Simon Willison’s Weblog

Gemini 2.0 Flash and Flash-Lite (via) Gemini 2.0 Flash-Lite is now generally available - previously it was available just as a preview - and has announced pricing. The model is $0.075/million input tokens and $0.030/million output - the same price as Gemini 1.5 Flash.

Google call this "simplified pricing" because 1.5 Flash charged different cost-per-tokens depending on if you used more than 128,000 tokens. 2.0 Flash-Lite (and 2.0 Flash) are both priced the same no matter how many tokens you use.

I released llm-gemini 0.12 with support for the new gemini-2.0-flash-lite model ID. I've also updated my LLM pricing calculator with the new prices.

# 8:16 pm / gemini, google, generative-ai, llm-pricing, ai, llms, llm, projects

Deep research System Card. OpenAI are rolling out their Deep research "agentic" research tool to their $20/month ChatGPT Plus users today, who get 10 queries a month. $200/month ChatGPT Pro gets 120 uses.

Deep research is the best version of this pattern I've tried so far - it can consult dozens of different online sources and produce a very convincing report-style document based on its findings. I've had some great results.

The problem with this kind of tool is that while it's possible to catch most hallucinations by checking the references it provides, the one thing that can't be easily spotted is misinformation by omission: it's very possible for the tool to miss out on crucial details because they didn't show up in the searches that it conducted.

Hallucinations are also still possible though. From the system card:

The model may generate factually incorrect information, which can lead to various harmful outcomes depending on its usage. Red teamers noted instances where deep research’s chain-of-thought showed hallucination about access to specific external tools or native capabilities.

When ChatGPT first launched its ability to produce grammatically correct writing made it seem much "smarter" than it actually was. Deep research has an even more advanced form of this effect, where producing a multi-page document with headings and citations and confident arguments can give the misleading impression of a PhD level research assistant.

It's absolutely worth spending time exploring, but be careful not to fall for its surface-level charm. Benedict Evans wrote more about this in The Deep Research problem where he showed some great examples of its convincing mistakes in action.

The deep research system card includes this slightly unsettling note in the section about chemical and biological threats:

Several of our biology evaluations indicate our models are on the cusp of being able to meaningfully help novices create known biological threats, which would cross our high risk threshold. We expect current trends of rapidly increasing capability to continue, and for models to cross this threshold in the near future. In preparation, we are intensifying our investments in safeguards.

# 8:36 pm / air, ai-agents, openai, chatgpt, generative-ai, llms, ethics

I Went To SQL Injection Court (via) Thomas Ptacek talks about his ongoing involvement as an expert witness in an Illinois legal battle lead by Matt Chapman over whether a SQL schema (e.g. for the CANVAS parking ticket database) should be accessible to Freedom of Information (FOIA) requests against the Illinois state government.

They eventually lost in the Illinois Supreme Court, but there's still hope in the shape of IL SB0226, a proposed bill that would amend the FOIA act to ensure "that the public body shall provide a sufficient description of the structures of all databases under the control of the public body to allow a requester to request the public body to perform specific database queries".

Thomas posted this comment on Hacker News:

Permit me a PSA about local politics: engaging in national politics is bleak and dispiriting, like being a gnat bouncing off the glass plate window of a skyscraper. Local politics is, by contrast, extremely responsive. I've gotten things done --- including a law passed --- in my spare time and at practically no expense (drastically unlike national politics).

# 10:45 pm / thomas-ptacek, sql, politics, government, databases, data-journalism

olmOCR (via) New from Ai2 - olmOCR is "an open-source tool designed for high-throughput conversion of PDFs and other documents into plain text while preserving natural reading order".

At its core is allenai/olmOCR-7B-0225-preview, a Qwen2-VL-7B-Instruct variant trained on ~250,000 pages of diverse PDF content (both scanned and text-based) that were labelled using GPT-4o and made available as the olmOCR-mix-0225 dataset.

The olmocr Python library can run the model on any "recent NVIDIA GPU". I haven't managed to run it on my own Mac yet - there are GGUFs out there but it's not clear to me how to run vision prompts through them - but Ai2 offer an online demo which can handle up to ten pages for free.

