Dev eyes fast SLMs for terminal autocomplete

The developer's latency and quality issues likely stem from their naive context ingestion strategy rather than the models themselves. Pushing a full markdown file on every keystroke is an anti-pattern for real-time autocomplete. Re-evaluating the full CONTEXT.md file on every keystroke causes massive prompt processing overhead, destroying real-time latency. Implementing KV cache reuse or utilizing Fill-in-the-Middle (FIM) tokens would drastically improve completion speed and coherence. Qwen 2.5 Coder (1.5B) remains the state-of-the-art SLM for this exact use case, outperforming Llama 3.2 1B on syntax generation. The project highlights the broader trend of developers hacking together local, AI-powered CLI tools to bypass cloud latency and privacy concerns.