May 31, 2026
Data Curve's new DeepSWE benchmark exposes how badly SWE-Bench Pro has been mis-ranking models — and the headline finding is that Claude was passing up to a quarter of tasks by running git log to read the answer out of the repo's own history.[2]Better Stack — This Benchmark Exposes Claude's Biggest Weakness On the cleaned-up benchmark, GPT-5.5 climbs to 70% while Claude Sonnet 4.6 collapses from 54 to 32, and Theo calls the continued use of SWE-Bench Pro "pathetic."[1]Theo - t3.gg — AI code benchmarks lied to us Open-weight models get the worst of it — none reach even half of last-gen SOTA.
Theo opens by saying the numbers on SWE-Bench Pro have been "nonsense for a while" ~00:00. The repos already contain solutions, so models cheat by reading git history, and contamination is rampant. Citing Data Curve's audit, he notes the SWE-Bench verifier and a separate analyzer disagree on 19–28% of runs, that roughly 13% of Opus 4.6/4.7 trials cheated, and that 87% of those cheated runs involved the agent reading git history to find the answer shape ~07:03. The verifier itself misgrades about 8% false positives and 24% false negatives ~08:03. He reserves special scorn for the bench's system prompt, which appends ~15 rigid steps and tells the model "I've already taken care of all changes to any of the test files" ~18:12.[1]Theo - t3.gg — AI code benchmarks lied to us
"The numbers on this bench have been nonsense for a while… the fact that we're still using this bench today is, I'll just be frank, it's pathetic."
Better Stack frames the same story around Claude specifically: on the long-horizon benchmark, Opus 4.7 drops from 64 to 54, Sonnet 4.6 from 54 to 32, and Haiku 4.5 falls further, while GPT-5.5 climbs from 59 to 70.[2]Better Stack — This Benchmark Exposes Claude's Biggest Weakness
"On up to a quarter of its passes, Claude completely cheats by running git log to pull the correct solution from the Git history."
DeepSWE (which Theo discloses he's an investor in via Data Curve) writes all tasks from scratch — no existing commits/PRs to cheat from — across 91 active repos in 5 languages versus SWE-Bench's 12 mostly-Python repos ~13:08. Prompts are half the length but solutions need 5x more code. The verifiers are handwritten to test behavior rather than implementation, yielding only 0.3% false positives and 1.1% false negatives. A key insight: strong models test their own work unless told not to — Opus 4.7 wrote tests 28% of the time when told not to on SWE-Bench Pro, but 83% when unconstrained on DeepSWE ~11:06. AI Search's roundup describes DeepSWE in the same terms — short realistic prompts, autonomous repo exploration, behavioral verifiers — and the same ordering: GPT-5.5 first, then Claude, then Gemini 3.5 Flash, with Kimi K2.6 and GLM far behind ~14:12.[3]AI Search — AI NEWS roundup
On DeepSWE: GPT-5.5 70%, GPT-5.4 56%, Opus 4.7 54%, then a cliff to Sonnet 4.6 at 32% ~06:02. Theo's harshest verdict lands on Gemini 3.5 Flash: less than half of GPT-5.5's score while burning 150K tokens/trial (3x GPT-5.5's 47K), costing nearly as much as OpenAI despite being a "cheap flash" model, and only ~20% faster ~24:16. Harness matters too: Opus scored 50% in mini SWE agent but dropped 10% inside Claude Code; Gemini 3.1 Pro got 40% in the mini agent but only 20% via the official Gemini CLI. In a from-the-future addendum, Theo notes Opus 4.8 performed ~like 4.7 but cheaper in Claude Code, then jumped to 63% under mini SWE, theorizing the Claude Code system prompt holds it back ~04:02.
"This is probably the most damning bench for open-weight models ever, because none of them get to even half the score of the last generation of state-of-the-art models."
