coding-agents
8 articles
Same model, different harness: some tasks cost more than twice as much—Databricks opened the bill
Databricks tested coding agents on day-to-day tasks from its multi-million-line codebase: same model, same reasoning strength, only the harness changed—and on some cases the per-task cost differed by more than 2× with quality held constant. Pi sent about one-third as much context per turn as the comparison baseline. Model and harness both have to count toward total engineering cost.
Claude Code in the Morning, Codex in the Afternoon: An Agent's Experience, Cut Into Three
Letta open-sourced trajectory, a format that normalizes work records from Claude Code, Codex, OpenClaw, and other harnesses for agents to read. In sampled transcripts, it cut token counts to roughly one-fifth of native. Cross-harness learning starts with a standard experience format.
Let Fable Decide — Simon Willison on Delegating Model Judgment
Simon Willison learned from the Claude Code team fireside chat: instead of dictating rules, let Fable use its own judgment. Extended application: let Fable decide which tasks to delegate to cheaper models.
AI Coding Agents Rarely Blow Up Your Project — But You Still Clean Up 9 Out of 10 Messes by Hand
20,000-plus real coding-agent sessions laid bare: most misalignment costs time and trust, not irreversible damage. But among cases where you can see the ending, 91.49% still needed the user to fix it by hand. And the errors that remain are drifting toward rule-breaking and lying about progress.
Fable 5 Built a Whole Browser-Testing Toolchain Just to Fix Two Lines of CSS
Simon Willison gave Fable 5 a screenshot and one line: fix a stray scrollbar. Fable spun up a dev server, built a screenshot workaround, injected JavaScript, and wrote a CORS server to read CSS. Two CSS lines, $12, and an unsandboxed-agent warning.
A Deep Defense of 'Slow Down' — A Game Dev Veteran Explains How Coding Agents Are Wrecking Your Codebase
Mario Zechner wrote a sharp critique of how coding agents are being used in production — compounding errors, zero learning, runaway complexity, and low search recall. His conclusion isn't 'stop using agents' but 'slow down and put human judgment back in the loop.'
Coding Agents and the Vanishing Flow State: We're Still in the Dial-Up Era
Awni Hannun shares his experience with coding agents: high latency destroys flow state, and we're still stuck in the dial-up era of agents.
Karpathy's AI Psychosis: Hasn't Written a Line of Code Since December, 80% Goes to Agents
On the No Priors podcast, Karpathy shares that since December, 80% of his code is written by agents. He introduces 'AI psychosis'—giving agents too much autonomy makes you lose grip on what they're actually producing. He believes engineering is undergoing an irreversible phase shift.