标签：#AR

Wall-OSS-0.5 is a new 4B VLA release from X Square Robot, built on a 3B VLM backbone with action experts in a Mixture-of-Transformers layout. What caught my eye is that the report evaluates the pretrained checkpoint on real robots before task-specific fine tuning, instead of only reporting downstream fine-tuned performance. The reported numbers are: zero shot on a 17-task real-robot suite, 4 tasks above 80 task progress, including a held-out deformable task (Rope Tightening, 82). After fine tuning on a 15-task suite, they report 60.5 average task progress, +17.5pp over pi0.5, and +26pp on the 10-task manipulation subset. They also report +21.8pp on embodied grounding while general VL ability stays stable. The method bits I am trying to sanity check are the gradient bridge and the optimizer claim. They argue that discrete action-token CE is the dominant gradient into the VLM backbone, while flow matching's contribution to backbone updates collapses to roughly 5 percent within a few thousand steps. The Vision-Aligned RVQ tokenizer is supposed to make those action tokens semantically grounded instead of just numerical compression. For continuous actions, they still use flow matching, but supervise in recovered action space rather than velocity space. They also include DMuon, a distributed Muon optimizer, with a pretty aggressive overhead reduction claim. Code: https://github.com/X-Square-Robot/wall-x . Hugging Face org: https://huggingface.co/x-square-robot . Project page: https://x2robot.com/oss#resources . Paper: https://x2robot.com/api/files/file/wall_oss_05.pdf The questions I had after reading it: if you have run an analogous gradient-bridge ablation in another VLA, did action-token CE dominate in the same way? For people already using Muon, does the DMuon overhead claim sound plausible? And has anyone seen RVQ-with-vision-alignment clearly beat FAST-style tokenization outside this paper? If anyone is already trying to reproduce this on real hardware, drop notes.

If almost all jobs got replaced by AI, here's what happens: 1) Corporate revenue collapses - since humans do not have the means to buy product. It leads to demand destruction at an all-time level. 2) At the same time, there's a massive deflationary supply shock, thanks to democratization of production and the ubiquity of AI-led labor. The direct consequence of the aforementioned is: a price collapse, across the board. Which in turn, also leads to unprecedented tax revenue collapse. Who're you going to tax when no individual or corporate is making any money? To me, all this heralds a post-capitalism society, and not a "I-lost-my-job-and-I'm-now-poor" society. Once everyone loses their jobs, capitalism is over. Sure you can have an interim period of distress - where the world is transforming toward post-capitalism but isn't squarely there yet. But the final equilibrium intuitively feels more Star Trek (or Terminator, if you're a doomer), and much less Elysium or Ready Player One (few oligarchs, most population under poverty line). Correct me if I'm wrong. submitted by /u/mhb-11 [link] [留言]

Europe, Australia, and Asia can still get it while the getting's good.

In 1994, Peter Deutsch published the Fallacies of Distributed Computing — eight assumptions that every developer building distributed systems makes, discovers are wrong, and pays for in production. The network is reliable. Latency is zero. Bandwidth is infinite. Each assumption sounds true. Each leads to system failures that could have been avoided. Thirty years later, we're making the same category of mistakes with generative AI. The trough of disillusionment for AI-assisted development has begun. Byron Cook, VP and Distinguished Scientist at Amazon, founder of AWS's Automated Reasoning Group (300+ scientists, 15+ teams), says it plainly: "Generative AI is sliding into the trough of disillusionment." The headlines are shifting. The "summer of vibe coding" is over. The disillusionment isn't caused by AI being useless. AI-assisted coding delivers real productivity gains. The disillusionment is caused by false assumptions about WHERE the gains come from and WHAT changes when generation gets fast. Teams expected 10x engineering. They got 10x code generation and 1x everything else. The gap between expectation and reality is the trough. This series names the eight assumptions, explains why each one fails, and presents the resolution — not from theory, but from domains that hit the same wall and climbed out. The Eight Fallacies 1. Faster code generation means faster engineering. You made one sub-system 10x faster. Seven others didn't change. The system doesn't get faster — it breaks at the interfaces. The CPU-memory wall tells you exactly what happens and what fixes it. 2. If the output looks correct, it is correct. AI-generated code is optimized for plausibility, not correctness. It compiles, passes tests, and reads well — while violating properties nobody tested. Plausible is not correct. The gap is where production failures live. 3. You can verify AI output with another AI. Guardrails, LLM-as-judge, AI code review — the verifier has the same failure modes as the thing