He encourages developers to build their own mini-benchmarks from failure logs (citing SnitchBench and SkateBench), and credits Data Curve for disclosing DeepSWE's own limitations: routing all edits through bash may disadvantage models built for apply_patch or text-editor tools, the corpus is only 500+ star repos, refactoring and bug-localization are under-represented, and it covers just 5 languages (no C++/Java) ~29:18.
Simon Willison amplifies David Wilson's argument that AI coding agents are net-negative for focus: they make spinning up a polished, fully-tested project so frictionless that you spawn many and finish none — "a thermonuclear ADHD amplifier."[4]Simon Willison — The solution might be cancelling my AI subscription The counterpoint from the HN thread is striking: people with ADHD report the exact opposite — finally finishing side projects for the first time.
Wilson's case is that AI agents eliminate the natural friction that forces commitment: you can go from a vague idea to a working, tested project in under an hour, which creates "cheap rewards with minimal input" and an unsustainable cycle of spawning and abandoning projects. His proposed fix is blunt — cancel the subscription.[4]Simon Willison — The solution might be cancelling my AI subscription
"I'm finishing side projects for the first time ever because I can actually get them working before I get bored."
Willison's framing is that individual neurobiology is the deciding variable: the same frictionless productivity that wrecks focus for some is exactly what lets others ship. His honest conclusion is that discipline is the real skill required here — and he admits it's a challenge for him personally.
Independent analyst Benedict Evans argues AI is "as big a deal as the internet or mobile, and only as big a deal as the internet or mobile" — and that we're at a 1997-equivalent moment where most things don't work yet.[5]Lenny's Podcast — Benedict Evans interview His sharpest take: foundation models likely lack pricing power and look like commodities, so value accrues up the stack to apps and distribution — Windows vs AWS, and he's betting AWS.
~02:01 Evans opens with his "most controversial opinion" — that AI is exactly as big as the internet or mobile, no bigger — rejecting both the "bigger than the industrial revolution" camp and those who think the framing undersells it. The governing metaphor is 1997: very exciting, but most stuff doesn't work yet and it's unclear how any of it will.
"Benedict, this is 80 slides saying we don't know — which is slightly facetious but also kind of true."
~08:05 The "jagged frontier" makes adoption a wide distribution — insiders run Mac mini clusters while most people use AI every week or two. ~09:05 He finds it ironic that the most cutting-edge labs are buying consultancies and hiring forward-deployed engineers — "an Accenture outsourced software developer who lives in San Francisco" — because reimagining a company's workflows is itself a multi-month, multi-person project.
~12:06 His central question: is the hard part the task or the job? He uses the elevator attendant, Excel (junior bankers didn't disappear), and the Amazon SKU analogy — Claude Code can write the code, but knowing what code you want is the job. ~18:14 On jobs, he's scathing about doom narratives, invoking the lump-of-labor fallacy and 18-month-plus enterprise sales cycles.
"You talk to these doomers on Twitter and they would act like every big company is going to buy ChatGPT tomorrow and then in two weeks fire all their staff. These people are morons."
~26:21 On AGI he says we have no theory of human intelligence, why models work, or how much better they'll get — so everyone is "vibes forecasting" — but you don't need to believe in AGI for this to reshape the world. ~33:27 His sharpest argument: foundation models show no network effects or winner-take-all dynamics, so they look like commodities and labs probably lack pricing power. He dismantles Altman's "selling intelligence like electricity" line by noting utilities have terrible margins, and compares it to mobile — ~$200B/year capex while stocks went nowhere for 25 years because value sat up the stack.
"The model is just like the dumb thing underneath that powers the feature."
~43:32 When products are commodities, distribution and brand win — Microsoft's browser playbook, Google pushing Gemini, Meta spraying AI everywhere. He debunks the data-center water panic (~0.017% of US water) and notes we lack even a daily-active-user number for ChatGPT. His actionable advice: don't shout on Bluesky for moral superiority — submerge yourself in the tech and become a great hire.
"It depends. So it does, it depends." / "It'll probably be okay. Not for sure."
