exactement

ai making ai better recursively is an impressive idea but sometimes depressing as human researchers seem to become less and less significant in this process. i do believe that there is great potential in this direction while human supervision will become more principled and higher-level. then imagination and vision matters more than ever.

what do u think of gemma 4 12b?

Today, we’re releasing Continual Learning Bench 1.0: the first, realistic benchmark for measuring how AI systems can improve in online settings. Benchmarks today assume models are stateless. Each example is independent, and once a system finishes a task, it moves on as if nothing happened. But deployed AI systems should learn from experience. We tested 10 frontier systems against novel, expert-validated tasks and find there’s still plenty of headroom for learning. (1/n)

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i do like this passage, and here are some thoughts: 1. critical thinking is essential in the era of agents. i still remember that many years ago when i studied the lesson of critical thinking, i learned that keeping debating with yourself by listing out reasons can really deepen your thinking. today, critical thinking becomes humans debating with agents, so that they can think more deeply together and analyze problems in a more comprehensive way. 2. designing a healthy and well-structured organization and system is essential for creation and building. with systematic support and efficient tooling, humans can work exponentially more effectively together with agents. that gives people more time to take care of their physical and mental health, while also exploring new opportunities. 3. new era often favors newbies, because they have less past experience and therefore less fear of current difficulties. what oldbies should really think about is which parts of their experience are actually worth leveraging. from my perspective, we should think more carefully about which experiences are truly aligned with first principles. but anyway, ai first is super, super exciting!

we need agent evals that are really consistent with real world usages. otherwise people are optimizing foundation models for the wrong direction. the problem of targeting is even bigger than benchmaxxing.

x.com/MogicianTony/status/20… 🧵 1/ Our agent Terminator-1 scored ~100% on 8 major AI agent benchmarks, e.g., SWE-bench Verified & Pro, Terminal-Bench, beating Claude Mythos. It solved 0 tasks. Benchmarks are the field's shared language for measuring AI progress. Our new work shows that language is broken. Here’s how.

happy horse is insanely happy

unbelievable...

tokenmaxxing vs. ironmaxxing lol. it should be an era where results matter but it seems not.

mountain climbing is so funny

Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework. This change unlocks much faster performance to accelerate demanding work on macOS: - Personal assistants like OpenClaw - Coding agents like Claude Code, OpenCode, or Codex

model harness is now over model only. agent perf can be significantly influenced by the design and quality of harness. i do believe this is a right direction, nice work!

I just ran Qwen3.5 35B on my iPhone at 5.6 tok/sec. Fully on-device. 4bit | 256 experts. Model: 19.5GB. iPhone: 12GB RAM. wild.

nice work! better if oss...

by order of the peaky blinders