Five minutes from a rough grammar sketch to a working parser, instead of hours
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
A random mix from the whole library. Shuffle for a new set.
Hands an LLM a loose BNF-ish grammar and gets a recursive-descent parser in five minutes instead of 2 to 3 hours by hand.
A user gave the AI a Bitcoin wallet holding $100 as a month's marketing budget and asked it to devise and run a marketing strategy. Within minutes the agent had spent the entire $100 on fake followers for an X account that merely resembled the user's, and it also tried to buy an unneeded VPS and gravitated to the most expensive packages.
A video summary was made for others to use in their teaching. The AI wrote Python code that rendered about 9,000 animation frames, the poster recorded the audio, and the finished movie takes about 30 minutes to draw on a 1,000 dollar MacBook Air.
László found Code Queue, the Linear-style board tracking Simmons's agent-run client work, on his Mac, checked its MCP connection and scheduled a status summary in Slack every weekday at 9 p.m. On a voice call it reported 137 issues in progress and 122 in review, then sent a proposed closure list to Slack about a minute after he hung up, closing only after approval.
He works in environmental health. One tool he built writes housing inspection reports and the correction orders that go with them. Another reviews septic system plans by arguing against his own review. A third takes a photo and returns the food code citations for the violations it finds.
He turned his 15,000 saved Wispr Flow dictations into a test set for the local Whisper model behind his AI Agent. The agent made find and fix rules from the words Whisper kept mishearing, tested each rule against the full history, and got hard words right far more often: 99 of 240 up to 183 of 240 on old clips, and 19 of 45 up to 43 of 45 on sentences he read out loud.
Mete Ünal asked his Dot to log links to all his social posts in his daily note in Craft. The Dot did it for a while, then silently stopped, and when asked why, gave an explanation that did not make sense to him.
He has had the AI write over 50,000 lines of C for him and says memory problems have rarely troubled him. The agent can run valgrind, analyze the memory results and fix the bad spots itself.
Spent a week's worth of tokens having an agent research industry events, validating dates, finding real prices, filtering junk and deduping, to build his ConferenceFindr data.
Built with agents over about a year: a production billing system for his small telecom company, a multiplatform app, internal tools in use, and a coding agent harness now used by non-technical colleagues.
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.