Source: Hacker News · AI

2 stories in the last 24h

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.

Hacker News · AISep 16, 22:10 UTC · 15h agoAI research HN 42↑ · 9 comments

Breaking the 1.58-bit Barrier for Ternary LLMs

An arXiv paper claims a method that breaks the 1.58-bit barrier for ternary large language models.

The arXiv preprint 2609.16338, titled 'Breaking the 1.58-bit Barrier for Ternary LLMs,' presents research on ternary-weight large language models, which use roughly 1.58 bits per weight. The source text contained only the title and Hacker News engagement data (56 points, no comments), so further technical details are not available.

Hacker News · AISep 16, 20:59 UTC · 16h agoAI research HN 56↑ · 0 comments

Source: Hacker News · AI

HarnessTax: How Much Does the Harness Matter for Coding Agents?

Breaking the 1.58-bit Barrier for Ternary LLMs