Evidence-based AI market signals, coding demand, and practical growth cases for builders.
A build debrief on swapping session-history compaction for a queryable context store: live SQLite records the agent revises via transactions, retired items pushed to a vector store.
A dev wished he could see which tool his model was calling during long waits. Here's a reproducible Python + hosted HTML board that streams thinking vs tool state.
Caijing's Aug 2024 test showed domestic subscriptions burning quotas in days. Build a Python monitor that logs cache rates and per-task burn before choosing a harness.
A reproducible approach to browser automation: parse user intent, execute Playwright commands, and write every DOM action to SQLite for audit and replay.
Karpathy's agentic engineering thesis applied to a concrete problem: automated quality gates that flag one-off logic, abandoned patterns, and batch inconsistencies.
Meta says Muse Spark 1.3 cuts tool calls ~20% and tokens ~25% (vendor figures, unverified). Here's how I turned that promise into something I could actually measure on VicroCode.