How I drew all 11,064 bird species with Claude Code and Codex, and why the reviewer mattered more than the artist A non-programmer directed Claude Code as orchestrator and Codex with GPT-image as illustrator to generate stickers for all 11,064 living bird species in the IOC World Bird List v15.2, publishing the images and structured species data (names in up to 43 languages, Wikidata/eBird/GBIF/iNaturalist IDs, range, measurements and IUCN status) on GitHub. Of the generated stickers, 7,676 met the project's quality bar while 3,388 did not, and the creator credits a reviewer loop that pastes specific field-mark corrections into redraw prompts as more important than the illustration model itself. I drew a sticker for every living bird species on Earth. All 11,064 of them, every species in the IOC World Bird List v15.2, and I've put them on GitHub along with structured data for each species: names in up to 43 languages, Wikidata / eBird / GBIF / iNaturalist IDs, range, subspecies, body measurements and IUCN status. Repo: https://github.com/alchaincyf/huashu-bird-sticker https://github.com/alchaincyf/huashu-bird-sticker Two things up front. First, I don't write code. Everything here was built by AI agents I directed: Claude Code as the orchestrator, Codex with GPT-image as the illustrator. Second, these are AI illustrations, not hand-painted, and not all of them are good. 7,676 meet the quality bar we set; 3,388 don't, and the data says which. I'd rather you hear that from me. This post is about how a project this size gets done without falling apart, because I think the method transfers to anything with "do this 10,000 times, correctly". Last December I spent some time in Sydney. Sulphur-crested cockatoos became my favourite bird almost immediately. There was a tree in the Royal Botanic Garden full of rainbow lorikeets, and an Australian white ibis smacked me on the head while charging at someone's chips. After that I started actually noticing the birds around me. That's my photo on the left, and the cockatoo sticker in the repo is drawn from it it scored 86, a pass . A few weeks ago I was testing a design tool and had Claude Code mock up a birding app as a test case. The mock-up was good enough that I decided to build it for real. The idea is a sticker book: every time you identify a bird, you stick it into your own notebook with a date-and-city postmark. That means every species needs a sticker. We started with China's birds, grew to 5,232 species, and then I said: all of them. The IOC list has 11,227 species; minus 163 extinct ones, that's 11,064. We did find a reference photo for every species. But by species, only 1,986 had openly licensed photos CC0, public domain, CC BY, CC BY-SA . Many others were non-commercial, no-derivatives or all rights reserved, and for 2,017 we didn't record the license at the time. And photos shot from every angle, against every background, in every light, don't sit together on one page. So photos became references only, and every sticker was redrawn. Roles first: The principles that held it together: An image model drawing a bird from memory gets the bill curve, the eye colour and the number of toes wrong all the time. So every species gets a real photo first, mostly from iNaturalist and Wikimedia Commons. The source page, author and known license are recorded in each record's sticker.reference field. For 453 obscure species there was no single usable photo only distant shots, backs, half a bird . For those we built a reference board: 2 to 4 imperfect images tiled together, plus a written description of the field marks that must be right. The rule I set was "done beats perfect". Reference photos above, all from Wikimedia Commons, left to right, top to bottom: Blue-throated Bee-eater Charles J. Sharp, CC BY-SA 4.0 , Chinese Crested Tern Oregon State University, CC BY-SA 2.0 , White-throated Dipper Mark Medcalf, CC BY 2.0 , Siberian Sand Plover Vedant Raju Kasambe, CC BY-SA 4.0 , Plaintive Cuckoo Charles J. Sharp, CC BY-SA 4.0 , Pallas's Rosefinch M.Nishimura, CC BY-SA 3.0 , Red-tailed Minla Shiv's fotografia, CC BY 4.0 , Blue-tailed Bee-eater Tisha Mukherjee, CC BY-SA 4.0 , Orange-headed Thrush JJ Harrison, CC BY-SA 3.0 . Species with reference boards were drawn one at a time instead. When a bird failed review, its next batch prompt got an extra "pay attention to" line with the reviewer's note pasted in verbatim, for example "toes must be zygodactyl, two forward and two back" or "the iris is red", and the redraw was checked specifically against it. The grid route alone ran 2,760 batches and 16,339 bird draws including redraws. At peak, early on 30 October, there were 8 Codex sessions drawing, 8 reviewer subagents reviewing and 10 subagents building reference boards for the last 195 species without photos. Things broke, of course. BiRefNet's memory crept up to 22 GB and froze the whole Mac. Codex keeps about 7 MB of session log per generated image, which quietly grew to 46 GB. Every AI sticker goes to a separate reviewer subagent that compares it with the reference and scores it out of 100. 80 or above passes. The reviewer works through a checklist: iris colour, bill shape and length, every colour patch on the head and body, legs, feet and claws, tail shape, feather texture and the white outline, and whether last round's problems were fixed. Any single error caps the score at 76–78, so it can't pass. Where something can't be verified the eye isn't visible in the reference, say , the reviewer has to write that down instead of assuming it's fine. And every bird has to be zoomed in on, because many errors are invisible at thumbnail size. The reviewer returns JSON, roughly like this illustrative, not a real record : {"