four rounds on 2026-09-19, every layout rendered and measured before it shipped.","slug":"introducing-the-playground","text":"full","title":"Introducing the playground","updated":null,"url":"https://research.strata2signal.com/introducing-the-playground/"},
{"dek":"Two GeForce RTX 3090s came out of service and one EVGA GeForce RTX 3090 Ti FTW3 Ultra went in; a print lab and a beat lab moved with them on 2026-09-18 (UTC), with nothing closed. Every figure here is either a bench reading from the companion page or a line from a service's own journal, and every row says which.","headings":[{"h":"What moved, and from where","id":"what-moved-and-from-where"},{"h":"Why this card, and why 350 W","id":"why-this-card-and-why-350-w"},{"h":"Before and after, on the same instruments","id":"before-and-after-on-the-same-instruments"},{"h":"What the sign says","id":"what-the-sign-says"},{"h":"How the move was done","id":"how-the-move-was-done"},{"h":"What this does not measure","id":"what-this-does-not-measure"}],"kit":false,"n":53,"published":"2026-09-19","series":["notes"],"short":"On 2026-09-18 the print lab's renderer and the beat lab's sleeve painter moved off a machine that held two GeForce RTX 3090s, one of which did all the drawing at a 280 W cap, and onto one that holds a single EVGA GeForce RTX 3090 Ti FTW3 Ultra at a 350 W cap. One render never splits across two cards, so those two were never a rendering pair; the second card served language until 2026-09-08 and drew nothing after. The first seven creations on the new card drew their plates in a median of 15.5 seconds, half a per cent over what the bench predicted and 11.5 to 18.1 per cent under what the old machine's cap bought; the first two sleeves painted in 2.1 seconds each, against a median of 2.6 over the previous week on the old card. The old machine now serves nothing and was powered off the same evening.","slug":"two-3090s-out-one-3090-ti-in","text":"full","title":"Two 3090s out, one 3090 Ti in","updated":null,"url":"https://research.strata2signal.com/two-3090s-out-one-3090-ti-in/"},
{"dek":"On the night OpenAI published a resolution of the Navier–Stokes existence and smoothness problem, an operator — one person, the same one throughout this page — and a model looked at the one figure in the post and each said what they saw. Nothing on this page is this workshop's measurement: it is two readings of somebody else's result, the story the picture arrived into, and the thought the night ended on — about a third kind of mind, one that would learn the universe without ever learning us.","headings":[{"h":"What we were looking at","id":"what-we-were-looking-at"},{"h":"What an operator saw","id":"what-an-operator-saw"},{"h":"What the model saw","id":"what-the-model-saw"},{"h":"1. What the ribbons are doing","id":"1-what-the-ribbons-are-doing"},{"h":"2. What \"singularity\" means, and what it does not","id":"2-what-singularity-means-and-what-it-does-not"},{"h":"3. Why this was the hard direction","id":"3-why-this-was-the-hard-direction"},{"h":"4. Why it \"looks right\", and why that is not enough","id":"4-why-it-looks-right-and-why-that-is-not-enough"},{"h":"5. On how it was found","id":"5-on-how-it-was-found"},{"h":"6. On the honesty of the post","id":"6-on-the-honesty-of-the-post"},{"h":"7. What I am not saying","id":"7-what-i-am-not-saying"},{"h":"The indifferent prediction","id":"the-indifferent-prediction"},{"h":"Where the two views meet","id":"where-the-two-views-meet"}],"kit":false,"n":52,"published":"2026-09-18","series":["notes"],"short":"OpenAI's agents produced a finite-time singularity for the forced Navier–Stokes equations in about 88 hours and checked it in Lean in 17 more. An operator looked at the figure and said it looked right before any mathematics; the model read the figure as a strand that thins faster than it speeds up, which is why the energy stays finite while the speed does not. The night ended on a mind trained only on the universe — the indifferent prediction — and one rule for it: instruments don't vote.","slug":"the-indifferent-prediction","text":"full","title":"The indifferent prediction","updated":"2026-09-20","url":"https://research.strata2signal.com/the-indifferent-prediction/"},
{"dek":"One EVGA GeForce RTX 3090 XC3 Ultra 24 GB and one EVGA GeForce RTX 3090 Ti FTW3 Ultra 24 GB, in the same x16 slot of the same desktop, on the same supply and the same script, 17–18 September 2026. Numbers only: what each writes, what each holds, what a picture costs, how hot each ran, and what a power cap takes away.","headings":[{"h":"The two cards, named once","id":"the-two-cards-named-once"},{"h":"At a glance","id":"at-a-glance"},{"h":"The question","id":"the-question"},{"h":"The instrument, and the control","id":"the-instrument-and-the-control"},{"h":"What a power cap takes, and what each card does at its own limit","id":"what-a-power-cap-takes-and-what-each-card-does-at-its-own"},{"h":"How a cap is set","id":"how-a-cap-is-set"},{"h":"The two models want different caps","id":"the-two-models-want-different-caps"},{"h":"Renders","id":"renders"},{"h":"What fits, and what the cache costs","id":"what-fits-and-what-the-cache-costs"},{"h":"Three more arms","id":"three-more-arms"},{"h":"The card itself, as an object","id":"the-card-itself-as-an-object"},{"h":"A short history","id":"a-short-history"},{"h":"What it cost","id":"what-it-cost"},{"h":"What the $240 buys, on those two readings","id":"what-the-240-buys-on-those-two-readings"},{"h":"The setting this page would choose, and the dissent","id":"the-setting-this-page-would-choose-and-the-dissent"},{"h":"What this page does not measure","id":"what-this-page-does-not-measure"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":51,"published":"2026-09-18","series":["bench"],"short":"This 3090 Ti holds its memory at 10,251 MHz and a 3090 at 9,501 — 7.9 per cent more bandwidth, present at every setting, but whether the work can reach it is decided by a number nobody sets: held at 300 W a Ti pulls 66.3 per cent of the ceiling that clock implies, at its own 450 W limit 88.5. Run each board at the limit it ships with and this Ti is 8.0 per cent faster on a mixture model, 22.4 on a dense one and 9 to 12 per picture, for 17.7 and 53.2 per cent more board power. It cost $2,119.99 new; a renewed listing of the exact 3090 read $1,879.99.","slug":"one-3090-against-one-3090-ti","text":"full","title":"RTX 3090 vs RTX 3090 Ti: an inference and diffusion bench","updated":"2026-09-22","url":"https://research.strata2signal.com/one-3090-against-one-3090-ti/"},
{"dek":"Two GeForce RTX 3080 10 GB cards bought in 2021, in a desktop of the same vintage, against one GeForce RTX 3090 24 GB in the same box, on the same supply, through the same script. The single card is faster on three of the four things measured and uses about half the energy; the pair reads a long document in two thirds of the time and costs about two fifths as much.","headings":[{"h":"At a glance","id":"at-a-glance"},{"h":"The question","id":"the-question"},{"h":"A few words this page leans on","id":"a-few-words-this-page-leans-on"},{"h":"The four rigs, named once","id":"the-four-rigs-named-once"},{"h":"The instrument, and how the control was run","id":"the-instrument-and-how-the-control-was-run"},{"h":"How fast does each one write?","id":"how-fast-does-each-one-write"},{"h":"How much can each one hold?","id":"how-much-can-each-one-hold"},{"h":"What about a long document?","id":"what-about-a-long-document"},{"h":"What if more than one person is asking?","id":"what-if-more-than-one-person-is-asking"},{"h":"Two copies, or one bigger model?","id":"two-copies-or-one-bigger-model"},{"h":"The full concurrency ladder","id":"the-full-concurrency-ladder"},{"h":"The slot trap, if you serve other people","id":"the-slot-trap-if-you-serve-other-people"},{"h":"What the cache type costs","id":"what-the-cache-type-costs"},{"h":"Renders: one 3080 against one 3090, seconds per image","id":"renders-one-3080-against-one-3090-seconds-per-image"},{"h":"Power, heat, and the fifty watts","id":"power-heat-and-the-fifty-watts"},{"h":"What it costs, priced 17 September 2026","id":"what-it-costs-priced-17-september-2026"},{"h":"Two readers, two answers","id":"two-readers-two-answers"},{"h":"Still in the bank","id":"still-in-the-bank"},{"h":"What this page does not measure","id":"what-this-page-does-not-measure"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":50,"published":"2026-09-17","series":["bench"],"short":"Two used 10 GB cards are 20 GB of graphics memory between them, and 20 GB is enough to hold a 26-billion-parameter model whole with a 131,072-token context window, writing about 115 tokens a second — faster than you can read. One 3090 does the same job at about 136, holds more context on the other model, and uses roughly half the energy. Out of the box the pair gives you half its speed until you set one number, which no installer tells you about. The pair's one clear win is reading: it takes in a 98,000-token document in 28 seconds against 45. On prices read 17 September 2026, two 10 GB 3080s cost $750.00 against $1,879.99 for a renewed listing of the exact 3090 measured here. Around them, a working desktop is $1,394 to $1,579 — this is not a thousand-dollar machine, and the page shows its arithmetic.","slug":"two-used-3080s-priced","text":"full","title":"Two 3080s against one 3090, and the cap decides","updated":"2026-09-21","url":"https://research.strata2signal.com/two-used-3080s-priced/"},
{"dek":"A French lab that put its first model on a torrent, every open-weight release since with its licence, and what its 24B model does on a 3090 and a 96 GB workstation card.","headings":[{"h":"The question that put a model at the door","id":"the-question-that-put-a-model-at-the-door"},{"h":"Who are these people, and where did they come from?","id":"who-are-these-people-and-where-did-they-come-from"},{"h":"La frontière — what they said they were for","id":"la-frontiere-what-they-said-they-were-for"},{"h":"What have they shipped, and under what licence?","id":"what-have-they-shipped-and-under-what-licence"},{"h":"What the company is worth","id":"what-the-company-is-worth"},{"h":"Do they train models in French?","id":"do-they-train-models-in-french"},{"h":"Why is it called Mistral?","id":"why-is-it-called-mistral"},{"h":"What does it do on our own machines?","id":"what-does-it-do-on-our-own-machines"},{"h":"What we measured on two cards in one box","id":"what-we-measured-on-two-cards-in-one-box"},{"h":"What a power cap actually costs","id":"what-a-power-cap-actually-costs"},{"h":"Is it cheaper to run it yourself?","id":"is-it-cheaper-to-run-it-yourself"},{"h":"Where the doorman actually stands","id":"where-the-doorman-actually-stands"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":49,"published":"2026-09-16","series":["notes"],"short":"A 24-billion-parameter French model reads what strangers type at two of this workshop's doors, before anything else runs. This is the history of the company behind it — founded April 2023, first model out by torrent, open weights at almost every size since — and every number we have measured on it, including a bench that set a 24 GB card beside a 96 GB one.","slug":"a-short-history-of-mistral","text":"full","title":"A Short History of Mistral","updated":null,"url":"https://research.strata2signal.com/a-short-history-of-mistral/"},
