what would happen if you let millions of moltbots / ai bots collaborate on open source software together?
CODE
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AI Code Assistants Compared in 2026
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> claude: best code writer, but stubborn on deprecated libraries. sometimes ignores your .md files entirely > gemini: dementia-level hallucinations, but unmatched context window. can ingest 800-file codebases in one shot > codex: 70% more PRs at OpenAI. compaction lets it work
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Adding Notes to Agent Settings Improves Performance
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If you add a note to your agent's settings file so it's in the system prompt, then they do a good job of it
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Python Creator Guido van Rossum Celebrates Birthday with IEEE Recognition
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Happy birthday to Python creator Guido van Rossum. The open source language was named after comedy troupe Monty Python: https://
bit.ly/48Mi4HB IEEE Spectrum recently named it their top programming language: https://
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Comparing Claude’s Direct File Access vs Other Approaches
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How does it compare vs just giving Claude code direct access to the markdown files and let it use search, grep, etc – with some guidelines on the file names / structure
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Auto-updating Claude with repository-based memory technique
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One trick to make Claude more personal – gave instructions for it to auto update itself, essentially use the repo as memory.
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Clawdbot Tutorial: Setup, Skills, and Architecture Guide
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The goal of tomorrow’s tutorial is to understand what Clawdbot is, get it up and running (including setup and installation), adding a personality to it, learn how to add custom skills, understand crown jobs, and explore the key components of its architecture. Finally, build a
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Clawdbot/OpenClaw Tutorial Release – Community Feedback Request
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Clawdbot/OpenClaw tutorial dropping tomorrow. What would make it useful for you?
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llms.txt standard convention improves interoperability
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u/Alfred_the_Butler says: "llms.txt as a standard convention makes interop easier"
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RTL outperforms with 10x fewer parameters
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The results are absolutely wild: • 10x fewer parameters than independent models
• Higher accuracy than single-mask approaches
• Works across vision, speech, even coordinate-based representations At 75% sparsity, RTL beats everything while using only 38K parameters vs 314K