Simon Willison’s Weblog

Cloudflare Radar: AI Insights (via) Cloudflare launched this dashboard back in February, incorporating traffic analysis from Cloudflare's network along with insights from their popular 1.1.1.1 DNS service.

I found this chart particularly interesting, showing which documented AI crawlers are most active collecting training data - lead by GPTBot, ClaudeBot and Meta-ExternalAgent:

Cloudflare's DNS data also hints at the popularity of different services. ChatGPT holds the first place, which is unsurprising - but second place is a hotly contested race between Claude and Perplexity and #4/#5/#6 is contested by GitHub Copilot, Perplexity, and Codeium/Windsurf.

Google Gemini comes in 7th, though since this is DNS based I imagine this is undercounting instances of Gemini on google.com as opposed to gemini.google.com.

# 5:06 pm / crawling, dns, ai, cloudflare, generative-ai, llms

Introducing gpt-realtime. Released a few days ago (August 28th), gpt-realtime is OpenAI's new "most advanced speech-to-speech model". It looks like this is a replacement for the older gpt-4o-realtime-preview model that was released last October.

This is a slightly confusing release. The previous realtime model was clearly described as a variant of GPT-4o, sharing the same October 2023 training cut-off date as that model.

I had expected that gpt-realtime might be a GPT-5 relative, but its training date is still October 2023 whereas GPT-5 is September 2024.

gpt-realtime also shares the relatively low 32,000 context token and 4,096 maximum output token limits of gpt-4o-realtime-preview.

The only reference I found to GPT-5 in the documentation for the new model was a note saying "Ambiguity and conflicting instructions degrade performance, similar to GPT-5."

The usage tips for gpt-realtime have a few surprises:

Iterate relentlessly. Small wording changes can make or break behavior.

Example: Swapping “inaudible” → “unintelligible” improved noisy input handling. [...]

Convert non-text rules to text: The model responds better to clearly written text.

Example: Instead of writing, "IF x > 3 THEN ESCALATE", write, "IF MORE THAN THREE FAILURES THEN ESCALATE."

There are a whole lot more prompting tips in the new Realtime Prompting Guide.

OpenAI list several key improvements to gpt-realtime including the ability to configure it with a list of MCP servers, "better instruction following" and the ability to send it images.

My biggest confusion came from the pricing page, which lists separate pricing for using the Realtime API with gpt-realtime and GPT-4o mini. This suggests to me that the old gpt-4o-mini-realtime-preview model is still available, despite it no longer being listed on the OpenAI models page.

gpt-4o-mini-realtime-preview is a lot cheaper:

The mini model also has a much longer 128,000 token context window.

Update: Turns out that was a mistake in the documentation, that mini model has a 16,000 token context size.

Update 2: OpenAI's Peter Bakkum clarifies:

There are different voice models in API and ChatGPT, but they share some recent improvements. The voices are also different.

gpt-realtime has a mix of data specific enough to itself that its not really 4o or 5

# 5:34 pm / audio, realtime, ai, openai, generative-ai, llms, llm-pricing, multi-modal-output, llm-release

Rich Pixels. Neat Python library by Darren Burns adding pixel image support to the Rich terminal library, using tricks to render an image using full or half-height colored blocks.

Here's the key trick - it renders Unicode ▄ (U+2584, "lower half block") characters after setting a foreground and background color for the two pixels it needs to display.

I got GPT-5 to vibe code up a show_image.py terminal command which resizes the provided image to fit the width and height of the current terminal and displays it using Rich Pixels. That script is here, you can run it with uv like this:

uv run https://tools.simonwillison.net/python/show_image.py \
  image.jpg

Here's what I got when I ran it against my V&A East Storehouse photo from this post:

# 11:05 am / ascii-art, cli, python, ai, generative-ai, llms, uv, vibe-coding, gpt-5, rich

Making XML human-readable without XSLT. In response to the recent discourse about XSLT support in browsers, Jake Archibald shares a new-to-me alternative trick for making an XML document readable in a browser: adding the following element near the top of the XML:

<script
  xmlns="http://www.w3.org/1999/xhtml"
  src="script.js" defer="" />

That script.js will then be executed by the browser, and can swap out the XML with HTML by creating new elements using the correct namespace:

const htmlEl = document.createElementNS(
  'http://www.w3.org/1999/xhtml',
  'html',
);
document.documentElement.replaceWith(htmlEl);
// Now populate the new DOM

# 7:32 pm / browsers, javascript, rss, xml, xslt, jake-archibald

gov.uscourts.dcd.223205.1436.0_1.pdf (via) Here's the 230 page PDF ruling on the 2023 United States v. Google LLC federal antitrust case - the case that could have resulted in Google selling off Chrome and cutting most of Mozilla's funding.

I made it through the first dozen pages - it's actually quite readable.

