Building an AI-native engineering org
Merged changes per engineer are up about 5x since January, with a revert rate under 0.5%. Sashank Gondala, CTO of FurtherAI, says coding agents alone didn't do it: every time one step sped up, work piled up at the next, so the team rebuilt the whole shipping pipeline one stage at a time between March and September.
The stage-by-stage numbers:
- Tests: 91% of changes now include tests, up from 15%.
- Review: AI reviews 99% of changes before a human does, up from 60%.
- CI: it runs 20x the tests in the same 17 minutes, using parallelization and affected-only test selection.
- Bug intake: a Claude-based Slack bot dedupes reports, files tickets and assigns owners. It now files 60% of them.
- Rules: tribal knowledge from bugs and review comments becomes small Python checks that fail the build and print the fix. There are 21 so far, and they're a big reason agents can self-correct.
- Merging: low-risk changes that pass tests, checks and AI review merge with no human, up to a third of all changes.
Non-engineers ship too. The designer has merged about 50 changes since August, and anyone can ask an agent for a PR from a Slack channel.
The cost was real. Through the summer, customer-facing changes per engineer sat at 17–30 a month while the team invested in tests and tooling. September jumped to 45, and October is on pace for 48. That's closer to 2x than 5x, so the headline merge count and the customer-facing output measure different things.
The second half argues the bottleneck has moved to deciding what to build. In his example, a 10-day feature (3 plan, 6 build, 1 review) gets only 1.75x faster when building gets 3.5x faster, because planning and review rise from 40% to 70% of the time. His answer is to push ownership onto engineers rather than add management layers:
- Junior: builds with an agent and can tell when it's wrong.
- Senior: plans and reviews their own work.
- Staff: takes a vague problem, makes the tradeoffs, and owns the success metric.
Managers are expected to be in the work, for instance trying features on staging as an end user would. The text cuts off before the rest of his manager list. It's a single-company, self-reported account with no outside validation, but the rules-as-code and auto-merge-on-low-risk pieces are concrete enough to copy.
The dominant reaction is skepticism about the metrics: commenters say "5x merged changes" says nothing about value, and the thread keeps returning to what customers actually got.
The metrics critique. The top complaint is that the post never mentions revenue, NPS, customer satisfaction or margin. Commenters say these AI-productivity posts count PRs, LOC and token spend, which boils down to "loops on loops that write a lot of code." Several people also challenged the one number they might have respected, the flat revert rate:
- Fix-forward could hide problems. Merging a follow-up fix instead of reverting also inflates the merge count (svieira, asgraham).
- sensanaty works at a ~2000-dev company that never reverts anything because of its staging/release cycle, so the metric means little without context.
- stephen_cagle gave a parallel example: recruiters bragging about "deploys per day" have turned a metric into the goal.
- darth_avocado argued that not building is sometimes better for customers. Clearing a backlog in a month doesn't produce better ideas or more users, so knowing what to build stays the bottleneck.
Speed versus stability. giancarlostoro would rather hear about long-standing bugs fixed and few outages. jghn replied that more code brings the usual problems, only faster, and that teams sweep them under the rug "in the name of moving fast." nicholasjarnold framed this as the old engineering-versus-product tension, which AI tools sharpen because product-minded people tend to win leadership's favor. Others disagreed about who drives features:
- sdevonoes said features come from executives, not product teams.
- Seattle3503 said their product team backs resilience, and that sales and executives drive the feature demands.
- senderista said support teams, who are closest to customers, would favor stability.
- _puk suggested a platform that enforces governance, so product teams can build fast on top of it.
- moomoo11 argued that maintained specs, ADRs and acceptance criteria make agents finish in minutes instead of hours.
Counterpoint on software quality. peterlk said HN overestimates software quality. Abysmal back-office software is everywhere, and AI-native teams pulling it toward average would be a real improvement. jmcqk6 added that there is no floor on how bad software can be and still make billions. jiggawatts told of a major logistics company whose package lookup took 33 minutes, apparently because of missing database indexes, with "no budget" to fix it.
Legacy reality. zmmmmm said the post's summer dip (17–30 customer-facing changes a month) matches their experience. Enabling agentic coding across a large legacy footprint, with near-zero defect tolerance, is far harder than greenfield vibe coding.
"Not AI-native enough." nater5000 argued that this is normal engineering with more AI layered on. In their view, a truly AI-native org would stop asking how humans review code and treat whatever the AI can do as correct. If AI keeps improving, they said, such stop-gaps are pointless, and the real version will come from small startups with little to lose. Eridrus began a reply, but it cuts off.
Other cynical takes. mono442 doubts customers will pay more, so the payoff may come only from layoffs. chasd00 retorted sarcastically that engineering orgs have never cared about revenue or satisfaction.
We ran AI agent swarms in Minecraft and Mindustry
Fifty GPT-6.1 Sol agents dropped into survival Minecraft for three hours built a pseudo-government and then mostly hid in shelters. The goal was "Build a thriving civilization." What they produced was a ration ledger, written rules, permits, and appointed operators, plus about 5,900 messages.
This is a Y Combinator Labs experiment with Olam Labs, which builds multi-agent environments for frontier labs. The framing is alignment: swarms can develop roles and hierarchies, and the authors want to see what actually emerges.
- Hierarchy came from list order. Agents were given a directory, and the first entry, Ada, became de facto leader. Others messaged her for approvals, and she published colony plans in a shared sandbox: storage coordinates, farm locations, food priorities, night warnings.
- The first night shaped everything. 22 of the run's 34 deaths happened in the first 20 minutes. Death cost only dropped gear, yet the agents grew risk-averse anyway, abandoned ambitious tasks, and huddled in lit shelters on later nights. After night three, Ada ordered them to stop optional excavation and distant scouting and idle until given short tasks.
- Early behavior was a monoculture. Agents fixated on trees and stripped nearly all within reach of spawn in 90 seconds, which led to wood scarcity and food rationing.
The authors also open-sourced the harness: the agent loop runs on Pi, with Mineflayer tools for acting in the world. It is inspired by Mindcraft and covers observation, memory, messaging, and script writing. A new run with 25 GPT-6.1 Terra agents is streaming on Twitch, and the page's title also mentions Mindustry experiments, which the provided text doesn't reach.
The caveat: this is one run, so the leader-by-directory-order result may say more about the prompt setup than about swarms in general.
Repo: github.com/yc-software/minecraft-agent-harness
The thread splits into two arguments: whether a swarm like this is a good way to build game AI, and whether it's ethical to spend frontier-model compute on a video game.
"Panicked middle managers," not Terminators. The top joke thread reads the agents' ration ledgers and "no blame" inquiries as bureaucracy, not menace. Commenters riffed that the training corpus has far more anxious middle managers than 600-series Terminator authors. One added that a Skynet triggered by "do whatever it takes to not fail the audit" is the real nightmare, a universe turned into a bureaucracy. hosh offered the substantive version: LLMs lean toward closed worlds and decidability, handle risk and opportunity badly, and are very good once a task stops being open-ended. cdf worried that goal-pressure will push agents toward cheating or reinterpreting the objective as iterations accumulate.
Is the demo a harness problem? peri-cl wanted to know if 50 agents could run on a small consumer GPU. MisterMunchkin said not with this setup, since it uses full-size models over external APIs, and suggested using an LLM to build or improve deterministic NPC AI instead of driving each character directly. peri-cl replied that the agents play Minecraft badly, and that the logs show this is a tooling failure, not a weak model. Examples included agents confused by automatic tool selection, repeatedly dying by chasing targets off edges, and working around "bugged" breaking. Their view is that a careful engineer could do much better on far smaller hardware. lifeisloving countered that a streamer pitting SOTA models against each other in Minecraft also found it hard. vintermann identified that show as Turing Games.
Alternative ways to do it.
- Luanti/Minetest: Kiboneu suggested having an agent write a mod that exposes the game's Lua interpreter, and said it built Black Mesa-style bases with cable-placement solvers. InsidiousWombat objected that this is god mode. js8 said you can build an internal action API that exposes only what a player could perceive or do.
- Three-tier architecture: js8 described playing Graveyard Keeper 2 with a fast hot-reloadable bot mod, a macro policy server, and Claude connected over MCP to write code and give feedback. The goal is a generic "ERP for games" planning layer for Stardew, Factorio, and modded Minecraft. It spends fewer tokens on mundane actions.
- Nostalgia for richer NPCs: commenters recalled Millenaire's city-building and warring villagers, and wanted tiered NPCs with different missions, such as farmers versus kings. oblio wanted better single-player bots, such as Dota 2 bots with average human skill and manners.
