Python

TL;DR AI agents fail when they don’t know what you know. I built a Slack bot that conducts structured 5-layer interviews to extract tacit knowledge — operating rhythms, decision criteria, dependencies, friction points, leverage opportunities — and generates soul.md, user.md, and heartbeat.md config files for provisioning agents. The interview surfaces ~30% more actionable context than documentation alone. Full source code below. The Problem Nobody’s Talking About Nate B. Jones has a video that nails the core issue with AI agents: they fail because they lack tacit knowledge. Not the stuff in your docs — the stuff in your head. The 20-year veteran who just knows that the staging deploy takes longer on Thursdays because the batch job runs. The designer who can feel when a color palette is wrong without being able to articulate why. ...

TL;DR PiKey is a Raspberry Pi project that spoofs a Logitech K380 Bluetooth keyboard and mouse. It jiggles the mouse to prevent idle detection and auto-types LLM-generated text to simulate human activity. The device appears as a standard Bluetooth HID peripheral – no drivers or software needed on the target machine. Three full implementations exist: Python (primary), Rust (static binary), and C (minimal dependencies). The whole thing was inspired by a Reddit thread on r/overemployed where someone asked for exactly this device. ...

TL;DR Cardboard is a TCG price tracker that monitors sealed product prices across 10 trading card games. It scrapes TCGPlayer and eBay using a three-tier strategy: pure API calls for bulk data, headless Selenium for product pages, and non-headless Selenium with a virtual display for sites that actively detect headless browsers. The scrapers run as Kubernetes Jobs on the same k3s cluster from Cluster Genesis. A Flask dashboard with Chart.js renders historical price data, profit/loss calculations, and portfolio tracking. All scraping is intentionally rate-limited to match normal human browsing patterns – the goal is polite data collection, not stress testing someone else’s infrastructure. ...

TL;DR The digital signage system was pulling weather from OpenWeatherMap, calendar events from Google Calendar, and device status from MQTT – three separate API keys, three separate failure modes. Home Assistant already had all of this data. I built an HA proxy service that exposes weather, forecasts, calendar events, temperature sensors, and arbitrary entity queries through a single Flask API backed by the Home Assistant REST API. Five new endpoints replaced three external dependencies. I also added API key authentication with role-based access control, wrote 37 tests, fixed MQTT addressing after a VLAN migration, and fought through 6 CI/CD fixes to get the pipeline deploying on self-hosted ARC runners. ...

TL;DR I built a complete swing trading research platform from an empty scaffold to 13,674 lines of Python in a single day. Five phases: data layer and backtesting, fundamentals and sentiment, portfolio construction, ML signals and Monte Carlo, then paper trading with a terminal dashboard. 199 tests across 48 test files. The platform fetches from Yahoo Finance, FRED, SEC EDGAR, and news APIs, runs technical and fundamental analysis, backtests strategies with walk-forward validation, and presents recommendations through a Rich terminal dashboard with human-in-the-loop approval. No cloud dependencies, no subscriptions, no vendor lock-in. ...

TL;DR A routine cluster health check surfaced seven simultaneous issues. Most were transient — Longhorn self-healed its replica fault, Prometheus recovered behind it, a stale manually-created Job was deleted in one command, and a liveness probe blip fixed itself. The real work was dnd-backend, which had been in CrashLoopBackOff and turned out to contain three separate bugs layered on top of each other. The AI identified all three during a single debugging session, authored the fixes across three PRs, and the service came up 1/1 Running with all 18 database tables created on the first boot after the final merge. ...