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Google is building a chip with Gemini baked into the silicon (3 minute read)
Google is reportedly working on a project that would bake Gemini's neural-network architecture right into the circuitry of a chip. This would lock the model into the chip, increasing efficiency and decreasing overhead. The model can still be refreshed by loading new weights, but the underlying structure will stay fixed. While the project may not end up as a finished product, it shows how the custom AI silicon industry may be moving away from running any model to fusing one model with the metal.
Jun 12 | Blog
I Tried to Build a Context Layer for My Agent in a Weekend. Reader, I Did Not Build a Context Layer for My Agent in a Weekend.
A "simple" weekend project turns into real infrastructure, and why agent context deserves a boring, reliable foundation.
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Jul 21 | Product
This Is How To Live A Good Life (7 minute read)
A good life is built through repeated action, not insight alone. We become better by practicing virtue, pursuing meaningful goals, and investing in relationships.
Jul 20 | AI
How Netflix Built Its LLM Serving Stack (18 minute read)
Netflix details how it deployed and operated LLM inference within its existing production infrastructure. The article covers engine selection, model packaging, API design, deployment strategy, output constraints, and trade-offs that emerged under real workloads.
Jul 20 | Dev
A Practical Guide to Reducing Token Spend (10 minute read)
A "swamp workflow" is a method of optimizing AI agents by structuring complex tasks into deterministic code, reducing resource consumption while preserving functionality. Implementing a swamp workflow decreased token usage by 8 times and cut execution time in half for a code review task compared to traditional skills, which inefficiently used numerous tokens and runtime.