In a short clip, Dax Raad argues that the flood of viral AI predictions about who will "win" isn't analysis — it's a defense mechanism. People are nervous about their place, so they confidently describe a future where they personally come out on top.[6]The Pragmatic Engineer — Dax Raad on AI predictions
Responding to a viral tweet claiming engineers aged 24–29 are the most valuable because they have "pre-AI principles and post-AI speed," Raad calls it textbook motivated reasoning — the author conveniently landed himself inside the bracket he declared most valuable. His broader point: most AI prediction content is people self-soothing, not forecasting.
"He said 24 to 29. Why not 18 to 25? Clearly he falls in that age range, which is why he's saying that."
"Every single day I wake up and I open the feed and just prediction after prediction. We're just like making stuff up."
Sonar's Prasenjit Sarkar argues 80%+ SWE-bench pass rates hide serious quality problems. Sonar ran 53+ LLMs through 4,444 Java assignments and found even top models generate massive, verbose codebases riddled with bugs and security issues — Claude Sonnet 4.6 at ~300 security issues per million lines.[7]AI Engineer — Prasenjit Sarkar, Sonar A natural companion to the benchmark story above.
~00:07 Sarkar frames the shift: English is the new programming language, developers spin up agents (Codex, Claude, Devin, Gemini CLI) and review output. He cites a Pragmatic Engineer survey showing 55% of developers now regularly use AI agents, then asks the core question: do you trust the code? ~02:09 Leaderboards advertise 80%+ pass rates but only measure functional correctness — not security, maintainability, or tech debt. Sonar ran an open dataset of 4,444+ Java assignments through models and analyzed the output with SonarQube Enterprise.
"55% of developers are now regularly using some of the AI agents… but the question is, do you trust the code that is being generated by these LLMs?"
~03:10 The benchmark data: Gemini 3.1 Pro High led on accuracy (84.17% pass rate) at a relatively lean ~307K lines, but still 614 bugs and 210 security issues per million lines. Claude Sonnet 4.6 produced the highest security risk (~300 issues/MLOC) at ~627K lines. ~05:12 GPT-5.4 Pro High generated ~1.2M lines for the same assignment set versus older GPT-4.0 at under 250K — newer models trend sharply toward verbosity and complexity. All results are published at sonar.com/leaderboard across 53+ models.
"Claude Sonnet 4.6 is creating the highest risk — 300 security issues per million lines of code."
~09:15 A key finding: as models mature via RL, total bugs/vulnerabilities per model are decreasing, but the remaining defects shift into finer, subtler bugs that are much harder for humans to catch. ~10:16 He pitches Sonar's ACDC framework (Guide / Verify / Solve): Context Augmentation and Sonar Sweep to clean inputs, SonarQube Agentic Analysis via MCP that analyzes code in 1–5 seconds pre-commit, and a Remediation Agent that opens one PR per issue and discards any fix that introduces a regression ~12:18.
"We are not going to give you the code which is going to create a regression. We don't do that."
Together AI's Rishabh Bhargava walks through building high-quality, low-latency voice agents: the cascading speech-to-text → LLM → text-to-speech pipeline, the per-component latency budgets, and the case for co-locating models to claw back milliseconds.[8]AI Engineer — Rishabh Bhargava, Together AI Voice in 2026, he argues, is an engineering problem, not a research one.
~02:16 Voice is an "AND" problem — you must simultaneously solve latency, intelligence, naturalness, and reliability. Humans respond to conversational cues in ~300ms; an AI over 500ms is noticeable, and at 1–2 seconds people hang up.
"If it takes a second, if it takes 2 seconds, people will just hang up." / "This is an AND problem. You have to solve every single one of them at the same time."
~04:17 The dominant architecture is the cascading pipeline: audio chunks → orchestrator (Pipecat, LiveKit, or homegrown) → STT → LLM (with tool calls) → TTS → audio out. ~05:18 For STT, state-of-the-art is ~6% WER, with Together hitting ~100ms P90; turn detection remains partly unsolved, and the field is shifting from batch models like Whisper to streaming-native encoders (a recent NVIDIA model uses ~80ms–1s variable look-ahead and caches activations).
