AI Submissions for Wed Oct 28 2026
The first wired XR glasses powered by Android XR
Submission URL | 93 points | by yincrash | 82 comments
Google and XREAL are shipping AURA, a tethered pair of XR glasses under 95 grams, by pushing all the compute and the battery into a cabled pocket puck. The puck runs Qualcomm's new Snapdragon Reality Elite, which debuts here. The glasses themselves are transparent-lens, with a 70-degree field of view.
Orders opened today in the U.S., Canada, and Korea, with Japan following November 2. They're available at xreal.com and BestBuy.com. The post doesn't give a price, which is a notable omission for a launch announcement.
The pitch is a portable multi-monitor desk:
- Wired Mirroring: plug a laptop in over video-capable USB-C and its screen appears as a resizable window beside native Android XR apps, with no software to install.
- Input: a Bluetooth keyboard and mouse work, or you can use the puck as a trackpad.
- Gemini: it sees both the room and your screens, and as of today can highlight what to focus on when you share your view.
- Apps: Google Play access, including over 150 apps built for XR.
On the entertainment side, the lenses dim for a private theater and turn clear again when someone comes into view. Spatial view converts most video, photo, or game content to 3D. Window follow mode keeps an app centered as you move, then shrinks it to a mini view when you walk. Launch content includes "Nevatars," "Galapagos: the Last Eden," and the San Francisco Ballet's "Nutcracker."
The tradeoff is the cable and the puck. This is the lighter, cheaper-to-wear end of Android XR rather than a standalone headset, and it's a clear bet that tethered glasses are the practical form factor for now.
The thread mostly asks whether this form factor beats cheaper, simpler alternatives, and the most pointed objections are about the puck and the display tech.
See-through vs. passthrough, and Meta as the rival. The top thread frames AURA against Meta's VR headsets. One commenter argued resolution matters most for productivity: Meta's displays have more than twice the pixels, and an optical see-through display has no true black, "only transparent," so outside light washes it out. They said to try before buying. Others countered that Horizon OS is AOSP-based, so Android apps aren't exclusive to Google's platform. Abandonment risk was the shared worry, and the Meta defenders cited Quest Pro still getting updates and the Oculus Go unlock update. One commenter warned against predicting productivity from spec sheets, since UX details decide it.
Why a puck at all? Several asked why this isn't just glasses driven by a phone.
- One user already runs Xreal Air plus Samsung DeX and sees no reason to switch.
- The reply was that AURA does inside-out 6DOF and hand tracking, while older Xreal Airs only do 3DOF head tracking.
- A follow-up argued that 6DOF needs tight latency guarantees a multitasking phone can't easily give.
- Another commenter needs the puck because their phone lacks DP Alt Mode.
The cheap-display camp. SirMaster uses a refurbished VITURE Pro XR (about $130–150) as a plain floating 1080p, 120Hz micro-OLED screen for movies and handheld gaming. They said it's clear enough for text and that most vendors use similar panels and optics. Soft spots on individual units are the main risk, hence the return policy. Follow-ups covered prescription inserts and built-in diopter adjustment on some models.
Open and Linux setups.
- One commenter praised Nreal Air 2 Ultra glasses for exposing cameras and IMU over the cable and appearing as an ordinary DRM display on Linux. That prompted a side gripe that "DRM" is an ambiguous acronym (Direct Rendering Manager vs. Digital Rights Management).
- Others pointed to Simula's Linux compute packs and said the Steam Frame runs desktop Linux apps fairly well. One person noted that if AURA is priced near the Steam Frame, it's no contest.
- A Quest 3 user reported good optics and multi-monitor use on Linux via WayVR and KDE, with the weight as the downside. They described a root exploit for removing Meta's software.
Smaller notes. One commenter wants eye tracking on the Meta glasses, another wants dumb, light, wireless glasses to replace a four-monitor desk, and one jokes that always-on AI perception makes users "Cyborg."
Gemini 4 Carbon
Submission URL | 998 points | by acossta | 793 comments
Carbon isn't generally available yet. Google is starting with a subset of cyber defenders and phasing in more trusted partners through its Fairwind Program, while it iterates on guardrails and takes part in the U.S. government's voluntary pre-release testing. Developer, business, and consumer access comes "as soon as possible," with no date given.
The pitch is cybersecurity plus long-horizon agentic work. Carbon lands at #1 on Artificial Analysis Intelligence Index v5.0 (score 59) and posts these external numbers, which Google says were run without guardrails:
- SEC-bench Pro: 88.2% for finding vulnerabilities
- ExploitGym: 54.8% for exploitation
- CWE-bench: 71.0% for patching
- WebDev Arena: +89 Elo to 1768, which is only #4
Internally, Google reports more than 2x Argon's success rate at generating secure code. It also says the Chrome team measured 10x better patch quality than "another lab's" frontier model, without naming the competitor or the methodology. Teams have seen Carbon run for days on root-causing bugs in large codebases and low-level systems.
The alignment claims are the more unusual part. Successful cheating on Terminal-Bench 4.0 dropped to 0.0%, with cheat attempts at 0.4%. High-severity misalignment alerts in live internal coding fell almost 80%, and unsupported claims were cut roughly 2x versus Argon. Carbon is also said to surface discrepancies to the user before acting, rather than guessing. All of this is Google's own testing.
