Benchmarks are great, but IMO the behavior change is a much bigger deal. Plans before it edits, recovers from its own errors, and finds creative ways around obstacles instead of stalling. Feels much more like a senior engineer than 4.7, and better at long-horizon work.
MACHINE LEARNING
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Claude Opus 4.8 achieves 69.2% on SWE Bench Pro, beating Opus 4.7
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ANTHROPIC : Claude Opus 4.8 achieves 69.2% score on SWE Bench Pro against 64.3% for Opus 4.7. Benchmarks
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RL Inference Stack Development in C for GB300 Hardware
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Next will be writing the inference stack in C for simultaneous high-speed RL across a large block of GB300s. (We do use a little C++ tbh, but not much)
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AI Agents Need to Learn from Executions, Not Just Complexity
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Exacto. Ese es el problema que nadie estaba atacando. Todos haciendo agentes más complejos pero ninguno que realmente aprenda de sus propias ejecuciones
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AI Self-Improving Agent Outperforms Karpathy’s Autoresearcher
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Esa gráfica es clave. Se ve claramente el momento en el que el self-improving agent se despega del autoresearcher de Karpathy y ya no baja
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Perplexity Computer now helps prepare federal tax returns
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https://t.co/b774dXzDJ7 https://t.co/NeC9DfB4rI
— Aravind Srinivas (@AravSrinivas) 28 mai 2026Perplexity Computer can now help prepare your federal tax return. Select “Navigate my taxes” on Computer to give it a shot.
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AI Agents Need to Learn from Executions for Meaningful Functionality
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I've been waiting for something like this for a while. Agents that don't learn from their executions make no sense.
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Self Improving AI beats Karpathy’s autoresearcher by improving itself
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Self Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itself!
— Sumanth (@Sumanth_077) 28 mai 2026
SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task.
Most agent frameworks are static. Fixed harness, fixed model… https://t.co/bk4BaLqsCE pic.twitter.com/kiByM7J0KvSelf Improving AI (SIA) beats Karpathy's autoresearcher agent by improving itself! SIA is a Self Improving AI framework to autonomously improve the performance of any AI system (Model / Agent) on a benchmark task. Most agent frameworks are static. Fixed harness, fixed model
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MIT Deep Learning Course: Breakthroughs in AI Applications
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Free MIT course breaks down deep learning: https://t.co/S9DmjSlveB
— MIT CSAIL (@MIT_CSAIL) 28 mai 2026
Here, MIT ass't prof. Sara Beery discusses how it's driven breakthroughs in areas like image generation, coding, & playing games (Lecture 1). pic.twitter.com/qXuJiI3MamFree MIT course breaks down deep learning: https://
bit.ly/4t9gHJL Here, MIT ass't prof. Sara Beery discusses how it's driven breakthroughs in areas like image generation, coding, & playing games (Lecture 1). -
General-Purpose AI Agent Surpasses Karpathy’s Auto-Researcher by Self-Improvement
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A GENERAL-PURPOSE AGENT SURPASSED KARPATHY'S AUTORESEARCHER It wasn't designed for research. It simply improved itself until it achieved it The framework behind it is called SIA by Hexo Labs and it's open source → The agent updates its own logic
→ Modifies its own weights