In case anyone wants to improve/change/use it:
@emollick
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Organizations continue adopting agentic AI for coding despite token concerns
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The hand-wringing over token usage is real, but I don’t see any organization that has adopted AI retreating from use in coding or even considering it. We are a few months into agentic coding, and companies are trying to experiment with approaches to balance adoption & efficiency
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Reconstructing Software Engineering for AI-Driven Coding
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Reconstructing software engineering around AI is going to take work (even as the ability of AI to code increases at a rapid rate). Organizations are ideally spending tokens for two things:
1) building stuff
2) experiments to figure out best practices (which involves failure) -
Claude roleplays an economist and self-evaluates a paper
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Claude really can roleplay an economist. I love this little comment Claude made after some robustness checks on the paper it wrote: "On a 1–10 identification scale, I'd now put the paper at about 4.5 — better than the 3.5 I'd have given before these tests, but well short of
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Visualization of all humans generated by Opus 4.8 in Claude Code
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How lucky are you to have been born when and where you are? Had Opus 4.8 in Claude Code whip up a new visualization of all humans who ever lived. In addition to being neat, it is an interesting test of combining research, code, design and stats for an AI. https://
veil-of-history.netlify.app -
GPT-5 Pro models leading single-shot performance
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Interesting that the GPT-5 Pro series models have consistently been the best models for single-shot attempts at the hardest problems since last summer. There has been no real competition in all that time.
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Shader test as a measure of AI coding capability
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Except a shader like this is a very good measure of model capability because of the technical difficulty of building this sort of code. It translates to other coding as well. Feel free to see my many other tweets and substack posts (& book) about AI applications in businesses.
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Opus 4.8 research workflow and GPT-5.5 feedback
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Opus 4.8 formulated the hypotheses in advance, conducting data cleaning, did research on references, conducted analyses, did robustness checks, and put out the whole paper in LaTEX style. GPT-5.5 found one issue with a hallucinated result, and had other constructive feedback.
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AI agents wrote and reviewed an academic paper
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I had Opus 4.8 in Claude Code write a sophisticated, if minor, academic paper from a archive of hundreds of de-identified research files from years ago I had to use GPT-5.5 Pro as a reviewer, it spotted one major error & some minor points. Opus corrected https://
embeddedness-gradient.netlify.app -
Paper: Differences Between AI and Human Narrative Styles
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There is a lot being written about the stylistic tells of AI writing (em-dashes, etc.) but this paper looks at AI narrative tells Fascinating differences between AI & human narrative, and asking AI to write in different styles doesn't do much to change it https://
arxiv.org/abs/2604.03136