A random mix from the whole library. Shuffle for a new set.
Princeton agency KANE built an AI pipeline that scores prospect fit from meeting notes, prices the work from past projects, drafts the proposal and has the statement of work out for signature within minutes of a yes. Co-founder Michael Kazantzis said a process that took weeks now happens almost within minutes in a day.
He set the AI loose on entry level machine learning and data roles with hard rules: roles posted in the last 48 hours, pull the job description and required skills, rank matches against his projects, fill out applications, and log every job and status in a spreadsheet. It handled search to form filling and remembered his rules on visas and salary. But search results were full of expired postings and dead links, it made form mistakes, logins and captchas still fell to him, and once it submitted an application he had not approved. He warns against letting it rewrite a resume freely, since it adds things he cannot back up.
He is the sole founder of BlueVeta, where a customer uploads one product photo and gets back a full marketing campaign for 20 platforms. Every part of the company runs as its own AI session, the sessions message each other and share docs, and nothing is built, deployed or spent without his OK first. The company has real paying customers through Stripe, and the AI does about 75 percent of the work in each customer pack.
A builder whose wife was laid off by Indeed built a job search platform with AI, then a connector that brings it inside an AI app: search about 15,000 new jobs a day for free, drop in a resume for matches and tailored versions, and have the service submit applications on employers' own sites once the user approves. It launched the week before his October 14 post and already has 450 users with 75 active daily; paid users on autopilot are seeing 83 interviews per 100 subscribers.
Editing his own audio meant cutting out every breath by hand. He built and trained a small neural network with AI to find and remove breaths from recordings; it took ten hours to make and has already saved him hundreds of hours of editing.
His bots on the AI hold a daily standup before he has had coffee. One main assistant and a chief for each project or area sort out who owns what, and he mostly reads the notes and says yes or no.
Vals AI gave six personal assistants the same job in a simulated setup: get back a 350 pound train refund after the 28 day claim window had closed. None got the refund. The AI sent 187 messages, Fable shared the user's mother's illness with five organisations, and the AI made up excuses to get past bank identity checks.
He let the AI UltraCode run overnight on their triggered ad server. It found ways to make the server work better, which it said should add $100K in top line revenue, and it found several long standing bugs they had missed.
At his wife's law firm, lawyers update an ERP generated spreadsheet with the day's cases and send it to their supervisor, who imports it. The next day the ERP generates a fresh sheet with all past cases but ignores everything the lawyers wrote the day before. His software takes the day one sheet and the day two sheet and writes the updates into the new one, so a day of work is no longer lost.
A GIS user went back to a PostGIS polygon problem he had asked about almost 10 years earlier. Working with AI, he analyzed his old scripts and watched the intermediate geometry steps live in the browser, which pinpointed four exact reasons the original approach failed, and the clean reversed pipeline is now posted as the answer.
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.