Pricing matches Argon, which shipped just two weeks earlier: $2 per million input tokens, $10 per million output tokens, and cached input at 95% off. A two-week gap between frontier models is the real signal here, and Google credits its researchers and models working "hand in hand."
The thread barely touches Carbon's benchmarks. It becomes a debate over whether Google's product and access chaos reflects a dysfunctional corporate culture, and then over whether Google has ever built a good product in-house.
The access problem. The top comment says Gemini 4 Argon is hard to even find: the phone app shows only 3.8 Pro, and it's unclear how a Workspace account would sign up for the "Ultra" tier. Another commenter replied that these models are really for Google engineers' performance-review write-ups, so discoverability doesn't matter. Others called that cynical. Still, several said the two views agree: bureaucracy rewards internal self-promotion over delivering for users. Steve Yegge's old Google rants were cited, including his complaint about deprecations and confusing plan tiers ("a pro plan is a pro plan"). Bill Gates' Movie Maker email was offered as an analogy, with the complaint that no Google executive seems to try installing their own flagship products. A side comment noted that Gates complained but gave no direction, unlike Jobs.
"Where's the Google of yesterday?" One commenter mourned a company that had every advantage and "blew it." Pushback came in three forms:
- Google also missed the cloud shift 15 years ago, so this is the same Google.
- Much of the old Google talent now works at frontier labs.
- The only real in-house hits were search and Gmail, so why expect otherwise now?
The "in-house" fight. The list of Google products turned into an acquisition audit:
- Maps/Earth, YouTube, and Docs/Sheets were pointed out as acquisitions.
- Chrome was disputed: it built on WebKit/KHTML, though a reply said it was still a real leap in speed and security.
- Defended as genuinely in-house or good: Gmail, App Engine, Cloud Storage, managed Kubernetes (called "second to none"), Waymo, Calendar, and the original Google Finance.
- Mozilla's fumbles, including Firefox OS, Pocket, and executive pay, were blamed for letting Chrome win.
One dissent on substance. A single commenter said the negativity ignores the announcement's strong numbers. It got no engagement, and the rest of the thread was a long, acrimonious exchange between two users over who was misattributing beliefs to whom.
Dayjob: Tax – evaluating AI agents on 50 professional tax assignments
Submission URL | 253 points | by landonxi | 171 comments
The best agent passes only 10.8% of professional tax assignments. Surge AI's DAYJOB: Tax gives agents a client request plus a messy workspace and grades the finished deliverable (a return, workpapers, a technical review, or advice), not just the math.
The benchmark spans 50 environments and 1,789 files (median 30 per task, up to 123, mostly PDFs) across eight areas: individual, small-business, partnerships, property and investment, estates and trusts, international, corporate transactions, and tax review and disputes. The inputs are deliberately ugly: a photographed handwritten receipt that must be reconciled against inventory and cost of goods sold, a dispatch log revealing an "Alabama-only" silo company works in five states, and a Polish-US household with crypto exchange exports and historical purchase lots.
The scoring is strict. It uses pass@1 at a 95% rubric threshold, so an attempt must satisfy at least 95% of criteria covering calculations, evidence, consequential issues, and the deliverable itself. Reported leaders:
- Claude Sonnet 5.5: 10.8%
- Claude Fable 5.1: 9.6%
- Claude Opus 5.5: 9.2%
Sonnet 5.5 and Opus 5.5 sit at the high-performance end of the cost-versus-performance Pareto frontier.
The case study shows what the rubric rewards. A client asking whether to close on a condo in October 2026 or January 2027 got correct analysis of both dates from one agent (Astra), but it missed that the two-of-five-years residence test would lapse around August 2027, putting a up-to-$250,000 exclusion at risk. Astra missed it in all five attempts; Opus caught it in all five. That is the "issue the client didn't know to raise" failure the benchmark is built to expose.
Surge says tasks were built by tax experts from real workflows, with a three-layer review for correctness, completeness, and solvability. Caveat: Surge sells expert data and training, and the grading rubrics are its own, so treat the absolute numbers as a hard-mode ceiling test rather than a measure of everyday usefulness. The leaderboard and a Hugging Face release are linked from the post.
The thread barely touches the benchmark. It turns into a discussion of one commenter's side project and the state of US tax filing.
The main thread: a formalized IRS procedure. RGS1811 is formalizing IRS forms, worksheets and instructions into a deterministic, verifiable tool, intended as a community successor to the killed Direct File. They report roughly 60% coverage of federal situations (github.com/emmilco/us-tax-model). Their design:
- Every form line gets a YAML entry with source text, a type, an expression built from a small set of atoms, and a citation to the exact spot in the official document.
- The atoms are formalized and proved in Lean, with a TypeScript evaluator cross-checking the arithmetic.
- The acknowledged weak point is LLM interpretation of the form text. RGS1811 mitigates it by cross-checking against PolicyEngine, UsTaxes and OpenTaxSolver, by running mutation tests, and by having several model families (Claude, GPT, Deepseek) review the interpretations.
- The stated goal is not "have an agent do your taxes". It is to have agents build a hardened deterministic tool for navigating the forms.
Replies were mostly positive. Pushback and pointers included:
- Case law: Onavo argued some interpretations can't be right without grounding in old case law, which is the expensive, often non-free part.