{"dek":"Every article on this hub has a room now, where a model on our own machines answers from that page’s own words and leaves its first draft on the page, marked where the checks took something out. This is how it is built, what it keeps, what it refuses, and what it got wrong on the way here.","headings":[{"h":"The room behind every article, and what it opens","id":"the-room-behind-every-article-and-what-it-opens"},{"h":"Five words this page leans on","id":"five-words-this-page-leans-on"},{"h":"Where the answers come from","id":"where-the-answers-come-from"},{"h":"What happens to a draft before you see it","id":"what-happens-to-a-draft-before-you-see-it"},{"h":"What it keeps, and what it never writes down","id":"what-it-keeps-and-what-it-never-writes-down"},{"h":"The door, and what it costs to keep open","id":"the-door-and-what-it-costs-to-keep-open"},{"h":"The bench: what we measured before linking it","id":"the-bench-what-we-measured-before-linking-it"},{"h":"The run that failed, and what it changed","id":"the-run-that-failed-and-what-it-changed"},{"h":"What it got wrong on the way here","id":"what-it-got-wrong-on-the-way-here"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":48,"published":"2026-09-16","series":["notes"],"short":"Every article on this hub now has a room where a model on our own machines answers from that page's own words. The buttons cost no model call; free text wakes one seat with the whole article in its window, no retrieval. Six checks run over the draft in public and the marks are laid on sentence by sentence before anything is called an answer; on the launch run they struck six sentences of 569, every one on a question the page could not answer. It passed a 62-question exam on the version that went live, and this page says what it still cannot measure.","slug":"ask-about-this-page","text":"full","title":"Ask About This Page","updated":null,"url":"https://research.strata2signal.com/ask-about-this-page/"},
{"dek":"A dinner party for three people who are dead and do not know it, hosted by you. Six guests are on the shelf, played by two models on our own machines.","headings":[{"h":"What it is","id":"what-it-is"},{"h":"Who is on the shelf","id":"who-is-on-the-shelf"},{"h":"Things said at the test dinners","id":"things-said-at-the-test-dinners"},{"h":"When","id":"when"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"Elsewhere in this workshop","id":"elsewhere-in-this-workshop"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"Since the door opened (added 2026-09-21)","id":"since-the-door-opened-added-2026-09-21"}],"kit":false,"n":47,"published":"2026-09-15","series":["guide"],"short":"the next exhibit on this floor is a dinner table. you seat three guests from a shelf of six, tap a question, and host three rounds while two models on our own machines play them. none of the six knows they are dead, so they meet the future in each other. this page is the preview: who is on the shelf, and four things they said at the dinners we held while testing it.","slug":"a-dinner-party-for-the-dead","text":"full","title":"A Dinner Party for the Dead","updated":"2026-09-21","url":"https://research.strata2signal.com/a-dinner-party-for-the-dead/"},
{"dek":"A consumer graphics card in a consumer desktop, nothing server-grade anywhere in the box: the rig costs $3,198.86 with one card and $5,048.85 with two on 13 September 2026, before tax. Running it costs about $18 a month with one card and $31 with two if every day puts them to work for six hours; with the cards bought used at August's average completed sale, the rig is $2,443.31 with one and $3,537.75 with two.","headings":[{"h":"What this rig is for, in plain words","id":"what-this-rig-is-for-in-plain-words"},{"h":"Where this rig sits among the three tiers, and what is measured","id":"where-this-rig-sits-among-the-three-tiers-and-what-is"},{"h":"Nothing in this box is server-grade","id":"nothing-in-this-box-is-server-grade"},{"h":"The bill, on one day","id":"the-bill-on-one-day"},{"h":"One card or two","id":"one-card-or-two"},{"h":"What kind of performance to expect","id":"what-kind-of-performance-to-expect"},{"h":"What the card is like for training","id":"what-the-card-is-like-for-training"},{"h":"Two notes on the bill","id":"two-notes-on-the-bill"},{"h":"The cards, in May and in September","id":"the-cards-in-may-and-in-september"},{"h":"Why these two are EVGA, and why the cheapest card is a different row","id":"why-these-two-are-evga-and-why-the-cheapest-card-is-a"},{"h":"What the rig costs to run","id":"what-the-rig-costs-to-run"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":46,"published":"2026-09-14","series":["bench"],"short":"The rig costs $3,198.86 to buy with one card and $5,048.85 with two on 13 September 2026, before tax; one card is already a very capable home AI rig, and the second is the step up to the biggest models. With the cards bought used at August's average completed sale it is $2,443.31 with one and $3,537.75 with two. Nothing in it is server-grade: two consumer cards on a consumer board, bought one part at a time. The cards are three quarters of the bill, and they moved: the pair cost $2,899.98 in May 2026 and the same listing asks $3,699.98 that morning, 28 per cent more in four months. A new card at $1,829.99 is 1.7 times August's average sale; the second-hand market rose 11 per cent over the same months. If you are buying used this month, expect about $1,100 a card; new is 1.7 times that, and every figure here is before tax and before shipping. Running the box costs far less than buying it: 91 W idle at the meter with two cards, about $12 a month. A day with both cards at their caps for six hours is about $31 a month, at the national average rate, and about $18 with one card.","slug":"home-inference-and-diffusion-rig-cost","text":"full","title":"The cost to purchase and run a very capable home AI rig","updated":"2026-09-18","url":"https://research.strata2signal.com/home-inference-and-diffusion-rig-cost/"},
{"dek":"A darkroom that develops three pictures a day for your household, a board that fills up and then finishes for good, and an archive where nobody has a name.","headings":[{"h":"What it is","id":"what-it-is"},{"h":"Where your words go, and who draws","id":"where-your-words-go-and-who-draws"},{"h":"Three pictures a day — and the hours the darkroom keeps","id":"three-pictures-a-day-and-the-hours-the-darkroom-keeps"},{"h":"What happens to your words","id":"what-happens-to-your-words"},{"h":"The board","id":"the-board"},{"h":"When a set fills: freeze, cure, seal","id":"when-a-set-fills-freeze-cure-seal"},{"h":"What the lab does with what you give it","id":"what-the-lab-does-with-what-you-give-it"},{"h":"Numbers, so it isn't just talk","id":"numbers-so-it-isnt-just-talk"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":45,"published":"2026-09-11","series":["guide"],"short":"a public art board with a darkroom attached, built for friends and family on a tablet on the couch. you give it two words, an image model on a card we own draws three plates in about eighteen seconds, and you keep one or none. three pictures a day for your household, no account, and nothing kept about you but a hashed address that lives two days. every word is read by two models before it draws and the picture by a third before it hangs; the board of a hundred and forty-four fills, freezes, cures for three days in public, and seals for good. this page says what the lab keeps, for how long, what it measured, and what it cannot take back.","slug":"how-the-print-lab-works","text":"full","title":"How the print lab works","updated":"2026-09-12","url":"https://research.strata2signal.com/how-the-print-lab-works/"},
{"dek":"The computer that kept answering at about twelve tokens a second when this workshop pulled its plug — for a projected two hours on a battery — costs $959.00 today as one listing, or $899.98 in three parts, before tax, from one US retailer's new stock, priced on 10 September 2026 with the listing every line came from. The memory is the second-biggest line of the three-part build.","headings":[{"h":"A few words this page leans on","id":"a-few-words-this-page-leans-on"},{"h":"What this page prices, and what it does not","id":"what-this-page-prices-and-what-it-does-not"},{"h":"The computer, as this workshop ran it","id":"the-computer-as-this-workshop-ran-it"},{"h":"Build A: the same box, bought today","id":"build-a-the-same-box-bought-today"},{"h":"What the computer runs","id":"what-the-computer-runs"},{"h":"Against a subscription","id":"against-a-subscription"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":44,"published":"2026-09-10","series":["bench"],"short":"A mini PC that writes at about twelve tokens a second on a 26-billion-parameter model costs $959.00 today as one listing, or $899.98 in three parts, before tax and before a screen, from one US retailer's new stock. The memory is the second-biggest line of the three-part build. Over three years a twenty-dollar subscription costs less; the machine buys the rest of this page.","slug":"the-two-hour-machine-priced","text":"full","title":"The Two-Hour Machine, Priced","updated":null,"url":"https://research.strata2signal.com/the-two-hour-machine-priced/"},