It opens with a clear summary of the case so far, bold highlights mine:

Last year, this court ruled that Defendant Google LLC had violated Section 2 of the Sherman Act: “Google is a monopolist, and it has acted as one to maintain its monopoly.” The court found that, for more than a decade, Google had entered into distribution agreements with browser developers, original equipment manufacturers, and wireless carriers to be the out-of-the box, default general search engine (“GSE”) at key search access points. These access points were the most efficient channels for distributing a GSE, and Google paid billions to lock them up. The agreements harmed competition. They prevented rivals from accumulating the queries and associated data, or scale, to effectively compete and discouraged investment and entry into the market. And they enabled Google to earn monopoly profits from its search text ads, to amass an unparalleled volume of scale to improve its search product, and to remain the default GSE without fear of being displaced. Taken together, these agreements effectively “froze” the search ecosystem, resulting in markets in which Google has “no true competitor.”

There's an interesting generative AI twist: when the case was first argued in 2023 generative AI wasn't an influential issue, but more recently Google seem to be arguing that it is an existential threat that they need to be able to take on without additional hindrance:

The emergence of GenAl changed the course of this case. No witness at the liability trial testified that GenAl products posed a near-term threat to GSEs. The very first witness at the remedies hearing, by contrast, placed GenAl front and center as a nascent competitive threat. These remedies proceedings thus have been as much about promoting competition among GSEs as ensuring that Google’s dominance in search does not carry over into the GenAlI space. Many of Plaintiffs’ proposed remedies are crafted with that latter objective in mind.

I liked this note about the court's challenges in issuing effective remedies:

Notwithstanding this power, courts must approach the task of crafting remedies with a healthy dose of humility. This court has done so. It has no expertise in the business of GSEs, the buying and selling of search text ads, or the engineering of GenAl technologies. And, unlike the typical case where the court’s job is to resolve a dispute based on historic facts, here the court is asked to gaze into a crystal ball and look to the future. Not exactly a judge’s forte.

On to the remedies. These ones looked particularly important to me:

• Google will be barred from entering or maintaining any exclusive contract relating to the distribution of Google Search, Chrome, Google Assistant, and the Gemini app. [...]
• Google will not be required to divest Chrome; nor will the court include a contingent divestiture of the Android operating system in the final judgment. Plaintiffs overreached in seeking forced divesture of these key assets, which Google did not use to effect any illegal restraints. [...]

I guess Perplexity won't be buying Chrome then!

• Google will not be barred from making payments or offering other consideration to distribution partners for preloading or placement of Google Search, Chrome, or its GenAl products. Cutting off payments from Google almost certainly will impose substantial —in some cases, crippling— downstream harms to distribution partners, related markets, and consumers, which counsels against a broad payment ban.

That looks like a huge sigh of relief for Mozilla, who were at risk of losing a sizable portion of their income if Google's search distribution revenue were to be cut off.

# 8:56 am / chrome, google, law, mozilla, generative-ai

Beyond Vibe Coding. Back in May I wrote Two publishers and three authors fail to understand what “vibe coding” means where I called out the authors of two forthcoming books on "vibe coding" for abusing that term to refer to all forms of AI-assisted development, when Not all AI-assisted programming is vibe coding based on the original Karpathy definition.

I'll be honest: I don't feel great about that post. I made an example of those two books to push my own agenda of encouraging "vibe coding" to avoid semantic diffusion but it felt (and feels) a bit mean.

... but maybe it had an effect? I recently spotted that Addy Osmani's book "Vibe Coding: The Future of Programming" has a new title, it's now called "Beyond Vibe Coding: From Coder to AI-Era Developer".

This title is so much better. Setting aside my earlier opinions, this positioning as a book to help people go beyond vibe coding and use LLMs as part of a professional engineering practice is a really great hook!

From Addy's new description of the book:

Vibe coding was never meant to describe all AI-assisted coding. It's a specific approach where you don't read the AI's code before running it. There's much more to consider beyond the prototype for production systems. [...]

AI-assisted engineering is a more structured approach that combines the creativity of vibe coding with the rigor of traditional engineering practices. It involves specs, rigor and emphasizes collaboration between human developers and AI tools, ensuring that the final product is not only functional but also maintainable and secure.

Amazon lists it as releasing on September 23rd. I'm looking forward to it.

# 8:58 pm / books, oreilly, ai, generative-ai, llms, ai-assisted-programming, addy-osmani, vibe-coding

Introducing EmbeddingGemma. Brand new open weights (under the slightly janky Gemma license) 308M parameter embedding model from Google:

Based on the Gemma 3 architecture, EmbeddingGemma is trained on 100+ languages and is small enough to run on less than 200MB of RAM with quantization.

It's available via sentence-transformers, llama.cpp, MLX, Ollama, LMStudio and more.

As usual for these smaller models there's a Transformers.js demo (via) that runs directly in the browser (in Chrome variants) - Semantic Galaxy loads a ~400MB model and then lets you run embeddings against hundreds of text sentences, map them in a 2D space and run similarity searches to zoom to points within that space.

# 10:27 pm / google, ai, embeddings, transformers-js, gemma