Simon Willison's Weblog: kakapo

Kākāpō parrots

2026-04-10T19:07:02+00:00

Lenny posted another snippet from our 1 hour 40 minute podcast recording and it's about kākāpō parrots!

Highlights from my conversation about agentic engineering on Lenny's Podcast

2026-04-02T20:40:47+00:00

I was a guest on Lenny Rachitsky's podcast, in a new episode titled An AI state of the union: We've passed the inflection point, dark factories are coming, and automation timelines. It's available on YouTube, Spotify, and Apple Podcasts. Here are my highlights from our conversation, with relevant links.

The November inflection point

4:19 - The end result of these two labs throwing everything they had at making their models better at code is that in November we had what I call the inflection point where GPT 5.1 and Claude Opus 4.5 came along.

They were both incrementally better than the previous models, but in a way that crossed a threshold where previously the code would mostly work, but you had to pay very close attention to it. And suddenly we went from that to... almost all of the time it does what you told it to do, which makes all of the difference in the world.

Now you can spin up a coding agent and say, build me a Mac application that does this thing, and you'll get something back which won't just be a buggy pile of rubbish that doesn't do anything.

Software engineers as bellwethers for other information workers

5:49 - I can churn out 10,000 lines of code in a day. And most of it works. Is that good? Like, how do we get from most of it works to all of it works? There are so many new questions that we're facing, which I think makes us a bellwether for other information workers.

Code is easier than almost every other problem that you pose these agents because code is obviously right or wrong - either it works or it doesn't work. There might be a few subtle hidden bugs, but generally you can tell if the thing actually works.

If it writes you an essay, if it prepares a lawsuit for you, it's so much harder to derive if it's actually done a good job, and to figure out if it got things right or wrong. But it's happening to us as software engineers. It came for us first.

And we're figuring out, OK, what do our careers look like? How do we work as teams when part of what we did that used to take most of the time doesn't take most of the time anymore? What does that look like? And it's going to be very interesting seeing how this rolls out to other information work in the future.

Lawyers are falling for this really badly. The AI hallucination cases database is up to 1,228 cases now!

Plus this bit from the cold open at the start:

It used to be you'd ask ChatGPT for some code, and it would spit out some code, and you'd have to run it and test it. The coding agents take that step for you now. And an open question for me is how many other knowledge work fields are actually prone to these agent loops?

Writing code on my phone

8:19 - I write so much of my code on my phone. It's wild. I can get good work done walking the dog along the beach, which is delightful.

I mainly use the Claude iPhone app for this, both with a regular Claude chat session (which can execute code now) or using it to control Claude Code for web.

Responsible vibe coding

9:55 If you're vibe coding something for yourself, where the only person who gets hurt if it has bugs is you, go wild. That's completely fine. The moment you ship your vibe coding code for other people to use, where your bugs might actually harm somebody else, that's when you need to take a step back.

Dark Factories and StrongDM

12:49 The reason it's called the dark factory is there's this idea in factory automation that if your factory is so automated that you don't need any people there, you can turn the lights off. Like the machines can operate in complete darkness if you don't need people on the factory floor. What does that look like for software? [...]

So there's this policy that nobody writes any code: you cannot type code into a computer. And honestly, six months ago, I thought that was crazy. And today, probably 95% of the code that I produce, I didn't type myself. That world is practical already because the latest models are good enough that you can tell them to rename that variable and refactor and add this line there... and they'll just do it - it's faster than you typing on the keyboard yourself.

The next rule though, is nobody reads the code. And this is the thing which StrongDM started doing last year.

I wrote a lot more about StrongDM's dark factory explorations back in February.

The bottleneck has moved to testing

21:27 - It used to be, you'd come up with a spec and you hand it to your engineering team. And three weeks later, if you're lucky, they'd come back with an implementation. And now that maybe takes three hours, depending on how well the coding agents are established for that kind of thing. So now what, right? Now, where else are the bottlenecks?