Every week another "AI agent for X" launches. Email triage. Calendar coordination. Sales follow-up. PR reviewer. Slack monitor. Meeting summarizer. I've installed enough of them to see the pattern. Here's the dirty secret nobody mentions in the launch posts: These tools don't reduce your work. They multiply your notifications. Each AI tool is configured to be helpful by default. "Helpful" means: "I noticed this thing — here's a notification." Stack a dozen of those, and instead of one inbox to ignore you have twelve. The signal-to-noise ratio gets worse every time you add an AI to your workflow. The mainstream answer is "just configure each one." Sure. Spend four hours tuning notification settings every time you add a tool, and another four hours when one of them ships a "smarter notifications" update. That's not productivity. That's notification janitorial work disguised as setup. This is a structural problem. Not a configuration problem. The wrong question Every AI tool asks the same thing: "Is this important?" Wrong question. There is no objective "important." Importance depends on you, right now. A Stripe webhook is important when you're debugging a checkout flow. The same webhook is pure noise during a deep work block. A Slack message from your cofounder is critical at 11am Tuesday and irrelevant at 11pm Friday. The right question is: Is this urgent enough to interrupt me, right now, given what I'm doing? That's not a question any individual AI agent can answer. It's a layer above all your AI agents. None of them have the context. None of them know what the others are doing. None of them know how you're spending the next hour. So they all default to "I'll just send you a notification, you decide." Which is exactly the experience you have right now: drowning. What an AI firewall actually looks like I'm building that layer. It's called Klorn . Here's how it works in practice. Every signal — email, calendar invite, agent action, webhook, push from another tool — g

I use "what is my IP" sites maybe once a month. Every time I end up on something covered in ads, calling three different tracking APIs, and showing me results I don't fully understand. So I spent a weekend building whatsmy.fyi. The thing I didn't expect: you don't need an IP geolocation API at all if you're on Cloudflare Workers. Every request comes with a cf object that already has your city, country, ISP, TLS version, HTTP protocol, and RTT. Free. Zero latency. The part I enjoyed most was the WebRTC leak test. It checks whether your browser is exposing your real IP through RTCPeerConnection even when you're on a VPN. I ran it on my own setup. It was leaking. Zero logs. Zero storage. Just your data, shown to you. https://whatsmy.fyi

If you're interviewing at Amazon this year, you've probably read that you need to "prepare STAR stories." What most guides don't tell you is exactly how Amazon uses STAR differently from every other company — and what interviewers are silently scoring you against while you talk. Here's the complete 2026 breakdown: the cheat sheet, the full question bank, scored example answers, and the four mistakes that get candidates rejected even when their stories are genuinely impressive. Why Amazon STAR Is Different Amazon evaluates every behavioral answer against its 16 Leadership Principles. This isn't just culture marketing — interviewers are trained to map your stories to specific LPs and give them discrete scores. A Bar Raiser isn't just listening; they're running a rubric. The STAR formula at Amazon has specific time allocations that most candidates ignore: Situation (10%): Set the context in 20–30 seconds max Task (10%): What was specifically your responsibility Action (50%): What you did — not your team, not your manager Result (30%): Quantified outcomes only That weighting is the whole game. Most candidates spend 60% of their answer on Situation and Task, then rush through Action and Result — which is exactly backwards from what gets high scores. The "I" Rule: The Single Biggest Reason Candidates Fail Bar Raisers flag one thing more than any other: candidates who say "we" during the Action phase. Weak answer: "We decided to refactor the codebase, and we deployed a caching layer to fix the latency issue." Strong answer: "I identified the bottleneck using distributed tracing. I proposed the Redis caching layer to my tech lead and personally implemented the proof-of-concept over a weekend before bringing it to the team." Amazon hires individuals. If you can't cleanly separate your contribution from the group's work, interviewers have no signal on whether you were the driver or just along for the ride. Every sentence in your Action phase should start with "I." 30 Amazon S

Hi, What are the things that surprised you that AI cannot do? Would you please also mention what is your work, since i assume most of this thread are coders etc? Ill start here. I work in corporate finance. Doing tons of stuff left and right. AI cannot do finance or accounting..... almost at all. Hundreds of billions on the line, every CEO and their mother pushing AI and nothing major happened. Sure, if you are just a link in chain where you receive the same excel sheet and produce the same powerpoint you are replacable but there are very few people like that anymore left in finance corps. However, if you just receive accounting memo written by random people AI is useless, if you receive bunch of random files and have to come up with valuation AI is useles, if you need to migrate product to a new system AI is useless........... so on and so forth. Hope i dont start a war where everybody is gonna be mad at this. submitted by /u/Zoltan1251 [link] [留言]

The pale-blue Ojai vehicles will start picking up members of the public in California and Arizona today.

After several months of testing, Waymo is finally ready to invite non-employee passengers into its newest vehicle, the Zeekr RT minivan, which has been rebranded as Ojai. Waymo says it will begin offering "select riders" access in San Francisco, Los Angeles, and Phoenix, before "gradually" expanding to more riders and cities. Trips will be free […]

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A new crop of AI labs are focused on recursive self-improvement — but the goal is proving elusive.