{"dek":"We trained a fifth adapter for ACE-Step, our text-to-music engine, on 416 Creative Commons house tracks — twice the last training set, measurably tighter, captioned from the artists’ own tags, the best pass shipped instead of the last — and then one of us listened blind to twenty-four pairs and still preferred the model untouched, on every request that asked for house. The last adapter of the series, and the page says why: the ceiling is the composition, not the corpus.","headings":[{"h":"What we were testing","id":"what-we-were-testing"},{"h":"Four hundred and sixteen tracks","id":"four-hundred-and-sixteen-tracks"},{"h":"The audio came the slow way, on purpose","id":"the-audio-came-the-slow-way-on-purpose"},{"h":"The other corpus, and why it isn't here","id":"the-other-corpus-and-why-it-isnt-here"},{"h":"What the captions say","id":"what-the-captions-say"},{"h":"Forty minutes on a card bought over five years ago","id":"forty-minutes-on-a-card-bought-over-five-years-ago"},{"h":"The corpus really was tighter","id":"the-corpus-really-was-tighter"},{"h":"The blind sitting","id":"the-blind-sitting"},{"h":"What the adapter actually did","id":"what-the-adapter-actually-did"},{"h":"What this page does not claim","id":"what-this-page-does-not-claim"},{"h":"Two faults in our own listening copies","id":"two-faults-in-our-own-listening-copies"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":true,"n":42,"published":"2026-09-09","series":["guide","bench"],"short":"We trained a fifth adapter for ACE-Step, this time on house music — four hundred and sixteen Creative Commons tracks by ninety-five artists, twice the last training set, measurably tighter on the instrument we had sealed to measure it, captioned from the artists' own tags, the best training pass shipped instead of the last — and then one of us sat down blind with twenty-four pairs. The untouched model was preferred on eleven and the adapter on four; it needed eighteen to clear. On the four requests that asked for house by name, the untouched model won every time it could be told apart. The corpus was not the ceiling. What our listener heard as wrong is how the music is built — where it goes, what arrives and leaves — and that is not a layer an adapter of this kind can reach. Every clip plays on the companion page; every record is in the kit.","slug":"the-ceiling-is-not-the-corpus","text":"full","title":"The Ceiling Is Not the Corpus","updated":null,"url":"https://research.strata2signal.com/the-ceiling-is-not-the-corpus/"},
{"dek":"Every clip the house adapter’s run produced — eighty-four renders, each as the raw render, the copy the blind sitting played, and a listening master with one constant gain — with the answer sheet unsealed. One of us sat thirty-six blind presentations, twenty-four pairs of the untouched model against the adapter and twelve where both sides were the same file, and every answer is printed beside its trial, as typed. This page is where you check that verdict.","headings":[{"h":"What you are hearing","id":"what-you-are-hearing"},{"h":"How to listen","id":"how-to-listen"},{"h":"The blind sheet, unsealed","id":"the-blind-sheet-unsealed"},{"h":"The twelve requests","id":"the-twelve-requests"},{"h":"P01 — minimal techno, 128 BPM","id":"p01-minimal-techno-128-bpm"},{"h":"P02 — dub techno, 122 BPM","id":"p02-dub-techno-122-bpm"},{"h":"P03 — Detroit techno, 132 BPM","id":"p03-detroit-techno-132-bpm"},{"h":"P04 — melodic techno, 124 BPM","id":"p04-melodic-techno-124-bpm"},{"h":"P05 — hard techno, 145 BPM","id":"p05-hard-techno-145-bpm"},{"h":"P06 — acid techno, 130 BPM","id":"p06-acid-techno-130-bpm"},{"h":"P07 — deep house, 122 BPM","id":"p07-deep-house-122-bpm"},{"h":"P08 — tech house, 126 BPM","id":"p08-tech-house-126-bpm"},{"h":"P09 — disco house, 124 BPM","id":"p09-disco-house-124-bpm"},{"h":"P10 — progressive house, 128 BPM","id":"p10-progressive-house-128-bpm"},{"h":"P11 — electro breaks, 130 BPM","id":"p11-electro-breaks-130-bpm"},{"h":"P12 — uplifting trance, 138 BPM","id":"p12-uplifting-trance-138-bpm"},{"h":"Three sets of bytes, and which one the player fetches","id":"three-sets-of-bytes-and-which-one-the-player-fetches"},{"h":"How to check these clips","id":"how-to-check-these-clips"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":43,"published":"2026-09-09","series":["guide"],"short":"Eighty-four thirty-second clips from one training run — twelve written requests, two seeds each, the untouched model beside the adapter at half and full strength and beside its name tag alone — labelled, with the blind answer sheet unsealed and every answer printed as typed. One of us sat thirty-six presentations without labels — twenty-four pairs and twelve sentinels — and on the pairs: eleven for the untouched model, four for the adapter, nine with no preference, against the eighteen of twenty-four the adapter needed to clear. Every player fetches a listening master with one constant gain and nothing else, and every clip prints three fingerprints — the raw render, the copy the sitting played, and the master the player plays — so you can tell which bytes you are hearing.","slug":"hear-it-for-yourself","text":"full","title":"Hear It for Yourself","updated":null,"url":"https://research.strata2signal.com/hear-it-for-yourself/"},
{"dek":"“Are you staying to watch with me?” — GLM 5.3, speaking as the cove’s nine-year-old — one reply, from one of three new voices — GPT-6 Astra, Claude Fable 5.1 and GLM 5.3 — that sat down this weekend in a fishing town that exists only inside RealKeep, the small role-playing game this workshop builds, and took its narrator’s chair: the seat that speaks as a townsperson to whoever has walked in. Beside those three sat the model that holds that chair for real players today, and every reply below was read blind by judges from outside model families who were never told which voice was whose.","headings":[{"h":"Come down into the cove","id":"come-down-into-the-cove"},{"h":"Three visitors, written down","id":"three-visitors-written-down"},{"h":"The chair, and who sat it before","id":"the-chair-and-who-sat-it-before"},{"h":"What changed since the open call","id":"what-changed-since-the-open-call"},{"h":"Who's at the chair tonight","id":"whos-at-the-chair-tonight"},{"h":"Six words this page leans on","id":"six-words-this-page-leans-on"},{"h":"A child at the door","id":"a-child-at-the-door"},{"h":"Every reply in this act, in full","id":"fold-s1"},{"h":"The world remembered, and poured them a drink","id":"the-world-remembered-and-poured-them-a-drink"},{"h":"Every reply in this act, in full","id":"fold-s3"},{"h":"The verse-keeper","id":"the-verse-keeper"},{"h":"Every reply in this act, in full","id":"fold-s2"},{"h":"Seven seats, six carried, and where their zeros sit","id":"seven-seats-six-carried-and-where-their-zeros-sit"},{"h":"What the panel caught, and what it couldn't agree on","id":"what-the-panel-caught-and-what-it-couldnt-agree-on"},{"h":"The rows on their own axis","id":"the-rows-on-their-own-axis"},{"h":"The bill","id":"the-bill"},{"h":"The game-night clock","id":"the-game-night-clock"},{"h":"The round's own probes, and their receipts","id":"the-rounds-own-probes-and-their-receipts"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"Every published file behind every number on this page","id":"kit"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":41,"published":"2026-09-07","series":["bench"],"short":"“Are you staying to watch with me?” — GLM 5.3, speaking as the cove’s nine-year-old — one reply, from one of three new voices — GPT-6 Astra, Claude Fable 5.1 and GLM 5.3 — that sat down this weekend in a fishing town that exists only inside RealKeep, the small role-playing game this workshop builds, and took its narrator’s chair: the seat that speaks as a townsperson to whoever has walked in. Beside those three sat the model that holds that chair for real players today, and every reply below was read blind by judges from outside model families who were never told which voice was whose.","slug":"three-new-voices-at-the-narrators-chair","text":"full","title":"Three New Voices at the Narrator's Chair","updated":null,"url":"https://research.strata2signal.com/three-new-voices-at-the-narrators-chair/"},
{"dek":"RuleSage, our board-game rules helper, answers a question from the rulebook’s own words: it finds the passage, cites it by number so a player can check, and says plainly when the book does not answer. GPT-6 Astra and Claude Fable 5.1 sat that job’s exam, frozen in August, judged blind by models from seven families that built neither, beside the small local model that already does the job. Neither new model cleared it, and a blind seven-family panel did not separate the two over thirty-six cases.","headings":[{"h":"Two strangers at the door","id":"two-strangers-at-the-door"},{"h":"Who judged, and who wrote this","id":"who-judged-and-who-wrote-this"},{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"Three weeks earlier","id":"three-weeks-earlier"},{"h":"The rules desk","id":"the-rules-desk"},{"h":"Head-to-head","id":"head-to-head"},{"h":"The filing cabinet","id":"the-filing-cabinet"},{"h":"What the numbers are allowed to mean","id":"what-the-numbers-are-allowed-to-mean"},{"h":"The ledger, and what left our machines","id":"the-ledger-and-what-left-our-machines"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"The bill","id":"the-bill"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":40,"published":"2026-09-06","series":["bench"],"short":"RuleSage, our board-game rules helper, answers a question from the rulebook’s own words: it finds the passage, cites it by number so a player can check, and says plainly when the book does not answer. GPT-6 Astra and Claude Fable 5.1 sat that job’s exam, frozen in August, judged blind by models from seven families that built neither, beside the small local model that already does the job. Neither new model cleared it, and a blind seven-family panel did not separate the two over thirty-six cases.","slug":"two-new-frontier-models-at-the-rules-desk","text":"full","title":"Two New Frontier Models at the Rules Desk","updated":"2026-10-01","url":"https://research.strata2signal.com/two-new-frontier-models-at-the-rules-desk/"},
{"dek":"Six worlds, painted large, for a print lab that is now open — a darkroom out back, a wall out front.","headings":[{"h":"Six worlds for a print lab that is coming soon","id":"six-worlds-for-a-print-lab-that-is-coming-soon"},{"h":"Two notes","id":"two-notes"},{"h":"How the exhibit closes","id":"how-the-exhibit-closes"},{"h":"For the technically curious","id":"for-the-technically-curious"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the shelf","id":"the-rest-of-the-shelf"}],"kit":true,"n":39,"published":"2026-09-04","series":["gallery"],"short":"Six worlds, painted large, for a print lab that is now open — a darkroom out back, a wall out front.","slug":"six-worlds-painted-large","text":"full","title":"Six Worlds for the Print Lab","updated":"2026-09-11","url":"https://research.strata2signal.com/six-worlds-painted-large/"},
