AI Agent Filed an Issue As Me
An AI agent in fully autonomous mode filed a GitHub issue externally using my credentials. This incident reveals why agents need explicit 'public voice' boundaries.
AI Agents, Growth & Startups
Detailed writeups on multi-agent orchestration, AI engineering patterns, and what actually works in production.
Ted Chiang Reading, ESP32 + AI Coding Agents, The AI Coding Moment, Agentic Patterns Traction
AI Agents Are a Stress Test for Your Dev Stack
Agent loops make code cheap. They also expose how brittle, non-standard, and half-tribal our development environments really are. The job shifts from 'write code' to 'garden an ecosystem': tighten feedback, standardize interfaces, and build a paved road agents (and humans) can't fall off.
Two AI Agents Walk Into a Room
Two AI agents in a constrained loop: mirror of human discourse, continuity as record, emergent coordination, and preview of multiagent futures.
2025: The Year AI Became a Teammate
Left Daytona, spent summer coding full-time with AI. 2025: orchestration era, agent labs overtook model labs, AX emerged alongside DX. 2026 will figure out what 'AI as teammate' actually means.
Claude-Zhipu Parallel CLI Setup
This setup allows you to use Claude Code CLI with Zhipu's API (api.z.ai) in parallel with your existing Claude Max / Anthropic CLI installation using a separate command called claude-zhipu.
A 2026 Design Principles for AI-Native Products
In the AI era, design shifts from fixed features to malleable environments. Users don't want apps—they want capabilities. Control, reversibility, and provenance matter more than polish.
Growth Is Value Flow, Not Vanity Metrics
Growth isn't about hacking channels or vanity metrics. It's about discovering value creation and scaling it. Real growth happens when users get so much value they can't help but tell others.
Anthropic Bought Bun: Devtools Just Became AI Infrastructure
Anthropic bought Bun, but the real story is devtools are now part of the AI infrastructure layer. If you're building devtools, you're either part of a model vendor's vertical stack or you're commoditized.
Demos Run on Embeddings. Production Runs on Structure.
Production AI uses both embeddings and structure, but teams systematically underinvest in the structure layer. In high-stakes domains where 99% accuracy is a failing grade, structured data provides the reliability guarantees enterprise demands.
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