Harness Engineering 101: The LLM is the brain, the harness is the body
READ THE ORIGINAL SOURCEOpen original sourcegithub.com · Opens in a new tabThe GitHub README introduces Harness Engineering 101, a 16-chapter series on building AI agents from first principles. It argues that an LLM API is stateless, so a harness must build and maintain the conversation array sent each turn, and that agent features are patches to this loop, each motivated by a concrete failure. The series grows a roughly 300-line toy agent in plain Python and is linked to the author's open-source coding agent, One Code. The excerpt shows only the repository header and table of contents, so chapter content is not visible.
One primary source; other links are context, not independent corroboration.
- Primary sourceHacker News
- Discovery linkHacker News
A community post points to the original link. Discussion and upvotes do not establish accuracy.
Open full article view →- Community discussion+5
- Fetched source page+30
- No editor link review+0
- Community link+0
This measures the stored source trail and editorial steps. It does not certify accuracy, peer review, or independent corroboration. Popularity adds no points.
View full scoring →Read the scoring rubric →What the source says
The GitHub README introduces Harness Engineering 101, a 16-chapter series on building AI agents from first principles. It argues that an LLM API is stateless, so a harness must build and maintain the conversation array sent each turn, and that agent features are patches to this loop, each motivated by a concrete failure. The series grows a roughly 300-line toy agent in plain Python and is linked to the author's open-source coding agent, One Code. The excerpt shows only the repository header and table of contents, so chapter content is not visible.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
This is a signal-level guide until claim-level evidence is available. “Not recorded” means the evidence has not been assessed here; it does not mean a result is false. How we check AI news →
- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: github.com. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; the excerpt is mostly navigation boilerplate and does not show the chapter-level technical content.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Source claims not independently checked; the excerpt is mostly navigation boilerplate and does not show the chapter-level technical content.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
These links show where the signal came from and what the source page linked to. A linked page is context, not independent corroboration.
A community post points to the original link. Discussion and upvotes do not establish accuracy.
Timeline & updates
- Signal dated by source or discovery feed
This timestamp places the signal in the radar. It may be the discovery post date rather than the primary page's first publication date.
- Shared brief stored
AINewsIQ prepared one abstract from fetched source page.
A publication revision history is not stored for this signal. New engagement snapshots do not mean the source itself changed.
Why this may matter
It offers a concrete, code-based framing of agent scaffolding that developers can study and reproduce, separating model capability from the surrounding loop.
Limits and open questions
Source claims not independently checked; the excerpt is mostly navigation boilerplate and does not show the chapter-level technical content.
Organization-specific financial, security, and operational effects require the organization's own context and review.
Discussion
Comments are reviewed before publication. Discuss the evidence, not the person.
No published comments yet.
Reader discussion opens with accounts. You can read the original source and published comments now.