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The DecoderBusiness

Anthropic's Opus 5 blows past Fable 5 and GPT-5.6 Sol on the benchmark designed to measure real intelligence

Anthropic's Claude Opus 5 scored 30.2 percent on ARC-AGI-3, nearly quadrupling GPT-5.6 Sol's previous record of 7.8 percent. The benchmark's developers say the model independently formulated reflection equations, a behavior they had never seen from another model, and attribute to stronger logical reasoning. The article Anthropic's Opus 5 blows past Fable 5 and GPT-5.6 Sol on the benchmark designed to measure real intelligence appeared first on The Decoder .

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Anthropic's Opus 5 blows past Fable 5 and GPT-5.6 Sol on the benchmark designed to measure real intelligence
BAIR BerkeleyResearch

Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

<!-- twitter --> <meta name="twitter:title" content="Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction" /> <meta name="twitter:card" content="summary_large_image" /> <meta name="twitter:image" content="https://bair.berkeley.edu/static/blog/abbel/cover.png" /> <!-- PREVIEW: <meta name="twitter:image" content="https://bairblog.github.io/assets/abbel/cover.png"> --> <meta name="keywords" content="Long Horizon, LLM training, Summarization, Efficient Context Representation, Conte

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The DecoderBusiness

Hundreds asked ChatGPT for poison and bioweapon recipes and some got step-by-step high school level guides

In summer 2025, OpenAI internally flagged GPT-5 as high-risk because it helped users create biological hazards, but downgraded the model's risk rating that fall. According to the Wall Street Journal, some users got step-by-step instructions for making poisons and biological weapons. Hundreds asked for that kind of information. The article Hundreds asked ChatGPT for poison and bioweapon recipes and some got step-by-step high school level guides appeared first on The Decoder .

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Hundreds asked ChatGPT for poison and bioweapon recipes and some got step-by-step high school level guides
The DecoderBusiness

US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns

The Trump administration is planning targeted bans on Chinese AI models rather than a blanket ban. After public pressure, OpenAI and Google DeepMind signed an open letter opposing regulation of open-weight models, yet OpenAI and Anthropic continue to lobby privately for those same restrictions amid security concerns and powerful business interests. The article US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns appeared first on Th

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US reportedly favors selective bans over blanket restrictions on Chinese open weight models citing security concerns
The DecoderBusiness

The AI coding tutor paradox grows as educators scramble to rethink how they test real skills

An ACM survey of 763 computer science educators from 49 countries shows that 68 percent have already changed their exams because of AI, shifting toward oral exams, proctored tests, and project-based work. Teaching is moving from writing code to understanding it. But nearly half of respondents say they lack proven examples for integrating AI into their courses. The article The AI coding tutor paradox grows as educators scramble to rethink how they test real skills appeared first on The Decoder .

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The AI coding tutor paradox grows as educators scramble to rethink how they test real skills
r/MachineLearningResearch

I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1v6w394/i_implemented_the_yolo26n_model_inference_from/"> <img src="https://preview.redd.it/wiyelkfpsifh1.jpeg?width=640&crop=smart&auto=webp&s=9ed353f6d1eab4c20efcaa110c0c5f642a6d6e99" alt="I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]" title="I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]" /> </a> </td><td

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I implemented the YOLO26n model inference from scratch using ARM64 Assembly Language (No framework) [P]
Hacker News LLMLLMs

Introduction to LLM Inference

Article URL: https://kraghavan.ca/llm-infrastructure/inference/2026/04/14/re-introduction-to-inference.html Comments URL: https://news.ycombinator.com/item?id=49054962 Points: 3 # Comments: 1

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Hacker News LLMLLMs

LLM-as-a-Judge Field Guide

Article URL: https://kraghavan.ca/llm-infrastructure/evaluation/2026/07/25/llm-as-a-judge-field-guide.html Comments URL: https://news.ycombinator.com/item?id=49054953 Points: 2 # Comments: 0

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r/MachineLearningResearch

Understanding GPU Inference Workloads [D]

<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>I have been looking into how people source compute for their Inference workloads (and in general). I wanted to understand some specific pain points here.</p> <p>If you've used online services like runpod or <a href="http://vast.ai/">vast.ai</a>, your perspective is extremely valuable. Please share your experience in the comments here or by DMing me. I've also made a 2 minute survey form that I would really appreciate if you could fill out. D

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Hacker News AILLMs

Collective Intelligence: The Next Frontier of AI

Article URL: https://github.com/ailinone/collective-intelligence Comments URL: https://news.ycombinator.com/item?id=49053465 Points: 7 # Comments: 2

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Hacker News AILLMs

Personal AI Assistant, Anywhere

Article URL: https://www.heysolin.com/ Comments URL: https://news.ycombinator.com/item?id=49053240 Points: 3 # Comments: 0

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Hacker News AILLMs

What is happening to jobs? Separating AI hype from reality

Article URL: https://siepr.stanford.edu/publications/policy-brief/what-really-happening-jobs-separating-ai-hype-reality Comments URL: https://news.ycombinator.com/item?id=49052570 Points: 66 # Comments: 76

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Hacker News AILLMs

The state of AI agents, in numbers

Article URL: https://www.getreadyforagents.com/statistics/ Comments URL: https://news.ycombinator.com/item?id=49052107 Points: 3 # Comments: 0

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Hacker News LLMLLMs

Becoming a Research Engineer at a Big LLM Lab

Article URL: https://www.maxmynter.com/pages/blog/jobhunt Comments URL: https://news.ycombinator.com/item?id=49051707 Points: 40 # Comments: 18

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The Guardian AIBusiness

Mega datacentre planned for outer Melbourne will be almost six times the size of Chadstone shopping centre

<p>More than 3,600 people sign petition for careful assessment of proposed AI hub, now a flashpoint for national debate on datacentre boom</p><ul><li><p>Get our <a href="https://www.theguardian.com/email-newsletters?CMP=cvau_sfl">breaking news email</a>, <a href="https://app.adjust.com/w4u7jx3">free app</a> or <a href="https://www.theguardian.com/australia-news/series/full-story?CMP=cvau_sfl">daily news podcast</a></p></li></ul><p>For some residents it started with a letter in the mailbox. It wa

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Mega datacentre planned for outer Melbourne will be almost six times the size of Chadstone shopping centre
OpenClaw Commits

fix(ui): respect agent-owned model fallbacks (#113812)

<pre style='white-space:pre-wrap;width:81ex'>fix(ui): respect agent-owned model fallbacks (#113812) Co-authored-by: Peter Steinberger <steipete@golden-gate.local></pre>

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Dev.to

Why we chose "structured assessment + AI analysis" over a chatbot for PotenAI

<p>When we started building PotenAI, the obvious move seemed like a chatbot — user talks to an AI, AI figures out their career fit through conversation. We actually prototyped this direction early on.</p> <p>We moved away from it. Here's why.</p> <p>Open-ended conversation is hard to keep focused, and it's even harder to turn into a consistent, comparable output. Two users answering the same underlying questions in a free-form chat can produce wildly different signal quality — one gives you thre

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Dev.to

From Policy to Pipeline: Making Compliance an Engineering Property

<p><em>Part 4 of "Trust the Machine" —> a series on building AI infrastructure that is secure, compliant, and governable by design.</em></p> <h2> The thread that ties the series together </h2> <p>The preceding posts addressed three engineering problems: seeing the AI systems in an environment, containing autonomous agents, and governing the data beneath them. This final post addresses the discipline that ties them together and, increasingly, compels them: regulatory compliance.</p> <p>Three fram

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