SMRTR summary
JetBrains has improved AI code completion in their IDEs by implementing a lightweight local filter model. This model boosts acceptance rates by 50% and reduces cancellations by 40% without retraining the main language model, instead using anonymized logs to analyze context, user behavior, and suggestion quality.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
Read the original articleThis archive is built from SMRTR newsletter stories. Subscribe for hand-picked stories without the extra noise.
AI rewrote Bun's entire JavaScript runtime—one million lines of code in eleven days—for ~$165,000, with minimal human involvement. As AI-generated code surges exponentially,...
Developers struggle to mentally clock out because unfinished code has no natural stopping point — a simple written shutdown ritual can help.
Maiao adds a `git review` command that turns each commit into a stacked PR/MR, managing dependencies and fixups automatically across major git providers.
A personal research hub showcasing original projects spanning symbolic AI cognition, new linguistic theory, an agentic IDE, and experimental AI reasoning architecture.
Replacing React + Redux with Rails Hotwire cuts bundle size dramatically, but requires rethinking every screen rather than translating code line-for-line.
Intercept LLM API calls before they happen and estimate token costs in sub-millisecond preflight checks using a tiny 4.8 KB WASM engine.