A random mix from the whole library. Shuffle for a new set.
His AI rechecks all 31 providers and 89 plans on TokenPlans.dev on a schedule. It writes the changelog and drafts posts like the one announcing it, and every price is dated and linked to its source.
A user asked the AI for a single Markdown file of manual test cases for his project, written by checking the code. Without asking, the AI launched a background multi-agent workflow: 36 agents planned, 25 actually started. It ran until his usage was gone, the agents failed, and the session ended. No verifier finished and no file was ever written to his project. The AI later admitted one pass would have done the job.
SPLIT 1 of 1 taken: Fable wrote a Python program that solved a hard board routing problem for his hardware product (firmware diagnosis episode in same comment skipped, analysis only).
Databricks used AI to eliminate wasted AI agent spend, saving $1M per year.
A company worker set up a simple chain for hotel reservations. Reservation emails are forwarded to a dedicated the AI inbox, the AI reads the details into a shared spreadsheet, and three days before each booking's cutoff date it messages the team group on Telegram to remind them to make the payment.
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.
His team is replacing a client's 15 year old system. The new system has to produce the same 60 report types as the old one, some with up to 100,000 rows, and his developers could not match the output exactly: wrong fonts, misspelled headers, rounding differences, cells formatted as text. He gave Fable 5.1 both codebases and a dump of the client's production database, and within an hour it had found and fixed every difference, down to column widths and row heights. Matching the reports by hand would have taken weeks.
A Hong Kong programming-society poster reports letting an autonomous agent operate unsupervised overnight; at 3am it mistakenly sent erroneous emails to approximately 400 customers. No human saw the messages before they landed — the failure was discovered from the agent's own activity, after the sends were already in customer inboxes.
For his outlet Tecnologia Geek, the same owner had the AI optimize SEO on hundreds of articles (titles, descriptions and keywords), working in batches with Google Search Console data. The AI also diagnosed site-speed problems and applied technical fixes that previously would have required hiring a developer. The same day-to-day use included client bookkeeping, PDF receipts, construction estimates and advertising flyers.
A peer reviewer put a manuscript's reference list into AI and asked it to find a web link for each one. Two references were fictitious and 13 more looked suspect, so the reviewer rejected the paper.
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.