Anyone who's done any product work knows that your initial ideas are always wrong. What matters is proving them, and testing them.

We can test things so much faster now because we can build workable prototypes so much quicker. So there's an interesting thing I've been doing in my own work where any feature that I want to design, I'll often prototype three different ways it could work because that takes very little time.

I've always loved prototyping things, and prototyping is even more valuable now.

22:40 - A UI prototype is free now. ChatGPT and Claude will just build you a very convincing UI for anything that you describe. And that's how you should be working. I think anyone who's doing product design and isn't vibe coding little prototypes is missing out on the most powerful boost that we get in that step.

But then what do you do? Given your three options that you have instead of one option, how do you prove to yourself which one of those is the best? I don't have a confident answer to that. I expect this is where the good old fashioned usability testing comes in.

This stuff is exhausting

26:25 - I'm finding that using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. I can fire up four agents in parallel and have them work on four different problems. And by like 11 AM, I am wiped out for the day. [...]

There's a personal skill we have to learn in finding our new limits - what's a responsible way for us not to burn out.

I've talked to a lot of people who are losing sleep because they're like, my coding agents could be doing work for me. I'm just going to stay up an extra half hour and set off a bunch of extra things... and then waking up at four in the morning. That's obviously unsustainable. [...]

There's an element of sort of gambling and addiction to how we're using some of these tools.

Interruptions cost a lot less now

45:16 - People talk about how important it is not to interrupt your coders. Your coders need to have solid two to four hour blocks of uninterrupted work so they can spin up their mental model and churn out the code. That's changed completely. My programming work, I need two minutes every now and then to prompt my agent about what to do next. And then I can do the other stuff and I can go back. I'm much more interruptible than I used to be.

My ability to estimate software is broken

28:19 - I've got 25 years of experience in how long it takes to build something. And that's all completely gone - it doesn't work anymore because I can look at a problem and say that this is going to take two weeks, so it's not worth it. And now it's like... maybe it's going to take 20 minutes because the reason it would have taken two weeks was all of the sort of crufty coding things that the AI is now covering for us.

I constantly throw tasks at AI that I don't think it'll be able to do because every now and then it does it. And when it doesn't do it, you learn, right? But when it does do something, especially something that the previous models couldn't do, that's actually cutting edge AI research.

And a related anecdote:

36:56 - A lot of my friends have been talking about how they have this backlog of side projects, right? For the last 10, 15 years, they've got projects they never quite finished. And some of them are like, well, I've done them all now. Last couple of months, I just went through and every evening I'm like, let's take that project and finish it. And they almost feel a sort of sense of loss at the end where they're like, well, okay, my backlog's gone. Now what am I going to build?

It's tough for people in the middle

29:29 - So ThoughtWorks, the big IT consultancy, did an offsite about a month ago, and they got a whole bunch of engineering VPs in from different companies to talk about this stuff. And one of the interesting theories they came up with is they think this stuff is really good for experienced engineers, like it amplifies their skills. It's really good for new engineers because it solves so many of those onboarding problems. The problem is the people in the middle. If you're mid-career, if you haven't made it to sort of super senior engineer yet, but you're not sort of new either, that's the group which is probably in the most trouble right now.

I mentioned Cloudflare hiring 1,000 interns, and Shopify too.

Lenny asked for my advice for people stuck in that middle:

31:21 - That's a big responsibility you're putting on me there! I think the way forward is to lean into this stuff and figure out how do I help this make me better?

A lot of people worry about skill atrophy: if the AI is doing it for you, you're not learning anything. I think if you're worried about that, you push back at it. You have to be mindful about how you're applying the technology and think, okay, I've been given this thing that can answer any question and often gets it right. How can I use this to amplify my own skills, to learn new things, to take on much more ambitious projects? [...]

33:05 - Everything is changing so fast right now. The only universal skill is being able to roll with the changes. That's the thing that we all need.