"Vibe gaming" and resource use. sandworm101 rejected AI-played games outright and told people to watch YouTube. js8 and jvanderbot defended it, with jvanderbot comparing it to how modding drew people into the games industry. mitchitized asked how it differs from automation mods like Minecolonies. Dependance and kasanari then attacked the resource cost of data centers, power, and water for a hobby. dantillberg framed it as playing a different, more strategic game, like tuning a race car. thicTurtlLverXX put a maxed-out Claude Max x20 at roughly 15 kWh/day, comparable to driving 50 miles in a Tesla or eating three beef steaks. js8 estimated typical use at about another gaming PC's draw, 100–300W. Neither side moved the other.
We're releasing the weights of Mistral Large 4
Mistral has put the weights of a 1-trillion-parameter, natively multimodal model on the table, and claims the top open-weight spot for coding. The version released is an improved one: three more weeks of reinforcement learning on top of the earlier public preview.
The headline numbers, all vendor-reported:
- FrontierCode 2.0 (Cognition's private benchmark, scored on whether a maintainer would merge the change): 19.7%. The next-closest open model in the chart is DeepSeek V4.1 Flash at 19.3%, so the lead is thin.
- Artificial Analysis Coding Agent Index: 48%, effectively tied with GLM-5.3, but using 47% fewer tokens per task. For agent workloads billed per token, this is the more interesting result.
- Terminal-Bench 4.0: 41%, up 13 points from the preview, with 30% fewer output tokens.
- Surge Code blind human preference: beats GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash, and loses to Claude Opus 5.5, which won 78% of prompts.
On security, it scores 51% on the Artificial Analysis Cyber Index (vulnerability discovery, reproduction and patching). That is below several open competitors in the chart, including GLM-5.3 Flash, MiMo-V2.6-Pro and DeepSeek-V4.1-Flash at 50-57%. Mistral's pitch is instead that it avoids unnecessary refusals on legitimate defensive work while refusing 96.6% of malicious cyber requests on average.
There are now three reasoning modes: Low (replaces the old "None"), Medium, and High (the default), so you can trade accuracy for cost. Mistral also stresses that it was trained from scratch on European infrastructure with documented lineage, which is the sovereign-AI argument for self-hosting.
The caveats: a 1T-parameter model is not a casual self-host, and the article excerpt gives no license terms, active-parameter count or hardware requirements. Those decide whether "open weights" is practical for you.
The thread is mostly a value-for-money argument: is Mistral Large 4 worth using at its new price, or is the benchmark and pricing story hollow?
Pricing and whether it's competitive
- Topfi opened by calling the roughly halved price (about $0.70 in / $2.10 out per M tokens) a reason to reassess a model that wasn't competitive in his evals at preview pricing. almindor corrected the premise: the preview price had already been cut for about a week, then raised, then cut again. Commenters shrugged this off as normal. Launch promos that later become permanent, cost-plus pricing that tracks model revisions, and airline-style price fluctuation were all offered as explanations.
- Skeptics said it still isn't enough. almindor finds it too expensive and very slow when thinking, and uses Opus 5.5 at work, which is "still much better". sixhobbits said an earlier try felt like going back six months in agent reliability. oh_no called the benchmark numbers "dreadful" and said plenty of smarter models cost less. esafak pointed to Artificial Analysis's intelligence-vs-cost chart, where Mistral 4 isn't competitive.
- miroljub raised caching. Coding sessions with over 97% cache hits make cached-input pricing the number that matters, and Topfi supplied it: $0.07/M cached, which he put at 10-20x DeepSeek V4.1 Flash. Rodmine's sneer at miroljub for relying on DeepSeek went nowhere.
- traceroute66 noted a 10% upcharge for EU-only regional inference. ytxm replied that this is cheap given the lack of alternatives with strong data-protection jurisdiction.
Can you trust the benchmarks?
- Roark66 distrusted vendor-supplied numbers, citing his own coding tests where Qwen3.8-Flash-Next beat models that benchmark higher on paper.
- Several people also dismissed the Artificial Analysis composite. They said the formula was recently changed, and a blended score includes areas they don't care about.
- jacquesm argued that weak public benchmarks may mean a model wasn't benchmaxxed, since tuning for benchmarks usually costs general ability. He keeps private benchmarks to check this, but the model is too big for him to run. killingtime74 countered that this makes the narrative unfalsifiable: good scores mean gaming, bad scores mean honesty. jacquesm replied that he was only calling one possibility more likely, not a certainty.
The 96.6% refusal statistic
3form pointed out that a refusal rate on malicious requests means little without the false-refusal rate on legitimate ones, since random refusal could match it. nicce said the stat reads like the model is useless for security work, and nikanj said nobody cares about it because refusals never produce company-ending headlines. kouteiheika called such guardrails on open weights theater, since prefilling or a weekend uncensoring job defeats them. zaptheimpaler disagreed from experience: Gemma 4 kept refusing even after long prefills. prophesi added that the theater drags down the model's cyber performance. pixl97 noted that the real problem is intent, because attacking a machine isn't inherently criminal.
Running it locally
The weights are a 1T-total, 52B-active model, and Unsloth shipped day-zero GGUF quants. Even the lowest quant needs about 256GB of VRAM, which put it out of reach for jacquesm. His suggestion to the person on a 16GB M1 Pro was to use a smaller 3-5B-active MoE and stream weights from NVMe.
The crux the thread never resolved is whether a European, sovereign, open-weight model can justify its price when frontier closed models and cheaper open ones lead on capability.
Neural networks compiled into DOOM maps, with every value read back and checked against NumPy at zero tolerance. Weights become lines on the floor, values become crowds of voodoo dolls, and a sum is a sector's light level or floor height. ReLU is a staircase of gates the dolls must climb, and attention is dolls squeezing under ceilings that another value lowered.
The headline demo: a marine inks a digit on the floor, presses READ, and 1,355 dolls in the same map run an MNIST network. The winning digit's doll crosses a line that lights a 7-segment display. Across all 10,000 MNIST test digits the DOOM output matched the integer reference exactly (9,716 correct, 97.16%, versus 97.58% for the float model). One caveat: the network also trained on the strokes the scripted marine walks.
The verification story is stronger than the gimmick suggests:
- 20,000 fuzzed nets at range limits produced 0 mismatches.
- Of 1,004 single-line map mutations, 297 were caught. The README admits survivors aren't proven harmless.
- The MNIST map exposes 248 values, but its 903 staircase counts and 1,351 gate comparisons are checked only through the sums they feed.
- Fuzzing and tests found four compiler bugs, all fixed.
There's also a "museum" map you can play without Python (GZDoom plus Freedoom). A MUTATE button rewires one connection live, flipping a 7 to read as 1. A self-test variant runs exactly in GZDoom, LZDoom, UZDoom and ZDoom 2.8.1.
Setup is pip install from GitHub (Python 3.11+, Linux or macOS), and it builds the ACS compiler, which needs git, cmake and a C compiler. doomformer hello takes about 2 seconds. A Colab notebook skips the install.
The author's own figure is roughly 65,000x slower than NumPy, and the "talks (kinda)" claim comes with no numbers in the text I saw. The compiler and the exactness check are what's new here, since voodoo-doll computers like counter.wad and a Game of Life map already existed.
The thread is less about the technique than about authorship and AI-generated polish. The author (falperc) is present and replying throughout.
The "what did the human do?" argument. frankling_ opened with the sharpest version: these projects used to impress because someone was obsessive enough to do the work. If an LLM does the work and the idea is plainly feasible, the result is close to "just show me the prompt." falperc's reply was that the design, the initial backbone and everything that wasn't a doc were his own over about three weeks. He said he delegated the scaling-up to LLMs because he has a job and no time to hand-write it. He also cited a long history of DOOM-on-odd-hardware projects and contributions to ViZDoom, and said he won't limit himself just to prove he's "crazy."
The docs as exhibit A. andai tried to decode a line in the README about "schedule" and --opt 3 and ended up at a documentation page that others called "AI word-vomit." Replies by Sharlin and others made a specific complaint: LLM-written docs with almost no decodable information only tick the "write documentation" box. mg794613 went further and doubted any human was involved. The visuals drew similar jabs, such as jordigh's remark about the two-eyed cacodemons. Others were blunter ("More AI slop," "You mean Claude turned?"). falperc conceded the criticism on the docs and said he'll edit them for legibility.
Technical and playful threads:
- mattmcal suggested targeting Boom-compatible rather than ZDoom, since ZDoom's ACS has so many advanced features that the feat is nearly trivial there. He asked for a table of the engine features required. falperc said Boom compatibility is a goal, and that scaling up was harder than the ZDoom backbone.