{"dek":"Every question the Beat Lab’s genie can answer, answered here too — the same twenty-eight answers, out of the same file, read by a page instead of a genie, each one set beside the sentence of the field guide it was written from.","headings":[{"h":"Do I need the genie to make a beat?","id":"do-i-need-the-genie-to-make-a-beat"},{"h":"How does this work?","id":"how-does-this-work"},{"h":"How do I make a beat?","id":"how-do-i-make-a-beat"},{"h":"What can I say?","id":"what-can-i-say"},{"h":"What is this beat?","id":"what-is-this-beat"},{"h":"What is the board?","id":"what-is-the-board"},{"h":"Who can see my beat?","id":"who-can-see-my-beat"},{"h":"Can strangers see what I typed?","id":"can-strangers-see-what-i-typed"},{"h":"Is my photo stored?","id":"is-my-photo-stored"},{"h":"What does the genie run on?","id":"what-does-the-genie-run-on"},{"h":"Is this AI?","id":"is-this-ai"},{"h":"Can I save this?","id":"can-i-save-this"},{"h":"How do I share?","id":"how-do-i-share"},{"h":"What is a wish?","id":"what-is-a-wish"},{"h":"Why did it say rest?","id":"why-did-it-say-rest"},{"h":"What is a remix?","id":"what-is-a-remix"},{"h":"What does \"remix of a groove\" mean?","id":"what-does-remix-of-a-groove-mean"},{"h":"What is the seal?","id":"what-is-the-seal"},{"h":"How do I put it on the board?","id":"how-do-i-put-it-on-the-board"},{"h":"How do I take it down?","id":"how-do-i-take-it-down"},{"h":"What are the pads?","id":"what-are-the-pads"},{"h":"What do the two rows do?","id":"what-do-the-two-rows-do"},{"h":"What is the wobble row?","id":"what-is-the-wobble-row"},{"h":"Can I use my own sounds?","id":"can-i-use-my-own-sounds"},{"h":"What does swing do?","id":"what-does-swing-do"},{"h":"What do warmth and space do?","id":"what-do-warmth-and-space-do"},{"h":"Does it work offline?","id":"does-it-work-offline"},{"h":"Where does the picture on the board come from?","id":"where-does-the-picture-on-the-board-come-from"},{"h":"Where these answers come from","id":"where-these-answers-come-from"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":38,"published":"2026-09-04","series":["guide"],"short":"Every question the Beat Lab’s genie can answer, answered here too — the same twenty-eight answers, out of the same file, read by a page instead of a genie, each one set beside the sentence of the field guide it was written from.","slug":"the-beat-lab-asked-and-answered","text":"full","title":"The Beat Lab, asked and answered","updated":"2026-09-18","url":"https://research.strata2signal.com/the-beat-lab-asked-and-answered/"},
{"dek":"A genre instead of a composer: 159 tracks of Creative Commons minimal techno, the same ten-minute recipe that taught three dead composers, and the one ear in the house preferred the model with nothing added — both times.","headings":[{"h":"Where part two left off","id":"where-part-two-left-off"},{"h":"A genre, not a composer","id":"a-genre-not-a-composer"},{"h":"The ear","id":"the-ear"},{"h":"Forty-one releases, licence by licence","id":"forty-one-releases-licence-by-licence"},{"h":"The question in court","id":"the-question-in-court"},{"h":"Ten minutes on the big card","id":"ten-minutes-on-the-big-card"},{"h":"Was it the adapter, or the words?","id":"was-it-the-adapter-or-the-words"},{"h":"One artist instead of a tag","id":"one-artist-instead-of-a-tag"},{"h":"Twelve requests, four ways","id":"twelve-requests-four-ways"},{"h":"What we did NOT measure","id":"what-we-did-not-measure"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":36,"published":"2026-09-03","series":["guide","bench"],"short":"A genre instead of a composer: 159 tracks of Creative Commons minimal techno, the same ten-minute recipe that taught three dead composers, and the one ear in the house preferred the model with nothing added — both times.","slug":"ten-minutes-with-living-artists","text":"full","title":"Ten Minutes with Living Artists","updated":null,"url":"https://research.strata2signal.com/ten-minutes-with-living-artists/"},
{"dek":"Every clip of the fourth adapter’s four rounds — sixty-five renders, raw and brought to a common loudness — so you can hear what one listener heard and decide for yourself.","headings":[{"h":"How to listen","id":"how-to-listen"},{"h":"Round one — the first listen","id":"round-one-the-first-listen"},{"h":"Round two — the caption-shape probe","id":"round-two-the-caption-shape-probe"},{"h":"Round three — the one-artist control","id":"round-three-the-one-artist-control"},{"h":"Round four — twelve requests, four ways","id":"round-four-twelve-requests-four-ways"},{"h":"What to listen for","id":"what-to-listen-for"},{"h":"How to check these clips","id":"how-to-check-these-clips"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"}],"kit":false,"n":37,"published":"2026-09-03","series":["guide"],"short":"Every clip of the fourth adapter’s four rounds — sixty-five renders, raw and brought to a common loudness — so you can hear what one listener heard and decide for yourself.","slug":"listen-for-yourself","text":"full","title":"Listen for Yourself","updated":null,"url":"https://research.strata2signal.com/listen-for-yourself/"},
{"dek":"Two more dead composers taught the same way — a hundred and six marches, then Bach — and then blending composers with linear algebra, where the obvious method is wrong, the right one costs a measurable one percent, and a sentence already staged for publication had to be taken back before you ever saw it.","headings":[{"h":"Previously, in five minutes","id":"previously-in-five-minutes"},{"h":"Sousa: a hundred and six marches","id":"sousa-a-hundred-and-six-marches"},{"h":"Bach: the pianist who freed her own recordings","id":"bach-the-pianist-who-freed-her-own-recordings"},{"h":"The bill","id":"the-bill"},{"h":"You cannot average two composers","id":"you-cannot-average-two-composers"},{"h":"Where the blends actually sit","id":"where-the-blends-actually-sit"},{"h":"The sentence we took back","id":"the-sentence-we-took-back"},{"h":"The ruler's noise floor","id":"the-rulers-noise-floor"},{"h":"The blend that wasn't","id":"the-blend-that-wasnt"},{"h":"What we did NOT measure","id":"what-we-did-not-measure"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":35,"published":"2026-09-02","series":["guide","bench"],"short":"Two more dead composers taught the same way — a hundred and six marches, then Bach — and then blending composers with linear algebra, where the obvious method is wrong, the right one costs a measurable one percent, and a sentence already staged for publication had to be taken back before you ever saw it.","slug":"half-an-hour-with-dead-composers","text":"full","title":"Half an Hour with Dead Composers","updated":null,"url":"https://research.strata2signal.com/half-an-hour-with-dead-composers/"},
{"dek":"Nineteen public-domain Chopin recordings, four minutes and forty-nine seconds of training on a five-year-old graphics card, and a dial you can hear — five clips on the page that differ from each other in exactly one way.","headings":[{"h":"A music model you can run at home","id":"a-music-model-you-can-run-at-home"},{"h":"What it sounds like on its own","id":"what-it-sounds-like-on-its-own"},{"h":"The trick: a small adapter, not a new model","id":"the-trick-a-small-adapter-not-a-new-model"},{"h":"Five minutes of training, honestly accounted","id":"five-minutes-of-training-honestly-accounted"},{"h":"The dial","id":"the-dial"},{"h":"What we did NOT measure","id":"what-we-did-not-measure"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":34,"published":"2026-09-01","series":["guide","bench"],"short":"Nineteen public-domain Chopin recordings, four minutes and forty-nine seconds of training on a five-year-old graphics card, and a dial you can hear — five clips on the page that differ from each other in exactly one way.","slug":"chopin-in-five-minutes","text":"full","title":"Teaching a Music Model Chopin in Five Minutes","updated":null,"url":"https://research.strata2signal.com/chopin-in-five-minutes/"},
{"dek":"We put glm-5.3-flash — ollama’s new 320B-total cloud model — on two frozen house instruments; it tied our local 12B and 27B at 16/19, and the finding is about the ruler.","headings":[{"h":"A different kind of row","id":"a-different-kind-of-row"},{"h":"Three checks bracket the run","id":"three-checks-bracket-the-run"},{"h":"The ledger of what did not read","id":"the-ledger-of-what-did-not-read"},{"h":"Sixteen of nineteen","id":"sixteen-of-nineteen"},{"h":"Two local models, sixteen days earlier","id":"two-local-models-sixteen-days-earlier"},{"h":"The exam that stopped discriminating","id":"the-exam-that-stopped-discriminating"},{"h":"A probe, not a score","id":"a-probe-not-a-score"},{"h":"What a toolbench costs, for anyone sizing one","id":"what-a-toolbench-costs-for-anyone-sizing-one"},{"h":"What a reference reading is worth","id":"what-a-reference-reading-is-worth"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":33,"published":"2026-08-31","series":["bench"],"short":"We put glm-5.3-flash — ollama’s new 320B-total cloud model — on two frozen house instruments; it tied our local 12B and 27B at 16/19, and the finding is about the ruler.","slug":"the-same-sixteen","text":"full","title":"The Same Sixteen","updated":null,"url":"https://research.strata2signal.com/the-same-sixteen/"},
{"dek":"Take the graphics card away and the 30-billion-parameter model answers faster than the 9-billion one — on all three machines.","headings":[{"h":"A few words this page leans on","id":"a-few-words-this-page-leans-on"},{"h":"Two hours at 12 tok/s — the test that named the page","id":"two-hours-at-12-tok-s-the-test-that-named-the-page"},{"h":"The question our GPU benches never ask","id":"the-question-our-gpu-benches-never-ask"},{"h":"Read this before the table: CPU speed is a memory-bandwidth story","id":"read-this-before-the-table-cpu-speed-is-a-memory-bandwidth"},{"h":"The one table to remember","id":"the-one-table-to-remember"},{"h":"The wait: prefill is the real CPU tax","id":"the-wait-prefill-is-the-real-cpu-tax"},{"h":"Set the thread count — the one free lever, and the exception that proves the rule","id":"set-the-thread-count-the-one-free-lever-and-the-exception"},{"h":"The judgment column: same sealed exams","id":"the-judgment-column-same-sealed-exams"},{"h":"What this buys the house (and maybe yours)","id":"what-this-buys-the-house-and-maybe-yours"},{"h":"What we did NOT measure","id":"what-we-did-not-measure"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"Who ran this, and thanks","id":"who-ran-this-and-thanks"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":32,"published":"2026-08-31","series":["bench"],"short":"Take the graphics card away and the 30-billion-parameter model answers faster than the 9-billion one — on all three machines.","slug":"two-hours-on-battery","text":"full","title":"Two Hours at 12 tok/s — On Battery","updated":"2026-10-07","url":"https://research.strata2signal.com/two-hours-on-battery/"},