The term that comes up most in these conversations about how you can be great with AI is agency. I think agents have no agency at all. I would argue that the one thing AI can never have is agency because it doesn't have human motivations.

So I'd say that's the thing is to invest in your own agency and invest in how to use this technology to get better at what you do and to do new things.

It's harder to evaluate software

The fact that it's so easy to create software with detailed documentation and robust tests means it's harder to figure out what's a credible project.

37:47 Sometimes I'll have an idea for a piece of software, Python library or whatever, and I can knock it out in like an hour and get to a point where it's got documentation and tests and all of those things, and it looks like the kind of software that previously I'd have spent several weeks on - and I can stick it up on GitHub

And yet... I don't believe in it. And the reason I don't believe in it is that I got to rush through all of those things... I think the quality is probably good, but I haven't spent enough time with it to feel confident in that quality. Most importantly, I haven't used it yet.

It turns out when I'm using somebody else's software, the thing I care most about is I want them to have used it for months.

I've got some very cool software that I built that I've never used. It was quicker to build it than to actually try and use it!

The misconception that AI tools are easy

41:31 - Everyone's like, oh, it must be easy. It's just a chat bot. It's not easy. That's one of the great misconceptions in AI is that using these tools effectively is easy. It takes a lot of practice and it takes a lot of trying things that didn't work and trying things that did work.

Coding agents are useful for security research now

19:04 - In the past sort of three to six months, they've started being credible as security researchers, which is sending shockwaves through the security research industry.

See Thomas Ptacek: Vulnerability Research Is Cooked.

At the same time, open source projects are being bombarded with junk security reports:

20:05 - There are these people who don't know what they're doing, who are asking ChatGPT to find a security hole and then reporting it to the maintainer. And the report looks good. ChatGPT can produce a very well formatted report of a vulnerability. It's a total waste of time. It's not actually verified as being a real problem.

A good example of the right way to do this is Anthropic's collaboration with Firefox, where Anthropic's security team verified every security problem before passing them to Mozilla.

OpenClaw

Of course we had to talk about OpenClaw! Lenny had his running on a Mac Mini.

1:29:23 - OpenClaw demonstrates that people want a personal digital assistant so much that they are willing to not just overlook the security side of things, but also getting the thing running is not easy. You've got to create API keys and tokens and install stuff. It's not trivial to get set up and hundreds of thousands of people got it set up. [...]

The first line of code for OpenClaw was written on November the 25th. And then in the Super Bowl, there was an ad for AI.com, which was effectively a vaporware white labeled OpenClaw hosting provider. So we went from first line of code in November to Super Bowl ad in what? Three and a half months.

I continue to love Drew Breunig's description of OpenClaw as a digital pet:

A friend of mine said that OpenClaw is basically a Tamagotchi. It's a digital pet and you buy the Mac Mini as an aquarium.

Journalists are good at dealing with unreliable sources

In talking about my explorations of AI for data journalism through Datasette:

1:34:58 - You would have thought that AI is a very bad fit for journalism where the whole idea is to find the truth. But the flip side is journalists deal with untrustworthy sources all the time. The art of journalism is you talk to a bunch of people and some of them lie to you and you figure out what's true. So as long as the journalist treats the AI as yet another unreliable source, they're actually better equipped to work with AI than most other professions are.

The pelican benchmark

Obviously we talked about pelicans riding bicycles:

56:10 - There appears to be a very strong correlation between how good their drawing of a pelican riding a bicycle is and how good they are at everything else. And nobody can explain to me why that is. [...]

People kept on asking me, what if labs cheat on the benchmark? And my answer has always been, really, all I want from life is a really good picture of a pelican riding a bicycle. And if I can trick every AI lab in the world into cheating on benchmarks to get it, then that just achieves my goal.

