2026-01-28 00:58:08
One Human + One Agent = One Browser From Scratch
embedding-shapes was so infuriated by the hype around Cursor's FastRender browser project - thousands of parallel agents producing ~1.6 million lines of Rust - that they were inspired to take a go at building a web browser using coding agents themselves.The result is one-agent-one-browser and it's really impressive. Over three days they drove a single Codex CLI agent to build 20,000 lines of Rust that successfully renders HTML+CSS with no Rust crate dependencies at all - though it does (reasonably) use Windows, macOS and Linux system frameworks for image and text rendering.
I installed the 1MB macOS binary release and ran it against my blog:
chmod 755 ~/Downloads/one-agent-one-browser-macOS-ARM64
~/Downloads/one-agent-one-browser-macOS-ARM64 https://simonwillison.net/
Here's the result:
It even rendered my SVG feed subscription icon! A PNG image is missing from the page, which looks like an intermittent bug (there's code to render PNGs).
The code is pretty readable too - here's the flexbox implementation.
I had thought that "build a web browser" was the ideal prompt to really stretch the capabilities of coding agents - and that it would take sophisticated multi-agent harnesses (as seen in the Cursor project) and millions of lines of code to achieve.
Turns out one agent driven by a talented engineer, three days and 20,000 lines of Rust is enough to get a very solid basic renderer working!
I'm going to upgrade my prediction for 2029: I think we're going to get a production-grade web browser built by a small team using AI assistance by then.
Via Show Hacker News
Tags: browsers, predictions, ai, rust, generative-ai, llms, ai-assisted-programming, coding-agents, codex-cli
2026-01-27 23:07:41
Kimi K2.5: Visual Agentic Intelligence
Kimi K2 landed in July as a 1 trillion parameter open weight LLM. It was joined by Kimi K2 Thinking in November which added reasoning capabilities. Now they've made it multi-modal: the K2 models were text-only, but the new 2.5 can handle image inputs as well:Kimi K2.5 builds on Kimi K2 with continued pretraining over approximately 15T mixed visual and text tokens. Built as a native multimodal model, K2.5 delivers state-of-the-art coding and vision capabilities and a self-directed agent swarm paradigm.
The "self-directed agent swarm paradigm" claim there means improved long-sequence tool calling and training on how to break down tasks for multiple agents to work on at once:
For complex tasks, Kimi K2.5 can self-direct an agent swarm with up to 100 sub-agents, executing parallel workflows across up to 1,500 tool calls. Compared with a single-agent setup, this reduces execution time by up to 4.5x. The agent swarm is automatically created and orchestrated by Kimi K2.5 without any predefined subagents or workflow.
I used the OpenRouter Chat UI to have it "Generate an SVG of a pelican riding a bicycle", and it did quite well:
As a more interesting test, I decided to exercise the claims around multi-agent planning with this prompt:
I want to build a Datasette plugin that offers a UI to upload files to an S3 bucket and stores information about them in a SQLite table. Break this down into ten tasks suitable for execution by parallel coding agents.
Here's the full response. It produced ten realistic tasks and reasoned through the dependencies between them. For comparison here's the same prompt against Claude Opus 4.5 and against GPT-5.2 Thinking.
The Hugging Face repository is 595GB. The model uses Kimi's janky "modified MIT" license, which adds the following clause:
Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2.5" on the user interface of such product or service.
Given the model's size, I expect one way to run it locally would be with MLX and a pair of $10,000 512GB RAM M3 Ultra Mac Studios. That setup has been demonstrated to work with previous trillion parameter K2 models.
Via Hacker News
Tags: ai, llms, hugging-face, vision-llms, llm-tool-use, ai-agents, pelican-riding-a-bicycle, llm-release, ai-in-china, moonshot, parallel-agents, kimi, janky-licenses
2026-01-27 07:55:29
Someone asked on Hacker News if I had any tips for getting coding agents to write decent quality tests. Here's what I said:
I work in Python which helps a lot because there are a TON of good examples of pytest tests floating around in the training data, including things like usage of fixture libraries for mocking external HTTP APIs and snapshot testing and other neat patterns.
Or I can say "use pytest-httpx to mock the endpoints" and Claude knows what I mean.
Keeping an eye on the tests is important. The most common anti-pattern I see is large amounts of duplicated test setup code - which isn't a huge deal, I'm much more more tolerant of duplicated logic in tests than I am in implementation, but it's still worth pushing back on.
"Refactor those tests to use pytest.mark.parametrize" and "extract the common setup into a pytest fixture" work really well there.
Generally though the best way to get good tests out of a coding agent is to make sure it's working in a project with an existing test suite that uses good patterns. Coding agents pick the existing patterns up without needing any extra prompting at all.