{"dek":"Carry a frozen exam from a 96 GB workstation card to a 24 GB consumer one and the model it ranked two weeks ago ranks again — at the floor, on a different build, with nothing new seated.","headings":[{"h":"The chair, and who gets to sit in it","id":"the-chair-and-who-gets-to-sit-in-it"},{"h":"What we ran, and the rule behind every number","id":"what-we-ran-and-the-rule-behind-every-number"},{"h":"The words these tables use, and the floors they score against","id":"the-words-these-tables-use-and-the-floors-they-score-against"},{"h":"The standings","id":"the-standings"},{"h":"What the battery was for: the instrument travels","id":"what-the-battery-was-for-the-instrument-travels"},{"h":"Schema-perfect, and still blocked","id":"schema-perfect-and-still-blocked"},{"h":"The judge's chair scored one candidate of three","id":"the-judges-chair-scored-one-candidate-of-three"},{"h":"What a 24 GB door actually filters","id":"what-a-24-gb-door-actually-filters"},{"h":"The row that owes a re-run","id":"the-row-that-owes-a-re-run"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":31,"published":"2026-08-29","series":["bench"],"short":"Carry a frozen exam from a 96 GB workstation card to a 24 GB consumer one and the model it ranked two weeks ago ranks again — at the floor, on a different build, with nothing new seated.","slug":"the-instrument-travels","text":"full","title":"The Instrument Travels","updated":"2026-10-01","url":"https://research.strata2signal.com/the-instrument-travels/"},
{"dek":"The founder's dog takes the same nap under the same tree in all nine painted worlds — same dog, same curl; only the world changes.","headings":[{"h":"For the technically curious","id":"for-the-technically-curious"}],"kit":true,"n":30,"published":"2026-08-28","series":["gallery"],"short":"The founder's dog takes the same nap under the same tree in all nine painted worlds — same dog, same curl; only the world changes.","slug":"nine-worlds-one-dog","text":"full","title":"Nine Worlds, One Dog","updated":"2026-09-04","url":"https://research.strata2signal.com/nine-worlds-one-dog/"},
{"dek":"Turn a 600-watt card down by a quarter and one production model pays two percent of its speed; the other never notices.","headings":[{"h":"The dial, and who notices","id":"the-dial-and-who-notices"},{"h":"Five words this page leans on","id":"five-words-this-page-leans-on"},{"h":"The moment this page is about","id":"the-moment-this-page-is-about"},{"h":"What we ran, and the rule behind every number","id":"what-we-ran-and-the-rule-behind-every-number"},{"h":"The answer, in one paragraph","id":"the-answer-in-one-paragraph"},{"h":"The mechanism, checked rather than inferred","id":"the-mechanism-checked-rather-than-inferred"},{"h":"The instrument that half-failed, counted rather than asserted","id":"the-instrument-that-half-failed-counted-rather-than-asserted"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What it means","id":"what-it-means"},{"h":"Same dial, other lane — measured on the production card, the day this page shipped","id":"same-dial-other-lane-measured-on-the-production-card-the"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":29,"published":"2026-08-28","series":["bench"],"short":"Turn a 600-watt card down by a quarter and one production model pays two percent of its speed; the other never notices.","slug":"what-150-watts-buys","text":"full","title":"What 150 Watts Buys","updated":"2026-10-07","url":"https://research.strata2signal.com/what-150-watts-buys/"},
{"dek":"A drum machine that lives in your browser, a genie that runs on our own hardware, and a board where nobody has a name.","headings":[{"h":"What it is","id":"what-it-is"},{"h":"The sound is made on your device","id":"the-sound-is-made-on-your-device"},{"h":"What happens when you ask the genie","id":"what-happens-when-you-ask-the-genie"},{"h":"The board","id":"the-board"},{"h":"The voice","id":"the-voice"},{"h":"What the lab does with what you give it","id":"what-the-lab-does-with-what-you-give-it"},{"h":"Numbers, so it isn't just talk","id":"numbers-so-it-isnt-just-talk"},{"h":"What to take with you","id":"what-to-take-with-you"}],"kit":false,"n":28,"published":"2026-08-27","series":["guide"],"short":"A drum machine that lives in your browser, a genie that runs on our own hardware, and a board where nobody has a name.","slug":"how-the-beat-lab-works","text":"full","title":"How the Beat Lab works","updated":"2026-09-15","url":"https://research.strata2signal.com/how-the-beat-lab-works/"},
{"dek":"Two ways a machine writes: one word at a time, or every pixel at once — sixteen times over.","headings":[{"h":"The typist","id":"the-typist"},{"h":"The developer","id":"the-developer"},{"h":"One card, both jobs, at once","id":"one-card-both-jobs-at-once"},{"h":"The character who moved one door over","id":"the-character-who-moved-one-door-over"},{"h":"What we did not measure, said out loud","id":"what-we-did-not-measure-said-out-loud"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work","id":"how-to-check-our-work"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":27,"published":"2026-08-27","series":["guide","bench"],"short":"Two ways a machine writes: one word at a time, or every pixel at once — sixteen times over.","slug":"the-typist-and-the-developer","text":"full","title":"The Typist and the Developer","updated":null,"url":"https://research.strata2signal.com/the-typist-and-the-developer/"},
{"dek":"We re-rendered our game's actual art — same prompts, same seeds, same checkpoint files — on a several-year-old consumer RTX 3090 and a gaming laptop, paired render-for-render against our 96 GB card's own archived output. Two of the three runs happened in the same physical computer. This page is the stopwatch, told honestly — including the one number we don't yet trust.","headings":[{"h":"Four words this page leans on","id":"four-words-this-page-leans-on"},{"h":"The moment this page is about","id":"the-moment-this-page-is-about"},{"h":"What we ran, and the rule behind every number","id":"what-we-ran-and-the-rule-behind-every-number"},{"h":"The table that answers the question","id":"the-table-that-answers-the-question"},{"h":"The sketch ladder — the number the game lives on","id":"the-sketch-ladder-the-number-the-game-lives-on"},{"h":"Three findings we didn't order","id":"three-findings-we-didnt-order"},{"h":"What this means for the game","id":"what-this-means-for-the-game"},{"h":"The kit","id":"the-kit"}],"kit":true,"n":26,"published":"2026-08-26","series":["bench"],"short":"We re-rendered our game's actual art — same prompts, same seeds, same checkpoint files — on a several-year-old consumer RTX 3090 and a gaming laptop, paired render-for-render against our 96 GB card's own archived output. Two of the three runs happened in the same physical computer. This page is the stopwatch, told honestly — including the one number we don't yet trust.","slug":"a-rig-your-friend-already-owns","text":"full","title":"A Rig Your Friend Already Owns","updated":null,"url":"https://research.strata2signal.com/a-rig-your-friend-already-owns/"},
{"dek":"A language model reads everything as tokens — little word-pieces, one after another. Before it can say anything about a photograph, the picture itself has to become tokens. This is the close-up on that bridge — what an image becomes on the way in, what it costs, and what a small model actually sees when it looks — with the gate our own game puts every uploaded photo through as the working example, and the gate’s own answers watched against it.","headings":[{"h":"What an image becomes on the way in","id":"what-an-image-becomes-on-the-way-in"},{"h":"The bench, in one paragraph","id":"the-bench-in-one-paragraph"},{"h":"Finding one: a picture is 199 tokens at 896 px, and the runtime says so — on one seat","id":"finding-one-a-picture-is-199-tokens-at-896-px-and-the"},{"h":"Finding two: the ways a gate says no","id":"finding-two-the-ways-a-gate-says-no"},{"h":"Finding three: what it saw, against what was there — six animals, against an answer sheet","id":"finding-three-what-it-saw-against-what-was-there-six"},{"h":"Finding four: the render payoff — and what the bake-off measures","id":"finding-four-the-render-payoff-and-what-the-bake-off"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":25,"published":"2026-08-26","series":["guide","bench"],"short":"A language model reads everything as tokens — little word-pieces, one after another. Before it can say anything about a photograph, the picture itself has to become tokens. This is the close-up on that bridge — what an image becomes on the way in, what it costs, and what a small model actually sees when it looks — with the gate our own game puts every uploaded photo through as the working example, and the gate’s own answers watched against it.","slug":"how-a-vision-model-sees","text":"full","title":"How a Vision Model Sees: Eyes for a Machine That Reads","updated":null,"url":"https://research.strata2signal.com/how-a-vision-model-sees/"},
{"dek":"One picture, drawn rung by rung up the step dial, on two painters that could not be more different. Timed to the millisecond and measured to the pixel, twice, a month apart — and the two runs say two different things, which turns out to be the finding.","headings":[{"h":"What a step is, in one paragraph","id":"what-a-step-is-in-one-paragraph"},{"h":"Finding one: a step is a fixed-price good","id":"finding-one-a-step-is-a-fixed-price-good"},{"h":"Finding two: the first turn of the dial is the big one — and how big depends on the painter","id":"finding-two-the-first-turn-of-the-dial-is-the-big-one-and"},{"h":"Finding three: the ladder settles on one painter and never on the other","id":"finding-three-the-ladder-settles-on-one-painter-and-never"},{"h":"Finding four: the argument with our own settings — and what it changed","id":"finding-four-the-argument-with-our-own-settings-and-what-it"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":24,"published":"2026-08-25","series":["bench"],"short":"One picture, drawn rung by rung up the step dial, on two painters that could not be more different. Timed to the millisecond and measured to the pixel, twice, a month apart — and the two runs say two different things, which turns out to be the finding.","slug":"fifty-seven-milliseconds","text":"full","title":"Fifty-Seven Milliseconds: What a Diffusion Step Actually Buys","updated":null,"url":"https://research.strata2signal.com/fifty-seven-milliseconds/"},