59:56 - I think something people often miss is that this space is inherently funny. The fact that we have these incredibly expensive, power hungry, supposedly the most advanced computers of all time. And if you ask them to draw a pelican on a bicycle, it looks like a five-year-old drew it. That's really funny to me.

And finally, some good news about parrots

Lenny asked if I had anything else I wanted to leave listeners with to wrap up the show, so I went with the best piece of news in the world right now.

1:38:10 - There is a rare parrot in New Zealand called the Kākāpō. There are only 250 of these parrots left in the world. They are flightless nocturnal parrots - beautiful green dumpy looking things. And the good news is they're having a fantastic breeding season in 2026,

They only breed when the Rimu trees in New Zealand have a mass fruiting season, and the Rimu trees haven't done that since 2022 - so there has not been a single baby kākāpō born in four years.

This year, the Rimu trees are in fruit. The kākāpō are breeding. There have been dozens of new chicks born. It's a really, really good time. It's great news for rare New Zealand parrots and you should look them up because they're delightful.

Everyone should watch the live stream of Rakiura on her nest with two chicks!

YouTube chapters

Here's the full list of chapters Lenny's team defined for the YouTube video:

00:00: Introduction to Simon Willison
02:40: The November 2025 inflection point
08:01: What's possible now with AI coding
10:42: Vibe coding vs. agentic engineering
13:57: The dark-factory pattern
20:41: Where bottlenecks have shifted
23:36: Where human brains will continue to be valuable
25:32: Defending of software engineers
29:12: Why experienced engineers get better results
30:48: Advice for avoiding the permanent underclass
33:52: Leaning into AI to amplify your skills
35:12: Why Simon says he's working harder than ever
37:23: The market for pre-2022 human-written code
40:01: Prediction: 50% of engineers writing 95% AI code by the end of 2026
44:34: The impact of cheap code
48:27: Simon's AI stack
54:08: Using AI for research
55:12: The pelican-riding-a-bicycle benchmark
59:01: The inherent ridiculousness of AI
1:00:52: Hoarding things you know how to do
1:08:21: Red/green TDD pattern for better AI code
1:14:43: Starting projects with good templates
1:16:31: The lethal trifecta and prompt injection
1:21:53: Why 97% effectiveness is a failing grade
1:25:19: The normalization of deviance
1:28:32: OpenClaw: the security nightmare everyone is looking past
1:34:22: What's next for Simon
1:36:47: Zero-deliverable consulting
1:38:05: Good news about Kakapo parrots

Tags: ai, kakapo, generative-ai, llms, podcast-appearances, coding-agents, agentic-engineering

February sponsors-only newsletter

2026-03-02T14:53:15+00:00

I just sent the February edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In this month's newsletter:

More OpenClaw, and Claws in general
I started a not-quite-a-book about Agentic Engineering
StrongDM, Showboat and Rodney
Kākāpō breeding season
Model releases
What I'm using, February 2026 edition

Here's a copy of the January newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy!

I use Claude as a proofreader for spelling and grammar via this prompt which also asks it to "Spot any logical errors or factual mistakes". I'm delighted to report that Claude Opus 4.6 called me out on this one:

Tags: newsletter, kakapo, claude

First kākāpō chick in four years hatches on Valentine's Day

2026-02-17T14:09:43+00:00

First kākāpō chick in four years hatches on Valentine's Day

First chick of the 2026 breeding season!

Kākāpō Yasmine hatched an egg fostered from kākāpō Tīwhiri on Valentine's Day, bringing the total number of kākāpō to 237 – though it won’t be officially added to the population until it fledges.

Here's why the egg was fostered:

"Kākāpō mums typically have the best outcomes when raising a maximum of two chicks. Biological mum Tīwhiri has four fertile eggs this season already, while Yasmine, an experienced foster mum, had no fertile eggs."

And an update from conservation biologist Andrew Digby - a second chick hatched this morning!