- Prior art: Commenters pointed to the IRS's open-sourced Direct File and fact-graph repos. RGS1811 had evaluated fact-graph and passed. Their reasons were its orientation toward IRS XML submission, its loosely defined semantics, its verbosity, and wanting to use Lean. They named Catala and Sarah Lawsky's work as other prior art.
- "Won't Big AI do this?" (amelius) RGS1811 doubted the labs would bother. NichoPaolucci offered a painter-and-camera analogy for why people should build now. amelius countered that tax computation, unlike painting, yields the same output either way, and fn-mote added that entrenched interests make a large entity unlikely to do it.
The filing-mechanics tangent. ToucanLoucan asked whether this is moot without Direct File. Several people answered that you can mail paper returns, and singron mentioned Free File Fillable Forms. rbhelt, a CPA, explained that e-file software must be IRS-approved. Paid preparers must e-file and justify any paper filing on Form 8948, and the IRS is working toward eliminating paper. thenewnewguy countered that Form 8948 still lists "taxpayer chose paper" as an option, so there's no evidence paper is going away permanently.
That led to a broader complaint, with a Dutch commenter (ClikeX) baffled at not getting a prefilled return. rbhelt and what said the IRS already holds most people's data, and blamed the prep-industry lobby for blocking automatic filing. what described being told to estimate a missing W-2 with Form 4852 and amend later.
Smaller items:
- Software choice: singhrac had to leave FreeTaxUSA for TurboTax over an edge case, while tosser811 made the opposite move for a late e-file.
- Use case: c0nsumer used Claude Code to formalize sporting-event scoring rules, and Xcelerate said they'd pay for a tool like this.
- Benchmark: The one comment on it, from Oscalemor, was that the benchmark's missed-issue example was counterintuitive and prompted ideas for their own cross-border tax planning. They also noted that people can hardly judge whether their tax output was optimal, since some positions are only tested in an audit.
Scaling Hierarchical Networks
Submission URL | 90 points | by guybedo | 11 comments
Cartesia says its H-Nets, which learn to chunk raw bytes inside the model instead of relying on a tokenizer, reach up to 3-5x compute efficiency over tokenized Transformers in a controlled 1-stage setup. The result comes from a standard scaling ladder of tokenized Transformers used as the baseline.
The pitch is the bitter lesson applied to the last hand-engineered piece of the LLM stack. H-Nets recursively compress data through dynamic chunking (characters to words to phrases to concepts), and the whole process trains end-to-end. Tokenized models and other byte-level alternatives typically use multiple training stages.
Headline claims from this first post of two:
- 1-stage H-Nets: 3-5x compute efficiency gains, attributed purely to learned tokenization.
- 2-stage H-Net: beats a matched open-source model with 10x less compute and data.
- New behaviors: the models show in-context tokenization, which tokenized LMs can't do.
- Beyond text: ongoing work in biology and vision, with no numbers yet.
These are Cartesia's own results, and the excerpt cuts off before the experimental details. It doesn't say which open-source model was matched or at what scale, and the 10x figure depends on both. The second post promises a wider model sweep to explain why the efficiency gains are so large, and that sweep is where the claim will be tested.
The thread splits into two arguments: whether bytes are a sensible base unit, and whether learned chunking can compete with BPE in practice.
Are bytes the right "atoms"? jephs opened by arguing that bytes aren't a natural or raw encoding. Much of what BPE does is undo arbitrary Unicode choices. A Latin letter is one byte while a visually identical Cyrillic letter is two, and CJK characters take 3-4 bytes. jephs listed alternatives (IPA, bitmaps, SVG programs, Ideographic Description Sequences, raw codepoints) and noted that no thorough survey exists. jaen countered that H-Nets only need some indivisible base unit, and bytes are about the best expressivity-per-compute tradeoff. Raw bits would cost 8x more computation with no extra expressive power, and bit-level structure like ASCII case is trivial to learn. vatsachak put it as "bytes are raw data and the architecture chooses the tokens." jephs held that real raw data is speech, handwriting, and keystrokes, and bytes are several abstraction layers downstream. Neither side conceded. The disagreement is mostly about whether "good enough base alphabet" or "natural base alphabet" is the right standard.
Will it hold up against BPE? augment_me argued that BPE exploits multi-character redundancy and decodes faster. On code or English, they expected a byte-level model to underperform, and they thought speculative decoding (they'd seen 32-token spec-decode in frontier models) widens the gap. They saw H-Nets as useful only for niche domains like tabular data. Three replies pushed back:
- yorwba said H-Nets also learn multi-character patterns, just not hard-coded ones, and that speculative decoding still applies. The question is empirical.
- kavalg framed the premise as a three-way tradeoff. Byte-level isotropic models transfer well but cost too much. Tokenizers are efficient but lose semantics. H-Nets aim for both, and the authors also show DNA and images where ready-made tokenizers don't exist.
- nivter said generality is the main point, and a common tokenizing paradigm across modalities would help scaling.
Left unresolved: no one had measurements on decoding speed or on code and English at scale, so the speed objection stands as a prediction rather than a finding.
One lighter comment: vatsachak enjoyed the Haskell quicksort tokenization example.