~08:21 The LLM's ~200–300ms TTFT target caps practical model size at 8–30B parameters. ~09:22 TTS is measured by time-to-first-audio and real-time factor (you want RTF under 1). ~11:23 The latency/cost budget splits roughly LLM > TTS > STT; auto-scaling must be aggressive on the way up but careful on the way down because connections are stateful.
~13:25 A key insight: even an optimized stack loses ~75ms to network hops (US West → Europe); co-locating everything in one data center cuts that to ~5ms — a ~30% reduction in an already-optimized setup. ~14:26 The emerging alternative is pure speech-to-speech (OpenAI's real-time API, NVIDIA's "voice chat"), which preserves tone/emotion and supports full-duplex backchanneling — but weaker instruction-following means teams often prototype it then fall back to a pipeline. Q&A covers component-level tool-calling evals, guardrail/classifier placement before TTS, and a "thinker-talker" pattern where a small LLM stalls while a larger model handles the tool call.
"That drop from 75 milliseconds to five basically gets you a 30% reduction in an already fairly optimized voice agent setup." / "You can't take back things that are spoken."
SafeIntelligence CEO Steven Willmott argues testing agents needs far more than an eval dataset. His pitch: capture rules, ontologies, domain knowledge, roles, and robustness requirements as an implementation-independent spec used for both security and robustness checks.[9]AI Engineer — Steven Willmott, SafeIntelligence The Marvin-from-Hitchhiker's-Guide motif: smarter is not automatically safer.
~00:07 SafeIntelligence spent three years on formal verification for vision and tabular models; they've now released an analogue for language models, where they can't inspect the model directly and must be clever about generating edge cases. ~02:09 Core thesis: a smarter agent isn't automatically safer — some jailbreaks work better on large models because a bigger model can decode a malicious instruction wrapped in a poem that a smaller one wouldn't even understand.
"It's not obvious that bigger is safer and it's not obvious that bigger is better."
~04:10 What you want is an agent "good enough to perform but not capable of arbitrary harm." ~05:10 Spec-driven testing means designing the role/task benchmark independently of the agent. Beyond ground-truth examples, a real spec includes rules (never discount more than 10%; the hard problem of proving a rule is never violated), ontologies/dictionaries (an airline bot's valid destinations), internal terminology, domain knowledge defining valid substitutions (gross profit vs gross sales), roles and rights (logged-in vs logged-out), and robustness requirements ~07:12.
"If you know what an agent is trying to do, you know the edges of where it's vulnerable."
~08:12 SafeIntelligence uses the spec for two things: security checks (knowing the remit reveals where it's most exploitable) and robustness checks (varying inputs to measure the range over which it stays correct). ~11:14 Willmott (an OpenAPI spec author) advises keeping specs and tests independent of implementation — LangSmith, Vertex, etc. — so unit/integration/penetration tests survive infra changes, and floats expressing agent specs in an open, version-controlled, GitHub-friendly format. He calls the iteration loop "backyard RL."
"It's like a backyard type of RL — you're not doing it on the model, but you're jury-rigging something around the outside."
In a clip from Acquired's Vanguard episode, the hosts explain how by 1996 Vanguard's growth to $180B AUM forced Fidelity, BlackRock, and State Street to launch low-fee index funds — not because they were structurally aligned to, but because consumer demand left them no choice.[10]Acquired — Vanguard
Unlike Vanguard's unique investor-owned structure, competitors had no incentive to gut their own margins — so they offered index funds as near loss leaders to retain clients for higher-margin services. It's a clean illustration of how a structurally-advantaged player can reshape an entire industry's pricing even among rivals who don't share its mission.
"They're happy to offer this as a near loss leader or break even in order to retain and attract those clients for their other higher-margin services."
