The AI ports his business software from DOS-era BASIC to Linux, right first try
The AI automated porting business software from QuickBASIC on DOS to Tcl on Linux, usually right first try, to add features management needed.
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Work
Jobs, side gigs and small business · 1,692 ideasBuild an AI workspace that searches 300+ job sources and fit-scores every lead
Buried in a thread about application exhaustion, a job seeker describes the workspace he built in an AI app: it searches 300+ job sources, pulls leads and fit-scores each against his experience, preferences, salary requirements and commute; for any lead worth pursuing it breaks down the posting and the organization, compares it with his background, suggests resume adjustments, creates a customized cover letter and drafts the application email - he reviews, attaches documents and submits.
Run my coding tasks overnight and report pass or fail
DevOps engineer Amir runs AI on a roughly CAD 200 Dell Wyse thin client as an always-on orchestrator: he sends tasks from Telegram, the AI delegates coding to the AI, runs the tests itself, and only commits and pushes after its own QA passes. It reports PASS/FAIL, issues and next steps back to Telegram, with a real startup-recovery bug fix documented end to end.
Five ways his unsupervised agents actually failed at night
A Portuguese-language operator who runs a small automation business (automatic posts, digital product distribution, nightly Python and the AI jobs) catalogs failures that really happened in his operation: a post sent twice because a timeout made the agent eagerly retry a send that had already landed; whole nights of jobs silently lost when a Windows update rebooted his PC and nothing raised an error; a job that only breaks under the 3 a.m. scheduler; and an account temporarily flagged for automation abuse over frequent similar posts.
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Orange Spain Employees Build 1,000+ Agents for Triage and Network Analysis
Orange Spain employees in HR, IT, sales and customer service built more than 1,000 custom the AI Enterprise agents. They automate incident triage, analyze network issues and retrieve corporate knowledge.
Run my surplus-auction flipping business on an AI-driven opportunity board
A three-person flipping operation runs on a custom app steered through AI over MCP: it identifies what is actually inside vaguely described government surplus lots by hunting service tags in the auction's spreadsheets and model numbers in its photos, values each lot against sold comps, and overlays a buy/watch/pass call with a max bid on GovDeals as they browse. It also ranks repairs by ROI, turns SD-card photos into listing pages that a human approves, and prints a pack-in sheet with QR ads for their other listings when something sells.
An agent built the tooling that routes his rental jobs to contractors
Agent built pricing and scheduling tooling that routes jobs to contractors and handles exceptions, and his complaints pipeline dried up (Airbnb and move-form parts duplicate his 50053301 records).
Agents watch the money across his nine store apps, reading RevenueCat, Stripe, analytics and error logs
He has about nine apps in the App Store and Play Store, and he is quick to say they are not one prompt builds: most of them he has hand guided and polished for months or years. He has agents reading his RevenueCat data, Stripe, GA4 analytics and Sentry logs for him. Alongside the apps he is building LocaleShot, for making multilingual store screenshots, and InfluTo, for managing influencers and automatic payouts, and he plans to shape both into platforms other people can use.
Research five competitors into a structured report in 15 minutes
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
This only partly worked. Worth knowing before you try it.
The AI checked a paper's references and found two were made up
A peer reviewer put a manuscript's reference list into AI and asked it to find a web link for each one. Two references were fictitious and 13 more looked suspect, so the reviewer rejected the paper.
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.