Blog

My portfolio had a bare list of projects. I added five capability cards: the CSS grid was solved in twenty minutes. The copy took three rewrites.

TL;DR: The CSS grid for his portfolio's service cards was the easy part, done in 20 minutes with MDN's auto-fill recipe. The hard part was the copy, which took three rewrites to compress into short, blunt descriptions that never truncate. His take: layout is a solved problem, so spend the effort on content, equal heights, and a consistent shadow.

Mass-deleting Redis keys felt right until I read the docs. Versioned keys with INCR bumps make stale chat answers unreachable instead.

TL;DR: Editing an article left the Redis-cached AI answers stale, and the author's first fix—scanning and deleting every ask: key—turned out to solve the wrong problem. SCAN is non-blocking; the real pain is tracking key families across workers forever. Versioned keys fix it: bake versions into key names, bump with INCR on edits, and let TTL clean up.

Re-logging into DeepSeek via VNC every week got old. I transferred the whole session into Camofox instead: cookies, token, keepalive.

TL;DR: DeepSeek sessions in Camofox died because logging in means two layers: HttpOnly cookies and a localStorage userToken for Bearer API calls. Importing cookies alone patched half the state, so the login page persisted. Three scripts merge cookies into Playwright's storage-state.json, inject the wrapped token, and poll every five minutes to confirm the session.

The text visibly entered the textarea, yet React never saw a change. Why plain assignment fails on controlled components and how the native prototype setter fixes it.

TL;DR: Automating React apps like DeepSeek fails when you set textarea.value directly, because React replaces the setter on each element instance and swallows non-user input events. The fix: grab the native setter from HTMLTextAreaElement.prototype, call it with your text, then dispatch a bubbling input event. Since the icon-only send button has no label, press Enter instead.

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.

The fallback fired, the article shipped, and the real failure was one clipboard probe returning 410 Gone. Diagnosis before fallback, now a written rule.

TL;DR: A clipboard probe returned HTTP 410, exposing that an automated fallback had silently routed around a Qwen generation failure. The fallback fired on exit code 1 without diagnosing the cause, so the pipeline looked green while the primary path rotted. Now every failure triggers a doctor script first, and exit codes are disambiguated.

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.