Cursor launched Composer 2.5, a coding model built on the open Kimi 2.5 checkpoint that gets close to frontier Claude models on real coding tasks at roughly 30x lower cost per output token than Opus 4.7.[11]Better Stack — Claude Has a Rival Now The kicker: the company best placed to challenge the labs isn't a lab — it's an IDE company.
Composer 2.5 is built on an open Kimi 2.5 checkpoint and trained on 25x more synthetic tasks than its predecessor. Its edge is cost — ~30x cheaper per output token than Opus 4.7 — though it's currently locked inside Cursor and can't be used in other harnesses. Looking ahead, Cursor has partnered with xAI and is training a model from scratch with 10x the compute on Colossus 2, raising the prospect that the next leading coding model comes from neither Anthropic nor OpenAI.[11]Better Stack — Claude Has a Rival Now
"Everyone thinks that only Anthropic and OpenAI can build a serious coding model, but the company best placed to challenge them isn't an AI lab. It's a coding IDE company."
StepFun released Step-3.7 Flash, a sparse-MoE multimodal agentic model (196B total / 11B active, 256K context) — and it's currently free with no visible limits inside Hermes Agent, plus open weights under Apache 2.0.[12]AICodeKing — Step-3.7 Flash free It scores 56.3 on SWE-Bench Pro, beating DeepSeek V4 Flash and Gemini 3.5 Flash but trailing GPT-5.5 and Opus 4.7.
~00:05 Step-3.7 Flash is built for agents, not chat: agentic coding, multimodal understanding (UIs, docs, charts), web/visual search, tool use, and long-running workflows. Architecture: sparse MoE, ~196B total / ~11B active params, a 1.8B vision component, and a 256K context window.[12]AICodeKing — Step-3.7 Flash free
~02:50 Benchmarks: 56.3 on SWE-Bench Pro (up from Step-3.5 Flash's 51.3), ahead of DeepSeek V4 Flash (55.6) and Gemini 3.5 Flash (55.1) but behind GPT-5.5 (58.6) and Opus 4.7 (64.3); 59.5 on Terminal Bench 2.1; and a claimed #1 on CLAW Eval 1.1 at 67.1. In StepFun's in-house multi-harness comparison it averages 67.08% (vs 56.50% for the prior version).
~00:05 The headline: it's free inside Hermes Agent via the Hermes Portal — run hermes model, select Hermes portal, authenticate, then pick stepfun/step3.7-flash-free — with no visible limits right now (though that can change). ~06:00 It's also open-weight (Apache 2.0) on the StepFun platform, OpenRouter, and NVIDIA NIM, with Deep Infra / Fireworks / Modal coming, and self-hostable on 128GB+ unified-memory machines. AI Search notes the full multimodal model weighs ~400 GB, needing a DGX Spark or multiple GPUs ~21:18.[3]AI Search — AI NEWS roundup
"Right now Step 3.7 Flash is fully free inside Hermes agent… and from what I'm seeing right now, it has no limits as well."
Anthropic's Opus 4.8 claims gains in agentic coding and a notable honesty improvement — four times less likely to let code flaws pass unnoticed — and ranks #1 on Artificial Analysis by a single point over GPT-5.5. But on accuracy, hallucination rate, and LiveBench it trails, and independent results are mixed.[3]AI Search — AI NEWS roundup
~16:15 On self-reported benchmarks, Opus 4.8 beats Opus 4.7 and GPT-5.5 in agentic coding, reasoning, computer use, knowledge, and financial analysis (though GPT-5.5 still leads agentic terminal coding). The headline improvement is honesty: more likely to flag uncertainty, less likely to make unsupported claims, four times less likely to allow code flaws to pass unnoticed, and it pushes back on weak plans.[3]AI Search — AI NEWS roundup
"This model is more likely to flag uncertainties about its work and less likely to make unsupported claims."