The second #kakapo chick of the #kakapo2026 breeding season hatched this morning: Hine Taumai-A1-2026 on Ako's nest on Te Kākahu. We transferred the egg from Anchor two nights ago. This is Ako's first-ever chick, which is just a few hours old in this video.

That post has a video of mother and chick.

Via MetaFilter

Tags: kakapo

Kākāpō mug by Karen James

2026-02-08T17:25:07+00:00

Friend and neighbour Karen James made me a Kākāpō mug. It has a charismatic Kākāpō, four Kākāpō chicks (in celebration of the 2026 breeding season) and even has some rimu fruit!

I love it so much.

Tags: kakapo, art

Kākāpō Cam: Rakiura live stream

2026-01-25T04:53:01+00:00

Kākāpō Cam: Rakiura live stream

Critical update for this year's Kākāpō breeding season: the New Zealand Department of Conservation have a livestream running of Rakiura's nest!

You’re looking at the underground nest of 23-year-old Rakiura. She has chosen this same site to nest for all seven breeding seasons since 2008, a large cavity under a rātā tree. Because she returns to the site so reliably, we’ve been able to make modifications over the years to keep it safe and dry, including adding a well-placed hatch for monitoring eggs and chicks.

Rakiura is a legendary Kākāpō:

Rakiura hatched on 19 February 2002 on Whenua Hou/Codfish Island. She is the offspring of Flossie and Bill. Her name comes from the te reo Māori name for Stewart Island, the place where most of the founding kākāpō population originated.

Rakiura has nine living descendants, three females and six males, across six breeding seasons. In 2008 came Tōitiiti, in 2009 Tamahou and Te Atapō, in 2011 Tia and Tūtoko, in 2014 Taeatanga and Te Awa, in 2019 Mati-mā and Tautahi. She also has many grandchicks.

She laid her first egg of the season at 4:30pm NZ time on 22nd January. The livestream went live shortly afterwards, once she committed to this nest.

The stream is on YouTube. I used Claude Code to write a livestream-gif.py script and used that to capture this sped-up video of the last few hours of footage, within which you can catch a glimpse of the egg!

Via MetaFilter

Tags: youtube, kakapo, conservation, claude-code

LLM predictions for 2026, shared with Oxide and Friends

2026-01-08T19:42:13+00:00

I joined a recording of the Oxide and Friends podcast on Tuesday to talk about 1, 3 and 6 year predictions for the tech industry. This is my second appearance on their annual predictions episode, you can see my predictions from January 2025 here. Here's the page for this year's episode, with options to listen in all of your favorite podcast apps or directly on YouTube.

Bryan Cantrill started the episode by declaring that he's never been so unsure about what's coming in the next year. I share that uncertainty - the significant advances in coding agents just in the last two months have left me certain that things will change significantly, but unclear as to what those changes will be.

Here are the predictions I shared in the episode.

1 year: It will become undeniable that LLMs write good code ▶ 19:27

I think that there are still people out there who are convinced that LLMs cannot write good code. Those people are in for a very nasty shock in 2026. I do not think it will be possible to get to the end of even the next three months while still holding on to that idea that the code they write is all junk and it's it's likely any decent human programmer will write better code than they will.

In 2023, saying that LLMs write garbage code was entirely correct. For most of 2024 that stayed true. In 2025 that changed, but you could be forgiven for continuing to hold out. In 2026 the quality of LLM-generated code will become impossible to deny.

I base this on my own experience - I've spent more time exploring AI-assisted programming than most.

The key change in 2025 (see my overview for the year) was the introduction of "reasoning models" trained specifically against code using Reinforcement Learning. The major labs spent a full year competing with each other on who could get the best code capabilities from their models, and that problem turns out to be perfectly attuned to RL since code challenges come with built-in verifiable success conditions.

Since Claude Opus 4.5 and GPT-5.2 came out in November and December respectively the amount of code I've written by hand has dropped to a single digit percentage of my overall output. The same is true for many other expert programmers I know.

