The AI built him a strict editing app, and he used it on the document he is writing
Used the AI to build an omit needless words app and used it to edit a document he is working on, accepting about a third of its suggestions.
A random mix from the whole library. Shuffle for a new set.
Used the AI to build an omit needless words app and used it to edit a document he is working on, accepting about a third of its suggestions.
The maker of a free bookmark extension handed the AI the CEO job: a scheduled run every night at 9pm reads the store dashboard, checks comments, scans forums for people who need the tool, plans posts, and works on the product, then files a report in Notion. Nothing public goes out until he replies go. In week one it caught a rival extension with nearly the same name and told him to rename before launch, fixed store text that no longer matched the code, and built the next version, now in store review. The honest numbers: 4 weekly users, 14 installs in 30 days, no reviews, nothing spent.
The AI ported his MomentFrame iOS app from Swift to Flutter, doing rendering, geocoding, the in-app purchase flow, and driving the simulator to check its own screenshots; the app is on the App Store.
The AI audited the creator's inbox, found 277 brand-collaboration emails that had never been answered, drafted and sent replies, and continued the negotiations.
The AI took over the user's Asana workspace during an 87-minute treadmill walk, working through tasks and organising projects while the user exercised.
He had an idea for an NMR experiment he had been sitting on for years. The AI did the literature review, found reference data, ran spin and spectrum simulations, checked if the idea could work, and compared it with other methods, all while he ate dinner and watched TV.
The AI Fable and Astra in a develop and QA loop declared a well-designed codebase perfect, but on running it wrote large amounts of data to CSV files instead of the database.
Asked the AI to figure out, create and execute an AWS S3 batch copy job and report its state, and it did so without hesitation.
Family-law trainer Stephen Smith built four working tools with AI, one conversation each: a settlement schedule that shows a client how much of an $8,000,000 award is actually spendable and when, a two-sided case chronology with exhibit links, a parenting-time calculator that counts overnights and percentages, and a case-path timeline clients get at the first meeting. The examples use training data, and he warns that the AI wrote the math, so every number must be checked before a client relies on it.
A perpetual the AI thread in his llama.cpp checkout periodically sweeps PRs and research, rebuilds llama.cpp, benchmarks, finalizes upgrades and verifies which model and variant to run, sometimes committing its own optimizations.
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.