The AI found why we were bouncing our own active SMS clickers
His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
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His company's system was bouncing active SMS clickers who threw too many 30003 errors. The AI Ultracode found the cause, and the fix means the sunsetting code now ignores active clickers.
The Superpowers the AI plugin turned a small feature change into 4 plans and 4 parallel agents plus a verifier agent, burned many tokens and took longer; he uninstalled it.
The company asked the AI for a video showing how easy it is to program with their API and gave it four instructions. The AI put the rest together itself, and made and published the post.
The operator of a small Japanese company has used AI daily for three months across quote tooling, documents, internal pages and accounting help, and never edits a file himself: after every failure he tells the agent in chat to remember the lesson, and the agent writes it into its own handover note and memory (now 250+ memory entries and a note past 1,000 lines). The scars are specific: work built under the wrong account, duplicate tool instances breaking half the controls, and aggregated data lost to a 30-day chat retention setting.
He runs Svippit, a small shop platform for creators. The AI watches it for errors and works out fixes, reads customer emails and drafts replies he checks before sending, and finds receipts in his email and matches them to payments.
He works in environmental health. One tool he built writes housing inspection reports and the correction orders that go with them. Another reviews septic system plans by arguing against his own review. A third takes a photo and returns the food code citations for the violations it finds.
Nokia built an ecosystem of specialized the AI Enterprise agents that automate troubleshooting. Issue resolution time has been cut by up to 80 percent.
Locally hosted agents rebuilt a Conductor flaw in an isolated lab and got root access inside the test container. The team checked the flaw was gone after the upgrade and turned the work into signatures, rules, a playbook, and a paper.
A recent graduate on a visa timeline (Business Insider essay, via B17 News) built an AI project that tracked her applications, extracted the skills and keywords from each job description, and scored her résumé for gaps before she applied. The dashboard she had the AI build shows the funnel that ended in a job: 186 applications, 44 cold outreaches, 19 interviews, six take-home assignments, three final rounds and one offer.
A video summary was made for others to use in their teaching. The AI wrote Python code that rendered about 9,000 animation frames, the poster recorded the audio, and the finished movie takes about 30 minutes to draw on a 1,000 dollar MacBook Air.
A legal librarian at a law firm had to check ten references for a Belgian lawyer working on warranty of liabilities cases. Three references had errors, one of them serious, and the AI Pro with skills and Google AI Overviews found the right reference in a badly protected PDF on revue-banque.fr where another tool pointed to the wrong one, and one more error was fixed using the next year's index of decisions.
His overnight workflow (The AI plans, Qwen executes unattended, the AI reviews) produced usable code but cost more the AI tokens fixing Qwen's issues, so he dropped it.
He asked the AI to make a marketing video for his app, Astro Editor. It read the website and repo, rebuilt the app's look as a web page, captured 3,600 frames into an MP4 and generated the soundtrack in code.
The AI added programmatic PowerPoint screenshot support to his markdown-pptx tool in under 5 minutes.
He fed a career vault tool 21 resumes from the last 10 years plus weekly journal entries about projects, wins and goals. Every morning the AI pulls jobs that match his profile, tailors his resume per posting from that real history, generates the PDF, and fills out the whole application, then stops and waits for his review before anything goes out. Review takes him about 30 seconds per application, against 4 to 6 minutes each when he did it with AI. The post also promotes his own paid tool, so it is marked promotional.
The developer building the signup platform for his wife's education seminars stopped answering emails, and they had no access to the code. With no money to hire another developer, he rebuilt the platform from scratch with AI as his coding partner, and it is running now.
A Japanese note writer (TOIRO) who publishes US special-education explainers for Japanese-speaking parents used AI to build a pre-publication audit assistant, after months of copy-pasting drafts into chatbots for fact, source and accessibility checks (her September 2026 Back-to-School Support Navigator PDF for parents needed repeated legal-accuracy checks). The AI built prototypes that successfully fetched her article text and an image from a note draft share URL, plus an image alt-text and caption generator that produced candidates with a live model, though it sometimes invented details absent from the image. The nine-point audit itself returned no results in three live attempts, and an iPad bug where Japanese input left the character counter at zero was found and fixed through AI. Offline tests pass (43); the live audit is still unverified.
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 Purdue professor built Docable, a working tool that turns PowerPoint, Word and PDF files into accessible versions for new disability rules. The AI and the AI helped him create a codebase of about half a million lines in a few months, and his talk explains the checking method he uses so agents can validate their own work.
Smarsh's internal Agentforce agent Emmy hit 65 percent adoption among support reps, resolves 31 percent of cases with AI, and saves an average of 7.5 hours per case.
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.