ai-agent

A 70/0/0 acceptance run made me think my agent pipeline was deterministic. It was the runner that deserved the credit; temperature 0 was no guarantee.

TL;DR: A flawless 70/0/0 acceptance run looked like proof of a deterministic agent pipeline, but it really validated the harness, not the model. Temperature zero doesn't guarantee reproducibility either; batch invariance and LLM-as-judge studies show outputs and verdicts still flip. Trustworthy results come from test isolation plus byte-level diffs against golden files, never from an agent grading its own work.

The OpenHands CLI is officially deprecated in 2026. Meet Agent Canvas, learn the right way to use it, and see how it pairs with Hermes Agent.

TL;DR: OpenHands dropped its pip CLI and pivoted to Agent Canvas, a canvas-based platform installable via npm or a safer Docker sandbox scoped by PROJECTS_PATH. The SDK adds security risk levels, LiteLLM provider flexibility, and named LLM configs for cost tiering. Pair it with Hermes Agent as an orchestrator that verifies diffs before anything gets merged.

Told to search for "nano banana", an agent without query rewriting gets two worlds of meaning. Here are the 5 rewrite patterns I installed.

TL;DR: The author's fix for ambiguous agent searches: expand every query into five variants—literal, contextual, English, industry synonym, full question—before searching. All five run and merge, so "nano banana" resolves correctly as Google's image model rather than a fruit. The approach costs almost nothing—no extra API calls—though the author honestly admits no benchmark data exists yet.

The chat surfaced a valid new angle and one unverifiable statistic. Finding angles and proving claims are different jobs; only one of them belongs to the chat.

TL;DR: An AI chat surfaced a genuinely new research angle, but then invented a stat attributed to Stanford HAI that couldn't be verified anywhere. The lesson: finding angles and proving claims are separate jobs, so the pipeline now asks AI for angles, then opens every primary source manually. Citations offer traceability, not truth; walking the trail is your job.

My agent's memory only ever grew, so I built a weekly no-agent cron that turns seven days of messages into a ratify-it-myself candidate report.

TL;DR: The author audited their agent's memory files and found everything only accumulates, never gets pruned or corrected. ChatGPT and Letta solve this with out-of-band batch synthesis, so the author built a weekly Python script that generates a candidate report instead. They insist on manual ratification since a single fabricated entry could poison a single-agent setup.

Queries returned just a path and a score. So I stole OpenViking's L0 summary pattern: one line per dossier, no new infrastructure.

TL;DR: Search results in my knowledge base showed only scores, so I kept opening dossiers just to judge relevance. Rather than install OpenViking, I stole its L0 tier pattern and added a mandatory one-line summary field to each dossier in about 40 lines of Python. Making summaries a hard requirement keeps them consistent, no new dependency needed.

ChatGPT memory is not a real-time summary of your sessions. I break down the real mechanism: periodic synthesis, system prompt injection, and the privacy cost.

TL;DR: ChatGPT doesn't scan your chat history with RAG. Instead, it periodically synthesizes your conversations into a static profile injected into every new session's system prompt, including roughly your last 40 typed messages. Since deleting one chat won't erase derived insights, treat memory as a managed archive, not a perfect record.

A repo with many coding agents needs more than one instruction file: AGENTS.md for procedures, user memory for preferences, a fact store for on-demand facts.

TL;DR: Repeated agent mistakes in a repo pointed to missing documentation, not a bad agent. The fix is a three-layer setup: AGENTS.md for repo procedures, user memory for personal preferences, and a fact store for reference material. Claude Code reads the same rules through a CLAUDE.md that imports AGENTS.md, keeping every session consistent.

A plain-language glossary of AI agent terms from primary sources: agent vs workflow, skills, three kinds of memory, RAG, and pipelines.

TL;DR: Agents decide their own next steps while workflows run on fixed code paths, and the distinction comes down to who holds the steering wheel. Skills are procedural SOP files loaded only when needed, unlike memory, which carries procedural, semantic, or episodic information across sessions. Pipelines add triggers, gates, and watchdogs to make autonomous agents trustworthy.