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A multi-agent DSPy program is really several prompts in a trench coat.
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I tried seven ways to make a DSPy program faster and cheaper: three adapters, TOON, a custom adapter I almost wrote, JSON Patch, and finally diffs.
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Natural language will make AI accessible.
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How I got Codex, FlowDeck, XCUITest, Loopback, and a local backend to test KIN's voice calendar path end to end.
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The least painful setup I found: Chrome owns the auth window, Supabase owns OAuth and PKCE, and a tiny relay page sends the result back.
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Why I think DSPy programs need a managed optimization loop, not scattered experiments.
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Introversion and diminished social skills can look identical from the outside, but they are not the same thing.
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Drawing parallels between Feature Engineering in traditional ML and Data Curation in AI Engineering.
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Content Authenticity is missing the catalyst that made HTTPS Universal.
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Introducing two Nouns that make working with DSPy clearer.
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Getting granular token usage from nested DSPy modules
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I failed at GEPA, then learned a few things.
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Streaming Named Chunks from Parallel DSPy Modules
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Real-Time Tool Call Updates in DSPy with Status Streaming
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My thinking behind this Blog Site.
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Type-Safe Tool Contracts for Multi-Module DSPy Programs