Simon Willison’s Weblog

How an empty S3 bucket can make your AWS bill explode (via) Maciej Pocwierz accidentally created an S3 bucket with a name that was already used as a placeholder value in a widely used piece of software. They saw 100 million PUT requests to their new bucket in a single day, racking up a big bill since AWS charges $5/million PUTs.

It turns out AWS charge that same amount for PUTs that result in a 403 authentication error, a policy that extends even to "requester pays" buckets!

So, if you know someone's S3 bucket name you can DDoS their AWS bill just by flooding them with meaningless unauthenticated PUT requests.

AWS support refunded Maciej's bill as an exception here, but I'd like to see them reconsider this broken policy entirely.

Update from Jeff Barr:

We agree that customers should not have to pay for unauthorized requests that they did not initiate. We’ll have more to share on exactly how we’ll help prevent these charges shortly.

My approach to HTML web components. Some neat patterns here from Jeremy Keith, who is using Web Components extensively for progressive enhancement of existing markup.

The reactivity you get with full-on frameworks [like React and Vue] isn’t something that web components offer. But I do think web components can replace jQuery and other approaches to scripting the DOM.

Jeremy likes naming components with their element as a prefix (since all element names must contain at least one hyphen), and suggests building components under the single responsibility principle - so you can do things like <button-confirm><button-clipboard><button>....

Jeremy configure buttons with data- attributes and has them communicate with each other using custom events.

Something I hadn't realized is that since the connectedCallback function on a custom element is fired any time that element is attached to a page you can fetch() and then insertHTML content that includes elements and know that they will initialize themselves without needing any extra logic - great for the kind of pattern encourages by systems such as HTMX. # 30th April 2024, 11:02 am

Why SQLite Uses Bytecode (via) Brand new SQLite architecture documentation by D. Richard Hipp explaining the trade-offs between a bytecode based query plan and a tree of objects.

SQLite uses the bytecode approach, which provides an important characteristic that SQLite can very easily execute queries incrementally—stopping after each row, for example. This is more useful for a local library database than for a network server where the assumption is that the entire query will be executed before results are returned over the wire. # 30th April 2024, 5:32 am

My notes on gpt2-chatbot. There's a new, unlabeled and undocumented model on the LMSYS Chatbot Arena today called gpt2-chatbot. It's been giving some impressive responses - you can prompt it directly in the Direct Chat tab by selecting it from the big model dropdown menu.

It looks like a stealth new model preview. It's giving answers that are comparable to GPT-4 Turbo and in some cases better - my own experiments lead me to think it may have more "knowledge" baked into it, as ego prompts ("Who is Simon Willison?") and questions about things like lists of speakers at DjangoCon over the years seem to hallucinate less and return more specific details than before.

The lack of transparency here is both entertaining and infuriating. Lots of people are performing a parallel distributed "vibe check" and sharing results with each other, but it's annoying that even the most basic questions (What even IS this thing? Can it do RAG? What's its context length?) remain unanswered so far.

The system prompt appears to be the following - but system prompts just influence how the model behaves, they aren't guaranteed to contain truthful information:

You are ChatGPT, a large language model trained
by OpenAI, based on the GPT-4 architecture.

Knowledge cutoff: 2023-11
Current date: 2024-04-29

Image input capabilities: Enabled
Personality: v2

My best guess is that this is a preview of some kind of OpenAI "GPT 4.5" release. I don't think it's a big enough jump in quality to be a GPT-5.

Update: LMSYS do document their policy on using anonymized model names for tests of unreleased models.

Update May 7th: The model has been confirmed as belonging to OpenAI thanks to an error message that leaked details of the underlying API platform. # 29th April 2024, 8:45 pm

How do you accidentally run for President of Iceland? (via) Anna Andersen writes about a spectacular user interface design case-study from this year's Icelandic presidential election.

Running for President requires 1,500 endorsements. This year, those endorsements can be filed online through a government website.