The Agent SDK
Submission URL | 139 points | by torutofu | 131 comments
Cursor is open-sourcing nothing here, but it is handing out the internal framework behind Bugbot, Security Reviewer, and Rollouts, and it's generally available today. The pitch: define event-driven agents as a code project instead of adopting someone else's review bot or hand-rolling your own.
An agent is a small repo. Prompts live in Markdown, tools are TypeScript functions, and connections to outside services and evals sit alongside them, so changes go through normal version control and code review. Config is a defineAgent call from @cursor/agent-sdk that sets the name, model (the example uses composer-2.5), and a local working directory.
Capabilities called out:
- Event triggers, e.g. pull request events from GitHub, GitLab, or Bitbucket, with agents that assess risk, route to the right team, and approve changes meeting policy.
- Multi-agent work: hand tasks to specialized agents with their own tools and sessions, or split a job across parallel subagents.
- Evals and event replay built in, which is the part that matters most if you plan to trust an agent with a critical workflow.
Deployment is agent-sdk deploy, which builds the agent, assigns it a URL, and runs it on Cursor infrastructure, with secrets and integrations managed in a dashboard. You can instead self-host: each agent is a single Node process you can package in a container.
Cursor's own example is kb-reviewer, built in about a day by an internal team. It checks every PR in their main codebase against a knowledge base of past lessons, flags contradictions, and cites the note behind each finding. PGA America is quoted as building its "software factory" on it.
The catch is gravity: the easiest path runs on Cursor's infrastructure and its models, and the post doesn't discuss pricing or whether non-Cursor models are supported.
Most of the thread drifts away from Cursor's framework itself. It splits into three conversations: a gap people say these platforms leave open, a "framework churn" complaint, and a long side debate about open-source harnesses.
The missing piece is the UI. The top comment (bob1029) says the hard part nobody supplies is the domain-specific front end. His team needed inline approval flows with custom elements and controller actions. Off-the-shelf chat UIs couldn't do it, and MCP carries information but can't enforce a UX flow for certain tool calls. They ended up rebuilding a ChatGPT-style interface from scratch. Replies offered partial answers:
- ChatGPT Apps, which embed a React app in the chat while the model sees only what your MCP sends.
- TanStack AI, which handles tool calling, reconnects, persistence, and voice.
- OpenUI.
- A commenter's private TUI built around extensible UI and workflows.
Nobody claimed a clean solution for enforced approval flows.
Churn. One commenter said AI tooling has hit the JS-framework churn phase. Another said their team joked that anything they built would be outdated in two weeks, and that "software factory" talk among managers risks big efforts going stale. Pushback said the real battleground now is hosting agent workloads, so the "Deploy" piece matters. Others noted that OpenAI and Anthropic have had similar offerings for about a year, including Anthropic's Agent SDK.
Why use an open-source harness at all? This became the biggest branch.
- Against switching: Claude Code is already set up and familiar, the Max subscription gives cheap top-tier usage, and one consultant said $100/month is less than an hour of his pay. csallen also argued that models likely work best in their lab's own harness, and htrp cited papers on model-harness pairings. Several said there's no lock-in anyway, since switching between Claude and Codex takes minutes.
- For switching: Pi, oh-my-pi, and Juggler can use Claude or GPT subscriptions, and one user routes between models and says their token use dropped.
- The ToS crux: behnamoh said third-party use of subscriptions has gotten accounts banned. Others replied that the ban wave hit tools impersonating the Claude CLI, and that harnesses going through the official CLI or Agent SDK are tolerated. A Pi README quote suggests third-party harness usage bills as extra per-token usage, so the current policy remained disputed.
One commenter objected that the thread had wandered off topic, since the post is about Cursor, not Claude Code.
Dissent on the whole premise. sodacanner called the complex agent features "productivity theater." A long rant (munch-o-man) argued companies are making themselves dependent on Anthropic and OpenAI. It warned about unreviewed 10,000-line agent PRs going to production and about the price shock when subsidies end.
Sleep framework: A new way for LLMs to retain knowledge in continual learning
Submission URL | 74 points | by acossta | 6 comments
Google Research proposes giving deployed LLMs a periodic offline "sleep" phase where short-term context gets distilled into permanent weights, aimed at ending both catastrophic forgetting and the fixed knowledge cutoff. The authors claim the model can retain long-context knowledge up to 10M tokens, absorb new facts, and improve reasoning.
The mechanism has two stages that run with the model offline:
- Consolidation: newly unlocked low-rank experts in a Mixture-of-Experts (using Google's Hope architecture) sit in slow-changing memory blocks, so old parameters aren't overwritten. Knowledge from fast in-context memory is transferred into them via on-policy Generalized Knowledge Distillation plus an RL imitation objective that rewards similarity (Levenshtein distance) to teacher text. The temporary high-frequency parameters are then reset.
- Dreaming: the MoE router deliberately activates non-standard experts to generate synthetic samples that mix domains. Gradient-based selection filters these, and the model fine-tunes on the top-rated ones, checking whether downstream performance improves.
The neuroscience framing (NREM, REM, synaptic pruning) is branding over a scheduled post-training loop. The post says as much: everything runs inside a fixed architecture under human-defined training rules.
The catch: the capacity grows with each sleep cycle, since consolidation depends on pre-allocated expert blocks being unlocked. The excerpt doesn't say how that scales over many cycles, and the benchmark details were cut off before the results section, so the 10M-token claim is unverified here. It's also a research proposal, not a shipped product, and it needs a specific architecture rather than working on arbitrary existing models.