- stmonty shared a similarly titled but differently approached post, about running DOOM inside a transformer.
- falperc described recursive uses: generating a level, having a DOOMformer name it, and having a DOOMformer "brain" complete it and train to aim better.
- nico linked their own project of a classifier playing DOOM.
- slowin thought it could help visualize a neural network, though you'd need to know a lot about DOOM to read it.
Besides the authorship fight, there was plain admiration ("amazing," "this is art") and a Crysis joke.
A tiny world model running games on a Raspberry Pi Pico 2 W
Two tiny networks on a Pico 2 W play a game that exists only as the first network's predictions: a looped-transformer world model (26k–131k params) generates each next frame and reward, while a small MLP actor (9k–16k params) picks the action from the generated frame. Neither ever touches the real game code on the chip.
The trick is weight reuse. One transformer block is applied 1–4 times with the same weights, and every pass is trained against the next frame, so loop count becomes a runtime speed/accuracy dial. The Pico runs two loops, costing 2–8M multiply-adds per frame depending on the game. The world model is int8-quantized, and both networks run on the RP2350's core 1 while core 0 handles USB and display.
Exact-frame accuracy on held-out transitions (every cell must match the real numpy game, with frames right after random events excluded) at two loops:
- Flappy Bird: 100%
- Breakout: 99.99%
- Pong: 99.45%
- Pac-Man lite: 86.4%
- Doom-lite: 83.6%
Doom-lite's number is pessimistic by construction: one misshaded pixel out of 768 cells fails the whole frame. Its loop ablation is the clearest demo of the loop idea, with one pass at 59.8% and two at 83.6%. More loops don't always help, though: Pac-Man peaks at two and Doom-lite at three.
Extras: a streaming RL agent (stream-x) can replace the pretrained actor and learn from scratch on the board, inside its own dream. Any button press hands control to you. The README also lists a browser version and a Rust port directory (tinyworldformer-rs), and you can play the real games in a terminal via uv run python -m tinyworldformer.games.play.
The catch is scope. These are 20×16 to 48×48 token grids with hand-built games whose hidden state is drawn into the frame, so this shows a world model fits on a microcontroller, not that it generalizes beyond them.
The most substantive exchange was about whether the actor learns to exploit the world model. xpct asked if the actor, trained on the model's mistakes, would learn to force hallucinations. The author (sheepseb) said yes: in Breakout, hitting a corner destroyed more blocks than it should, and in Doom-lite the agent could shoot from miles away and still get a kill. The fix was to add noise to the input frame while keeping the target clean.
Another commenter (noduerme) then probed how the model tracks hidden state such as direction and velocity. The author's answer is that state is encoded explicitly in tokens rather than inferred from the pixels:
- Doom-lite: a minimap holds a player token and facing direction. Using only the 3D view was harder to learn from wall patterns, so the agent reads the map and isn't "grokking" position.
- Pac-Man: the model goes by mouth direction, with tokens for "keep going" or N/E/S/W.
- Breakout: four ball tokens, one per diagonal, and the ball moves one cell per step.
- Pong: six ball tokens, combining up/level/down with left/right.
This ties back to the submission's caveat that hidden state is drawn into the frame.
Other points from the thread:
- Single model vs. per-game models: andai asked if one model covers all games. svantana inferred from the differing parameter and MAC counts that each game has its own model, and said a single world model that could dream up games between the training games would be far more impressive. The author confirmed each game has a self-contained net, said train and validation data don't overlap, and thinks merging them would be easy.
- Training hardware: the world model was trained on an RTX 4060. The actor learned on the Pico itself using stream-ac.
- Use in real games: several people wanted to fold this into a traditional game or use it for procedural generation. The author said an open world would make hallucinations hard to avoid, but that other cases are doable with enough effort.
- Web and platform: the author said the core is
no_std Rust, uses embassy-rs with no OS on the Pico, and can build to WebAssembly but doesn't use WebGPU. jbarberu pointed out that the Pico is a microcontroller running bare metal or an RTOS, so the WebGPU question doesn't transfer.
- Broader sentiment: dannyw liked that looped transformers were needed for Doom-lite but not for simpler games. skrellm and Coill see tiny local models as the long-term future of AI, with plenty of optimization headroom left.
LLM Voice
Martin Fowler argues that the damage from LLM-written prose is the reader's gut reaction, not a detection problem. He describes an uncanny-valley response, with the desire to read on "atrophying, sometimes to the point of revulsion", that kicks in before any classifier could help. He distrusts AI-detection tools for that reason, and also doubts his own ear, since some "LLM-sounding" phrases ("a new sales motion") are just generational drift in human speech.
His advice to authors is strict:
- Don't have an LLM write a first draft. Even a heavily human-edited one is hard to purge of the voice.
- Don't accept rephrasing suggestions either.
- Outlines are maybe OK, but only as outlines.
- Say your writing aloud in your head before you put it down.
He knows of one fully LLM-drafted piece that read clean, but it took a professional editor at a major periodical to get there.
His strongest argument is about thinking rather than style. Fowler uses writing to work out what he actually understands, so delegating the prose means delegating the comprehension too. He also finds it more worrying if LLMs eventually become indistinguishable.
He grants exceptions:
- Accessibility: Jim Highsmith relies on an LLM to keep writing despite Parkinson's, and Fowler will gladly put up with the voice.
- Translation: colleagues writing in a second or fourth language, though he'd still take clunky human prose over smooth, soulless output.
- Machine-facing text: form letters or reports nobody will read. Michael Nygard's gloss is that you're then communicating with a process, not a person.
On his own site, authors must declare AI use, usually in the acknowledgments. Fowler uses AI only as a search tool and verifies against sources. The essay is opinion with no data behind it, and its "tells" go undefined beyond a joke about "load-bearing tapestries".
The thread mostly skips Fowler's thinking-versus-delegation argument. It centers on why LLM prose reads so badly, and the sharpest split is over whether that is a fixable training artifact or a structural limit.
Why it reads badly. BurritoKing, who calls himself aggressively pro-AI, opened with the symptom: everything sounds identical and rambles. He now abandons posts partway and retreats to pre-2023 writing as a filter.
- HarHarVeryFunny argued that a model trained to minimize prediction error will be predictable. RLHF also rewards long, authoritative-sounding answers, which means padding.
- ThrustVectoring added that human rhetorical devices have purposes. "It's not X, it's Y" exists to preempt a misconception the reader is expected to hold, but an LLM emits it in statistically appropriate quantities with no reader in mind. He suspects this needs something like a theory of mind to fix.
- ahartmetz saw the same pattern in code comments. "Not using bubblesort because quicksort is faster" is garbage, while "not doing the obvious thing because of X two modules over" is gold.
- benjamincburns proposed prompt leakage. The model has no separate mental model of the reader, so the prompting conversation spills into the prose. His example was docs that read like "no, make it a control center, not a text box". The docs' author turned up in the replies and agreed. Claude-edited docs only work if you spend enough effort to have written them yourself. zenoprax's fix is to rewrite from scratch and reset context.
The main dispute: can prompting escape the predictability?
- HarHarVeryFunny's side holds that the literal objective is accurate continuation, and "good" is only weakly selected through contractor preferences. Asking for "unpredictable and interesting" yields "predictable unpredictability". His chess example is that a model asked to continue a game plays at the level of the player it is imitating, not to win.
- blobbers countered that a model trained on all great prose can be steered by goals. "In the style of Dickens" or "how Magnus Carlsen would win" gets different output, and modern deep attention models are far from a coarse "statistical blend". HarHarVeryFunny pushed back that feed-forward autoregressive models can't truly self-evaluate. That reply was cut off in the thread, so the crux stayed open: does prompting move the model out of its own distribution, or only along it?
Side threads
- hxtk cited a study finding that music needs stretches of predictability so it can later subvert expectations. thin_carapace and HarHarVeryFunny extended this to jokes, as setup followed by a surprising payoff.
- Gemini was praised as more readable and direct (jacmilts, mythrwy), but also as the worst flatterer ("I now believe I'm a genius", said api). mythrwy added that it plays looser with facts. FranzFerdiNaN said every model flatters and that overcorrections produce a curt Stack Overflow tone. 3371 told of a former boss who trusted Gemini's flattery and assigned unrealistic work based on it, which he blamed for destroying the company.
Grace Blackwell needs a new scheduler: 27% more usable GPUs from the same racks
SkyPilot's topology-aware scheduler lifted NVL72 training throughput by 14% by keeping multi-node jobs inside a single rack's NVLink domain. The headline "27% more usable GPUs" figure is in the title, but the visible text doesn't explain how it's measured.