The page for collecting endorsements originally had two sections - one for registering to collect endorsements, and another to submit your endorsement. The login link for the first came higher on the page, and at least 11 people ended up accidentally running for President! # 29th April 2024, 3:31 pm

Zed Decoded: Rope & SumTree (via) Text editors like Zed need in-memory data structures that are optimized for handling large strings where text can be inserted or deleted at any point without needing to copy the whole string.

Ropes are a classic, widely used data structure for this.

Zed have their own implementation of ropes in Rust, but it's backed by something even more interesting: a SumTree, described here as a thread-safe, snapshot-friendly, copy-on-write B+ tree where each leaf node contains multiple items and a Summary for each Item, and internal tree nodes contain a Summary of the items in its subtree.

These summaries allow for some very fast traversal tree operations, such as turning an offset in the file into a line and row coordinate and vice-versa. The summary itself can be anything, so each application of SumTree in Zed collects different summary information.

Uses in Zed include tracking highlight regions, code folding state, git blame information, project file trees and more - over 20 different classes and counting.

Zed co-founder Nathan Sobo calls SumTree "the soul of Zed".

Also notable: this detailed article is accompanied by an hour long video with a four-way conversation between Zed maintainers providing a tour of these data structures in the Zed codebase. # 28th April 2024, 3:25 pm

Everything Google’s Python team were responsible for. In a questionable strategic move, Google laid off the majority of their internal Python team a few days ago. Someone on Hacker News asked what the team had been responsible for, and team member zem relied with this fascinating comment providing detailed insight into how the team worked and indirectly how Python is used within Google. # 27th April 2024, 6:52 pm

Food Delivery Leak Unmasks Russian Security Agents. This story is from April 2022 but I realize now I never linked to it.

Yandex Food, a popular food delivery service in Russia, suffered a major data leak.

The data included an order history with names, addresses and phone numbers of people who had placed food orders through that service.

Bellingcat were able to cross-reference this leak with addresses of Russian security service buildings—including those linked to the GRU and FSB.This allowed them to identify the names and phone numbers of people working for those organizations, and then combine that information with further leaked data as part of their other investigations.

If you look closely at the screenshots in this story they may look familiar: Bellingcat were using Datasette internally as a tool for exploring this data! # 26th April 2024, 1:59 am

Blogmarks that use markdown. I needed to attach a correction to an older blogmark (my 20-year old name for short-form links with commentary on my blog) today - but the commentary field has always been text, not HTML, so I didn't have a way to add the necessary link.

This motivated me to finally add optional Markdown support for blogmarks to my blog's custom Django CMS. I then went through and added inline code markup to a bunch of different older posts, and built this Django SQL Dashboard to keep track of which posts I had updated. # 25th April 2024, 4:34 am

No, Most Books Don’t Sell Only a Dozen Copies. I linked to a story the other day about book sales claiming "90 percent of them sold fewer than 2,000 copies and 50 percent sold less than a dozen copies", based on numbers released in the Penguin antitrust lawsuit. It turns out those numbers were interpreted incorrectly.

In this piece from September 2022 Lincoln Michel addresses this and other common misconceptions about book statistics.

Understanding these numbers requires understanding a whole lot of intricacies about how publishing actually works. Here's one illustrative snippet:

"Take the statistic that most published books only sell 99 copies. This seems shocking on its face. But if you dig into it, you’ll notice it was counting one year’s sales of all books that were in BookScan’s system. That’s quite different statistic than saying most books don’t sell 100 copies in total! A book could easily be a bestseller in, say, 1960 and sell only a trickle of copies today."

The top comment on the post comes from Kristen McLean of NPD BookScan, the organization who's numbers were misrepresented is the trial. She wasn't certain how the numbers had been sliced to get that 90% result, but in her own analysis of "frontlist sales for the top 10 publishers by unit volume in the U.S. Trade market" she found that 14.7% sold less than 12 copies and the 51.4% spot was for books selling less than a thousand. # 25th April 2024, 3:41 am

Snowflake Arctic Cookbook. Today's big model release was Snowflake Arctic, an enormous 480B model with a 128×3.66B MoE (Mixture of Experts) architecture. It's Apache 2 licensed and Snowflake state that "in addition, we are also open sourcing all of our data recipes and research insights."

