Token Efficiency: Where the Tokens GoApril 30, 2026 Karpathy's Wiki, OpenBrain, and Picking a Context Layer for AI Two memory models for AI work. One synthesises on write, one on query. Which to pick depends on whether you're a solo researcher or running multi-agent.
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ilo: A Programming Language for AI Agents, Not HumansApril 29, 2026 OpenUISpec and the Case for Telling LLMs What's On the Page A spec for AI-readable component libraries. The same idea would cut the cost of scraping the web in half, if anyone published it.
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ilo: A Programming Language for AI Agents, Not HumansApril 9, 2026 Six Ways to Give an AI Agent a Map of Your Code Prowl, CodeBoarding, Memgraph GraphRAG, CodeGraphContext, SymDex, and jCodeMunch all solve the same problem differently. So does ilo graph. A comparison.
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Token Efficiency: Where the Tokens GoMarch 30, 2026 jCodeMunch: Use 80% Less Context jCodeMunch-MCP uses tree-sitter AST parsing to build a symbol index of your codebase, letting AI agents pull individual functions instead of whole files. The token savings are significant.
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ilo: A Programming Language for AI Agents, Not HumansMarch 12, 2026 --explain: Reading a Language You Don't Know ilo's annotation tool shows the structural role of every statement. Built for agents, useful for humans too.
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