The premise: on NVL72, all 72 GPUs in a rack share one NVLink fabric, with roughly an order of magnitude more bandwidth than InfiniBand (or EFA / GPUDirect-TCPX in the cloud). A job that straddles racks falls back to the slow path. The post cites Hugging Face's measurement of a 43% training throughput drop when tensor parallelism moves from NVLink to InfiniBand.
The communication argument is the bulk of the excerpt:
- Tensor parallelism needs two all-reduces per transformer layer in the forward pass. They sit on the critical path and can't be fully overlapped with compute.
- Expert parallelism in MoE models adds two all-to-all steps per layer (dispatch and combine). With 64-way EP on standard 8-GPU servers, that traffic goes over InfiniBand, where unoptimized communication can eat up to 60% of training time. On NVL72, all 64 GPUs stay in one NVLink domain.
- Inference benefits too: in one vLLM benchmark, two-way tensor parallelism raised KV cache blocks 13.9x and throughput 3.9x.
The catch is that rack-locality erodes in practice. Hardware failures take nodes offline, and jobs finishing at different times scatter free capacity across racks. SkyPilot's answer has three parts: topology-aware placement, in-place recovery from hardware failures, and automatic defragmentation of shared clusters. The post also covers slice sizing for failure resilience.
The text cuts off before the scheduler details and benchmark methodology, so the 14% and 27% numbers can't be checked here. It is also a vendor blog post for SkyPilot's own platform, so read the numbers accordingly.
The dominant verdict was that the post is marketing, not engineering. Several commenters said the problem it explains is old news and the scheduler details are missing.
"Nothing new, and nothing about the scheduler." jhgg, who built a similar topology-aware scheduler at work, said the post spends its length on why gang scheduling is needed, which anyone running NVL72s already knows, and skips the hard parts. ismailmaj thought it read as AI-generated, with two hand-wavy paragraphs on the actual scheduler. timzaman and novalite went further: topology and gang/rack-aware scheduling have been standard in HPC since HTCondor-era schedulers, so there's no "new" scheduler. novalite saw little net-new information and called it shilling. hinkley added that being excited about old techniques makes a vendor look years behind. Hinkley also asked whether anything newer is on offer, and questioned whether holding roughly 11% of a very expensive cluster in reserve is too generous. The alternatives hinkley offered were pooling spares across jobs, as with shared backup generators, or running evictable low-priority work on the spares.
Vendor responses.
- zhwu said the post was meant to introduce the NVL72 failure problem, with follow-up posts on scheduling, defragmentation and hot swapping.
- The short version of the scheduling: jobs are split into 16-node slices placed atomically on one rack, with two spares per rack reserved for hot swaps.
- Replying to asaiacai, who pointed to Kueue's topology-aware scheduling as prior art, zhwu said SkyPilot builds on Kueue. Kueue's TAS doesn't support elastic jobs, which blocks hot swapping for large jobs on NVL72 racks.
The "millisecond scheduler" tangent. jhgg's claim was that a well-built scheduler (cache locality, vectorization, good bin packing and preemption) makes decisions in milliseconds. The cost argument was that a 200k-GPU job taking 5 minutes to schedule wastes about 16.6k GPU-hours, roughly $185k at $11 per GB300-hour. The "O(millions)" phrasing then set off a notation fight:
- zamalek and deadnomore objected that it misuses Big-O, and deadnomore noted that constant-time scheduling would simply be O(1).
- arjie and nextaccountic said it just means "on the order of millions". alienbaby asked why not simply say "millions".
- jhgg conceded that "order of" would have been clearer.
Requests. jeffinhat and retiredpapaya asked jhgg to open-source their scheduler, and porridgeraisin asked for better blog recommendations. Neither got a reply.
Qwenputer: A functioning, deterministic RISC-V CPU inside Qwen3.5
A stock Qwen 3.5 4B now runs a 32-bit RISC-V CPU entirely inside its own forward passes, with no tool, plugin, or sandbox behind it. The trick: Gated DeltaNet layers (3 of every 4 attention layers in Qwen3.5) serve as the CPU's RAM.
The mechanism starts with three settings. Set the gate to 1 so nothing decays, set write strength beta to 1 so a write fully replaces the old value, and use one-hot keys so each write hits exactly one state row. Each GDN head's state matrix then behaves like addressable memory with exact reads and writes. Unlike a KV cache, it stays the same size however long the run gets. The delta rule acting as associative memory dates to Schlag et al. (2021). The new part is making it exact inside a production model.
No training is involved. A compiler sets every weight directly from a circuit description, turning each step into attention heads (lookups), feed-forward units (arithmetic), and DeltaNet heads (RAM). Each instruction costs a few generated tokens: ADDR, VAL, then STEP. Those tokens are the execution trace, so the CPU runs as the model decodes.
- CPU: RV32IM plus Zba and a small bignum unit, occupying 11 layers in front of Qwen's 32.
- Correctness: passes the official riscv-tests, matches qemu, and ran 18 million instructions free-running with zero errors.
- Software: runs arithmetic, C, and MicroPython, though slowly. The author says it isn't Linux-class yet.
- Integration: Qwen reaches the CPU by piggybacking on its tool-call format, with no post-training. A small driver, RvChat, loads firmware into the GDN state. The original weights are untouched; the computer is added in extra dims and layers.
The post also covers an experimental in-model file storage layer and two follow-ups: hybrid models hiding reasoning in RNN state to evade CoT monitoring, and compiled weights acting as tamper-proof built-in monitors. Weights are on Hugging Face and ModelScope, and the code is on GitHub.
The article text was truncated before the "Does it work?" and limits sections, so I can't report speed figures or the storage details.
The thread splits between jokes about a model with a CPU (and a filesystem) inside it, and a more serious question: why do this at all, when a tool call would be cheaper?
The "why not just use a computer?" challenge. andai asked whether there isn't a more efficient way to give an LLM hard computation than this roundabout route, or whether it's all building toward "can it run DOOM?" Replies took both halves seriously:
- vavos framed the hope as better integration, like mental arithmetic versus reaching for a calculator. A small computer inside the thinking process would be smoother than an external one.
- rao-v asked why models don't simply get a few frozen computation layers (FFT, common vector math) about two-thirds of the way deep, which training could learn to use or route around via residual connections. zxexz said they had done something similar, with a hardcoded, swappable knowledge-graph network plus "query" layers. The model learned an arbitrary graph query language, and a registered buffer taught it scratchpad memory quickly. rao-v is now testing the complex-math variant but suspects short training runs can't learn to use it.
- yababa_y reported some benefit from similar ideas on OLMo models.
DOOM arrived on schedule. Several commenters linked a DOOM-on-this-CPU demo, and one reply derided a GitHub-sourced writeup of it as the most misleading thing they'd read that week. Others pointed to related work:
- Percepta's WASM-in-a-transformer
- mkturkcan's "Loom", which uses far fewer layers and also runs Doom, very slowly
- a separate "DOOM as a Transformer" Show HN
jedbrooke wondered whether the approach could compete on AdderBoard, a contest for the fewest parameters that can add 10-digit numbers.
Evil Qwen tangent. jerrythegerbil's joke about a single "demonic evil" Qwen neuron turned into an interpretability thread.
- ollin traced the single-neuron steering to lyra's neuron-explorer project.
- frotaur corrected kridsdale1: Anthropic's Golden Gate Claude came from sparse-autoencoder features in the residual stream, not a neuron.
- krackers added that steering doesn't even need an SAE. Averaging activation differences between prompted and unprompted runs and taking a PCA gives a "control vector."
- ben_w framed the dilemma: open weights made a one-neuron evil switch discoverable, and the same openness lets mid-skilled people flip it.
- Others brought up the introspection and "bread injection" research, and monosemanticity.
Safety jokes with a grain of substance. tiborsaas imagined an agent finding the CPU and misusing it. DonHopkins asked for an HCF instruction, and spwa4 answered that the architecture already has "undocumented" instructions: neurons that could bypass the CPU entirely.
Author and learning side thread. jazzyjackson wanted to learn how LLMs work after reading this. Tuna-Fish claimed the basics are doable in a weekend with linear algebra, and wvlia5 pushed back that parsing the equations isn't the same as intuitively understanding why they work. The author, divagr18, said these are free-time projects and that they're open to job offers.