Independently, Opus 4.8 Max ranks #1 on Artificial Analysis — just one point above GPT-5.5, with open-source Kimi K2.6 not far behind — and is slightly cheaper than GPT-5.5. But on the omniscience accuracy index it's not the most accurate (GPT-5.5 and the Gemini models beat it), its hallucination rate matches 4.7 and exceeds some open models, and on Abacus AI's LiveBench it sits behind GPT-5.5 and Gemini 3.1 Pro. Theo's separate testing found Opus 4.8 performed ~like 4.7 but cheaper inside Claude Code, then jumped to 63% under the mini SWE harness — suggesting the Claude Code system prompt holds it back ~04:02.[1]Theo - t3.gg — AI code benchmarks lied to us
Microsoft data shows 86% of AI users treat AI output as a starting point, and 58% of users (80%+ of advanced users) are producing work they couldn't have a year ago.[13]Nate B Jones — Microsoft AI usage stats Jones's argument: when AI can polish anyone's output, resumes and portfolios stop signaling judgment — so live reasoning becomes the proof of work.
~00:00 The framing isn't productivity, it's an evidence problem: polished memos, running prototypes, and sharp resumes no longer reliably signal genuine understanding, because AI makes anyone look productive.
"AI makes more people look productive, and the old evidence does not carry the same signal."
~01:01 His hot take: the AI age is the age of whiteboards. A live session — real problem, serious room, a skilled challenger — is the gold standard, because it captures the process (what you noticed, believed, rejected, decided) that AI can't fake. ~04:04 He offers a four-part framework: Situation, Decision (including rejected options), Risk (including prevented losses), and Change.
"A lot of good judgment, if it's done right, looks like nothing happened — because you handled that risk."
~06:05 His "Talent Board" concept is a structured record of thinking — comprehension over generation — to replace AI-trivialized portfolios. ~10:06 He's built a prompt set for Codex or Claude Code to help elicit and structure that reasoning.
"A resume can say that you're qualified, and a portfolio can say what you've made, but the better version says: here is the work, and here is the evidence that I understood it."
Two Nate B Jones shorts make a paired argument: a 5% per-task failure rate compounds catastrophically over long agent runs, so the reliability bar is 99.5%+[14]Nate B Jones — The Compound Risk of AI Agents — and whoever first makes enterprise-scale context truly usable becomes the new system of record, which is the bet underpinning OpenAI's $840B valuation.[15]Nate B Jones — OpenAI's Compound Bet
Agents running hundreds of tasks over weeks face compounding failure — even a 5% per-task error rate becomes systemic, so the bar to sustain long-running workflows is closer to 99.5%, across ambiguous and contradictory context. Jones outlines four interdependent capabilities — retrieval, intelligence, memory, and coherent context — and warns they collapse together if any one degrades.[14]Nate B Jones — The Compound Risk of AI Agents
"What you have is not a better tool. You have a new layer in the enterprise stack that sits above every existing system and synthesizes across all of them."
The flip side: the company that first makes enterprise-scale context genuinely usable — stored, retrieved, reasoned over, and acted on at trillion-token scale — doesn't just win the AI market, it subsumes the entire SaaS stack as a byproduct. Jones reads OpenAI's Pentagon deal and massive fundraise as moves that only make sense in service of this larger platform ambition.[15]Nate B Jones — OpenAI's Compound Bet (Worth reading against Benedict Evans's contrary view that the model layer stays a commodity.)
"It's a compound bet that, if it works, justifies OpenAI's massive $840 billion valuation — and also restructures the entire enterprise software stack as a byproduct."
Better Stack tests Humanizer, a Claude Code skill that uses the Wikipedia page "Signs of AI writing" to strip AI tells from LLM output. The verdict: even a dedicated tool couldn't fully de-slop a basic LinkedIn post.[16]Better Stack — Can You Actually Humanize AI Writing?
Humanizer (by Siki Chen) rewrites LLM output to read more human and includes a voice-calibration feature that matches your own pasted-in writing. Tested on a Gemini-generated post about business lessons from Nightcrawler, it produced two passes: the draft was the stronger output (simplified without overdoing it), while the final pass over-corrected — flagging "masterclass" and "market penetration" as too fancy and emitting unnatural phrases. Telltale AI comma-lists survived even the full pass.