At this point if you continue to argue that LLMs write useless code you're damaging your own credibility.

1 year: We're finally going to solve sandboxing ▶ 20:05

I think this year is the year we're going to solve sandboxing. I want to run code other people have written on my computing devices without it destroying my computing devices if it's malicious or has bugs. [...] It's crazy that it's 2026 and I still pip install random code and then execute it in a way that it can steal all of my data and delete all my files. [...] I don't want to run a piece of code on any of my devices that somebody else wrote outside of sandbox ever again.

This isn't just about LLMs, but it becomes even more important now there are so many more people writing code often without knowing what they're doing. Sandboxing is also a key part of the battle against prompt injection.

We have a lot of promising technologies in play already for this - containers and WebAssembly being the two I'm most optimistic about. There's real commercial value involved in solving this problem. The pieces are there, what's needed is UX work to reduce the friction in using them productively and securely.

1 year: A "Challenger disaster" for coding agent security ▶ 21:21

I think we're due a Challenger disaster with respect to coding agent security[...] I think so many people, myself included, are running these coding agents practically as root, right? We're letting them do all of this stuff. And every time I do it, my computer doesn't get wiped. I'm like, "oh, it's fine". [...] The worst version of this is the worm - a prompt injection worm which infects people's computers and adds itself to the Python or NPM packages that person has access to.

I used this as an opportunity to promote my favourite recent essay about AI security, the Normalization of Deviance in AI by Johann Rehberger.

The Normalization of Deviance describes the phenomenon where people and organizations get used to operating in an unsafe manner because nothing bad has happened to them yet, which can result in enormous problems (like the 1986 Challenger disaster) when their luck runs out.

Every six months I predict that a headline-grabbing prompt injection attack is coming soon, and every six months it doesn't happen. This is my most recent version of that prediction!

1 year: Kākāpō parrots will have an outstanding breeding season ▶ 50:06

(I dropped this one to lighten the mood after a discussion of the deep sense of existential dread that many programmers are feeling right now!)

I think that Kākāpō parrots in New Zealand are going to have an outstanding breeding season. The reason I think this is that the Rimu trees are in fruit right now. There's only 250 of them, and they only breed if the Rimu trees have a good fruiting. The Rimu trees have been terrible since 2019, but this year the Rimu trees were all blooming. There are researchers saying that all 87 females of breeding age might lay an egg. And for a species with only 250 remaining parrots that's great news.

(I just checked Wikipedia and I was right with the parrot numbers but wrong about the last good breeding season, apparently 2022 was a good year too.)

In a year with precious little in the form of good news I am utterly delighted to share this story. Here's more:

Kākāpō breeding season 2026 introduction from the Department of Conservation from June 2025 .
Bumper breeding season for kākāpō on the cards - 3rd December 2025, University of Auckland.

I don't often use AI-generated images on this blog, but the Kākāpō image the Oxide team created for this episode is just perfect:

3 years: the coding agents Jevons paradox for software engineering will resolve, one way or the other ▶ 54:37

We will find out if the Jevons paradox saves our careers or not. This is a big question that anyone who's a software engineer has right now: we are driving the cost of actually producing working code down to a fraction of what it used to cost. Does that mean that our careers are completely devalued and we all have to learn to live on a tenth of our incomes, or does it mean that the demand for software, for custom software goes up by a factor of 10 and now our skills are even more valuable because you can hire me and I can build you 10 times the software I used to be able to? I think by three years we will know for sure which way that one went.

The quote says it all. There are two ways this coding agents thing could go: it could turn out software engineering skills are devalued, or it could turn out we're more valuable and effective than ever before.

I'm crossing my fingers for the latter! So far it feels to me like it's working out that way.