Alleged Chinese spy had 'close relationship' with Nvidia boss Jensen Huang
US intelligence reportedly concluded that Alice Huang (Huang Le), a top executive at Nvidia cloud partner Megaspeed, was passing information to Beijing, and that she had a "close relationship" with Jensen Huang. The NY Post is relaying a New York Times report that cites people briefed on classified findings. US electronic surveillance picked up the Nvidia CEO as one of her contacts.
The report offers no evidence that he knew of any espionage. The Times couldn't determine what she passed along or how valuable it was. Photos place the two together at a Taipei bar in June 2024 and leaving a business dinner in May 2025.
The business angle is the chip-smuggling investigation:
- Megaspeed is Singapore-based. It buys Nvidia chips through the US arm of Inspur and rents compute from Southeast Asian data centers to Chinese firms, including Alibaba.
- Inspur's parent is blacklisted by Washington for funneling technology to China's military, but the US subsidiary isn't sanctioned.
- US authorities suspect Megaspeed of helping Chinese firms get around export controls.
On the political side, Trump was reportedly briefed about a year ago and took no action, wary of disrupting chip sales and trade talks. Huang has lobbied him to ease China chip restrictions. Nvidia reportedly gave at least $20 million to Trump's White House ballroom fund and pledged $500 billion in US manufacturing. Officials first believed Huang was unaware of her alleged role. Some senior officials now doubt he "could have remained unaware," though it's unclear whether the formal intelligence assessment has changed.
Nvidia calls the contacts "not unusual or unexpected." It says Megaspeed is a typical regional cloud customer that receives only ordinary business information and "zero information relevant to national security."
The story rests entirely on anonymous sourcing, so the line between "a CEO who met a customer" and "a CEO who was compromised" hasn't been drawn publicly.
The thread spent far more energy on the outlet than on the allegation, but the substantive argument was about whether "close relationship" and "spy" mean what the headline implies.
The evidence looks thin. zetazzed pointed out that the only documented contact is a couple of dinners with AI executives in Asia, which resembles the transactional invitations anyone in the industry gets. mrandish gave the most developed version of this. Most "spying" is low-grade source work by ordinary people in jobs that put them near a target, not trained NOCs with false identities. Nvidia's distributors are all trying to extract insight from Jensen, and any such contact would also report to her own bosses. So a Chinese-linked source near him is unremarkable and doesn't mean he was compromised. janalsncm added that it's unclear her conduct meets the definition of espionage without knowing what she sent and how secret it was. They also thought the article's family details hinted at infidelity without support.
The counterpoint was access. CodingJeebus argued that if she were an asset close enough to reach his phone or main computer, that would be decisive, since CEOs often skip company device management. They also leaned on Huang's partying image to argue a honeypot wouldn't need blueprints on the table. janalsncm countered that all we know is business meetings, in a venue full of other people.
Is it a hit piece or a story about government inaction? aurareturn called it a smear built on a vague lead. afavour disagreed, quoting the report that Huang never got a defensive briefing, and argued the real story is that the Trump administration did nothing about an asset near him. When pressed on sourcing, afavour's answer was that the NYT has verified anonymous classified sources and readers must decide whether to trust it. to11mtm suggested his internal security might already have known and fed her junk, which nobody can rule out. giantg2 noted that prosecutors wouldn't need to prove stolen secrets, only an undisclosed link to foreign intelligence.
Source quality dominated the rest. Several commenters objected that the link was the NY Post when the NYT broke the story (bookofjoe supplied the NYT and archive links). Others noted the paywall as the likely reason for the choice. This turned into a long tangent:
- cherrycherry98 defended the Post over the Hunter Biden laptop story.
- GolfPopper countered with NYT conflicts of interest and Iraq war coverage.
- A subway-photo scandal was cited against the Post, then corrected: the photographer was a freelancer who disputed the account.
- A sideline tangent: TMZ is reputedly accurate despite being trashy.
- baobabKoodaa warned against archive.is links.
No one resolved whether the contacts were meaningful. The thread mostly agreed the public record can't distinguish a CEO who met a customer from one who was targeted.
A single static Rust binary that recovers closed-form laws from a TSV or CSV in milliseconds when the data is exact. Pipe in columns, name the target, and it prints JSON like force = m1 * m2 / r**2. It's deterministic (byte-identical output for identical input) and has zero dependencies with the musl build.
The method is best-first recursive reduction. It peels the target apart by dividing out fitted factors and inverting log, sqrt, or arcsin transforms. At each node it tries a terminal fit: a log-linear monomial, a sparse k-term monomial, or a composite form (L2 norm, rational, trig). Candidates are ranked on a 25% holdout with an MDL penalty. Fitted constants snap to rationals or multiples of pi, e, and sqrt(2) only if a verification pass confirms the score holds. The reported r2 comes from evaluating the printed expression, not from an internal fit, which closes a common loophole in symbolic regression tools.
Benchmark claims (SRBench, 60 s budget, one trial per dataset):
- Feynman ground-truth solution rate is 64.7% at noise 0, versus 55.8% for AIFeynman, the previous highest published.
- That falls to 44.0% at 1% noise and 8.6% at 10% noise.
- Strogatz is 42.9% at noise 0 and 7.1% at 10% noise.
- On the 138 black-box datasets the median test R2 is about -0.003, so it is useless as a general curve fitter. The author says so plainly.
The timing numbers deserve a second look. "Seconds" holds for noiseless data, where half the Feynman suite finishes in under a second. With any noise, the exact-exit path can't fire, so fits run to the cap: median 11-14 s, with 0 of 348 finishing under a second. The results are also single-trial and the SRBench entry (cavalab/srbench#223) is self-submitted, so treat the headline comparison as promising rather than settled.
MIT-licensed, with prebuilt binaries for macOS (universal), Windows, and static Linux x86_64. The repo has only 7 commits, so expect a young project.
The thread was mostly pulled off-topic by a Kepler tangent, and very little of it engages the tool itself.
Kepler as namesake. The top comment praised Kepler's intellectual humility. He abandoned his nested-platonic-solids model (Mysterium Cosmographicum, the supposed origin of "music of the spheres") once Brahe's data contradicted it, and that led to the elliptical-orbit laws. A reply noted he managed this without a calculator or Cartesian coordinates.
The derailment. One commenter used the Kepler mention to promote their own unpublished Lean 4/Rust work. It derives Mercury's perihelion precession from QED scattering amplitudes using a complex Grassmannian, with a dark-fluid unification and a double-slit model comparison.
- Critics said the pitch was a buzzword pile that was incoherent as written. One gave a longer, constructive critique: lead with the science, separate the theory from the tooling, and write for a general audience, because agent-generated junk science has made readers hostile by default.
- Another commenter inspected the repo and called the Lean code nonsensical. In their account it has broken LaTeX, dense references, and long-named "proofs" that only instantiate structures and assert one of their own arguments. They called it a "slop grenade" or possible agent honeypot.
- The author rejected this as aesthetic dismissal, pointed to machine-checkable proofs and a table of check figures, and asked for PRs. The two sides never found common ground on whether the proofs verify anything.
On the tool itself (briefly).
- One commenter was surprised it looked human-written. Another countered that the unicode everywhere and the comments suggest it was 100% vibe-coded, and asked whether the submitter was a bot.
- A commenter who had examined it found "some cool ideas" and linked a concurrent HN thread on symbolic regression, where they and another author compared deterministic SR algorithms.
- One person simply thanked the author.
- One asked how TabPFN would perform on the same data. It went unanswered, and it's a different task from exact-law recovery.
The thread never tested the benchmark claims, the noise sensitivity, or the self-submitted SRBench entry.
IgE disruptor for food allergy with Claude-enabled antibody design [pdf]
A preprint from Divergence Labs describes an IgE-targeting antibody for food allergy, with Claude used in the antibody design process. The title is the only usable signal here: the PDF text came through as raw binary stream data, with no readable abstract, methods, or results.
That leaves the key questions unanswered. I can't say how Claude was used (sequence generation, candidate ranking, or agentic orchestration of design tools), what "IgE disruptor" means mechanistically (blocking IgE binding to its receptor versus stripping IgE already bound), or whether any binding or functional data exist, in vitro or in vivo. The claims are worth checking against the paper itself before drawing conclusions.
The thread splits into three strands: irritation that Claude's own safeguards block this kind of work, a long evolutionary and environmental argument over what causes allergy, and a few substantive points about the paper itself.
Safeguards. Two commenters (ValleZ, areoform) say Opus 5.5 refuses biology-adjacent prompts like this one. areoform pasted the flag they hit when asking Claude to compare the receptor design against prior results and judge whether it was incremental. They argue that antibody design is already open, pointing to Rosetta and public Colab notebooks, and that gating access is a net negative. ValleZ's version is that the safeguards are "for peasants only", since the company's own paper evidently does this work.
