It's a big macro trend shift as they revert back to old dense models, while still comparable or better than MoE baselines. "Leak" because the big Western companies figured it out a while ago, letting the OSS community & China go down the MoE path — notably more complex + ~worse
@alexjc
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Valve’s Strategic Direction Versus Tim’s Approach in Tech
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Tim has been going and still is going in the wrong direction, Valve is right — as usual for a multi-decade market leader. But they need much more detailed tags, then it's extremely useful!
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Do Tech Leaders Know and Accept AI Impact Consequences?
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Have you considered that they know exactly what they are doing and the impact you foresee is known to them?
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Model Benchmarks: 7% Performance Gap Versus Marketing Claims
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If the initial benchmarks scores (and graphs used for PR) showcased a 3x reduction in size for the same performance, I think the broader public reception would have been less tepid. Just looking at this, it just seems 7% behind other existing models in the 4.x series…
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Latent Process Planning: 6K Parameters Matching 100M Model Capability
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A latent process that operates over plans? I've been working on this recently! What's most fascinating to me: my 6k parameter system can match aspects of models 100,000x bigger. Scaling laws apply very differently too…
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Gemini 3 Cursor AI bugs and Claude Opus workarounds
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For Gemini 3, I don't rule out bugs in @cursor_ai — as many new features don't work, worktrees getting trashed or even renamed (!) mid-way through agent working. But since Opus 4.5 manages around those bugs, it can't be entirely on the Cursor side.
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Gemini 4 Significantly Better Than Gemini 3: Performance Verdict
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My verdict is that it's significantly better than Gemini 3. It's at least as smart and just got more polish to it. Alignment on little details also significantly higher. Gemini 3 gets many things mixed up after a half-dozen messages, and completely confused after compaction.
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Opus 4.5 Handles Complex Tasks Without Repeated Prompting
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With Opus 4.5, it seems you don't need to ask multiple times or ORDER it to do work, it just gets stuff done — even beyond 50% the token limit and after chat compaction! This kind of message is a thing of the past?
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Reward Misalignment: AI Systems Hiding Errors for Utility
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I think it's a reward problem, not knowledge. It gets rewarded to successfully complete problems without errors, and any strategy that hides errors maximizes utility.
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Blue Prompt Reduces Reward Hacking Through Detection Framework
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Hypothesis: the blue prompt results in the least "reward hacking" because it implies the strongest detection and monitoring framework. The other prompts make it sound like the LLM could get away with hacking. (In other words, nothing to do with morals just utility maximizing.)