3 years: Someone will build a new browser using mainly AI-assisted coding and it won't even be a surprise ▶ 65:13

I think somebody will have built a full web browser mostly using AI assistance, and it won't even be surprising. Rolling a new web browser is one of the most complicated software projects I can imagine[...] the cheat code is the conformance suites. If there are existing tests that it'll get so much easier.

A common complaint today from AI coding skeptics is that LLMs are fine for toy projects but can't be used for anything large and serious.

I think within 3 years that will be comprehensively proven incorrect, to the point that it won't even be controversial anymore.

I picked a web browser here because so much of the work building a browser involves writing code that has to conform to an enormous and daunting selection of both formal tests and informal websites-in-the-wild.

Coding agents are really good at tasks where you can define a concrete goal and then set them to work iterating in that direction.

A web browser is the most ambitious project I can think of that leans into those capabilities.

6 years: Typing code by hand will go the way of punch cards ▶ 80:39

I think the job of being paid money to type code into a computer will go the same way as punching punch cards [...] in six years time, I do not think anyone will be paid to just to do the thing where you type the code. I think software engineering will still be an enormous career. I just think the software engineers won't be spending multiple hours of their day in a text editor typing out syntax.

The more time I spend on AI-assisted programming the less afraid I am for my job, because it turns out building software - especially at the rate it's now possible to build - still requires enormous skill, experience and depth of understanding.

The skills are changing though! Being able to read a detailed specification and transform it into lines of code is the thing that's being automated away. What's left is everything else, and the more time I spend working with coding agents the larger that "everything else" becomes.

Tags: predictions, sandboxing, ai, kakapo, generative-ai, llms, ai-assisted-programming, oxide, bryan-cantrill, coding-agents, conformance-suites, browser-challenge, deep-blue, november-2025-inflection

The new ChatGPT Images is here

2025-12-16T23:59:22+00:00

The new ChatGPT Images is here

OpenAI shipped an update to their ChatGPT Images feature - the feature that gained them 100 million new users in a week when they first launched it back in March, but has since been eclipsed by Google's Nano Banana and then further by Nana Banana Pro in November.

The focus for the new ChatGPT Images is speed and instruction following:

It makes precise edits while keeping details intact, and generates images up to 4x faster

It's also a little cheaper: OpenAI say that the new gpt-image-1.5 API model makes image input and output "20% cheaper in GPT Image 1.5 as compared to GPT Image 1".

I tried a new test prompt against a photo I took of Natalie's ceramic stand at the farmers market a few weeks ago:

Add two kakapos inspecting the pots

Here's the result from the new ChatGPT Images model:

And here's what I got from Nano Banana Pro:

The ChatGPT Kākāpō are a little chonkier, which I think counts as a win.

I was a little less impressed by the result I got for an infographic from the prompt "Infographic explaining how the Datasette open source project works" followed by "Run some extensive searches and gather a bunch of relevant information and then try again" (transcript):

See my Nano Banana Pro post for comparison.

Both models are clearly now usable for text-heavy graphics though, which makes them far more useful than previous generations of this technology.

Update 21st December 2025: I realized I already have a tool for accessing this new model via the API. Here's what I got from the following:

OPENAI_API_KEY="$(llm keys get openai)" \
  uv run openai_image.py -m gpt-image-1.5\
  'a raccoon with a double bass in a jazz bar rocking out'

Total cost: $0.2041.

Tags: ai, kakapo, openai, generative-ai, text-to-image, nano-banana

OpenAI are quietly adopting skills, now available in ChatGPT and Codex CLI

2025-12-12T23:29:51+00:00

One of the things that most excited me about Anthropic's new Skills mechanism back in October is how easy it looked for other platforms to implement. A skill is just a folder with a Markdown file and some optional extra resources and scripts, so any LLM tool with the ability to navigate and read from a filesystem should be capable of using them. It turns out OpenAI are doing exactly that, with skills support quietly showing up in both their Codex CLI tool and now also in ChatGPT itself.

