429 new ideas this week

Level up how you use AI

Real things everyday people got it to do, and how to try them yourself.

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Turn player comments into GitHub issues and shipped fixes automatically

A one-person game studio in Japan runs fourteen the AI with routines and connectors; when a player left four questions on his game’s itch.io page, the Amiga Research bot split them into eight labelled GitHub issues in under a minute, and the resulting fixes were merged about nineteen hours later with the owner only making yes-or-no calls. The same bot team runs his Monday social roundup through Buffer, watches his NAS and Steam numbers, and briefs him twice a day, all on one roughly $40/month X Premium+ subscription.

$40a month

The AI ran Blender and built my dental design tools

A dental worker had the AI run Blender on his own computer and write Python add-ons for his design work. It built a tool that turns a shell into a based model, one for basic aligners, an automated baseplate designer, and a custom impression tray designer. A fifth add-on, for copying dentures, did not come out as well.

The AI and the AI rebuilt my 20 year old teaching site

His free French teaching site Ralentir travaux, used by millions of visitors, had been more or less abandoned because its old code ran to thousands of pages. Over about six months the AI and the AI cleaned, fixed and modernised the code and added new tools, and the rebuilt site relaunched for its 20th anniversary.

Crawl both client websites through our own SEO tool — 44 and 111 pages

Connecting the agency's own MCP-based SEO service to Toby took an afternoon of wrong turns between desktop and web plugin setups, but once authenticated it created both monitored sites and completed full crawls: 44 pages for the law firm and 111 for the medical practice. Google data imports were still skipped because the service had no Google connection configured.

Let the AI audit and automate my agency's work for a week

Agency owner Nick Saraev documented handing his business's work to the AI for a week: auditing every task, having the AI plan the automation systems, building evals, and turning the winners into reusable skills. The tasks that scored well were scheduled to run without him; he kept judgement calls for himself.

A semester of lectures turned into an hour long animated recap video

He downloads all his lecture slides and feeds them into NotebookLM for an audio overview. A pipeline then transcribes the audio, has the AI write a timed spec, uses AI Design for the animations and the AI for the filler content, then renders and stitches everything into an hour long recap of the whole semester.

The AI ported my 1993 Amiga game to phones. It is out now.

The creator of Babylonian Twins had the AI port his 2010 game engine and the original 72,758 lines of 1993 assembly into Godot. The Definitive Edition is live on iOS and Android, the 1993 original is free on itch.io, and a fix found from a one star review shipped as version 2.0.3.

Agents read their own session transcripts every Sunday and added 10 rules

Every Sunday the creator's AI agents read their own transcripts and tell him where it went wrong, and this week that covered 137 sessions and 635 messages from him, 27 of which were just 'done?' or 'update?'. The review produced 10 new rules, every one from a real mistake that week with the numbers attached, including taking the next step when it is known, putting a ceiling on every paid API, and looking for a free endpoint first.

MoneyPartly worked

Called Xfinity, navigated the phone tree to a human, but stalled on

In shipwithmuse.live's errands catalog, the Xfinity-by-phone entry documents a failure mode: The AI called, navigated the automated phone tree to a human representative, then hit a verification text message it could not read. Its fallback was to patch the user into the live call.

This only partly worked. Worth knowing before you try it.
WorkWent wrong

I let Astra run a software factory for 35 hours. It built nothing of value.

Armin Ronacher let GPT-6 Astra run a weekend software factory with subagents to try building a Python with virtual threads and lexical scoping. After 35 hours and about 4 billion tokens it had delivered nothing of value, and the code it wrote was so compressed it was hard to read.

This one went wrong. See what happened before you try it.

I stopped writing my listings and let AI read my buyer messages instead

His shop sells electronics accessories into Thailand and Malaysia, and one wrong word, waterproof instead of water resistant, once cost two months of returns. Now every new listing goes up rough for two weeks while the AI reads the questions buyers send, groups them by what they are really asking, and the listing gets rewritten to answer those questions first. Buyer questions and returns both dropped.

I left Wix and the AI built my whole blog in a day

He left Wix and had the AI build his own blog, with AI in Chrome doing the setup clicks in Azure, GitHub and DNS. The new blog went live on 17 September 2026 with all eleven old posts moved across, and since then it has had about a hundred updates and four new posts from him.

Five shop photos and a one-line memo became a published Naver post in 3m20s

A Korean automation instructor sells an AI skill kit for Naver Blog and documents his own test on a real shop: he handed the skill five shop photos and a one-line memo, and it wrote the post, placed photos, a video, a map and tags, and completed a scheduled publication in 3 minutes 20 seconds. With a reservation set, Naver itself posts at the chosen time even with the computer off.

HobbiesPartly worked

I designed a keyboard with AI and sent it off to production

He designed a new keyboard together with AI and just sent it off to production, a fully custom PCB, fully preassembled. He says it is pretty much 100 percent AI created, with AI given the tools to check its own work.

This only partly worked. Worth knowing before you try it.

Orchestrate the AI over SSH and review its work

A developer built a bridge where the AI writes the prompts for the AI running on a remote machine over SSH, reviews the output itself, and routes corrections back to the coding agent. One agent manages and quality-checks another agent's code work.

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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.