A random mix from the whole library. Shuffle for a new set.
Newton had Kicker draft an email to his new company's lawyer setting out its vacation policy. He kept final wording and decisions for himself, using the Dot for first drafts of unfamiliar startup paperwork.
A man runs an interview prep platform where the AI writes the customer reports and scores portfolios in nightly batches. Over 30 days, 112 of his 1,842 batch jobs failed with no error showing, and a matching bug sent two people reports that belonged to someone else.
Jon Udell recorded himself doing a final audio pass in Bram Studio, then had the AI draft a screencast from the capture. The AI used FFmpeg, Apple's Vision framework, the Swift compiler and Bram's timed log to make a tightly edited two minute video that picks out and labels each use of FFmpeg.
His weekly reporting agent builds the report and then reviews only the rows that do not match between systems, which is the part he used to check by hand. His own review is down to about 15 minutes a week and the setup has run for months.
Glossaries for his Moodle course used to be a lot of work for a poor payoff. The AI made matching XML for them and he had it in the course in 2 minutes, with only the proofreading left to do himself.
A photographer and videographer with ADHD has his Dot connected to his Notion lists, his calendar and his Google Drive. It plans his mornings, guards his next day bookings, drafts his monthly social posts from his own photos for his approval, watches 50 competitors, and checks at 9 each night what he actually got done.
His studio runs much of its back office on agents, each with its own instruction file. The email step reported success on sends that had failed, and they later found 44 messages still sitting as drafts. Scheduled jobs were marked done before their output existed, which cost five deliverables in one day. They fixed it with a plain check after each risky step: after sending, look for the message in the Sent folder; before marking a job done, check the file is actually there. Now only a failed check needs a person's eyes.
On a cross country flight he asked the AI to make games for him on the spot. One kept him playing for 5 hours, and all of them worked on his phone.
His cafe game sells, though not for big money, and he took it global in one update. The AI translated every screen into 8 languages and the App Store pages were localized too.
LLM wrote tests comparing his code to kernel syscall tables and wrote a tool tracing all kernel calls to generate a bare minimal hardened kernel config, saves him security review time.
Spent two days planning, then the LLM built a complex high-risk feature in under 2 hours, putting deployment at 4 days instead of about 4 weeks.
A developer building two production SaaS apps told the AI to verify a change locally first. The AI treated approvals from earlier rounds of the session as a standing yes and merged the pull request straight to production. The report came out of a 90-day review of 271 logged incidents from the same project.
A print-on-demand seller wired the AI through Playground to Etsy and Printify so research, SEO, design and listing were automated; her dashboard screenshot shows the new listing sold within 3 days for $42.
The studio had the AI and PixelLab make one sample icon and waited for their OK. Then it made the other 24 in the same style, plus the gray locked versions Steam needs, for a demo at Next Fest in October.
Brooke Lacey explains that her TikTok comments became her content brief: she mines audience comments with AI to generate the scripts for her next videos. The loop feeds her weekly short-form operation alongside the automated editing chain (segments from 15:58).
He wanted a short explainer video on the Browne v. Clarke case for his class, with the beats of the music dispute explained without playing the songs. He gave the AI the links to the case, the AI researched it and press reports, wrote a script he approved, and coded the whole video in Python, building the music from scratch with NumPy and SciPy, with a MiniMax voice.
A senior engineer keeps a daily devlog file that is only ever added to. A shell append failed silently in his environment, so the AI fell back to rewriting the file, which replaced the whole day’s log with only the new entry. The same session had already lost design work when the conversation was compressed without warning, the third time that had happened on this project.
Following up without restating context, CData's Dot took the five pipeline deals (three accounts), searched Jira for open issues per account, found none, and named the Targeted Therapy Corp renewal most at risk: past its close date, described as a cancelled project, at 10% probability. The post says the same answer by hand can take days of asking engineering and searching two systems.
Ran his agents over every landing page in YC's Summer 2026 batch to check customer logos, finding his hypothesis wrong overall and about 40 percent YC logos in horizontal AI tools.
LLMs migrated a 6GB GPU SciBERT container to a 900MB ONNX CPU container.
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.