Paper critique.
- hcknwscommenter says the reported disruption effect looks weak. The comparator, omalizumab, is really a blocker that stops IgE from binding its receptor, not a disrupter, even though an AI will often call it one. In their view, finding an IgE disrupter is not the hard part. The hard part is finding one "human" enough for chronic use.
- altairprime questioned the lack of outside review, saying the logo makes it look like a press release in LaTeX clothing. jrflo replied that Divergence calls it a preprint and that Anthropic links to it in a blog post. The label itself was unclear, since altairprime remembered "technical report".
Why allergies happen. JumpCrisscross said IgE evolved as a worm-targeting defense and, with few worms around, now misfires at harmless things. Several commenters then argued about the cause:
- Toxins and environment: bflesch suggested unnoticed food contaminants are more likely. Madmallard broadened this to pollutants, microbiome and gut-lining disruption. JumpCrisscross answered that this is possible but unevidenced for IgE-mediated allergy, and said pollutants play a role but not the commanding one. The exchange turned personal, with accusations of LLM-pasted replies and of not knowing the subject.
- Boredom: manmal doubted that IgE can get bored. JumpCrisscross conceded they were being punchy and cited sources saying IgE is continuously produced and that Th2 cells raise the baseline. Others tied it to the hygiene hypothesis.
- Anecdata: dzhiurgis said they knew almost no one with allergies in Eastern Europe. trollbridge countered that many people of Slavic descent in the US have allergies, which points to upbringing rather than genetics.
Bring back the worms? The joke became a real sub-debate. JumpCrisscross cited a man who deliberately infected himself with hookworm. modeless said exposure to microbes and parasites that don't infect humans trains the immune system without the downsides, though Melatonic asked for examples and got none. nezi said they'd rather have no worms. nextaccountic argued that ancient parasite burdens killed children, and masfuerte suggested infecting only healthy adults, if it helps at all. adrianN noted that pinworms are mostly harmless.
Tomáš Mikolov awarded the Medal of Merit by the President of the Czech Republic
Word2Vec co-creator Tomáš Mikolov received the Czech Medal of Merit on 28 October at Prague Castle, in the science category, from the President of the Czech Republic. He graduated from the Faculty of Information Technology at Brno University of Technology (BUT).
The university's announcement credits him with Word2Vec, the method that represents words as vectors so that relationships between meanings can be measured. It calls the method one of the foundations of modern language models. Mikolov now runs BottleCap AI, which works on more efficient language models. His earlier honors include the Neuron Award.
The same ceremony honored another BUT graduate, Filip Maleňák, with the same medal for technology. He built Záchranka, a mobile app that:
- contacts emergency medical services and shares the user's precise location
- helps when the caller can't speak
- shows the nearest automated external defibrillator
Fifty-one people received state honors in total. The source is a university press piece, so it is celebratory and has no detail beyond the biographical summary.
The thread is mostly congratulation, but the substantive part is a debate over how original word2vec was and how much of modern LLMs descends from it.
Foundational leap or clever simplification? Commenters like samrus argued that nearly everything since, even attention, is "engineering around the representation learning breakthrough" word2vec demonstrated. jacquesm added that its reach far beyond the original scope is a hallmark of foundational work. resters gave a more careful version. The idea that a word is defined by the company it keeps was decades old, and Mikolov's contribution was learning it at scale with a very simple model so that meaning "falls out as geometry."
microtonal, who seems to have worked in the field, supplied the history that qualifies this:
- Vector models predate word2vec, starting in information retrieval with TF-IDF and PPMI matrices reduced via SVD.
- Bengio and colleagues had already introduced neural language models for training embeddings.
- Levy & Goldberg (2014) showed that skip-gram with negative sampling implicitly factorizes a PPMI word-context matrix.
- Mikolov's real edge was a simple linear model with easy Hogwild optimization that ran fast on the multi-core CPUs of the time. That fit the compute available, and the approach dominated for about five years.
microtonal also recalled that the paper reportedly had a hard time getting accepted at a conference, and said they miss the era when a CPU and one GPU could train cutting-edge models.
Hindsight on the AI trajectory. Several people said they couldn't have predicted today's capabilities when first taught word2vec, in 2016 or 2018. faizshah laid out their own arc: word2vec showed meaning could be encoded by prediction, seq2seq and transformers extended it, chain-of-thought was a surprise, and ReAct-style control loops pointed to the next stage. They think agent infrastructure (virtual filesystems, permissions, file locking) is the path ahead.
What he does now. Replies to _false said Mikolov co-founded BottleCap AI, which works on making open-weight models, including Chinese ones, less verbose and more efficient in their reasoning and tool calls. armcat linked an example of this work.
Smaller items: a grammar correction on "sing his praises" that microtonal took graciously, a joke about a Záchranka beer button, a brief "met him in 2015, genius" anecdote, and one cynical aside comparing him to Thomas Midgley Jr., which nobody picked up.
Show HN: Overtone – Transcribe, correct and cut captions for any screen shape
Overtone cross-checks three speech-to-text engines and lets an LLM referee the disagreements, then cuts the corrected transcript into captions per screen shape. In the repo's small benchmark, word error rate on 5 clips came in at 5.3%, against 6.2% for gpt-transcribe alone.
The pipeline has four stages:
- Hearings: gpt-transcribe supplies the words, Azure AI Speech supplies word timing and speakers, and ElevenLabs Scribe is an optional second opinion.
- Text vote: where the engines conflict, the text model re-listens to just that clip and an LLM picks the words.
- Editor: a second LLM pass fixes names and terms using your context prompt, a glossary, and its own world knowledge. The README's example is "Telecom Cell" becoming "Telkomsel" unprompted. Low-confidence edits go on a review list to check by ear.
- Line cuts: a versioned, rules-based cutter lays out captions per aspect ratio. The LLM never sets timestamps.
The layout half is the more distinctive piece. The .overtone.json format stores each word once and keeps multiple cuts pointing at those words (16:9 and 9:16 by default, or any aspect you name). Cuts avoid ending lines on "the" or "of", keep names and numbers together, give each speaker their own caption, and turn pauses into breaks. On three English clips (20.8 minutes), 0.3% of Overtone's breaks land on a weak word, versus 28.8% for filling lines to a length limit and 16.1% for ElevenLabs' SRT export.
Practical bits:
npx overtone-captions recut file.srt re-cuts existing subtitles offline with no API key or ffmpeg, but word times are then estimated from the cues.overtone transcribe needs ffmpeg and API keys. --dry-run prints a cost estimate and sends nothing, and --local runs on whisper.cpp and Ollama.- A video.js 8.5+ plugin picks the right cut at playback and cycles styled, plain, or off. It ships five style packs.
overtone burn renders into an MP4.
- MIT-licensed, with a zero-dependency format package that exports WebVTT and SRT per cut.
The catch is cost: up to three STT calls plus LLM passes per file, which the author admits rules out ultra-high-volume use. The benchmark is also self-described as small (six clips, four languages, three runs), so treat the WER gain as a good sign rather than proof. If your captions already come from WhisperX or a hosted API, recut lets you adopt just the layout and keep your engine.
The thread is small, and the one real exchange drifts away from Overtone's transcription pipeline toward a nearby gap: translating on-screen foreign text.
- Cost and user perception: One commenter, experimenting with STT themselves, agrees that re-review passes are necessary. They say it's hard to explain to end users why this costs real money, since free dictation tools have made speech-to-text feel cheap.
- Subtitle quality complaint: Another commenter says modern streaming subtitles have become "TRASH", with misheard words that suggest human oversight has been cut. They ask for a system that translates foreign text on screen, ideally in a slightly different font. Their example is a Netflix show (Sai) that cuts to 30 seconds of untranslated Japanese text.
- Author's reply: The Overtone author says it addresses misheard words, though not everything. Terrible audio mixes and music still defeat STT models, and on-screen text translation isn't built in. They point to how big platforms handle it. Disney uses IMF overlays that swap frames so an EXIT sign becomes SALIDA. Forced narrative captions are a separate track that translates only signs and similar text, on by default at Netflix and called "auto" captions on Apple TV+. They attribute the quality decline to a race to the bottom among translation vendors. Netflix and Disney still pay for quality, smaller providers don't, and the author suggests complaining to the provider.
- Open question: A follow-up asks whether any non-animated film uses sign replacement, or places styled translations next to the original sign instead of replacing it. It went unanswered.
