Hermes is an open-source AI agent that lives on my own machine, answers in Telegram, runs shell commands, drives a browser, and — most importantly — remembers. These are the projects and automations it operates for me: a household operations stack built in about three weeks of conversation.
Three properties make the difference between a chatbot and an operator.
It executes on my desktop with terminal, filesystem, and browser access. Real files, real commands, real deployments — nothing uploaded to a third-party workspace.
A Telegram DM is the control room. I text it from my phone the way I'd text a chief of staff; it replies with results, files, and voice notes.
Everything it learns becomes a written skill — a procedure with pitfalls and verified commands — or persistent memory. Each task makes the next one cheaper.
Full products, not demos. Each one started as a sentence in a chat window.
A recipe library with family ratings feeds a weekly meal-picker web app: pick dinners from real recipes we've actually cooked, get a merged, quantity-collapsed shopping list, and store the plan for next year's rotation. Recipes live as markdown, the picker deploys to a public URL in one command.
Ratings are one-to-three stars recorded per recipe after dinner, so the picker learns what the kids will actually eat — not what looks good on paper.
A private, one-listener podcast: every night it reads the long-form articles I've saved, writes a magazine-style spoken script, and generates an episode with an AI voice. A YouTube channel I follow gets ingested the same way. Served from my machine over a private VPN, straight into Apple Podcasts.
Source articles are kept verbatim for attribution; audio is transcoded to broadcast-safe MP3; the feed verifies with byte-range requests like a real CDN.
A custom home screen on an e-ink panel that redraws itself with fresh renders: tomorrow's weather with clothing advice, bitcoin price beside its 20/50/200-day moving averages, moon phase, family birthdays, and the school lunch menu.
HTML screens are designed, screenshot-verified, and pushed through the device's API on a schedule — the layout code is mine, checked pixel-by-pixel.
An interactive explainer for the US individual tax return: click any line and see the computation traced backward through schedules and source documents. Includes a cross-check engine that reconciles every stored figure, and a specification for a full rebuild written by the agent itself.
Working prototype with sample data; a detailed spec exists for the production version with a real calculation engine.
Scheduled jobs that fire without me. When one breaks, the agent diagnoses and fixes it from the run logs — usually before I've noticed.
Smaller builds and utilities, all live.
A print-ready magazine compiled from saved articles — layout, proofreading gates, and single-copy print ordering.
A local budget application with bank aggregation. Financial data never leaves the house.
Household federal and state tax estimators, with data flows kept strictly private.
An Obsidian knowledge base with a compiler and query layer for planning across sessions.
A photo-metadata CLI that extracts timestamps and GPS into a YAML feed for a family location map.
An offline-first children's operating environment — source-available, carefully licensed.
Declarative capture and restore of the whole desktop — used for a complete machine-to-machine migration.
A keyboard-driven terminal application for market data, styled and extended pair-programming style.
Web articles converted to files for a small e-ink reader, with typographic tuning per device.
Word, Excel, PowerPoint, and PDF workflows — reading, editing, filling, OCR — plus meeting notes turned into tracked action items.
Terminal email triage over IMAP/SMTP, and full GitHub workflows: issues, pull requests, code review, CI status.
Hue lighting control, direct-to-printer IPP printing, Linux troubleshooting, audio denoising, Bluetooth pairing, maps and routing.
The compounding layer: written procedures the agent loads before it works on something, so lessons are never paid for twice.
Each skill is a small operating manual — when to use it, exact commands, verified workflows, and a pitfalls section written from real failures. When a task goes wrong, the post-mortem becomes a skill patch. The library now covers everything from deploying web apps to transcoding audio for finicky podcast clients.
Personal skills are layered on top of a shared open-source baseline, and the same library runs across a terminal CLI, a desktop app, and this Telegram gateway — one brain, many surfaces.