An LLM keeps the index of his browsing archive tidy for his writing
LLM maintains the taxonomy and index for his automatically collected browsing archive in an Obsidian vault, used for his writing.
A random mix from the whole library. Shuffle for a new set.
Work
Jobs, side gigs and small business · 1,665 ideasFive bots put 39 candidates into my ATS (until a $20 bill stopped the morning)
A recruiter’s five-bot the AI desk pushed 39 people into his applicant tracking system before the morning run stopped on a $20 extra bill. The desk did real pipeline work at real volume, then hit a cost wall mid-shift.
This only partly worked. Worth knowing before you try it.
An agent harness converted his framework to the modern API and rescued a rotting project
An agentic harness for Sonnet 3.5 converted a medium-large vue-class-component framework to Vue 3 composition API, rescuing a personal project from software rot.
Agents write most of the code for a solo blockchain project
A solo developer is building an independent Rust blockchain from a white paper. AI coding agents write most of the code under his direction, and AI models review it. An agent implemented the fix for a stake counting overflow in the consensus check, and the developer decides what merges and runs the tests.
Every two hours my agent checks my app's issues, fixes them and opens the pull requests
A developer gave the AI a token for his code repo and set a two hour cycle. The AI checks the issues page, makes a branch for each issue, writes the fix or change, and opens a pull request. Merges rebuild the app and serve it on his home network, and the AI texts him on Telegram when it has a question or when work is done. He uses the app daily and files features as issues, which are handled within a couple of hours.
Local AI submitted almost 200 job applications in about 5 days
A man ran a local AI on his own machine that submitted almost 200 job applications over almost 5 days. His only part in the applications was clicking the captcha boxes, and running the work locally saved at least $400 in token costs at the AI rates.
$400one time
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 163 co-author profiles for me and found no Mastodon links
He asked the AI to check a researcher's co-authors on ORCID for possible Mastodon links. The AI checked 163 co-authors, 120 of whom had ORCID profiles, and found none.
Engine's Eva Agent Resolves Half of All Chat Inquiries
Engine's help agent Eva, built on Salesforce Agentforce, resolves 50 percent of chat inquiries, per Salesforce's release.
Triage every dependency update like a supply-chain investigation
The solo maintainer of the karasu modeling language runs dependency updates through an AI skill that investigates each Dependabot PR before anyone merges: release notes, the diff between tags, publisher or ownership changes, install-time scripts, lockfile edges and known advisories, written up as merge, hold or reject recommendations in a design doc. In the week of Oct 10–17 three triage rounds fed 88 merged PRs (16 from Dependabot) and 12 architecture decision records, and the process caught a rebuilt lockfile silently moving a package off the reviewed version.
An AI coworker handles his mom's wedding venue inquiries and cut the reply time to five minutes
His mom runs a wedding venue in southern Italy and got 15 to 20 inquiry emails a day, each taking 20 to 30 minutes, which was 70 to 80% of her week. The AI coworker he built now works the inquiries inside her 10 year old booking system, which has no API, and she approves only special quotes. After three weeks the typical inquiry is processed within 5 minutes and the system saves her over 120 hours a month for about $350 a month.
She had the AI build a pre-publication audit assistant; the audit failed three times, the alt-text tool worked
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.
This only partly worked. Worth knowing before you try it.
His work pattern: LLMs test and rewrite legacy modules until users feel the speed
At work he prompts LLMs to write extensive tests for legacy modules, rewrite them cleanly, and pass the tests again; users report the software suddenly responsive in under a second and customers are happy.
An AI coding tool built his device certification app in about a day, tests upgraded too
An AI IDE built a device certification app in about a day, then upgraded his production test codebase to a v1.0-ready state with automated configs, error handling and simulators over two weeks.
Monitored 14 local community Facebook groups and alerted the tradesman whenever anyone
A middle-aged plumber wearing a Vic's Plumbing shirt addresses trades business owners who get work from Facebook groups, saying they will have nothing to sell without this tool. He programmed the AI to watch his 14 local-area community groups for service requests; each alert carries the post link so he can go directly to engage, answer questions, and offer his phone number.
A bot fixes his team's production errors overnight, every night
A bot fixes the team's production errors overnight, on an ongoing basis.
My agent emailed a prospect from an old page. The company had moved on months ago.
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.
This one went wrong. See what happened before you try it.
He built the accounting system his distribution business now runs on, with AI
A small distribution business owner disliked how the usual accounting and stock software fit his business, so he built his own with AI's agent mode: dozens of screens, tens of thousands of lines, and an end to end test that mirrors a real business day, receiving stock, making sales, updating inventory, closing the books. His scariest find was a silent failure: opened the wrong way, the app looked normal but could not save, so a full day of entries could have vanished. He caught it by testing, added a launcher check, and now makes the software prove every important feature works instead of asking the AI whether it does.
Three beauty shops, one owner: the blog writes itself from treatment photos
The owner of three Korean beauty treatment shops says she used to end most days having skipped the blog again; now she hands the AI a few treatment photos and one line of treatment information and a post in her stores' tone and her own voice comes back nearly finished, which she then saves as a Naver draft. Posting frequency is visibly up, the setup handles the recurring work, and she now teaches the same sequence to her staff.
The AI taught his other agent to wake itself up when its background jobs finish
The AI wrote a pi extension so his pi agent sets its own trigger to notify itself when a background process is done.
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.