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15:48

Practical Guide to Setting Up a Local Coding Agent Stack with Open-Weight Models

手把手搭建本地编程智能体：Qwen3.6、Codex与Claude Code的实践指南

This is a step-by-step tutorial for building a fully local coding agent using open-weight LLMs (primarily Qwen3.6 35B-A3B) served via Ollama and the Qwen-Code harness. The author covers model selection, speed/memory benchmarking with a custom script, a small agent capability evaluation (5 tasks), and a security audit checklist before running any harness. It then compares the same local model across three harnesses—Qwen-Code, Codex (open-source), and Claude Code—finding that Codex achieves the same task success rate with roughly half the token usage of Claude Code. The guide also explains SSH tunneling to run the model on a dedicated machine (e.g., DGX Spark) while using the harness on the main workstation. Targeted at engineers comfortable with the CLI who want a transparent, inspectable, and free alternative to proprietary coding agents.

magazine.sebastianraschka.com · 45 min · Coding Agent · Local LLM · Ollama · Qwen · Token-Optimization
06:01

how to be good at research

研究者的可训练技能栈：从挑选问题到刻意犯错

A thread by @itsreallyvivek arguing that research skill is a stack of trainable sub-skills, not a gift. Core moves: pick problems you genuinely want to exist (Schulman), upgrade inputs by reading old papers and skipping summaries, write everything down to expose hidden gaps (Graham, Feynman, Darwin), tighten the experimental loop with scripted tooling (Karpathy), stare directly at failure cases instead of loss curves (Andrew Ng), deliberately wander across subfields to find your unfair advantage, and cultivate collaborators who will tell you an idea is bad. The post synthesizes concrete tactics from Hamming, Sutton, Shannon, and others, emphasizing falsifiable forecasts, reproducible tooling, and reading raw data over third-hand threads. Actionable for research engineers and PhD students tired of surface imitation.

x.com · 10 min · AI Engineering · Career Advice · Experiment Design · Research Methodology
06:01

Context Engineering for AI Agents: The Complete Playbook

AI Agent 上下文工程完全手册：为什么你的 Agent 在第 15 步开始变糟

This article systematically explains why context engineering is the most critical skill for building reliable AI agents. It argues that agent degradation usually stems from poor context window management rather than model limitations. The context window is likened to RAM, and as tool outputs, retrieval results, and conversation history accumulate, attention thins and the “Lost in the Middle” effect kicks in. Four core strategies are presented: Write (persist information outside context), Select (just-in-time retrieval), Compress (proactively reduce tokens), and Isolate (separate contexts for different jobs). The article details four failure modes—poisoning, distraction, confusion, and clash—and offers concrete evidence: Chroma benchmarks show continuous performance decline well before token limits, RAG‑MCP improved tool selection accuracy from 14% to 43% while halving token usage, and KV‑cache hit rates can yield a 10× cost reduction. A real-world workflow that shipped ~35,000 lines of Rust code in 7 hours using frequent intentional compaction is presented. The target audience is engineers building production‑grade agents.

x.com · 21 min · Agents · AI Engineering · Context Engineering · KV‑Cache · Tool Selection