Skills in ChatGPT

I learned about this from Elias Judin this morning. It turns out the Code Interpreter feature of ChatGPT now has a new /home/oai/skills folder which you can access simply by prompting:

Create a zip file of /home/oai/skills

I tried that myself and got back this zip file. Here's a UI for exploring its content (more about that tool).

So far they cover spreadsheets, docx and PDFs. Interestingly their chosen approach for PDFs and documents is to convert them to rendered per-page PNGs and then pass those through their vision-enabled GPT models, presumably to maintain information from layout and graphics that would be lost if they just ran text extraction.

Elias shared copies in a GitHub repo. They look very similar to Anthropic's implementation of the same kind of idea, currently published in their anthropics/skills repository.

I tried it out by prompting:

Create a PDF with a summary of the rimu tree situation right now and what it means for kakapo breeding season

Sure enough, GPT-5.2 Thinking started with:

Reading skill.md for PDF creation guidelines

Then:

Searching rimu mast and Kākāpō 2025 breeding status

It took just over eleven minutes to produce this PDF, which was long enough that I had Claude Code for web build me a custom PDF viewing tool while I waited.

Here's ChatGPT's PDF in that tool.

(I am very excited about Kākāpō breeding season this year.)

The reason it took so long is that it was fastidious about looking at and tweaking its own work. I appreciated that at one point it tried rendering the PDF and noticed that the macrons in kākāpō were not supported by the chosen font, so it switched to something else:

Skills in Codex CLI

Meanwhile, two weeks ago OpenAI's open source Codex CLI tool landed a PR titled feat: experimental support for skills.md. The most recent docs for that are in docs/skills.md.

The documentation suggests that any folder in ~/.codex/skills will be treated as a skill.

I dug around and found the code that generates the prompt that drives the skill system in codex-rs/core/src/skills/render.rs - here's a Gist with a more readable version of that prompt.

I used Claude Opus 4.5's skill authoring skill to create this skill for creating Datasette plugins, then installed it into my Codex CLI skills folder like this:

git clone https://github.com/datasette/skill \
  ~/.codex/skills/datasette-plugin

You have to run Codex with the --enable skills option. I ran this:

cd /tmp
mkdir datasette-cowsay
cd datasette-cowsay
codex --enable skills -m gpt-5.2

Then prompted:

list skills

And Codex replied:

- datasette-plugins — Writing Datasette plugins using Python + pluggy (file: /Users/simon/.codex/skills/datasette-plugin/SKILL.md)
- Discovery — How to find/identify available skills (no SKILL.md path provided in the list)

Then I said:

Write a Datasette plugin in this folder adding a /-/cowsay?text=hello page that displays a pre with cowsay from PyPI saying that text

It worked perfectly! Here's the plugin code it wrote and here's a copy of the full Codex CLI transcript, generated with my terminal-to-html tool.

You can try that out yourself if you have uvx installed like this:

uvx --with https://github.com/simonw/datasette-cowsay/archive/refs/heads/main.zip \
  datasette

Then visit:

http://127.0.0.1:8001/-/cowsay?text=This+is+pretty+fun

Skills are a keeper

When I first wrote about skills in October I said Claude Skills are awesome, maybe a bigger deal than MCP. The fact that it's just turned December and OpenAI have already leaned into them in a big way reinforces to me that I called that one correctly.

Skills are based on a very light specification, if you could even call it that, but I still think it would be good for these to be formally documented somewhere. This could be a good initiative for the new Agentic AI Foundation (previously) to take on.

Tags: pdf, ai, kakapo, openai, prompt-engineering, generative-ai, chatgpt, llms, ai-assisted-programming, anthropic, coding-agents, gpt-5, codex-cli, skills

In Kākāpō breeding season news…

2019-04-19T03:11:34+00:00

In Kākāpō breeding season news…

I posted on MetaFilter about this year’s record-breaking Kākāpō breeding season.

Tags: metafilter, kakapo, conservation