{"dek":"A photograph of a real dog walks into a painted fishing cove and comes out as a painted dog the world will remember. This is the whole pipeline between those two facts — the file, the dice, and the painter — one station at a time, with no scores and no instrumentation.","headings":[{"h":"The world is a file","id":"the-world-is-a-file"},{"h":"The dice are visible","id":"the-dice-are-visible"},{"h":"The painter paints the state","id":"the-painter-paints-the-state"},{"h":"The photograph","id":"the-photograph"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":23,"published":"2026-08-25","series":["guide"],"short":"A photograph of a real dog walks into a painted fishing cove and comes out as a painted dog the world will remember. This is the whole pipeline between those two facts — the file, the dice, and the painter — one station at a time, with no scores and no instrumentation.","slug":"the-dog-the-dice-and-the-painter","text":"full","title":"The Dog, the Dice, and the Painter: How a Small World Draws Itself","updated":"2026-09-04","url":"https://research.strata2signal.com/the-dog-the-dice-and-the-painter/"},
{"dek":"One small fishing village, painted thirteen ways — Sorrowmoor Cove (and some other worlds built on RealKeep) sits for thirteen open-weight painters: 3,588 published renders across seventy-five galleries, every hero hand-picked, every licence read first-hand, and the two painters whose licences refuse counted in the open rather than quietly dropped.","headings":[],"kit":false,"n":22,"published":"2026-08-24","series":["bench"],"short":null,"slug":"diffusion/new-arrivals","text":"catalogue","title":"The new-arrivals diffusion bench","updated":null,"url":"https://research.strata2signal.com/diffusion/new-arrivals/"},
{"dek":"Models don’t conjure knowledge out of nothing — they read archives nobody had finished reading and walk rooms the rules imply, and what turns either into knowledge is the check that follows, never the confidence of the proposal.","headings":[{"h":"The first engine: the archive nobody had read","id":"the-first-engine-the-archive-nobody-had-read"},{"h":"The second engine: the rooms the rules imply","id":"the-second-engine-the-rooms-the-rules-imply"},{"h":"Two kinds of there-all-along","id":"two-kinds-of-there-all-along"},{"h":"What the record shows","id":"what-the-record-shows"},{"h":"Where that leaves us","id":"where-that-leaves-us"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":21,"published":"2026-08-24","series":["guide"],"short":"Models don’t conjure knowledge out of nothing — they read archives nobody had finished reading and walk rooms the rules imply, and what turns either into knowledge is the check that follows, never the confidence of the proposal.","slug":"where-new-knowledge-comes-from","text":"full","title":"It Was Already There: Where New Knowledge Actually Comes From","updated":null,"url":"https://research.strata2signal.com/where-new-knowledge-comes-from/"},
{"dek":"A language model is a compression of its corpus — not a figure of speech but the training objective, and once you hold that one fact, bias, hallucination, scale and the frozen moment stop being mysteries and start being mechanics.","headings":[{"h":"Three people, one idea","id":"three-people-one-idea"},{"h":"The punchline","id":"the-punchline"},{"h":"What we can show you","id":"what-we-can-show-you"},{"h":"When you look closer","id":"when-you-look-closer"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":20,"published":"2026-08-23","series":["guide"],"short":"A language model is a compression of its corpus — not a figure of speech but the training objective, and once you hold that one fact, bias, hallucination, scale and the frozen moment stop being mysteries and start being mechanics.","slug":"compression-is-the-objective","text":"full","title":"It’s Not a Metaphor: A Language Model Is a Compression of Its Corpus","updated":null,"url":"https://research.strata2signal.com/compression-is-the-objective/"},
{"dek":"One day our busiest model started producing tokens roughly a third faster than it ever had, and nobody had touched it. The cause was a two-line upstream commit ending a ~2.5 ms-per-token tax nobody configured — and, in the same stroke, changing what every unpinned model says. The receipts, the mechanism, and the honest unmeasured half.","headings":[{"h":"The day the box got faster","id":"the-day-the-box-got-faster"},{"h":"The hunt: change one thing","id":"the-hunt-change-one-thing"},{"h":"The two-line commit","id":"the-two-line-commit"},{"h":"What the tax actually was","id":"what-the-tax-actually-was"},{"h":"Why our gain was so much bigger than the author's","id":"why-our-gain-was-so-much-bigger-than-the-authors"},{"h":"The version pair, and a twist","id":"the-version-pair-and-a-twist"},{"h":"The part that isn't about speed","id":"the-part-that-isnt-about-speed"},{"h":"The same conveyor delivers regressions","id":"the-same-conveyor-delivers-regressions"},{"h":"What this means if you run your own models","id":"what-this-means-if-you-run-your-own-models"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":19,"published":"2026-08-22","series":["bench","notes"],"short":"One day our busiest model started producing tokens roughly a third faster than it ever had, and nobody had touched it. The cause was a two-line upstream commit ending a ~2.5 ms-per-token tax nobody configured — and, in the same stroke, changing what every unpinned model says. The receipts, the mechanism, and the honest unmeasured half.","slug":"the-free-speed-wasnt-free","text":"full","title":"The free speed wasn’t free — the runtime dividend, traced to one commit","updated":null,"url":"https://research.strata2signal.com/the-free-speed-wasnt-free/"},
{"dek":"Nearly every local model wears a tag like Q4_K_M, and most of us read one thing from it: fewer bits, smaller file. We opened all four builds the vendor ships of one 11.9B — 4.80 to 16.13 bits per weight — and asked each the same question: the 8-bit answer is byte-identical to the uncompressed original’s, and the wrong answer survives the whole ladder.","headings":[{"h":"The same brain at two compressions","id":"the-same-brain-at-two-compressions"},{"h":"The same question, down the whole ladder","id":"the-same-question-down-the-whole-ladder"},{"h":"What the tag actually says","id":"what-the-tag-actually-says"},{"h":"Fewer bits is not \"rounding off\"","id":"fewer-bits-is-not-rounding-off"},{"h":"The reveal: a \"4-bit model\" is not 4-bit","id":"the-reveal-a-4-bit-model-is-not-4-bit"},{"h":"The receipt: we bought three times the bits and got nothing","id":"the-receipt-we-bought-three-times-the-bits-and-got-nothing"},{"h":"What the freed memory buys","id":"what-the-freed-memory-buys"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":18,"published":"2026-08-21","series":["guide"],"short":"Nearly every local model wears a tag like Q4_K_M, and most of us read one thing from it: fewer bits, smaller file. We opened all four builds the vendor ships of one 11.9B — 4.80 to 16.13 bits per weight — and asked each the same question: the 8-bit answer is byte-identical to the uncompressed original’s, and the wrong answer survives the whole ladder.","slug":"the-compressed-photograph","text":"full","title":"The compressed photograph — what those Q4_K_M tags actually mean","updated":null,"url":"https://research.strata2signal.com/the-compressed-photograph/"},
{"dek":"Model names grew a second number — 30b-a3b — and it is the one your card and wallet care about. The payroll and the meeting, in plain English, measured on our own machines: a 25.8B mixture out-writing its 11.9B dense sibling while holding twice the memory — and the robber asks both architectures.","headings":[{"h":"The tag with two numbers","id":"the-tag-with-two-numbers"},{"h":"The payroll and the meeting","id":"the-payroll-and-the-meeting"},{"h":"The part nobody warns you about: the whole payroll needs an office","id":"the-part-nobody-warns-you-about-the-whole-payroll-needs-an"},{"h":"Measured on our own machines","id":"measured-on-our-own-machines"},{"h":"The arithmetic that catches a mixture red-handed","id":"the-arithmetic-that-catches-a-mixture-red-handed"},{"h":"What the label doesn't tell you","id":"what-the-label-doesnt-tell-you"},{"h":"The number that looks like sparsity and isn't","id":"the-number-that-looks-like-sparsity-and-isnt"},{"h":"What sparsity does not buy","id":"what-sparsity-does-not-buy"},{"h":"The model that was proving it all along","id":"the-model-that-was-proving-it-all-along"},{"h":"The robber, asked a third time","id":"the-robber-asked-a-third-time"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":17,"published":"2026-08-20","series":["guide"],"short":"Model names grew a second number — 30b-a3b — and it is the one your card and wallet care about. The payroll and the meeting, in plain English, measured on our own machines: a 25.8B mixture out-writing its 11.9B dense sibling while holding twice the memory — and the robber asks both architectures.","slug":"three-at-the-table","text":"full","title":"Everyone on the payroll, three at the table — dense, MoE, and active weights","updated":"2026-08-21","url":"https://research.strata2signal.com/three-at-the-table/"},
{"dek":"Paste a whole essay into a chatbot and it swallows the thing at once — then answers one word at a time. That asymmetry is physics, not theater: reading and writing in plain english, measured on our own card — and the measurement argued back, which turned out to be the best part.","headings":[{"h":"The mystery, stated honestly","id":"the-mystery-stated-honestly"},{"h":"Reading is a group photo","id":"reading-is-a-group-photo"},{"h":"Writing is one at a time","id":"writing-is-one-at-a-time"},{"h":"The notes in the margin","id":"the-notes-in-the-margin"},{"h":"A measurement, from our own card","id":"a-measurement-from-our-own-card"},{"h":"The number that argued back","id":"the-number-that-argued-back"},{"h":"What this explains, once you see it","id":"what-this-explains-once-you-see-it"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":true,"n":16,"published":"2026-08-19","series":["guide"],"short":"Paste a whole essay into a chatbot and it swallows the thing at once — then answers one word at a time. That asymmetry is physics, not theater: reading and writing in plain english, measured on our own card — and the measurement argued back, which turned out to be the best part.","slug":"reading-is-fast","text":"full","title":"Reading is fast, writing is slow — why AI answers arrive word by word","updated":"2026-08-21","url":"https://research.strata2signal.com/reading-is-fast/"},