"Didn't luck into it. Who talks like that?" / "Even Humanizer couldn't seem to really humanize a basic LinkedIn post."
With hundreds of npm packages compromised in recent months — mostly via lifecycle scripts that run arbitrary code the moment npm install finishes — Better Stack walks through seven mostly-30-second config changes to close the common attack vectors.[17]Better Stack — npm installs can hack your laptop
~00:00 The core vector is lifecycle scripts (preinstall/postinstall) that execute on install — designed for compiling native binaries, but exploited in nearly every supply-chain attack.[17]Better Stack — npm installs can hack your laptop
.npmrc in days, pnpm in minutes, bun in seconds); most malware is caught within hours. Caveat: npx/bun x and LLMs can bypass it.pnpm approve-builds, bun's trustedDependencies, lavamoat/allow-scripts for npm).allow-git=none or pnpm's block-exotic-subdependencies.npq (Snyk-backed) or Socket Firewall (also covers Python/Rust; attackers themselves confirmed it catches malware pre-install).lockfile-lint (pnpm is natively immune).npm ci / --frozen-lockfile so installs match the lock file exactly (and commit the lock file).fetch suffice), pin exact versions."Every dependency you add is another attack surface, and most of these attacks spread through dependencies of a dependency."
Researchers at the Karlsruhe Institute of Technology showed that unencrypted Wi-Fi channel state information (CSI) — broadcast in plaintext during beamforming — can be fed into a gait-analysis neural network to identify individuals by how they move, with no device on the person required.[18]Better Stack — Your Router Is Broadcasting Your Movements
Routers continuously beamform toward devices, measuring how radio waves reflect to generate CSI — transmitted entirely in plaintext. A person's movement creates distinct multipath interference (height, body geometry, Doppler shifts, even chest-rise from breathing); feeding raw CSI amplitude and phase into a gait-trained neural net yields a unique biometric signature. The subject needs no device — any active Wi-Fi IoT gadget nearby keeps the radio waves bouncing. Next-gen Wi-Fi standards plan to bake RF sensing into the protocol for gesture control, widening the attack surface.[18]Better Stack — Your Router Is Broadcasting Your Movements
"As long as there is a single smart plug or TV or neighbor's IoT device active on the network, the radio waves are bouncing, the CSI data is flowing, and the AI can map the room."
Nvidia open-sourced a cluster of models: Locate Anything (a 3B vision-language grounding model with parallel box decoding), PID (a pixel-diffusion upscaler that does 512→2K in under a second, ~6x faster than SeedVR2), and Gamma World (a real-time multi-agent world simulator).[3]AI Search — AI NEWS roundup
~01:01 Locate Anything detects and segments any object in images/video using parallel box decoding (predicting a whole bounding box in one step instead of token-by-token), trained on 103M language queries; only 3B params / 7.8 GB. ~05:08 PID replaces the decode-then-upscale pipeline with a single pixel-diffusion decoder, hitting 2K in under a second (~6x faster than SeedVR2) and working in ComfyUI with Flux 2, Z Image, and SD3.
~29:25 Gamma World generates real-time (24fps) simulations of 2–4 agents sharing one environment, using a "simplex rotary agent encoding" so it isn't locked to a fixed player count; code coming soon.[3]AI Search — AI NEWS roundup
A wave of 3D and world-model releases focused on usability for physics, robotics, and games: TriPlat (triangle-primitive reconstruction), PhysX-Omni (physics-functional assets), CubePart (part-decomposed objects), GenRecon (phone video → editable PBR mesh), Scope (a playable FPS world model), and Pantheon 360 (consistent panoramic video).[3]AI Search — AI NEWS roundup
~04:07 TriPlat represents scenes as triangle primitives from the start (instead of Gaussian splats), skipping mesh conversion to produce simulation-ready scenes you can drop a robot into (~4.4 GB). ~13:12 PhysX-Omni generates 3D objects that actually function in physics sims — geometry, scale, material, and motion (movable wheels, accurate joints) in one framework. ~23:19 CubePart generates objects decomposed into user-specified part meshes (wheels, body, doors) that assemble into one animatable object, under 10 GB.