The research insights will be shared on this Arctic Cookbook blog - which currently has two articles covering their MoE architecture and describing how they optimized their training run in great detail.

They also list dozens of "coming soon" posts, which should be pretty interesting given how much depth they've provided in their writing so far. # 25th April 2024, 2:47 am

openelm/README-pretraining.md. Apple released something big three hours ago, and I’m still trying to get my head around exactly what it is.

The parent project is called CoreNet, described as “A library for training deep neural networks”. Part of the release is a new LLM called OpenELM, which includes completely open source training code and a large number of published training checkpoint.

I’m linking here to the best documentation I’ve found of that training data: it looks like the bulk of it comes from RefinedWeb, RedPajama, The Pile and Dolma. # 24th April 2024, 2:57 am

microsoft/Phi-3-mini-4k-instruct-gguf (via) Microsoft’s Phi-3 LLM is out and it’s really impressive. This 4,000 token context GGUF model is just a 2.2GB (for the Q4 version) and ran on my Mac using the llamafile option described in the README. I could then run prompts through it using the llm-llamafile plugin.

The vibes are good! Initial test prompts I’ve tried feel similar to much larger 7B models, despite using just a few GBs of RAM. Tokens are returned fast too—it feels like the fastest model I’ve tried yet.

And it’s MIT licensed. # 23rd April 2024, 5:40 pm

The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions (via) By far the most detailed paper on prompt injection I’ve seen yet from OpenAI, published a few days ago and with six credited authors: Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke and Alex Beutel.

The paper notes that prompt injection mitigations which completely refuse any form of instruction in an untrusted prompt may not actually be ideal: some forms of instruction are harmless, and refusing them may provide a worse experience.

Instead, it proposes a hierarchy—where models are trained to consider if instructions from different levels conflict with or support the goals of the higher-level instructions—if they are aligned or misaligned with them.

The authors tested this idea by fine-tuning a model on top of GPT 3.5, and claim that it shows greatly improved performance against numerous prompt injection benchmarks.

As always with prompt injection, my key concern is that I don’t think “improved” is good enough here. If you are facing an adversarial attacker reducing the chance that they might find an exploit just means they’ll try harder until they find an attack that works.

The paper concludes with this note: “Finally, our current models are likely still vulnerable to powerful adversarial attacks. In the future, we will conduct more explicit adversarial training, and study more generally whether LLMs can be made sufficiently robust to enable high-stakes agentic applications.” # 23rd April 2024, 3:36 am

timpaul/form-extractor-prototype (via) Tim Paul, Head of Interaction Design at the UK’s Government Digital Service, published this brilliant prototype built on top of Claude 3 Opus.

The video shows what it can do. Give it an image of a form and it will extract the form fields and use them to create a GDS-style multi-page interactive form, using their GOV.UK Forms design system and govuk-frontend npm package.

It works for both hand-drawn napkin illustrations and images of existing paper forms.

The bulk of the prompting logic is the schema definition in data/extract-form-questions.json

I’m always excited to see applications built on LLMs that go beyond the chatbot UI. This is a great example of exactly that. # 22nd April 2024, 10:01 pm

No one buys books (via) Fascinating insights into the book publishing industry gathered by Elle Griffin from details that came out during the Penguin vs. DOJ antitrust lawsuit.

Publishing turns out to be similar to VC investing: a tiny percentage of books are hits that cover the costs for the vast majority that didn't sell well. The DOJ found that, of 58,000 books published in a year, "90 percent of them sold fewer than 2,000 copies and 50 percent sold less than a dozen copies."

UPDATE: This story is inaccurate: those statistics were grossly misinterpreted during the trial. See this post for updated information.

Here's an even better debunking: Yes, People Do Buy Books (subtitle: "Despite viral claims, Americans buy over a billion books a year"). # 22nd April 2024, 9:55 pm