The AI found a serious security hole in a state legislature website while archiving it
She was using AI to archive legislative material when it flagged a serious vulnerability in a state legislature's website. She had to stop and report it.
348 new ideas this week
Level up how you use AI
Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
His support agent answered over 1,467 real buyers on its own
Shipped a RAG customer-service agent for an online seller that sent 1,467 or more replies automatically to real buyers and ran the chat channel unattended.
The AI added meaning-based search to his personal archive search, in daily use
The AI vibe-coded a RAG and semantic search augmentation to his full-text SQLite search, now in use on a corpus of under 2 million rows.
My agent built my budget tracker and updates it with my balances
Commenting on the 26 use cases post, she says she gave the AI access to her budget tracker sheets. It updates the tracker with her balances, and she set up alerts for when a category goes over plan. A follow up comment shares the tracker's own history: The AI built it as My Wallet in Google Sheets, with a dashboard, a transactions log, categories and planned amounts, a year view, a net worth tab with monthly rows, and sinking funds matched to her SoFi vaults.
57 billion tokens spent rewriting a small tool, and nothing usable came out
57B tokens burned on an AI jq rewrite and nothing usable came out.
This one went wrong. See what happened before you try it.
An LLM read a pencil sketch, built antenna models, and ran the simulation
A ham radio operator had a frontier LLM read his pencil sketch of an antenna, build NEC models from it, test and iterate on numerical stability until the approximation held together, and then run a real simulation that answered his questions. He uses the same approach for SPICE circuit simulation.
A sandboxed agent checks daily whether the product spec and the code still agree
Sandboxed agent checks spec and code drift daily, triages findings, and surfaces issues; it revealed product miscommunications on the team.
An agent migrated his whole setup to a declarative system, backups included
Migrated a fairly complex setup to NixOS 1 to 1 with an LLM agent and gained declarative services, backups and redeploy tooling.
The AI cleaned up my exported bookkeeping in 30 minutes
He exported all his bookkeeping to text and let the AI take care of it. Thirty minutes later it was in better shape than ever.
Agents OCR'd years of his journals into searchable text, checked by multiple models
AI sessions and local LLMs OCR'd years of journals with multi-model averaging and a sanity check, producing free-text searchable journals plus photo backups, settled at 80 percent accuracy.
This only partly worked. Worth knowing before you try it.
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.