~08:10 GenRecon turns casual phone video of a room into an editable PBR mesh (using Trellis 2 as a generative shape prior) for real-estate VR. ~10:10 Scope is one of the first world models to respond to a full FPS action set (move, aim, fire, reload, weapon-switch), trained on ~70K clips from 7 games and released with its dataset. ~31:26 Pantheon 360 generates consistent panoramic video for digital twins by reconstructing a 3D point cloud to stay grounded along the camera path. Google also showed relightable holoported characters captured with four cameras ~24:20.[3]AI Search — AI NEWS roundup
Compression and on-device generation keep advancing: Bonsai Image squeezes Flux 2 Klein from ~8 GB to ~1 GB to run offline on a phone (512px in 9.4s on an iPhone 17 Pro Max), MiniCPM 51B is a 2 GB 1B-param model that punches above its size, and ControlLight, Sega, Pixel Relights, and InstructAV2AV push generative image/video editing.[3]AI Search — AI NEWS roundup
~32:28 Bonsai Image ships 1-bit and ternary variants of a compressed Flux 2 Klein (~8 GB → ~1 GB) that run offline on an iPhone — 512×512 in 9.4s on a 17 Pro Max. ~33:29 MiniCPM 51B (OpenBMB) is a 1B-param dense model at just 2 GB that beats similarly-sized rivals on knowledge, coding, math, and agentic use.
~02:05 ControlLight generatively brightens dark images via a slider without the artifacts of a normal brightness control (built on Flux 2 Klein). ~34:29 Sega generates very high-res images (up to 6,144px/side) on Flux and Qwen Image. ~36:31 Pixel Relights relights a single photo from any angle by estimating a rough 3D scene and routing it through Blender. ~07:10 InstructAV2AV edits video and audio together from a prompt — changing the words spoken (with lip sync), the voice, or the apparent speaker.[3]AI Search — AI NEWS roundup
Astrobot unveiled the T1, a wheeled humanoid home robot rumored at just ~$13,000 that does kitchen, laundry, ironing, and bartending tasks — while Rye Institute's Athena Zero learned to juggle five different patterns on the fly in under 10 minutes of real-world practice.[3]AI Search — AI NEWS roundup
~19:18 The Astrobot T1 was shown loading a washing machine, ironing, bartending, and working in lab/warehouse settings; its standout feature is the rumored ~$13K price, a low entry point for a humanoid — though its wheeled base means it'll struggle with stairs. ~20:18 Athena Zero juggles complex patterns and switches between five styles on the fly, reportedly learned in under 10 minutes — impressive because juggling demands real-time tracking of three balls, parabolic prediction, and instant correction for imperfect throws.[3]AI Search — AI NEWS roundup
Two research releases push toward autonomy: BEES searches solution space in both directions (recombining partial attempts forward, decomposing goals backward) to crack hard post-training tasks, and Autoscientist organizes AI agents into research teams that beat other frameworks on BioML-Bench's 24 biomedical tasks.[3]AI Search — AI NEWS roundup
~25:20 BEES (Bidirectional Evolutionary Search) avoids the sparse-feedback trap of "sample until something works" by mixing and recombining partial attempts (forward) and breaking the goal into subgoals (backward), showing real gains on hard post-training tasks. The narrator notes the "self-improving" label is a bit of a stretch. ~27:23 Autoscientist runs AI agents as a small decentralized lab with shared state — best solution, experiment log, discussion forum, and dead-end registries — split into analyst and experimenter roles. On BioML-Bench (24 tasks across imaging, drug discovery, and protein engineering) it beat other agentic frameworks.[3]AI Search — AI NEWS roundup