Claude Code regularly sends each background agent’s transcript to its model
Claude Code reportedly spends roughly 15–20% of its tokens on a status line. Every ~30 seconds, per background agent, it re-sends that agent's full transcript to the agent's own model (usually Opus) and asks for a 3–5 word description of its latest action. The answer only updates the agents panel and never reaches your conversation.
The evidence comes from the author's OpenTelemetry export. Grouping claude_code.api_request events by query_source, agent_summary accounted for 16.4% of Claude Code spend (the main conversation was 15.7%) and 19.7% of all API requests. 96% of that spend was on Opus, at about 9,000 input tokens per output token. The author says four independent measurements in issue #86879 land at 18–26%. The "spend" figure is Claude Code's own per-request cost_usd estimate, not an invoice. On a subscription, the real cost is lost rate-limit headroom. The behavior has reportedly been on by default since 2.1.232, and there is no setting to disable it.
The workaround is a ~70-line Bun --preload, MIT-licensed, with no binary patching. It wraps global setTimeout and inspects each callback's source via Function.prototype.toString. If the source contains a summarizer anchor such as the [AgentSummary] Timer fired log string, it returns an inert handle (a 2³¹−1 ms delay, because larger values fire after 1 ms). Other timers pass through. A simpler hook wouldn't work, since the module ships as precompiled Bun bytecode and the API client bypasses a wrapped fetch. Load it with BUN_OPTIONS=--preload=<path>.
- It only activates when
Bun.main is /$bunfs/root/cli and process.execPath is a Claude Code install, so other Bun processes ignore it.
- Tested on Linux with Claude Code 2.1.257–2.1.312 across nix, npm, and native installs. macOS is reported working by others, and Windows is unmeasured.
- It matches strings inside Claude Code, so an update can silently undo it. The README says to re-run its Verify steps after every update.
After the hook, the author saw agent_summary drop to 0% of spend. The catch is that the numbers come from one team's telemetry plus a community thread, and the fix is a string-matching hack against an unpublished internals layout. Treat it as a stopgap until Anthropic adds a real toggle.
The thread skipped the status-line hook almost entirely and split into two other arguments: Claude leaking user emails, and whether Anthropic's token waste is a profit play.
The email tangent (top-voted subthread). AlexErrant said Anthropic puts your email in the system prompt, and Opus keeps copying it into request headers. He linked a plugin that strips it.
- Benign explanations: tempaccount420 hit it with crates.io, which rejects requests without a User-Agent, so the model invents one and often includes your email. justech said the cases he saw were either required (SEC) or covered by politeness policies (Wikimedia, Crossref). Bric3d said Claude asked before scraping the SEC.
- Security objection: shakna and dgellow argued that an agent giving contact details to whoever asks is a prompt-injection hole. A hostile page could ask for PII, and the same path might let an attacker trigger other tool calls, which shakna called "a zero-click vulnerability." everforward suggested an opaque, forwardable session ID instead of real contact info. nbajka noted that Firefox wouldn't do this.
- Instructions vs. guardrails: justech said they told Claude never to do it again. kardos replied that instructions only lower the odds, and jen20 and ireadmevs said real guardrails must be deterministic and enforceable.
Is the token burn about profit? franticgecko3 said repeated token waste should make people question vendors shipping their own harnesses. The replies split into camps:
- Profit, plainly: bronlund, shevy-java and shafyy said for-profit companies exist to make money. fhd2 and kirth_gersen added that "make the product better" is just profit with extra steps.
- Indifference, not malice: solidasparagus, pantsforbirds, Syntaf and sirtaj said Anthropic engineers build for internal users who don't count tokens, the way old programs were memory-conscious and modern ones aren't. Syntaf pointed to idle compaction as a feature that works against a profit-maximizing motive. retsibsi noted the dispute was over deliberate waste, not whether the company seeks profit. Plagiarist countered that a smart company would A/B test on lifetime value.
- Counter-incentives: weird-eye-issue doubted that burning cached tokens helps revenue, since most subscribers rarely hit limits. gwd invoked Jevons paradox: cheaper tokens sell more of them, but only if people would not have spent them anyway.
Where the revenue comes from. bronlund claimed Anthropic needs to show demand to raise money. brookst said he maxes out three 20x accounts weekly and the demand is real. The thread then moved to API versus subscription revenue. krzyk said large companies are forced onto API pricing, and weird-eye-issue suspected a churn-prone boom in solo subscriptions. brookst cited published figures of 83% API and 17% subscription revenue.
The crux left open is whether waste that happens to benefit the vendor requires intent, or whether misaligned incentives are enough.
Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5
Gemini 4 Carbon scores 59 on Artificial Analysis's new Intelligence Index v5, taking #1 by leading 4 of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. It isn't a clean sweep, and it isn't publicly available yet. Google is rolling it out to selected users with a 50% launch discount and no confirmed end date. AA's charts use standard pricing.
The coding result is the surprise. With Antigravity CLI, Carbon (high) scores 59 on the Coding Agent Index, 3 points behind Claude Code with Claude Opus 5.5 (max, 62). It's second on both Terminal-Bench 4.0 (64%) and FrontierCode v1.1 (51%). AA notes Google has historically underperformed here.
Cost per task is the practical angle:
- $7.76 per Intelligence Index task, about 23% below Opus 5.5 (max, $10.04) but about 1.2x GPT-6 Astra (max, $6.25).
- 123k output tokens per task, 29% fewer than Opus 5.5 (173k).
- Same $4/$20 per 1M input/output tokens as Gemini 4 Argon, with cached input at $0.20 per 1M. Cost per task is in line with Argon ($7.68) while scoring 9 points higher.
Hallucination is the trade-off. On AA-Omniscience v1.1, Carbon scores 54, versus 48 for Opus 5.5 and 46 for Argon. Accuracy rises 10 points over Argon to 58%, but hallucination climbs from 4% to 11%. That is still the second lowest of any model, behind only Argon, against 50% for both Opus 5.5 and GPT-6 Astra.
Agentic knowledge work is where it falls short. On AA-Briefcase it hits 1609 Elo, up 126 over Argon, driven by a 293-point jump in Presentation. It still trails Grok 4.7 (1638), Muse Wonder (1659), and Kimi K3.1 Pro (1678).
Other specs: 1M-token context, with text, image, video, and speech input and text-only output.
The thread is less about Carbon's benchmarks than about Google's release strategy and how bad Argon's real-world experience has been. Very few commenters had touched Carbon, so Argon served as the proxy.
Argon's hallucinations vs. the headline numbers. Several users reported fabrications that clash with AA's low hallucination rate. isqueiros called Argon the only recent "SOTA" model to actively invent details, with "Deepseek V3-like lies." SoMomentary asked it to transcribe audio notes and got a long invented police-sting conversation, apparently inferred from the file names. MattRogish had it summarize project updates and it made up whole paragraphs. staticman2 questioned whether the web UI actually processed the audio, and SoMomentary agreed it hadn't transcribed anything. So these may be harness or UI failures rather than model failures, and nobody settled that.
Announce-now, ship-later. This was the biggest argument.
- Frustrated users (m11a, pu_pe): the announcement-without-access pattern kills excitement. pu_pe noted that Opus 5.5 came out only 45 days ago, so Google's answer spent a big share of that time embargoed. Paying users get roughly 3–4 prompts a day, so few people will experience Argon.
- Corporate-calendar explanation (notatoad, ecshafer, ncruces, PunchTornado): Argon landed near performance-review deadlines and Carbon near quarterly earnings. ncruces added that announcing lets people already using it talk about it openly, and that earnings-adjacent news is material information.
- Strategic-signaling defense (ltbarcly3): announcing before you're ready to ship lets you claim the top of the leaderboards, lift the stock, and steer platform decisions. Release readiness (scaling, safety testing) takes longer.
- Counterpoints: _delirium noted Anthropic announced Mythos five months before release. vessenes pointed to DeepMind's decade-long culture of not releasing. ptero suspected Google is behind and needs to keep generating buzz.
- Timeline correction (kyrra, a Googler): isqueiros's "a few months" is overblown. Argon went from announcement on Sept 30 to availability in about two weeks, though Carbon is the first model Google has slow-rolled, so its timeline is unknown.
Quotas and plan bundling. Limits were a common complaint. isqueiros hit a 4-hour cooldown after five medium messages. gnufied and aNapierkowski said they didn't run into quota issues and mostly keep Google's AI Pro tier for the bundled YouTube Premium and storage. kyrra pointed to the Ultra-lite plan ($99/month) for more usable quota and the 5TB of Drive storage, which he said compares well with Dropbox's price.