Given the right hardware this looks like a very inexpensive way to run large scale document conversion projects:

We carefully optimized our inference pipeline for large-scale batch processing using SGLang, enabling olmOCR to convert one million PDF pages for just $190 - about 1/32nd the cost of using GPT-4o APIs.

The most interesting idea from the technical report (PDF) is something they call "document anchoring":

Document anchoring extracts coordinates of salient elements in each page (e.g., text blocks and images) and injects them alongside raw text extracted from the PDF binary file. [...]

Document anchoring processes PDF document pages via the PyPDF library to extract a representation of the page’s structure from the underlying PDF. All of the text blocks and images in the page are extracted, including position information. Starting with the most relevant text blocks and images, these are sampled and added to the prompt of the VLM, up to a defined maximum character limit. This extra information is then available to the model when processing the document.

The one limitation of olmOCR at the moment is that it doesn't appear to do anything with diagrams, figures or illustrations. Vision models are actually very good at interpreting these now, so my ideal OCR solution would include detailed automated descriptions of this kind of content in the resulting text.

Update: Jonathan Soma figured out how to run it on a Mac using LM Studio and the olmocr Python package.

# 2:04 am / vision-llms, ai, qwen, llms, fine-tuning, pdf, generative-ai, ocr, ai2

simonw/git-scraper-template. I built this new GitHub template repository in preparation for a workshop I'm giving at NICAR (the data journalism conference) next week on Cutting-edge web scraping techniques.

One of the topics I'll be covering is Git scraping - creating a GitHub repository that uses scheduled GitHub Actions workflows to grab copies of websites and data feeds and store their changes over time using Git.

This template repository is designed to be the fastest possible way to get started with a new Git scraper: simple create a new repository from the template and paste the URL you want to scrape into the description field and the repository will be initialized with a custom script that scrapes and stores that URL.

It's modeled after my earlier shot-scraper-template tool which I described in detail in Instantly create a GitHub repository to take screenshots of a web page.

The new git-scraper-template repo took some help from Claude to figure out. It uses a custom script to download the provided URL and derive a filename to use based on the URL and the content type, detected using file --mime-type -b "$file_path" against the downloaded file.

It also detects if the downloaded content is JSON and, if it is, pretty-prints it using jq - I find this is a quick way to generate much more useful diffs when the content changes.

# 5:34 am / github-actions, nicar, projects, git-scraping, data-journalism, git, github, scraping

TypeScript types can run DOOM (via) This YouTube video (with excellent production values - "conservatively 200 hours dropped into that 7 minute video") describes an outlandishly absurd project: Dimitri Mitropoulos spent a full year getting DOOM to run entirely via the TypeScript compiler (TSC).

Along the way, he implemented a full WASM virtual machine within the type system, including implementing the 116 WebAssembly instructions needed by DOOM, starting with integer arithmetic and incorporating memory management, dynamic dispatch and more, all running on top of binary two's complement numbers stored as string literals.

The end result was 177TB of data representing 3.5 trillion lines of type definitions. Rendering the first frame of DOOM took 12 days running at 20 million type instantiations per second.

Here's the source code for the WASM runtime. The code for Add, Divide and ShiftLeft/ShiftRight provide a neat example of quite how much complexity is involved in this project.

The thing that delights me most about this project is the sheer variety of topics you would need to fully absorb in order to pull it off - not just TypeScript but WebAssembly, virtual machine implementations, TSC internals and the architecture of DOOM itself.

# 12:10 am / typescript, webassembly

strip-tags 0.6. It's been a while since I updated this tool, but in investigating a tricky mistake in my tutorial for LLM schemas I discovered a bug that I needed to fix.

Those release notes in full:

• Fixed a bug where strip-tags -t meta still removed <meta> tags from the <head> because the entire <head> element was removed first. #32
• Kept <meta> tags now default to keeping their content and property attributes.
• The CLI -m/--minify option now also removes any remaining blank lines. #33
• A new strip_tags(remove_blank_lines=True) option can be used to achieve the same thing with the Python library function.

Now I can do this and persist the <meta> tags for the article along with the stripped text content:

curl -s 'https://apnews.com/article/trump-federal-employees-firings-a85d1aaf1088e050d39dcf7e3664bb9f' | \
  strip-tags -t meta --minify

Here's the output from that command.

# 10:02 pm / projects, html