Thoughts on AI, engineering, and building products
Most non-technical AI marketing workflows fail not because the tools are weak, but because they skip the operating model. Here's the stack I'd actually hand a marketing team in 2026, plus the last-frame stitching trick that turns Seedance 2.0 from a 15-second toy into something usable for real campaigns.
Lambda Durable Functions absorbed the core value proposition of every durable execution platform. But Inngest's flow control primitives and developer experience remain genuinely superior for certain architectures. A systems-level comparison for teams choosing in 2026.
Anthropic just built the most powerful AI model in the world. Then they decided you can't use it. Notes on the Claude Mythos system card, Project Glasswing, and what it means when offense structurally outpaces defense.
Google went full Apache 2.0 with Gemma 4, Alibaba closed its flagship model behind an API, a startup got GPU futures on Bloomberg Terminal, and OpenAI bought a media company. All on the same Wednesday.
Every frontier AI model scored below 1% on ARC-AGI-3. Humans scored 100%. The new benchmark abandons pattern-matching grids for interactive video game environments, exposing a fundamental gap between memorization and genuine intelligence.
Large language models waste enormous computational depth reconstructing facts they already know. DeepSeek's Engram module fixes this by adding a fast-lookup memory system alongside neural computation, yielding surprising gains not just in knowledge retrieval but in reasoning, coding, and math.
Most copy-trading tools are glorified Telegram bots with no risk controls and zero transparency. So I built CopyAlpha, a full-stack platform that processes signals, enforces risk rules, and executes across CEX and DEX.
How SK Hynix went from near-bankruptcy after a devastating factory fire to controlling the AI supply chain through a decade-long bet on High Bandwidth Memory (HBM) that everyone else thought was insane.
An analysis of Meta's VL-JEPA paper and Yann LeCun's vision for non-generative AI models that predict semantic embeddings instead of tokens.
Why relying too much on LLMs weakens your critical thinking - and how to use AI as a partner rather than a replacement.
My takeaways from Tesla AI Day - from neural networks predicting in vector space to the Dojo supercomputer and the Tesla Bot.