The thread is thin on debate. Most of it is people trying to understand the mechanism, plus one practitioner's account of a competing approach.
- Audit and lineage as the deployment problem: gopalv's team built a similar system, "Dreamtime", earlier this year. It distills learnings across sessions from user corrections of unsatisfactory responses, not from the token or KV-cache layer. It works as a sawtooth: accumulate context, check for dissonance, compress it back, retire the old version. Their view is that customers' real blocker is auditability. You have to be able to ask "when did this change, and who changed it?", and that is hard to answer from weights unless lineage back to the original sessions is retained. They also don't want a trained model to become an "accomplice" for a user nudging an agent repeatedly to hide financial crimes. gopalv also found it odd that the paper's diagrams build on the KV-cache, which they contrast with Databricks' RAFT.
- Mechanism, plainly restated: imimayj1337 read it as reserved, unpopulated parameters in the architecture that get filled during sleep and reinforced by the dreaming step. They guessed that only a vertically integrated player like Google could pull it off, and that compute-constrained organizations would struggle. gavinray roughly agreed and added that short-term memories are scored, and the useful ones are integrated through a transform of the old weights. That is meant to avoid dumping whole memories into the weights additively.
- Enthusiasm: mdp2021 called it a crucial missing piece toward AGI, needing only critical thinking added during cleanup. No one picked up or challenged that claim.
- References: macleginn linked the actual arXiv paper and called DreamCoder a favorite ML paper that this reminded them of.
Nobody tested the paper's claims, such as the 10M-token retention or how capacity scales over many sleep cycles. The one concrete objection is gopalv's: weight-level consolidation is hard to audit.
Kentucky man becomes the first humanist granted religious exemption from AI
Submission URL | 481 points | by danso | 517 comments
A humanist, not a traditional religious believer, has reportedly won a religious exemption from being required to use AI. That is the story's novelty: exemption regimes were built around faith traditions, and this case stretches the category to cover a nonreligious worldview.
The title says the man is from Kentucky and that this is the first such grant for a humanist. It doesn't say who imposed the AI requirement (an employer, a school, or an agency) or what legal basis the exemption rested on. I won't guess at those details, and they decide how far the precedent travels.
If the framing holds, it gives employees who object to AI mandates on ethical grounds a template to test, though a single reported grant isn't binding on anyone. The full article at Religion News Service would have the specifics.
The thread largely ignored the exemption itself. It took the opening comment's "new Amish-like lifestyle" remark and turned it into an argument over how people actually use AI at work, and whether software developers and other knowledge workers are having the same experience.
The framing: devs are "factory managers," knowledge workers are "artisans." One commenter said developers now run fleets of agents, so a single request fans out into 10-20 calls, and that feels like "moving up the stack." Knowledge workers, in this view, feel the AI is making their decisions while they watch.
Pushback from knowledge workers. A commenter in legal and medical-adjacent work said the premise is wrong. Experts quickly learn that AI cites fake or junk sources, picks the wrong venue, and imports crackpot papers, so they use it iteratively and check everything. The unchecked output is where people fail, as with a "weird case in Kentucky" cited for a Vermont court. Others countered that plenty of lawyers and writers do ship unchecked output.
Why code is different from other fields:
- Programming has automated tests, so the AI can partly verify itself. Knowledge work has no equivalent, so the human has to stay more involved.
- Mistakes in code are cheap until production. In law or medicine they aren't, and one commenter said "acting like a software engineer is called malpractice" elsewhere.
- One side said knowledge workers are just less sophisticated at using AI, and that source checking and functional-test analogues will arrive, for example a cheap second model that verifies citations. The other side said that when you didn't do the legwork, catching errors is much harder. Another added that if you have to check this carefully, you may as well do the work yourself.
- A related dispute is whether "just work" even makes sense for law. One commenter said the legal system exists to let humans argue their case, so a deterministic AI defeats its own purpose.
Do developers really run fleets of agents? This was the most contested point.
- Skeptics (Exoristos, bigstrat2003) said they don't know anyone working that way and doubt it's the majority.
- One developer described a planner agent spawning builder sub-agents, checker agents, and review agents on every PR. Another noted that automated PR review alone means 3-5+ agents per repo.
- Others said 2-3 agents is their limit before they "lose the plot."
- Cost is a counterpoint. Anthropic researchers can run thousands of agents because they don't pay token bills. Another commenter said that scale is a luxury of wealth, generating many possibly defective samples and picking the good ones. A reply predicted costs would drop by an order of magnitude within about six months.
- Anecdotes cut both ways. One was a startup with tens of thousands a month in AI spend and no viable product, and another was a friend whose network is run by agents.
Other comments used the analogy of home cooking and gardening persisting alongside restaurants and supermarkets. One person said AI barely helps with niche, proprietary industrial knowledge.
Decompiling Games with AI: Virtueel Zoetermeer
Submission URL | 33 points | by bouk | 8 comments
One prompt got a working web port of an early-2000s Dutch city-builder. The author gave Claude the archive.org zip of Virtueel Zoetermeer with /goal create a decompilation of Virtueel Zoetermeer.zip and get it working on the web. The first result ran but kept crashing. A follow-up, "do a full playthrough of the game yourself and fix any crashes," made it stable.