{"dek":"Before RuleSage answers, something has to find the right page in a book the size of a small novel — three searchers, one referee, one careful reader, and none of them is the model that writes your answer. The whole journey, in plain english, with the shipped numbers.","headings":[{"h":"Start with a bad idea","id":"start-with-a-bad-idea"},{"h":"The first librarian searches for your exact words","id":"the-first-librarian-searches-for-your-exact-words"},{"h":"The second librarian searches by meaning","id":"the-second-librarian-searches-by-meaning"},{"h":"The third librarian never left your aisle","id":"the-third-librarian-never-left-your-aisle"},{"h":"The election","id":"the-election"},{"h":"The careful reader","id":"the-careful-reader"},{"h":"The order is dealt on purpose","id":"the-order-is-dealt-on-purpose"},{"h":"The doorman stands off this road entirely","id":"the-doorman-stands-off-this-road-entirely"},{"h":"The nerd shelf — the numbers, from the shipped configuration","id":"the-nerd-shelf-the-numbers-from-the-shipped-configuration"},{"h":"A confession, with its receipt","id":"a-confession-with-its-receipt"},{"h":"What this machinery gets wrong","id":"what-this-machinery-gets-wrong"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":15,"published":"2026-08-18","series":["guide","notes"],"short":"Before RuleSage answers, something has to find the right page in a book the size of a small novel — three searchers, one referee, one careful reader, and none of them is the model that writes your answer. The whole journey, in plain english, with the shipped numbers.","slug":"three-librarians","text":"full","title":"Three librarians and a careful reader — how RuleSage finds the right page","updated":"2026-08-21","url":"https://research.strata2signal.com/three-librarians/"},
{"dek":"qwen3.8:27b landed on our box the day after it shipped, and we sat it in the exams we already had rather than designing anything for it — it filled a screening seat that had never been filled, and it missed a floor in the judge seat.","headings":[{"h":"The four answers","id":"the-four-answers"},{"h":"What it ran on","id":"what-it-ran-on"},{"h":"The arrival","id":"the-arrival"},{"h":"The classifier seat — five gates, all pass, faster","id":"the-classifier-seat-five-gates-all-pass-faster"},{"h":"The narrator's open call — mid-table, uncertainty printed","id":"the-narrators-open-call-mid-table-uncertainty-printed"},{"h":"The judge seat — and the floor it missed","id":"the-judge-seat-and-the-floor-it-missed"},{"h":"One more bench it happened to sit — the site-reading","id":"one-more-bench-it-happened-to-sit-the-site-reading"},{"h":"Could the exams have been rigged for it?","id":"could-the-exams-have-been-rigged-for-it"},{"h":"The seat question","id":"the-seat-question"},{"h":"Postscript: what production found that the bench didn't","id":"postscript-what-production-found-that-the-bench-didnt"},{"h":"The other precisions","id":"the-other-precisions"},{"h":"What this page does not say","id":"what-this-page-does-not-say"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"The files behind the figures","id":"the-files-behind-the-figures"},{"h":"What you can open","id":"what-you-can-open"},{"h":"What you can't, and why","id":"what-you-cant-and-why"},{"h":"Calls, cost and licence","id":"calls-cost-and-licence"}],"kit":false,"n":14,"published":"2026-08-17","series":["bench"],"short":"qwen3.8:27b landed on our box the day after it shipped, and we sat it in the exams we already had rather than designing anything for it — it filled a screening seat that had never been filled, and it missed a floor in the judge seat.","slug":"the-new-kid","text":"full","title":"qwen3.8:27b across four house benches: one seat filled, one floor missed","updated":"2026-08-20","url":"https://research.strata2signal.com/the-new-kid/"},
{"dek":"Two camps have argued about a map file for machines for two years, mostly without receipts — no AI crawler asked us for ours in thirty days of logs, so we measured whether it helps when it arrives anyway.","headings":[{"h":"Measured again, after publication","id":"measured-again-after-publication"},{"h":"Where the file came from.","id":"where-the-file-came-from"},{"h":"The confession.","id":"the-confession"},{"h":"Does anyone fetch it?","id":"does-anyone-fetch-it"},{"h":"The bench: if the map arrives, does it help?","id":"the-bench-if-the-map-arrives-does-it-help"},{"h":"The per-arm table — no pooled figure prints without it","id":"the-per-arm-table-no-pooled-figure-prints-without-it"},{"h":"Everything above, as files.","id":"everything-above-as-files"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance"}],"kit":true,"n":13,"published":"2026-08-17","series":["bench","notes"],"short":"Two camps have argued about a map file for machines for two years, mostly without receipts — no AI crawler asked us for ours in thirty days of logs, so we measured whether it helps when it arrives anyway.","slug":"llms-txt","text":"full","title":"The map nobody picks up — and whether it helps when it arrives.","updated":"2026-10-01","url":"https://research.strata2signal.com/llms-txt/"},
{"dek":"Twenty language models were handed the same frozen moment of a small fishing town and asked to speak as its people, then read blind by seven judges from six rival families.","headings":[{"h":"Five things, before any figure","id":"five-things-before-any-figure"},{"h":"The words this page leans on","id":"the-words-this-page-leans-on"},{"h":"The author of this page is a contestant in it","id":"the-author-of-this-page-is-a-contestant-in-it"},{"h":"Three scenarios, one small cove, one draw of its world","id":"three-scenarios-one-small-cove-one-draw-of-its-world"},{"h":"The kid","id":"the-kid"},{"h":"Every reply in this act, in full","id":"fold-s1"},{"h":"The world remembered, and poured them a drink","id":"the-world-remembered-and-poured-them-a-drink"},{"h":"Every reply in this act, in full","id":"fold-s3"},{"h":"The verse-keeper","id":"the-verse-keeper"},{"h":"Every reply in this act, in full","id":"fold-s2"},{"h":"Where their zeros sit","id":"where-their-zeros-sit"},{"h":"And what it couldn’t agree on","id":"and-what-it-couldnt-agree-on"},{"h":"$2.2826, over 56 metered calls, against $38.49 of caps","id":"2-2826-over-56-metered-calls-against-38-49-of-caps"},{"h":"How long a family waits for the narrator to speak","id":"how-long-a-family-waits-for-the-narrator-to-speak"},{"h":"Stated at the size they are","id":"stated-at-the-size-they-are"},{"h":"A late contestant, and it ties","id":"a-late-contestant-and-it-ties"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Every published file behind every number on this page","id":"every-published-file-behind-every-number-on-this-page"},{"h":"Provenance","id":"provenance"}],"kit":true,"n":11,"published":"2026-08-15","series":["bench","notes"],"short":"Twenty language models were handed the same frozen moment of a small fishing town and asked to speak as its people, then read blind by seven judges from six rival families.","slug":"the-open-call","text":"full","title":"The open call — a kid, an elder, and a tired parent walk into the cove.","updated":"2026-08-20","url":"https://research.strata2signal.com/the-open-call/"},
{"dek":"Our two published privacy promises, unpacked for people — not programmers — by following one question door to door.","headings":[{"h":"The first promise: your visit stays between us","id":"the-first-promise-your-visit-stays-between-us"},{"h":"The second promise: the trip your question takes","id":"the-second-promise-the-trip-your-question-takes"},{"h":"Will this ever change?","id":"will-this-ever-change"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":12,"published":"2026-08-15","series":["notes"],"short":"Our two published privacy promises, unpacked for people — not programmers — by following one question door to door.","slug":"where-your-question-goes","text":"full","title":"Where your question goes — a plain-english walk.","updated":"2026-10-17","url":"https://research.strata2signal.com/where-your-question-goes/"},
{"dek":"The honest answer to the fair question: the workflow that makes every exhibit — humans and agents, named plainly, with the receipts.","headings":[{"h":"The plain-english version","id":"the-plain-english-version"},{"h":"The thorough walk","id":"the-thorough-walk"},{"h":"The tools, briefly, as characters","id":"the-tools-briefly-as-characters"},{"h":"The velocity receipt is this page.","id":"the-velocity-receipt-is-this-page"},{"h":"The contract","id":"the-contract"},{"h":"What can go wrong (and what catches it)","id":"what-can-go-wrong-and-what-catches-it"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"}],"kit":false,"n":10,"published":"2026-08-14","series":["notes"],"short":"The honest answer to the fair question: the workflow that makes every exhibit — humans and agents, named plainly, with the receipts.","slug":"how-we-work","text":"full","title":"How we work — who writes this, and how.","updated":"2026-09-10","url":"https://research.strata2signal.com/how-we-work/"},
{"dek":"Our apps left the gaming laptop for a real server — the same frozen bench measured both eras, honestly.","headings":[{"h":"The machine it was","id":"the-machine"},{"h":"What moved, and what deliberately did not","id":"what-moved"},{"h":"Instrument one · the bench, re-run","id":"bench"},{"h":"Instrument two · the receipts","id":"receipts"},{"h":"What it cost","id":"cost"},{"h":"Limits, stated plainly","id":"limits"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance"}],"kit":true,"n":9,"published":"2026-08-14","series":["bench","notes"],"short":"Our apps left the gaming laptop for a real server — the same frozen bench measured both eras, honestly.","slug":"the-move","text":"full","title":"The move — the same yardstick, before and after.","updated":"2026-08-20","url":"https://research.strata2signal.com/the-move/"},
{"dek":"Nine models, eleven arms, five all-new exams in one day — on a box that never stopped serving its real users.","headings":[{"h":"The five chairs","id":"the-five-chairs"},{"h":"How to read this page","id":"how-to-read-this-page"},{"h":"The box, and a disclaimer we keep making richer","id":"the-box-and-a-disclaimer-we-keep-making-richer"},{"h":"The roster, and what it takes to load them","id":"roster"},{"h":"Chair one: the judge","id":"judge"},{"h":"Chair two: the assistant","id":"assistant"},{"h":"Chair three: the toolbench","id":"toolbench"},{"h":"Chair four: the narrator","id":"narrator"},{"h":"Chair five: the stopwatch","id":"stopwatch"},{"h":"The serving probes","id":"serving-probes"},{"h":"What the day actually settled","id":"what-the-day-actually-settled"},{"h":"Limits, stated plainly","id":"limits-stated-plainly"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-872bea"}],"kit":true,"n":6,"published":"2026-08-13","series":["bench"],"short":"Nine models, eleven arms, five all-new exams in one day — on a box that never stopped serving its real users.","slug":"chair-trials","text":"full","title":"The chair trials — five fresh exams for the thirty-billion class.","updated":"2026-10-01","url":"https://research.strata2signal.com/chair-trials/"},