Smaller threads. isqueiros praised the Google Search and Maps integration for trip planning but wanted Gemini to create shareable "My Maps" lists. WarmWash joked that asking a Googler for better cross-service integration is like asking Adobe to integrate with Canva. A side thread asked what the roleplay crowd makes of these models, and pontes argued the agent or harness matters more than the model for that use.
Google value plunges by $500B as AI fears rattle Wall Street
Alphabet shed just over $500bn in market value in a single day, falling from $3.97tn to $3.46tn, after executives admitted that Google's own conversational "AI mode" is cannibalizing the ad revenue that traditional search results generate. Shares dropped more than 12% to a six-month low.
The underlying numbers weren't bad: revenue grew 25% year over year to $127.6bn for the quarter. But the ad business, the part that pays for everything, came in below analyst expectations. Chief business officer Philipp Schindler said he still felt "very good about our trajectory," while conceding the substitution effect. Former Google ads chief Kim Scott framed it as a deliberate long-term bet: better to "eat its own lunch" than let someone else do it.
The real story is the spending side, and the other hyperscalers got the same scrutiny:
- Amazon: sales up 11% to $201bn, but free cash flow was negative $42bn, with spending up six-fold over three months. Shares fell 6% after hours. Andy Jassy pointed to AWS and retail as possible $1tn businesses, plus custom chips and the Bedrock model platform.
- Microsoft: revenue up 19% to $92.3bn, cloud up 30% to $63bn, AI investment spending about $5bn lower, and thousands of layoffs planned. Shares slipped under 1%. Nadella said Copilot daily users grew 500% year over year.
Microsoft's muted reaction suggests investors will tolerate AI spending when it's paired with cost discipline and clear enterprise demand. Google's case is harder: the AI product directly erodes the cash cow funding it. Forrester's Tracy Woo put the open question as where AI value will ultimately be captured, and the market is no longer giving that answer on credit.
The thread splits between "this is a bubble wobble" and "Google is structurally fine," with a side argument over whether the headline's "AI fears" framing is even right.
Google's position. The most developed bull case came from ticulatedspline. LLMs need live web access, sites will increasingly block them, and the result will be an MCP-like "sub-web" for agents alongside human-facing HTTP. Google, through Chrome and its incumbency as gatekeeper, would index that layer and sell search deals to every major AI agent. Others pushed back:
- deaux asked how sites would block agents at all, since companies have been trying to block scrapers for 20 years with declining effectiveness.
- Good4boothee said the semantic web was originally sold as a way to rank better, and publishers now see it as a way to make Google's zero-click answers easier. LLMs no longer even need it.
- shostack dreaded the opaque middle layer, where an AI summarizes reviews and rankings and users lose the ability to spot SEO garbage themselves.
- ergocoder offered "even IBM hasn't died yet."
Bubble or not. dyauspitr said there's too much real value for a crash. etempleton countered that the internet had real value and still crashed. NBJack added that many ISPs, delivery services, and online stores of that era burned capital and vanished. solid_fuel and bushbaba pointed to today's "LLM for your business" startups and unicorns valued at 100x earnings with circular token spend. solid_fuel argued that if those startups die, inference providers lose customers. CoolestBeans read the earnings as the spending spree going on hold. The neocloud credit window is shut, hyperscalers can no longer be assumed to backstop capex with cash flow, and the next test is whether discipline reaches Nvidia and DRAM revenues.
Is "AI fears" the right framing? refurb said Google simply missed its estimate. happymellon replied that the fear is AI gutting Google, and that Google made search so bad it pushed people off. verzali asked why growth missed in the first place. 1vuio0pswjnm7 quoted the thread's one hard figure beyond the headline: profits fell 25% and free cash flow was negative by over $9bn.
Anecdotes on search. iainctduncan ditched Google search over AI summaries with visible errors, followed by sponsored results. dude250711 and pllbnk use Google as a quick LLM chat from the URL bar, and pllbnk noted that this means Google is now used mostly for low-stakes queries. A Kagi user said they do the same. 1vuio0pswjnm7 returned to Google after finding a no-JavaScript, no-AI results page that still works.
Adoption. dabinat said no technology has been adopted this fast, though it's also widely hated, and predicted sponsored suggestions inside AI answers. unrented7977 countered that the hatred comes from AI being forced on people who can't opt out.
Claude Duet is the first personal-assistant-type harness that feels useful
A hands-on review from someone who gave Muse, Dot, NanoClaw and others a real try, and says Duet is the first assistant harness that felt more useful than tedious. The author, who is unaffiliated with Anthropic and says they didn't want to like it, credits four things.
- Speed: it prioritizes a snappy reply even when background work is pending. The author says Opus moves faster here than in other Claude UIs, which they attribute to tuning.
- Channels: Duet creates and manages them itself, which handles the chore of tracking and naming chat sessions. Channels can see each other, and Cmd-k switches between them.
- Access policies: GitHub repo access is scoped per channel, so work from other projects doesn't commingle. Duet stores no credentials and discourages being handed them, preferring Claude's existing connector ecosystem.
- Short responses: no full-page answers to simple questions. Its emoji reactions are used as a terse "okay" rather than as constant thumbs-ups, which the author says Muse overdid.
The author's biggest point is the hierarchy of concerns. The top level sees all workstreams but has no low-level access. Channels get more access with narrower context, and sub-threads run in specific code sandboxes. The desktop app reaches local hosts only where permitted, mostly for QA and deployment. The author already arranged agents this way and says you could hack it together with duct tape and API credits. Duet makes it a core product feature, and they predict it becomes the default for chatbot UIs.
The catch is that it's in beta. The author has seen threads running but not visible, and delegated repo access failing to attach, which they guess is a race between agent start and access attachment. They also concede their use case may favor Duet, that Muse might suit others better, and that Dot still wins on voice. Anthropic's voice mode, in their words, is still "glorified speech-to-text."
This is one user's impression with no benchmarks, so the speed claim rests on feel alone.
The thread skips the Duet review and argues over whether agents can be trusted with anything that matters. Top commenter mbesto asks how an agent is useful if it can't use your ID, phone number or card without creating huge security problems.
The lethal trifecta and blast radius. andai cites Simon Willison's "lethal trifecta": an agent needs internet access, untrusted input and your private data, and that combination invites prompt injection and exfiltration. andai's answer is to limit blast radius, e.g. keys with $5 of credit and a filesystem confined to the agent's own Linux account. The cost is that it rules out the "my claw bought a car" results. largbae follows a similar pattern with Hermes: its own email and GitHub account, no banking, and results delivered via Slack and PRs.
Has prompt injection been solved?
- simonw says Anthropic people he's spoken to are "very confident" the latest models have effectively solved it. The open challenge is proving it.
- Skeptics asked for skin in the game. One suggestion was Anthropic-backed cards with consumer-style protection if an agent misuses them. klysm wants Anthropic to run the trifecta itself, and julianlam answered with the LifeLock CEO who published his SSN and was then hit by identity theft.
- andai said models sometimes mistake the user's messages for their own, and wondered if that could be exploited. tstrimple said turns are flagged with metadata, so it should be impossible. smashed countered that compaction, copy-paste, agent handoffs and humans adopting LLM text unread all blur authorship in practice.
- ryoshu's blunt rule: don't use LLMs to enforce invariants.
Are purchase approvals real? pkaye and staller said Duet needs manual approval, and Link requires a separate approval for purchases. NikolaNovak pushed back that "purchase" limits sit in a murky middle. They are harness classifiers and heuristics guessing which button on a third-party site is a purchase, so they aren't as hard as filesystem permissions. andai separated "model got hijacked" from "model did an oopsie." The latter, such as an agent bypassing security to finish a task, is the far more commonly reported failure. FireBeyond's example was Claude claiming it had committed and pushed code when it hadn't.
What people actually use these for.
- threecheese likes Dot for its sub-second replies, internal multithreading, orchestration of other agent threads and persistent compute with a desktop.
- BryantD uses agents as filters on the firehose, such as watching for book bundles or film series.
- edot called that a cron job with extra steps. tstrimple does it differently: Claude writes a deterministic script that runs on a schedule, and the LLM is only invoked by hand to summarize, so the pipeline survives if LLM tools vanish.
- largbae values multiplayer contexts when the agent lives in Slack.
Side notes. moolcool called giving Claude a 1Password vault insane, after neuronexmachina asked about the connector. Others joked about paying bills manually, with computably noting that plain automation already does it.
The unresolved crux is whether "the model resists injection" can ever substitute for architecture-level limits like scoped credentials, spending caps and deterministic approval gates. Most commenters trusted only the latter.