A random mix from the whole library. Shuffle for a new set.
An engineer at Sumasate writes that his team connected the AI Tag to GitHub, Sentry, their database, log tools and Notion, and let it do the first look at every alert in Slack. After three months, fixes finished inside alert handling went from about 2 of 38 alerts a month (5.3 percent) to about 7 of 44 (15.9 percent).
Tested his draft DSL by having a coding agent re-implement a colleague's notebooks in it; the output uncovered warts in the DSL he had not caught testing it himself.
His business scans every invoice into a Google Drive folder. The AI reconciles them there: checking they were billed correctly, that credits for returned goods actually arrived, and that there is no slippage. He says it is far faster than reconciling by hand.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
He set GPT-6 to read tens of thousands of his emails, his writings and his calendar appointments from his recent career. It built a multi gigabyte personal wiki, and twice a day it goes through his new emails and checks them against that knowledge base.
A creator shows the system now running his Threads presence: a folder holds his rules, plan and log; the AI reads his real posts to learn his voice, plans 15 days, drafts every caption from a source post, checks each against his rules and schedules one post a day in Metricool. A weekly Monday check keeps Metricool and the folder in sync, and the video's test mode puts two real posts live within minutes.
Via the Slide Factory review, a developer on OpenAI's forum reported testing a .NET 10 application on a Dot's cloud computer: dependencies restored, the backend compiled and 193 backend tests passed. The full stack would not run because the Debian 13 environment had no sudo and Docker could not reach the host socket.
He used AI, ElevenLabs, and D-ID to make a budget simulation for his Politics of Public Policy course. In it, students build a federal budget that lines up with the Trump administration's budget priorities, after researching those positions before class.
His team is replacing a client's 15 year old system. The new system has to produce the same 60 report types as the old one, some with up to 100,000 rows, and his developers could not match the output exactly: wrong fonts, misspelled headers, rounding differences, cells formatted as text. He gave Fable 5.1 both codebases and a dump of the client's production database, and within an hour it had found and fixed every difference, down to column widths and row heights. Matching the reports by hand would have taken weeks.
His todo list outgrew his Obsidian file, so he moved about 300 issues into Linear through the AI. He then refined the roadmap by talking with AI while driving.
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.