A developer frustrated with coding agents' mechanical problem-solving — regex where judgment is needed, excessive self-asserting tests, mocking everything — distilled corrective principles into two markdown files and packaged them as llm-cure, an MIT-licensed npm CLI. The tool installs the principles across supported coding agents and offers an optional Agent Skill for Claude Code and Codex. It targets common LLM shortcuts and encourages wider, evidence-driven solution search.
A developer posted a 9-rule 'thinking discipline' instruction block that reduced wasted reasoning tokens in GLM 5.3 and GLM 5.3 Flash coding-agent sessions by up to 70% across 360 A/B runs. The rules target premise-checking, approach-switching, and re-verification loops. The author released the exam harness so others can replicate the test.
A developer posted a 9-rule 'thinking discipline' instruction block that reduced wasted reasoning tokens in GLM 5.3 and GLM 5.3 Flash coding-agent sessions by up to 70% across 360 A/B runs. The rules target premise-checking, approach-switching, and re-verification loops. The author released the exam harness so others can replicate the test.
A developer built a skill for Claude Code that forces structured diagnosis before code edits, addressing the agent's tendency to jump to speculative fixes. The skill is shared on Reddit for community use.
A developer built a skill for Claude Code that forces structured diagnosis before code edits, addressing the agent's tendency to jump to speculative fixes. The skill is shared on Reddit for community use.
A new empirical study analyzes bug reports from LLM agent systems like Codex and LangChain, revealing that many failures arise from interactions between model outputs and harness code rather than from either component alone. The research categorizes these 'agent-reactive bugs' and provides a taxonomy to help developers debug and design more robust agents.
A new empirical study analyzes bug reports from LLM agent systems like Codex and LangChain, revealing that many failures arise from interactions between model outputs and harness code rather than from either component alone. The research categorizes these 'agent-reactive bugs' and provides a taxonomy to help developers debug and design more robust agents.
A Reddit post offers a mechanical explanation for agent failure loops: because models are stateless and re-read the full session transcript each turn, repeated failed attempts become the dominant text pattern, biasing the model toward reproducing them. The author cites an agent that opened the same file eleven times while apologizing. The takeaway for practitioners is that prompt-level pleas like 'try a DIFFERENT approach' don't reliably break the loop.
A Reddit post offers a mechanical explanation for agent failure loops: because models are stateless and re-read the full session transcript each turn, repeated failed attempts become the dominant text pattern, biasing the model toward reproducing them. The author cites an agent that opened the same file eleven times while apologizing. The takeaway for practitioners is that prompt-level pleas like 'try a DIFFERENT approach' don't reliably break the loop.
A practitioner reports that logging failed AI outputs is more useful for prompt improvement than saving successful ones. Categorizing failures reveals recurring patterns, shifting the focus from optimizing prompts to addressing specific failure modes.
A practitioner reports that logging failed AI outputs is more useful for prompt improvement than saving successful ones. Categorizing failures reveals recurring patterns, shifting the focus from optimizing prompts to addressing specific failure modes.