Enterprise AI is entering a different phase now, one where enterprises are no longer evaluating whether AI is exciting. They are evaluating whether it is safe to deploy broadly.

Hey everyone, I just sent issue #34 of the AI Hacker Newsletter , a weekly roundup of the best AI links and the discussions around them. Here are some of title you can find in the issue: Using AI to write better code more slowly I think Anthropic and OpenAI have found product-market fit Can we have the day off? Google’s AI is being manipulated. The search giant is quietly fighting back Intuit to lay off over 3k employees to refocus on AI If you want to receive a weekly email with over 30 links like these, please join here: https://hackernewsai.com/ submitted by /u/alexeestec [link] [留言]

Spent the last few months building a deeper context layer over arxiv. Each paper gets a Tomesphere page with a TLDR + key findings (LLM-curated), OpenReview reviews where the venue is public, linked GitHub repos, HuggingFace models, conference videos, the citation graph in both directions, and a SPECTER2-based semantic neighbor graph. Same panel renders inline on arxiv via a Chrome extension (MV3 side panel API), or you can browse directly at tomesphere.com. 3M arxiv papers indexed. Caveats: reviewer scores only cover venues that publish openly on OpenReview (NeurIPS, ICLR, ICML, TMLR, COLM). Blind-review venues like CVPR, AAAI, ECCV are out of scope until contributors fill them in. GitHub, Hugging Face, and conference video matches are best-effort. Free, no signup. Site: tomesphere.com Chrome: chromewebstore.google.com/detail/tomesphere/nopoigoclhjcopjppnehidnkljmabllk Would love feedback, especially: which paper did you check first, and what's missing that you'd actually use? submitted by /u/RegretAgreeable4859 [link] [留言]

After overwhelming demand from founders around the world, TechCrunch has extended the Startup Battlefield 200 application deadline to June 8. Nominate a standout startup or apply yours today.

Savings of up to $410 on TechCrunch Disrupt 2026 tickets end tomorrow, May 29, 11:59 p.m. PT. Register now to save and join 10,000+ tech leaders on October 13-15 in San Francisco.

Today, I’m talking with Wassym Bensaid, the chief software officer at Rivian, and the co-CEO of Rivian’s platform joint venture with Volkswagen, which everyone just calls RV Tech. That joint venture kicked off about a year and a half ago with a nearly $6 billion investment from Volkswagen. It effectively puts Wassym in charge of […]

Primeira pergunta, quem vai pagar por essa porcaria? Cara, a parte mais inacreditável dessa história toda da Meta não é nem cobrarem assinatura. É cobrarem assinatura numa IA que ninguém genuinamente quer usar como principal. Tipo, vamos ser honestos: quem acorda e pensa “caralho deixa eu abrir o Meta AI pra resolver isso aqui”? Ninguém. O bagulho sempre teve vibe de feature enfiada no Instagram igual aquelas abas aleatórias que aparecem do nada depois de atualização. E mesmo assim os caras meteram: “agora o Thinking vai ser limitado 😃” “quer mais raciocínio? 20 dólares 😃” MAS QUEM TÁ PEDINDO ISSO IRMÃO??? Esse é o ponto que faz essa notícia parecer meme. Se pelo menos fosse: - uma IA absurda em código - monstruosa em escrita criativa - insana em vídeo - referência em imagem - ou um modelo amado pela comunidade Mas não. As imagens deles parecem IA de filtro do Facebook de 2023. Vídeo bugado. Interpretação de prompt toda torta. Código ninguém leva a sério. Escrita criativa então nem se fala. E aí os caras resolveram fazer o quê? Capar o reasoning de um modelo que já era nota de rodapé. É tipo um restaurante vazio começar a cobrar entrada VIP pra acessar o cardápio premium sendo que ninguém nem queria comer lá em primeiro lugar. E o mais bizarro é a lógica de público-alvo. Porque quem realmente usa raciocínio prolongado: - dev - pesquisador - power user - nerd de benchmark - gente que vive comparando modelo …essa galera já tá usando outras coisas faz tempo. Então o Meta AI não é forte o suficiente pra roubar os usuários hardcore, mas também não faz sentido pro casual pagar assinatura. Usuário casual do Instagram não vai precisar de “Thinking avançado”. A tia do WhatsApp não vai abrir cadeia de raciocínio de 8 mil tokens pra perguntar receita de bolo. O creator médio não vai abandonar GPT, Gemini ou ferramentas dedicadas pra gerar vídeo bugado no Meta AI. Então fica parecendo que os caras criaram um problema artificial pra vender solução artificial. E isso tudo vindo d