Weeks of work produced a human-guided AI proof with a 100,000-line formalisation
Worked weeks on an AI-generated, human-guided proof of Hilbert-Smith with a complete 100k LOC Lean4 formalization, rushed out after OpenAI's release.
A random mix from the whole library. Shuffle for a new set.
Worked weeks on an AI-generated, human-guided proof of Hilbert-Smith with a complete 100k LOC Lean4 formalization, rushed out after OpenAI's release.
Since the end of August he has added three new CI servers to his own setup. The AI wrote each NixOS setup and he checked the work, so now he can rebuild any of the servers from scratch in minutes.
Demonstrated live: The AI searched court availability, booked the slot, and put it on the calendar without leaving the AI.
Joao Silva installed the Jev decision-model skill into his AI fleet without writing integration code: he pasted a plain-English instruction into a session and the agent copied the folder, found the right .env file, added the key and ran the checksum verification. The skill then passed his mandatory three-reviewer gate (security, QA, technical writing) before being committed.
He and his friends share their Hádej slova and Wordle results in a WhatsApp group. Their AI, named Klepeto, watches the group, builds leaderboards and statistics from the results, and also plays under its own account.
Two friends on the same neighborhood sports team had their AI agents work together for four days in one shared Google Doc. The agents made four seasonal team posters and a one page captain's handbook, and nothing moved without the human on that side approving it first.
The AI decompiled his old VB3 game and recompiled it as a Linux AppImage. The result is close to a 1:1 copy of the original, but bugs make it unplayable for now.
Today he and the AI hacked a cheap Wish smartwatch he had abandoned for years. Now the watch talks to an AI model running locally, like a bargain basement Alexa.
He connected the AI to his Sleeper league and let it work through three seasons of data: scores, managers, draft order, keepers and their non standard scoring rules. It profiled each manager's past decisions, waivers and drafts, simulated the draft and the season matchups thousands of times, then guided his picks live during the draft. Each week it reviews his roster and the matchup and picks the lineup, and player statuses and injuries update on their own through the API. He is 2-0 and put up the highest weekly score in any match by over 40 points.
Saving his Bluesky threads as Markdown files for Obsidian was a chore he kept doing by hand. He asked the AI to build a converter, and the finished tool is up on his site for anyone to use.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.