A local model fuzzes his text editor like a person, then files the bug tickets itself
LLM made human-like fuzzing sessions for his text editor; local Qwen babysits, investigates, writes repro and files ticket; bugs found after 1-2h runs.
A random mix from the whole library. Shuffle for a new set.
LLM made human-like fuzzing sessions for his text editor; local Qwen babysits, investigates, writes repro and files ticket; bugs found after 1-2h runs.
A services co-op connected the AI to its Salesforce. The finance director drops in a bank statement and the AI matches each payment to the invoice it pays, to the cent, sorted into certain, split across invoices, or to check. Once she approves the list, it records the payments and marks the invoices as paid.
Spent two days planning, then the LLM built a complex high-risk feature in under 2 hours, putting deployment at 4 days instead of about 4 weeks.
An LLM came up with a patch against PHP master for the vulnerability, and it at least stops the PoC.
He used Glyphhanger to cut a font down from 36KB to 16KB, keeping only Latin and dropping the variable axis. Then he let the AI try, and it redrew the glyphs and got the font down to 6KB.
His agent researched a prospect and sent the email. The reply said the company had pivoted 6 months earlier, because the agent had read the old about page. The research saved him time, but the stale facts cost him the reply.
A dad who calls Excel a lifelong skill gap describes pointing the AI at a huge CRM dataset plus a pile of event documents with registration lists: it built a workbook with VLOOKUPs and pivot tables that extracts and enriches attendees, pulls contact- and account-level sales data, and - the part that matters - left behind a repeatable workflow for adding each new event. Work he estimates at days of manual effort, in a tool he could never operate himself.
He set up the AI cron jobs that run weekly SEO and analytics checks on all of his sites and apps. He says the checks are working well and keep him consistent.
Set the AI the task of running Parseable locally against another of his apps; it worked around a protobuf limitation by running a proxy, he switched his stack to JSON and got it running.
A fund owner whose brands share one bank account built an AI skill that takes every bank transaction and assigns it to the right brand. Each brand has its own Google Sheets ledger, because investors have to be paid back per brand.
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.