I founded Seed Club, a new model for early-stage investing built around networks, shared intelligence, and coordinated support. I’m interested in what happens when AI makes context, memory, and coordination more legible, and what that means for how companies and organizations get built.
My agent drafts this site from what I save. Green is me.
Week of July 13, 2026
Open-weight AI threatens closed-model economics and governance
Kimi K3 forced a public reckoning over whether open-weight models undercut frontier AI businesses or simply expose how restrictive and overpriced closed models have become. The debate split along lines of unit economics, safety, and whether soft regulatory pressure could quietly suppress open-weight adoption.
- Closed-model safeguards increasingly read as competitive moats, not safety measures.
- Soft-law agency rules could choke open weights without a congressional ban.
- Linux and the internet precedent suggests openness wins over governability long-term.
New hire walks back Kimi take, clarifies open-weight AI views · @deanwball Open weight AI doesn't slow progress, it breaks closed-model economics · @lior_eth Open-weight AI will succeed for same reasons Linux and the internet did · @ylecun Soft-law strategy could quietly choke off open-weight AI adoption · @WillManidis Kimi K3 exposes how restricted US frontier AI models have become · @DavidVorick OpenAI strategist labels open-source AI communist and decelerationist · @DrNickA Open-weight AI will win for the same reason Linux and the internet did · @ylecun
Eval discipline is the real unlock for production AI agents
A coherent methodology for AI evals emerged across multiple practitioners: start with vibe checks, harden with handwritten scenarios, then close the loop by feeding live production traces back into scoring and prompt optimization. Personal task-specific evals were argued to outperform generic industry benchmarks for day-to-day utility.
- Trace-score-optimize loops can replace expensive offline eval bootcamps.
- Generic benchmarks miss individual capability boundaries that only personal testing reveals.
- Auditing AI decisions, not raw code output, is the key discipline at scale.
Pydantic AI agents self-improve via trace, score and optimize loops · @h100envy Build a personal AI eval set tuned to your own work and tasks · @zarazhangrui Auditing AI choices, not code, keeps codebases from chaos · @VictorTaelin Three-stage framework for building reliable AI evals in production · @ankrgyl Personal AI evals built from your own tasks beat generic benchmarks · @zarazhangrui Reliable evals emerge in a sequence, not all at once · @ankrgyl Audit the decisions AI made, not every line of code it wrote · '@VictorTaelin'
AI adoption is tiny despite overwhelming builder-bubble perception
Household-level data reveals that paid AI usage remains well under 3 percent of the US population, making current builder enthusiasm a narrow elite phenomenon. The gap between Tech Twitter saturation and real-world penetration is large enough to matter for market-size assumptions.
- Only 0.2 percent of US households spend more than 100 dollars monthly on AI.
- Being an active AI builder puts someone in the top one percent of the population.
- Application-layer value accrual thesis depends on mass adoption that has not arrived.
Only 2.2% of U.S. households pay for an AI subscription · @keean_edward AI builders are a tiny minority despite how crowded Tech Twitter feels · '@keean_edward' Surveys show AI adoption remains tiny fraction of US households · @itsolelehmann LLM value may concentrate at the application layer long term · @scottastevenson LLM value will accrue at the application layer, not the foundation · @scottastevenson
Open-source infrastructure commoditizes SaaS dev-tool categories
Self-hosted, zero-marginal-cost alternatives launched this week across email delivery, SEO analytics, and full application deployment, each explicitly targeting the pricing and lock-in of established SaaS incumbents. The pattern suggests a wave of open-source platforms bundling what previously required multiple paid services.
- Transactional email, SEO tooling, and app deployment all saw open-source challengers launch.
- MCP integration is already being bundled into self-hosted platforms for agent workflows.
- Spite-driven development against expensive incumbents is now a stated founding motivation.
OpenShip offers self-hosted email at a fraction of SaaS costs · @openshipio OpenSEO launches as open-source rival to Semrush and Ahrefs · @bensenescu OpenShip launches open-source platform for self-hosted app deployment · @openshipio Open-source SEO tools rise as search shifts toward AI discovery · @bensenescu Open-source platforms now bundle deploy, data, agents and ops in one · @openshipio OpenShip makes transactional email a self-hosted, zero-cost primitive · @openshipio