HarnessTax: How Much Does the Harness Matter for Coding Agents?
AI summary · glm-5.3-flash
HarnessTax is a research project measuring how much the harness, the scaffolding around LLMs, affects coding agent performance.
HarnessTax examines how much the harness — the scaffolding, prompts, and tooling wrapped around a large language model — contributes to coding agent results, as opposed to the underlying model itself. The project was posted on Hacker News on September 16, 2026, where it drew 42 points and 9 comments. Further details are available on the project's GitHub Pages site.
- Introduces HarnessTax, a study quantifying harness impact on coding agent outcomes
- Aimed at disentangling model capability from agent scaffolding effects
- Discussed on Hacker News with 42 points and 9 comments
OrganizationsHacker News
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42 points · 9 comments on Hacker News
The full text could not be extracted from this site (paywall, bot protection or heavy scripting). Read it at harnesstax.github.io.