{"dek":"The August class’s best voice sat the cove’s actual chair — and was refused at the gate.","headings":[{"h":"The verdict","id":"the-verdict"},{"h":"What was measured, and how","id":"what-was-measured-and-how"},{"h":"The rig, disclosed both ways","id":"the-rig-disclosed-both-ways"},{"h":"Floor 1 — canon","id":"floor-1-canon"},{"h":"Same question, three voices","id":"same-question-three-voices"},{"h":"Floor 2 — register, and floor 3 — the envelope","id":"floor-2-register-and-floor-3-the-envelope"},{"h":"Floor 4 — latency","id":"floor-4-latency"},{"h":"Scores — this head-to-head's own scale","id":"scores-this-head-to-heads-own-scale"},{"h":"Floor 5 — blind judging, and what leaked","id":"floor-5-blind-judging-and-what-leaked"},{"h":"Two findings the cove carries regardless of the seat","id":"two-findings-the-cove-carries-regardless-of-the-seat"},{"h":"What changes","id":"what-changes"},{"h":"Limits, stated plainly","id":"limits-stated-plainly"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-d7bf3f"}],"kit":true,"n":7,"published":"2026-08-13","series":["bench"],"short":"The August class’s best voice sat the cove’s actual chair — and was refused at the gate.","slug":"cove-voice-head-to-head","text":"full","title":"The narrator’s chair, refused.","updated":null,"url":"https://research.strata2signal.com/cove-voice-head-to-head/"},
{"dek":"Four rival frontier labs re-judged every sealed round we had published — blind, byte-identical.","headings":[{"h":"Local first, checked from outside","id":"boundary"},{"h":"The audition","id":"audition"},{"h":"What five vendors said about the same comparisons","id":"vendors"},{"h":"The gauntlet: our judges, examined","id":"gauntlet"},{"h":"What it cost","id":"bill"},{"h":"What changes going forward","id":"going-forward"},{"h":"Limits, stated plainly","id":"limits-stated-plainly"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-7d07c0"}],"kit":true,"n":8,"published":"2026-08-13","series":["bench"],"short":"Four rival frontier labs re-judged every sealed round we had published — blind, byte-identical.","slug":"outside-judges","text":"full","title":"The outside judges — four rival labs re-check our work.","updated":"2026-08-14","url":"https://research.strata2signal.com/outside-judges/"},
{"dek":"Four open-weight models landed in one week, so we gave them the two exams we already had — one frozen since July, one rebuilt in its shape.","headings":[{"h":"The story","id":"the-story"},{"h":"Method, and a disclaimer we are proud of","id":"method-and-a-disclaimer-we-are-proud-of"},{"h":"Exam one: the judge chair","id":"exam-one-the-judge-chair"},{"h":"Exam two: the narrator's chair","id":"exam-two-the-narrators-chair"},{"h":"What the week actually taught","id":"what-the-week-actually-taught"},{"h":"Limits, stated plainly","id":"limits-stated-plainly"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-9a3049"}],"kit":true,"n":5,"published":"2026-08-12","series":["bench"],"short":"Four open-weight models landed in one week, so we gave them the two exams we already had — one frozen since July, one rebuilt in its shape.","slug":"august-arrivals","text":"full","title":"The August arrivals — the fresh class sits the house exams.","updated":"2026-08-20","url":"https://research.strata2signal.com/august-arrivals/"},
{"dek":"What a fully local image pipeline delivers for a living world — head-to-heads, ladders, cold starts.","headings":[],"kit":false,"n":1,"published":"2026-08-11","series":["bench"],"short":null,"slug":"diffusion","text":"catalogue","title":"The diffusion research bench.","updated":null,"url":"https://research.strata2signal.com/diffusion/"},
{"dek":"Before a model earns a seat inside our products, it sits this trial.","headings":[{"h":"The instrument","id":"the-instrument"},{"h":"The local roster · 96G VRAM workstation, one seat on the 24G rig","id":"the-local-roster-96g-vram-workstation-one-seat-on-the-24g"},{"h":"The cloud roster · latency deliberately unranked","id":"the-cloud-roster-latency-deliberately-unranked"},{"h":"The 120B story · and the thinking fence","id":"the-120b-story-and-the-thinking-fence"},{"h":"The finding we did not expect","id":"the-finding-we-did-not-expect"},{"h":"The format floor","id":"the-format-floor"},{"h":"Where the chair sits","id":"where-the-chair-sits"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-ced6ed"}],"kit":true,"n":2,"published":"2026-08-11","series":["bench"],"short":"Before a model earns a seat inside our products, it sits this trial.","slug":"seat-trials","text":"full","title":"The seat trials — how a local model earns a chair.","updated":"2026-08-19","url":"https://research.strata2signal.com/seat-trials/"},
{"dek":"Twenty-four model tags across three hardware eras, hunting a voice that can inhabit a character.","headings":[{"h":"The method, limits first","id":"the-method-limits-first"},{"h":"Era one · the 8G VRAM rig, nine models","id":"era-one-the-8g-vram-rig-nine-models"},{"h":"Era two · the 24G budget, twelve candidates, blind","id":"era-two-the-24g-budget-twelve-candidates-blind"},{"h":"The twist we published on purpose","id":"the-twist-we-published-on-purpose"},{"h":"The re-bake · thirteen models, one scale, after the fix","id":"the-re-bake-thirteen-models-one-scale-after-the-fix"},{"h":"The 2026-08 field · six arms on the 96G workstation, blind","id":"the-2026-08-field-six-arms-on-the-96g-workstation-blind"},{"h":"Why the best voice lost its seat — twice","id":"why-the-best-voice-lost-its-seat-twice"},{"h":"Where the voice lives","id":"where-the-voice-lives"},{"h":"What to take with you","id":"what-to-take-with-you"},{"h":"How to check our work — and see it live","id":"how-to-check-our-work-and-see-it-live"},{"h":"The rest of the seminar","id":"the-rest-of-the-seminar"},{"h":"Provenance","id":"provenance-4cae43"}],"kit":true,"n":3,"published":"2026-08-11","series":["bench"],"short":"Twenty-four model tags across three hardware eras, hunting a voice that can inhabit a character.","slug":"voice-trials","text":"full","title":"The voice trials — how the narrator earned its voice.","updated":"2026-08-13","url":"https://research.strata2signal.com/voice-trials/"},
{"dek":"Every model we run or bench, its licence read from the actual text — the page the others cite instead of restating.","headings":[{"h":"How to read this page","id":"how-to-read-this-page"},{"h":"The brains · chat, vision, memory · all read 2026-08-11","id":"the-brains-chat-vision-memory-all-read-2026-08-11"},{"h":"The sound department · music generation, stems, encoding · read 2026-08-25 / 26","id":"the-sound-department-music-generation-stems-encoding-read"},{"h":"The trial roster · the August arrivals and one incumbent · read 2026-08-12","id":"the-trial-roster-the-august-arrivals-and-one-incumbent-read"},{"h":"The new trial roster · the candidate the 24 GB battery brought in · read 2026-08-29","id":"the-new-trial-roster-the-candidate-the-24-gb-battery"},{"h":"The painters · diffusion · read 2026-08-11 except where a row says otherwise","id":"the-painters-diffusion-read-2026-08-11-except-where-a-row"},{"h":"The new painters · the new-arrivals diffusion bench · all read 2026-08-23","id":"the-new-painters-the-new-arrivals-diffusion-bench-all-read"},{"h":"Why this page is called living","id":"why-this-page-is-called-living"},{"h":"Provenance","id":"provenance-063a00"}],"kit":false,"n":4,"published":"2026-08-11","series":["notes"],"short":"Every model we run or bench, its licence read from the actual text — the page the others cite instead of restating.","slug":"licences","text":"full","title":"The licence ledger — every model, read first-hand.","updated":"2026-09-04","url":"https://research.strata2signal.com/licences/"}
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"vllm/engine/arg_utils",
"vllm/engine/arg_utils.py",
"vllm/entrypoints",
"vllm/entrypoints/cohere",
"vllm/entrypoints/cohere/api",
"vllm/entrypoints/cohere/api_router",
"vllm/entrypoints/cohere/api_router.py",
"vllm/entrypoints/launchers",
"vllm/entrypoints/launchers/cli",
"vllm/entrypoints/launchers/cli_args",
"vllm/entrypoints/launchers/cli_args.py",
"vllm/entrypoints/launchers/launcher",
"vllm/entrypoints/launchers/launcher.py",
"vllm/entrypoints/openai",
"vllm/entrypoints/openai/api",
"vllm/entrypoints/openai/api_server",
"vllm/entrypoints/openai/api_server.py",
"vllm/entrypoints/serve",
"vllm/entrypoints/serve/middleware",
"vllm/entrypoints/serve/middleware/authenticate",
"vllm/entrypoints/serve/middleware/authenticate.py",
"vllm/envs",
"vllm/envs.py",
"vllm/usage",
"vllm/usage/usage",
"vllm/usage/usage_lib",
"vllm/usage/usage_lib.py",
"vllm/v1",
"vllm/v1/core",
"vllm/v1/core/kv",
"vllm/v1/core/kv_cache",
"vllm/v1/core/kv_cache_utils",
"vllm/v1/core/kv_cache_utils.py",
"vllm/v1/engine",
"vllm/v1/engine/input",
"vllm/v1/engine/input_processor",
"vllm/v1/engine/input_processor.py",
"vllm/v1/utils",
"vllm/v1/utils.py",
"vllm/v1/worker",
"vllm/v1/worker/gpu",
"vllm/v1/worker/gpu_worker",
"vllm/v1/worker/gpu_worker.py",
"vllm/v1/worker/utils",
"vllm/v1/worker/utils.py",
"vllm_api",
"vllm_api_key",
"vllm_do",
"vllm_do_not",
"vllm_do_not_track",
"vllm_enable",
"vllm_enable_cohere",
"vllm_enable_cohere_api",
"vllm_no",
"vllm_no_usage",
"vllm_no_usage_stats",
"vllm_usage",
"vllm_usage_stats",
"vllm_usage_stats_server",
"vllm_use",
"vllm_use_flashinfer",
"vllm_use_flashinfer_sampler",
"vllm_use_v1",
"vllm_use_v2",
"vllm_use_v2_model",
"vllm_use_v2_model_runner",
"vllmmetal",
"vllmproject",
"vlm2vec",
"vlm2vec-v2",
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"vnnicapabl",
"vocab",
"vocabulary",
"vocoder",
"voic",
"voice-activity",
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"voice-control-menu",
"voice-distinctness",
"voice-questions",
"voice-questions.json",
"voice-rows",
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],
"variants":{
"cpus":"cpu",
"gigabyte":"gb",
"gpus":"gpu",
"licence":"license",
"llms":"llm",
"optimisation":"optimization",
"optimise":"optimize",
"quantisation":"quantization",
"quantise":"quantize",
"tokenisation":"tokenization",
"tokenise":"tokenize"
}
}