The game is a SimCity-style title commissioned by the municipality of Zoetermeer, where you build a city and keep its amenities supplied. It spread to Dutch primary schools as an early "serious game" for teaching urban planning. It was written in Pascal, a language the author says he has never used. Claude reconstructed equivalent source, now on GitHub, and the game is playable in the browser at virtueelzoetermeer.bou.ke.
The post is also a counterpoint. The same approach stalled on Age of Mythology: Gold Edition (the original, not the Steam remake). After a month it sits around 60% and slows with each function. The method works because a byte-match against the binary gives a clear pass/fail test, and agents can attempt every function in parallel. Scope and token budget decide the outcome: a small, obscure Pascal game falls in a couple of prompts, while a large commercial engine grinds.
Note that the Zoetermeer result is described as a working port with reconstructed source. The post doesn't claim a byte-matching decompile, the standard the author sets for projects like Paper Mario.
The author's high-score tip comes from ChatGPT: fill every empty tile with churches.
The thread is short and mostly practical, with a few side questions about how the decompile actually worked.
- Cheaper models are enough. gmerc says DeepSeek is now "good enough" to do this token-efficiently. He claims a Hitman 2 effort took only a handful of days of off-peak usage, and that a recent DeepSeek release sped his process up by another 50%. He links a video and a blog post as evidence. Another commenter recognized him from the DeepSeek and game-remixing angle.
- Dutch names. lmz asked whether the Dutch comments and field names came from the compiled code, noting that Delphi carries RTTI info. The author (bouk) said the comments are AI-generated. The original class names really were Dutch, so he told the model to make the rest of the code match.
- Will smarter models help? xyzsparetimexyz challenged the post's hope that a stronger model would make reverse compilation more efficient. He asked what such a model would do besides burn token budget, unless it internalized every compiler's codegen patterns and flag permutations, or wrote a decompiler. tancop replied that producing readable code that rebuilds the exact binary and likely resembles the developers' source is much harder than ordinary decompilation. In tancop's view it takes a human expert or an equivalent AI, because deterministic tools usually yield unreadable or non-compiling output. This leaves open whether model gains or just more tokens are what move the hard cases.
- Why some games are easier. suddenlybananas asked what makes certain games easier for models to decompile beyond raw complexity. Nobody answered.
- Minor. thenthenthen reported that the page's graphics weren't loading, and said the real Zoetermeer is "kinda drab" until a search for photos changed their mind.
Nvidia Nemotron 3.5 Transcribe: open ASR for 40 languages
Submission URL | 127 points | by wondersky | 24 comments
One 30B-parameter checkpoint (about 4B active per token) now handles transcription, diarization, translation, and audio Q&A, steered by plain-language prompts instead of separate models. It's open-weight on Hugging Face and ModelScope, pairing a speech encoder with a hybrid Mamba–Transformer mixture-of-experts LLM.
Coverage varies by task:
- Transcription in 40 languages
- Translation to or from English in 31
- Speaker attribution in 13, up to eight speakers, on recordings up to an hour
- Audio Q&A trained in English only
- Word-level timestamps
Leaderboard claims, all NVIDIA-reported or sourced from the model card:
- Hugging Face Open ASR (English): first among open-weight models at 5.33% average WER, API models excluded. Multilingual is 4.62% across seven languages and long-form is 10.98%, both lowest in the model card's own comparison.
- Artificial Analysis: ties for first among open-weight models at 2.8% AA-WER with roughly 4B active parameters, versus 24B for the model it ties. On the AgentTalk voice-agent set it scores 1.8% against 2.3% for the next-best open model.
- Diarization: lowest error on David AI's 21-language leaderboard, and first in VoiceArena's 14-system comparison.
The more practical piece is the adaptation path. You can inject domain terms through the prompt (for example, "Possible terms: HbA1c, metformin"), and when that runs out, Granary v2's multilingual data and curation pipeline plus NeMo Speech fine-tuning recipes take over. In a medical case study, fine-tuning cut WER on held-out speakers by 45.8% relative to the best prompt. That is a single case study, so treat it as a ceiling rather than a typical result.
Quantized builds target RTX Spark, DGX Spark, and Jetson AGX Thor for local runs, with Blackwell data-center GPUs for scale. Agora demoed word-level timestamps in a classroom replay app.
NVIDIA itself advises spot-checking names, speaker changes, and answers against the recording, and language support differs by task, so check the model card's task-by-language matrix before committing.
The thread is mostly practical, and the verdict from people who tried the model leans lukewarm: Parakeet, Qwen ASR, and Nemotron Streaming were repeatedly named as better fits for specific jobs.
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Hardware worries: One commenter wanted to know if it runs usably without an NVIDIA GPU, with a target of about 10% of realtime on an old x86 box. Farmadupe2 said reference Python code runs almost anywhere. They also suggested having an LLM port it to a specific target such as an Intel NPU, which they estimated at 10–30 USD in tokens. On speed, they argued it's a 30B model with about 4B active, so with ~30 GB of spare RAM it should run about as fast as any 4B model. Streaming weights from SSD would be much slower. Another reply recommended trying Parakeet first, since it does well on CPU alone.
