GLM Built Its Own Inference Infrastructure
GLM developed its own inference infrastructure to support large language model deployment. This custom system enhances the efficiency and scalability of GLM's AI models. (z.ai)
HarnessTax: How Much Does the Harness Matter for Coding Agents?
harnesstax.github.io144 points55 comments
Commenters debated whether the “harness tax” - extra tokens, complexity, and cost from heavy agent/harness system prompts - meaningfully matters for coding agents. Several argued harnesses are important but differences are overstated: corv said a thin, well-understood wrapper works fine, mikert89 noted smarter models need less prompting, and roywiggins and others reported Pi feels snappier than OpenCode or Claude Code. x312 and others criticized Claude Code/Codex as bloated and costly, while jswelker pushed back that much of that bulk is deliberate security and alignment overhead, and calling it merely a “tax” ignores those tradeoffs.
Opinion split on where gains actually come from. Supermancho argued single-harness comparisons are shallow and emphasized execution patterns (concurrent/subagent use) and model delegation as major levers. joshheitzman reported that provider middleware can change outcomes between harnesses, and spike021 asked whether proxying via OpenRouter preserves provider caching and other benefits. On safety, roywiggins said lightweight sandboxing can be nearly token-free, while seanmcdirmid warned prompt-based sandboxing is unsafe and tool-hook sandboxing is preferable. Overall, views diverged on whether to prioritize minimal prompts for speed and cost, or larger harnesses for reliability and alignment, with infrastructure and execution strategy seen as crucial modifiers.
Summary generated by AI from the linked article. hn.today is not affiliated with Hacker News or Y Combinator.
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