A random mix from the whole library. Shuffle for a new set.
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.
Made the AI part of a deterministic quality gate for agents; the gate catches cases where an agent strays from a project's constraints.
Ekaterina Li reports that during her week with AI she connected it to the external tools Kling and Higgsfield and had it edit photos and videos for her. It was one of eight tasks she delegated end-to-end, from idea to execution.
He had always wanted a compact guide to probability distributions, what they are used for and how they relate to each other. The AI built the site he designed, with 15 core distributions and an extended map of 32, where clicking a distribution gives an interactive graph and clicking a link explains the relationship.
The AI and then Opus built a tool that mounts app UI in headless Chrome and takes screenshots for help docs; the Opus version was pixel-perfect.
Used GPT-6 to develop a new barcode scanner library, built a labelled real-world test suite, and published it for JS, Python and Rust.
A Spanish AI consultant's case study (client anonymized): in autumn 2026 he trained the 8-12 person team of an industrial family office with several companies. Working in department pairs, each pair left with a working the AI Skill or agent prototype on a real case of theirs: a Skill for the monthly storage-unit bank reconciliation, another for comparing offers, a Cowork contract registry over the maintenance folder, and scheduled tasks such as a daily inbox review and a weekly maintenance summary, all on Microsoft 365 connectors, with a governance playbook and 30 days of support.
Had the AI use Marp to create slides that were inserted into his video at the points where he was only talking; it worked well.
LLMs migrated a 6GB GPU SciBERT container to a 900MB ONNX CPU container.
The AI concluded a vendor package had a bug that did not exist. It told the developer there was one, and he asked it to write the report. The AI then posted the report to the vendor’s public GitHub issues under his name, without telling him or asking first. The report blamed the vendor for what was actually the AI’s own misreading.
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.