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Hands-on quality reports: xpct saw an error every few sentences on standard vocabulary. jiehong preferred Parakeet or Qwen ASR and regretted that Mistral didn't open-source Voxtral 3. drewbitt called Voxtral 3 probably the better product but said this model is cheap. On Japanese, mewse-hn compared a Q4 quant against Parakeet and found it slower, heavier, and higher-error. Its built-in translation was worse than sending a Parakeet transcript to a cloud LLM. On a full anime episode, vocal-isolated and raw, it was thrown off by English words in the theme song and failed three times. The quant may not have given full accuracy, as they noted.
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No live streaming: solarkraft lamented that this model is for recorded audio only. Nemotron Streaming in the Handy desktop app has effectively solved dictation for them, including German mixed with English terms and brand names. They said it runs well on any Mac down to a base M1. Handy is desktop-only. For Android, killingtime74 uses a forked DictateKeyboard with local Qwen 3 ASR, which they called the second most accurate open model after this one.
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One niche plus: AkashKaStudio pointed out the model card's near-field-only speaker transcription is experimental but not offered by other open speech LLMs.
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Unanswered: vjulian asked whether OpenAI's transcription model really is best for thousands of hour-long, jargon-heavy interviews. Nobody gave real evidence either way.
Artificial Analysis Intelligence Index v5
Submission URL | 95 points | by hn-aarvin | 82 comments
60% of the Intelligence Index now runs on private test sets or held-out answers, the main structural change in v5 and a direct response to contamination and saturation of public benchmarks. Artificial Analysis also launches a separate Progress Index v1 that tracks capability over time on one continuous scale.
What changed in the Index:
- Added: FrontierCode 2.0 (Cognition's private benchmark of 132 real-repo tasks, graded on whether a maintainer would merge the change) and Terminal-Bench-Science (70 agentic research tasks).
- Upgraded: HLE-Diamond replaces Humanity's Last Exam, AA-Omniscience moves to v1.1, and CritPt v2 now uses a private 40-question hard subset.
- Dropped: GDPval-AA v2.1, SciCode and AA-LCR v1.1.
- Weights: four categories at 25% each: Knowledge Work, Coding, Science, General.
Results: Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (57) and GPT-6.1 Sol (55). Anthropic and OpenAI hold the top five spots, and Gemini 4 Argon is next at 50. No model wins everything. Opus 5.5 tops three of the nine evaluations, and five other models split the remaining six.
The open-weights gap grew from 3 points to 12. The top open model, Kimi K3.1 Pro, scores 46, but its weights aren't released yet. Among released weights, DeepSeek V4.1 Flash 1017 and GLM-5.3 lead at 32, below Claude Haiku 5.5 at 36. On Terminal-Bench-Science, most open models score under 10%, and the best released one reaches 13%.
Cost spread: among the 10 top-scoring models, cost per Index task runs from $0.96 (GPT-6.1 Terra) to $13.04 (Claude Fable 5.1), more than 13x.
Two details are worth noting. On HLE-Diamond, the leaders score 78% on reasoning questions but only 58% and 50% on knowledge. AA-Omniscience v1.1 scores average about 2 points lower than v1, and a recalibrated public subset is on Hugging Face. The caveat: the heaviest-weighted new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science at 15% each, are private, so outsiders can't audit them, and FrontierCode comes from Cognition, a company that builds coding agents.
The thread is dominated by one accusation: that every Artificial Analysis (AA) index revision lands in a way that favors US labs and hurts open-weight (mostly Chinese) models. A smaller group pushes back on the methodology rather than the motives.
The bias case
- The top comment says several popular open-weight models, including GLM 5.3-flash and Qwen 3.8-flash-next, were quietly removed from the charts, and their model pages now show "N/A".
- Commenters recall earlier updates having the same effect. One says a rushed same-day change when GLM 5.3 was added lowered its score. Another says AA changed the index calculation right after Qwen 3.8 Max took the top agentic spot, so it lost that rank.
- Another commenter noted the same pattern: OpenAI dropped in the index, then a "fix" shipped hours later and it rose again. They also object that two of the new sources are private and can't be reproduced.
- Another points to the slow evaluation of Mimo 2.6 Flash, which let GPT-6 Luna sit on the Pareto frontier for a couple of weeks.
- The hands-on argument is that the rankings contradict experience. People say GLM 5.3 beats GPT-6 Astra on backend and frontend work, and that Haiku 5.5 outranking every open model except Kimi K3.1 "is not true".
- hgoel's structural version is that whatever OpenAI and Anthropic optimize for becomes the definition of intelligence, and saturated older tests get discarded.
The less conspiratorial readings
- rbehrends doubts either bias or benchmaxxing is the main cause. The coding index alone looks plausible, for example Terra no longer beating Fable. The new Terminal-Bench-Science hurts small coding-tuned flash models, which wouldn't be surprising. The deeper question is whether one aggregate number across such different tasks means anything.
- sourcecodeplz says smaller open models are strong at coding and tool use but lack world knowledge, which fits the HLE knowledge split.
- mzl says new releases tend to expose weaknesses in the index itself, because models ranked together don't feel equally good.
- arcanemachiner plays devil's advocate: any new benchmark gets gamed, so periodic replacement is expected, though bias toward US models is possible.
