标签：#ci

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

Amazon is turning a corner with its launch providers, but ULA's Vulcan remains grounded.

Iron-rich immune cells in the liver may act as sensors for magnetic fields, serving as an internal compass.

submitted by /u/Rare-Grapefruit-3982 [link] [留言]

Anthropic just dropped Claude Opus 4.8 today, an incremental but meaningful upgrade over Opus 4.7. Here are the highlights: Model improvements Better performance across coding, agentic, reasoning, and knowledge work benchmarks Significantly improved honesty: the model is reportedly ~4x less likely to let flaws in its own code go unremarked compared to Opus 4.7 Alignment assessment shows lower rates of deceptive or misaligned behavior, on par with their Claude Mythos Preview model Scores 84% on Online-Mind2Web for computer use and browser agent tasks, ahead of both Opus 4.7 and GPT-5.5 New features launching alongside it Dynamic workflows (Claude Code): Claude can now spin up hundreds of parallel subagents in a single session to tackle large-scale problems like full codebase migrations. Available for Enterprise, Team, and Max plans. Effort control: Users on claude.ai can now choose how much compute effort Claude puts into a response, from faster/cheaper to deeper/slower. API update: The Messages API now accepts system entries inside the messages array, letting developers update instructions mid-task without breaking prompt cache. Pricing Same as Opus 4.7: $5/M input tokens, $25/M output tokens. Fast mode (2.5x speed) is now 3x cheaper than it was for previous models, at $10/$50 per million tokens. What's next Anthropic mentioned they are working on bringing Mythos-class models (currently in limited preview for cybersecurity use cases under Project Glasswing) to general availability in the coming weeks. Full details and system card: anthropic.com/news/claude-opus-4-8 submitted by /u/Direct-Attention8597 [link] [留言]

Here’s why Anthropic and OpenAI are on board with Illinois safety testing.

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]

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.

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

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]

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

submitted by /u/UFOsAreAGIs [link] [留言]

Trisha Ballakur discusses her journey from a backend software engineer to CTO and CEO, using her startup Pointz as a case study. She explains how to implement bottom-up customer discovery to find product-market fit, effectively delegate to global contractors to reduce build times, customize open-source repos like Valhalla, and apply engineering test-case models to business development. By Trisha Ballakur

OpenClaw the open source AI agent platform with 346K+ GitHub stars had four chainable CVEs disclosed on May 15. But that was just the latest chapter. The crisis started in january and it's worse than most people realize. The numbers 245,000 instances exposed to the public internet (Shodan + ZoomEye scans) 30,000+ actively compromised and used by attackers (Flare) 1,184 malicious marketplace skills across 12 publisher accounts (Antiy Labs) 12% of the entire ClawHub marketplace was compromised 4 chainable CVEs including a CVSS 9.6 sandbox write escape (Cyera Research) 9 CVEs disclosed in a 4-day window in March 50,000+ instances exploitable via one-click RCE (CVE-2026-25253) The Claw Chain (Cyera Research, May 15) Four CVEs that chain together into a complete kill chain CVE-2026-44113 (CVSS 7.7) - TOCTOU filesystem read escape. Race condition lets you swap paths with symlinks to read outside the sandbox CVE-2026-44115 (CVSS 8.8) - Credential disclosure. Gap between command validation and shell execution leaks API keys through unquoted heredocs CVE-2026-44118 (CVSS 7.8) - MCP loopback privilege escalation. Trusts client-controlled senderIsOwner flag without session validation CVE-2026-44112 (CVSS 9.6) - Filesystem write escape. Same TOCTOU race in write ops. Backdoor placement on the host The chain malicious plugin -> read escape + credential theft -> privilege escalation -> persistent backdoor. Every step mimics normal agent behavior. Traditional monitoring cannot distinguish this from legitimate operations. ClawHavoc supply chain attack (Jan-Feb 2026) First malicious skill appeared January 27 By February 5, 1,184 malicious packages identified Skills disguised as crypto bots and productivity tools Installed keyloggers on Windows, Atomic Stealer on macOS 76 distinct malicious payloads ClawHub had zero verification for skill publishers until March 26 - eight weeks after the attack started Timeline Jan 27 - First malicious skill on ClawHub Feb 1 - Koi Security names "Cla

I've spent 18 months building agent infrastructure and watched a lot of impressive demos. Here's the uncomfortable pattern: the demo works beautifully, the founder posts it, everyone claps and then it touches real users and quietly dies. Not because GPT-5 / Claude / whatever isn't smart enough. The model is almost never the problem anymore. It dies for three boring reasons nobody wants to talk about because they're not sexy: 1. AMNESIA. Your agent forgets everything the moment the process restarts. Crash, redeploy, pod cycle gone. So everyone hacks together a pickle file or a Postgres table, and it works until they have more than one agent and the memory needs to be shared. Then it's a mess. 2. SUICIDE BY LOOP. An agent has no idea it's in a loop. It will call the same tool with the same args 400 times and cheerfully burn $200 of tokens overnight, because it has no metacognition. It literally cannot detect its own failure. The defense has to live OUTSIDE the agent and almost nobody builds that. 3. NO BLACK BOX. The agent does something weird in front of a customer. They ask "why did it do that?" and you stare at logs that show inputs and outputs but no chain of reasoning. You have no answer. Trust evaporates. The whole industry is obsessed with the brain (the model and ignoring the nervous) system (memory , the immune system (loop detection), and the flight recorder (audit).) The unsexy truth: the next wave of agent winners won't have better prompts. They'll have better infrastructure. The model is commoditising. The reliability layer is where the actual moat is. I got annoyed enough about this that I built the layer myself persistent memory, automatic loop detection, and a tamper-evident audit trail, framework-agnostic (LangChain/CrewAI/AutoGen/OpenAI/MCP . It's at) octopodas.com if you want to tear it apart genuinely want feedback from people who've shipped agents and hit this wall. But honestly even if you never touch my thing: stop optimising the prompt and star

AI bias mirrors human bias; both stem from our language and lived experiences. Ethics and AI are inseparable, but AI changes affordances, making harmful actions easier to carry out. The EU regulations apply to AI, since digital products are products. The ultimate goal is accountability: companies must ensure transparency, and laws should favor using the simplest AI that gets the job done. By Ben Linders

New mothers working in software development are staring down an AI-pilled workplace they barely recognize.