Simon Willison’s Weblog

After months of coding with LLMs, I’m going back to using my brain. Interesting vibe coding retrospective from Alberto Fortin. Alberto is an experienced software developer and decided to use Claude an Cursor to rewrite an existing system using Go and ClickHouse - two new-to-him technologies.

One morning, I decide to actually inspect closely what’s all this code that Cursor has been writing. It’s not like I was blindly prompting without looking at the end result, but I was optimizing for speed and I hadn’t actually sat down just to review the code. I was just building building building.

So I do a “coding review” session. And the horror ensues.

Two service files, in the same directory, with similar names, clearly doing a very similar thing. But the method names are different. The props are not consistent. One is called "WebAPIprovider", the other one "webApi". They represent the same exact parameter. The same method is redeclared multiple times across different files. The same config file is being called in different ways and retrieved with different methods.

No consistency, no overarching plan. It’s like I'd asked 10 junior-mid developers to work on this codebase, with no Git access, locking them in a room without seeing what the other 9 were doing.

Alberto reset to a less vibe-heavy approach and is finding it to be a much more productive way of working:

I’m defaulting to pen and paper, I’m defaulting to coding the first draft of that function on my own. [...] But I’m not asking it to write new things from scratch, to come up with ideas or to write a whole new plan. I’m writing the plan. I’m the senior dev. The LLM is the assistant.

# 3:43 pm / vibe-coding, ai-assisted-programming, generative-ai, ai, llms

cityofaustin/atd-data-tech issues. I stumbled across this today while looking for interesting frequently updated data sources from local governments. It turns out the City of Austin's Transportation Data & Technology Services department run everything out of a public GitHub issues instance, which currently has 20,225 closed and 2,002 open issues. They also publish an exported copy of the issues data through the data.austintexas.gov open data portal.

# 6:18 pm / open-data, github, github-issues

Gemini 2.5: Our most intelligent models are getting even better. A bunch of new Gemini 2.5 announcements at Google I/O today.

2.5 Flash and 2.5 Pro are both getting audio output (previously previewed in Gemini 2.0) and 2.5 Pro is getting an enhanced reasoning mode called "Deep Think" - not yet available via the API.

Available today is the latest Gemini 2.5 Flash model, gemini-2.5-flash-preview-05-20. I added support to that in llm-gemini 0.20 (and, if you're using the LLM tool-use alpha, llm-gemini 0.20a2).

I tried it out on my personal benchmark, as seen in the Google I/O keynote!

llm -m gemini-2.5-flash-preview-05-20 'Generate an SVG of a pelican riding a bicycle'

Here's what I got from the default model, with its thinking mode enabled:

Full transcript. 11 input tokens, 2,619 output tokens, 10,391 thinking tokens = 4.5537 cents.

I ran the same thing again with -o thinking_budget 0 to turn off thinking mode entirely, and got this:

Full transcript. 11 input, 1,243 output = 0.0747 cents.

The non-thinking model is priced differently - still $0.15/million for input but $0.60/million for output as opposed to $3.50/million for thinking+output. The pelican it drew was 61x cheaper!

Finally, inspired by the keynote I ran this follow-up prompt to animate the more expensive pelican:

llm --cid 01jvqjqz9aha979yemcp7a4885 'Now animate it'

This one is pretty great!

# 8:34 pm / llm-release, gemini, llm, google, generative-ai, pelican-riding-a-bicycle, ai, llm-reasoning, llm-pricing, google-io

We did the math on AI’s energy footprint. Here’s the story you haven’t heard. James O'Donnell and Casey Crownhart try to pull together a detailed account of AI energy usage for MIT Technology Review.

They quickly run into the same roadblock faced by everyone else who's tried to investigate this: the AI companies themselves remain infuriatingly opaque about their energy usage, making it impossible to produce credible, definitive numbers on any of this.

Something I find frustrating about conversations about AI energy usage is the way anything that could remotely be categorized as "AI" (a vague term at the best of the times) inevitably gets bundled together. Here's a good example from early in this piece:

In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023.

ChatGPT kicked off the generative AI boom in November 2022, so that six year period mostly represents growth in data centers in the pre-generative AI era.

Thanks to the lack of transparency on energy usage by the popular closed models - OpenAI, Anthropic and Gemini all refused to share useful numbers with the reporters - they turned to the Llama models to get estimates of energy usage instead. The estimated prompts like this:

• Llama 3.1 8B - 114 joules per response - run a microwave for one-tenth of a second.
• Llama 3.1 405B - 6,706 joules per response - run the microwave for eight seconds.
• A 1024 x 1024 pixels image with Stable Diffusion 3 Medium - 2,282 joules per image which I'd estimate at about two and a half seconds.

Video models use a lot more energy. Experiments with CogVideoX (presumably this one) used "700 times the energy required to generate a high-quality image" for a 5 second video.

AI companies have defended these numbers saying that generative video has a smaller footprint than the film shoots and travel that go into typical video production. That claim is hard to test and doesn’t account for the surge in video generation that might follow if AI videos become cheap to produce.

I share their skepticism here. I don't think comparing a 5 second AI generated video to a full film production is a credible comparison here.

This piece generally reinforced my mental model that the cost of (most) individual prompts by individuals is fractionally small, but that the overall costs still add up to something substantial.

The lack of detailed information around this stuff is so disappointing - especially from companies like Google who have aggressive sustainability targets.

# 10:34 pm / ai-energy-usage, llms, ai, generative-ai, ai-ethics

Chicago Sun-Times Prints AI-Generated Summer Reading List With Books That Don’t Exist. Classic slop: it listed real authors with entirely fake books.

There's an important follow-up from 404 Media in their subsequent story:

Victor Lim, the vice president of marketing and communications at Chicago Public Media, which owns the Chicago Sun-Times, told 404 Media in a phone call that the Heat Index section was licensed from a company called King Features, which is owned by the magazine giant Hearst. He said that no one at Chicago Public Media reviewed the section and that historically it has not reviewed newspaper inserts that it has bought from King Features.

“Historically, we don’t have editorial review from those mainly because it’s coming from a newspaper publisher, so we falsely made the assumption there would be an editorial process for this,” Lim said. “We are updating our policy to require internal editorial oversight over content like this.”

# 3:03 pm / ai-ethics, slop, generative-ai, journalism, ai, llms, jason-koebler