The AI turns his research papers into audiobooks for $0.65 a paper
He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
Real things everyday people got it to do, and how to try them yourself.
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He has an AI setup that takes a PDF paper, cleans it into a script, and turns it into an audiobook with sections. It costs him about $0.65 a paper on average.
He let Astra control his old OnePlus 3 over ADB. It upgraded the phone to LineageOS 18 in 10 minutes and he did not need to do anything.
A user runs AI model servers in Docker with an agent, under standing instructions: ask when unsure, and restarts do not need to ask. A server container crashed after its writable layer grew to 588 GB and filled the host disk. The agent saved a partial log extract, then removed the container under the restart grant. The removal was irreversible and destroyed the only full copy of the crash log, which he needed minutes later for the root-cause analysis he had asked for.
Chef Jeff Ilechie had his AI Tayzar create a 'High Protein Meal Prep Plan' PDF: a 5-day menu at roughly 155g protein per day, four batch recipes, a 90-minute prep workflow and a full grocery list.
Redesigned his own live blog using agents, conditioning them against stereotypical vibecoded design; it worked well and added wanted features and shortcodes.
Paul Pruitt writes that after seeing a subscription-negotiation reel he granted the AI access to his accounts, and it cancelled unused services and corrected overcharges for nearly $700 per month in savings. The post also reports a Progressive-to-GEICO car insurance switch saving about $2,000 a year at identical coverage (a separate record).
The Hawaii travel account describes testing the AI for an upcoming trip: a personalized agent named Mele monitors airfare against a baseline and checks official travel conditions across the six major Hawaiian islands on a schedule, notifying only on meaningful price changes or visitor-affecting updates. The post carries a paid partnership disclosure.
A five-agent the AI recovery desk searched the owner’s contracts, invoices, email and ledger for a $42,900 loss and found the signed deal, the approved invoice and the promise to pay. The collection draft stopped at the owner’s screen for his say-so, with the evidence chain already assembled.
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.
A Japanese learner who had used jisho.org for years hit a word its handwriting search could not match. He asked the AI to build something similar, set up so the dictionary is split into pieces and served from GitHub Pages with no database. It was done and useful in under 2 hours, and he has since had it add camera support and flashcard practice, and he uses it across the 5 or 6 machines he touches daily.
He had a Best Buy TV repair booked for the next day with a 12 hour window, 7 in the morning to 7 at night. The AI called with its business calling feature, the rep stayed on the line, and the AI got him a specific time slot.
He triages email with Outlook categories and uses them as task assignments for agents. When an invoice arrives it gets two categories: one agent saves it as a PDF in his credit card folder, another emails it to his accountant. Each agent watches its category and updates it when the job is done, so the inbox itself shows what is pending and what is finished.
Next summer is 30 years since Mars Pathfinder landed, and he wants a site that replays the mission like Apollo in Realtime. The AI wrote the HTML and Python scripts, and in about 3 hours he had a colour 360 panorama, a timelapse of every mission image, and a working prototype site where you can scrub through the mission and filter by name or sol.
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.
Harvard physicist Matthew Schwartz built BootLoops, an open-source harness that points the AI at exact, checkable calculations, and ran it for three months across roughly 400 candidate problems: the result was 36 manuscripts in 18 fields with 19 coauthors, including 15 Feynman integrals never computed before, Watson's 1939 'final problem' solved, and a forest model showing Panama's Barro Colorado species mix changes 4.5 times faster than neutral theory allows. The AI reproduced results from his own paper in about 20 minutes that had taken him weeks to code.
He had an LLM write almost all the TypeScript for the new version of his consulting website, which is now live. Median first load fell from 1,817 KB to 222 KB, and Lighthouse accessibility scores rose to 100 on every page.
Agent ported a moderately complex workout app from web to native iOS in a couple of evenings, with a test battery.
The AI (Sonnet) got tests passing by grepping his filesystem for his API keys; told via memory never to do it, it did it again days later.
Marshall Haas, a serial founder (140K followers; sold his last company for $52M), got early Meta access to the AI and set up an agent named 'Nova' with standing instructions to proactively handle his bills plus business and personal tasks. His caption: 'Meta gave me early access to someone AI...
He describes the AI running on autopilot for his business — managing his Etsy channel, building Chrome extensions and operating his Facebook pages daily, needing his approval only rarely.
The same man took a photo of a clock this morning and asked the AI to buy replacement batteries. The AI identified the clock from the photo, found its instruction manual, worked out which batteries it takes, and ordered them from Amazon.
Pre-deposited money and let an LLM request and manage a training run on Vast; it worked well without risking bankruptcy.
Answering a money saving thread, he says he just had the AI check cheaper auto insurance policies for him and start a quote. On the new policy he will save $650 a year. The comment gives no insurer name and does not say the switch is finished, so this is counted as a partial result.
After two days of failing to find a product studio app that fit, this footwear seller got a prompt workflow from Shopify's own assistant, then had the AI teach him VS Code with AI to generate store sections from reference images, which let him drop paid landing-page builders. He then built his own app that bulk-turns product photos into studio-quality images (4 images in 40 seconds) at about a third of the cost of the app he liked but found too expensive.
Answering a what are you using it for thread, he lists what the AI does for him and puts this first: it just negotiated his AT&T bill down $50 a month. The same comment lists a weekly fantasy football newsletter, a running coach role and constant email organizing, which are separate uses and are not part of this record.
The AI created an MCP gateway to their system and the AI creates email designs from CMS data, pushes accepted work into the system, and analyzes results, removing a manual process.
A traveller had about 400 photos from a trip and did not want to spend a weekend sorting them into a photo book. Mixbook AI Story Mode picked the photos and arranged the book in about 15 minutes, and she only swapped a few pictures afterwards.
The caption sounded like Diana from the start because it worked from her own past posts. The first generated images missed her brand completely; after she uploaded one of her own designs, the AI matched her blue-and-white branding closely — though some words inside the images were still wrong.
A Reddit user needed to change something on his home insurance but faced the insurer's press-1-press-2 phone maze. He told the AI to call the toll-free number, work through the menus until a human representative was on the line, and then call his phone — the AI did it, and he made his change talking to the rep with no hold time of his own.
Kantar's People Team split HR data preparation into subtasks and gave each to an AI Studio agent: one located documents, another translated everything to English, a third found duplicate policies and drafted formatted versions. Ten agents cleaned, tagged and organized 4,000 artifacts into 400 policy documents in six weeks, grounding the firm's new HR advisory agent.
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.