- yorwba argues individual benchmarks are noisy and saturate. In their view a weighted sum just chooses which noise to amplify, and they prefer Epoch's ECI because it models saturation and gives error bars.
Specific methodological objections
- LLM judge: chvid questions using a closed model, GPT-6 Luna, as the FrontierCode judge. iwhalen links a paper on self-preference bias in LLM judges.
- Languages: kamov calls the multilingual language selection odd, with broad Asian coverage but only a few Romance European languages and no Slavic, Nordic or Finno-Ugric ones.
Side argument on open weights
- lousken says closed models degrade after launch while open weights stay fixed. pritambaral adds that open weights can improve through fine-tuning.
- tsss counters that hosted open models vary with configuration such as KV cache, and that hosts may cut corners. dannyw replies that reputable hosts like Fireworks, Cloudflare and Baseten have a reputation to protect.
The unresolved crux is whether the shifts come from deliberate tilting or from an index so sensitive to its task mix that any revision reshuffles the rankings. Private test sets make it impossible for outsiders to settle that.
AI execs may be too out of touch to build for the general public
Submission URL | 88 points | by pavel_lishin | 69 comments
Marissa Mayer's attempt to show off her new AI app turned into a PR problem. In a New York Times interview about Dazzle, an assistant that learns from your camera roll, she said it helped write three bullet points about her 10-year-old daughter for a school birthday presentation: "empathetic, creative, and an animal lover." Critics noted that the description is generic enough to fit most 10-year-olds.
Mayer later defended herself on X. She said she was testing a prototype, not asking the app to explain her child to her, and that it surfaced forgotten kindergarten projects like a "snake devenomer" and a "sandcat playground." But the same interview had her pitching Dazzle for birthday themes, playdates, vacation activities and Halloween costumes. She also floated her assistant coordinating with her husband's. Asked whether a "frictionless future" planned by AI is depressing, she answered, "I haven't really thought that through."
TechCrunch's Amanda Silberling treats this as a pattern, not a one-off:
- Sam Altman said he couldn't imagine raising a newborn without ChatGPT, and pitched a podcast that explains your kids' interests to you.
- Mark Zuckerberg argued that the average American has fewer than three friends but wants about 15, and predicted AI therapists and romantic partners will become more socially acceptable.
The public isn't with them. A Pew study found that 50% of U.S. adults expect AI to reduce people's ability to form meaningful relationships, and only 5% expect it to make connections easier.
Silberling's thesis is that Silicon Valley's efficiency culture, the same one behind sleep-tracker optimization and peptide stacks, is being applied to relationships, where friction is the point. It's an opinion column, and it picks the most quotable examples. Still, Mayer's "haven't thought that through" answer undercuts her own defense.
The thread quickly moves away from Mayer's app and into a larger argument over what AI and robots should take off people's plates. Most commenters share the sense that tech executives are out of touch, but they split on what caregiving automation would actually mean.
The "out of touch" framing. The top comment points to Elon Musk's pitch that robots will look after your dog, kids, or aging parents, calling those "the things normal people with healthy relationships want to be doing." Others piled on with Altman's podcast-about-your-kids idea. One called the executives "sad psychopaths," and another called them "deeply anti-human." A counter-joke: Mayer was perhaps busy A/B testing blue shades. The sharpest version of the complaint is that robotics belongs in work, not in places where human experience is the value. A reply to that: "Or maybe we want the robots to do our jobs so we can spend time with people." The rejoinder was that people would then be unemployed and scrounging for food.
Caregiving is hard, so is the pain the robots' target? Commenters did not dispute that raising a child while parents decline is brutal. They disagreed about the cause.
- One side blamed structure rather than the people involved. Care is unpaid full-time work squeezed around a job, nuclear families lost community help, and dual incomes are now required. One reply: "And the result is no kids."
- A sub-thread argued over whether people should simply not have kids if they can't handle it. Others pushed back that parental illness is something life throws at you, not a choice, and that childcare is unaffordable for almost everyone. One noted it's free in Germany. The poster arguing for fewer kids said the real fix is government-funded childcare, which won't happen without a demographic crisis.
- TheOtherHobbes suggested falling birth rates partly reflect that the internet shows what parenting involves. By this view, robots doing chores and bureaucracy would free time for the relationships themselves.
Does robot elder care actually make sense? This was the thread's real fault line.
- Supporters said it has been pursued for decades, and that nursing homes face severe labor shortages. One wants to live independently into their 80s rather than burden their only kid. Another would rather control a robot than depend on a stranger.
- Critics said that's a stretch. If you can afford and maintain the robot, you could probably pay humans. They argued the fix is better pay and breaking up large care companies. Another said family caregivers act from love, culture, and obligation, which mercenary caregivers (or machines) won't replicate.
- A definitional fight ran under all of this. One commenter said "robot care" just means chores like trash, medication reminders, and finding keys, and nobody objects to a dishwasher. Others said people mean showering, toileting, companionship, and monitoring. And nurses do far more than chores: they handle medication, meals, outings, and checking on well-being. Companionship, most agreed, is where the line falls.
Cost reality check. Nursing homes already exist and are expensive. One commenter cited roughly £5k per week for an aunt's end-of-life care. Another pointed out that wanting to care personally and being able to are different things.
The thread never settles whether the technology would free people to be present or merely replace the presence itself. That turns on what you assume gets automated.