I find that once a project has clean basic tests the new tests added by the agents tend to match them in quality. It's similar to how working on large projects with a team of other developers work - keeping the code clean means when people look for examples of how to write a test they'll be pointed in the right direction.
One last tip I use a lot is this:
Clone datasette/datasette-enrichments
from GitHub to /tmp and imitate the
testing patterns it uses
I do this all the time with different existing projects I've written - the quickest way to show an agent how you like something to be done is to have it look at an example.
Tags: testing, coding-agents, python, generative-ai, ai, llms, hacker-news, pytest
2026-01-27 03:19:31
One of my favourite features of ChatGPT is its ability to write and execute code in a container. This feature launched as ChatGPT Code Interpreter nearly three years ago, was half-heartedly rebranded to "Advanced Data Analysis" at some point and is generally really difficult to find detailed documentation about. Case in point: it appears to have had a massive upgrade at some point in the past few months, and I can't find documentation about the new capabilities anywhere!
Here are the most notable new features:
subprocess module.pip install package and npm install package both work now via a custom proxy mechanism.container.download tool to download that file and save it to a path within the sandboxed container.This is a substantial upgrade! ChatGPT can now write and then test code in 10 new languages (11 if you count Bash), can find files online and download them into the container, and can install additional packages via pip and npm to help it solve problems.
(OpenAI really need to develop better habits at keeping their release notes up-to-date!)
I was initially suspicious that maybe I'd stumbled into a new preview feature that wasn't available to everyone, but I tried some experiments in a free ChatGPT account and confirmed that the new features are available there as well.
My first clue to the new features came the other day when I got curious about Los Angeles air quality, in particular has the growing number of electric vehicles there had a measurable impact?
I prompted a fresh GPT-5.2 Thinking session with:
Show me Los Angeles air quality over time for last 20 years
Here's the shared transcript. Annoyingly that doesn't make the thinking trace available, so here's a screenshot:
This bit instantly caught my eye:
[...] The URL from the error message leads to an Excel file containing air quality trends. I can use the container.download tool to fetch it. [...]
What's container.download? I asked ChatGPT:
Without running any searches tell me about your container.download tool
And got back:
Sure.
container.downloadis a built-in tool I can call that fetches a file from a URL and saves it into the sandboxed container filesystem for this chat session.What it does
- Takes a publicly reachable URL and a destination filepath in the container.
- Downloads the bytes from that URL and writes them to the given path.
- After that, I can read/process the file locally in the container (e.g., unzip it, parse it with Python, open it as an image, convert it, etc.).
Signature (inputs)
It’s called like this (conceptually):
{ "url": "https://example.com/somefile.zip", "filepath": "/mnt/data/somefile.zip" }
Further experimentation confirmed that container.download behaves as described.
I had it download one file from a server with logs enabled and captured the IP address and user-agent. Here are the most interesting headers:
User-Agent: Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko); compatible; ChatGPT-User/1.0; +https://openai.com/bot
Accept: text/html, application/xhtml+xml, application/xml;q=0.9, image/avif, image/webp, image/apng, */*;q=0.8, application/signed-exchange;v=b3;q=0.9
Cf-Connecting-Ip: 52.230.164.178
That 52.230.164.178 IP address resolves to Microsoft Azure Cloud (centralus) in Des Moines, Iowa.
On the one hand, this is really useful! ChatGPT can navigate around websites looking for useful files, download those files to a container and then process them using Python or other languages.
Is this a data exfiltration vulnerability though? Could a prompt injection attack trick ChatGPT into leaking private data out to a container.download call to a URL with a query string that includes sensitive information?
I don't think it can. I tried getting it to assemble a URL with a query string and access it using container.download and it couldn't do it. It told me that it got back this error:
ERROR: download failed because url not viewed in conversation before. open the file or url using web.run first.
This looks to me like the same safety trick used by Claude's Web Fetch tool: only allow URL access if that URL was either directly entered by the user or if it came from search results that could not have been influenced by a prompt injection.
(I poked at this a bit more and managed to get a simple constructed query string to pass through web.run - a different tool entirely - but when I tried to compose a longer query string containing the previous prompt history a web.run filter blocked it.)
So I think this is all safe, though I'm curious if it could hold firm against a more aggressive round of attacks from a seasoned security researcher.
The key lesson from coding agents like Claude Code and Codex CLI is that Bash rules everything: if an agent can run Bash commands in an environment it can do almost anything that can be achieved by typing commands into a computer.
When Anthropic added their own code interpreter feature to Claude last September they built that around Bash rather than just Python. It looks to me like OpenAI have now done the same thing for ChatGPT.
Here's what ChatGPT looks like when it runs a Bash command - here my prompt was:
npm install a fun package and demonstrate using it
It's useful to click on the "Thinking" or "Thought for 32s" links as that opens the Activity sidebar with a detailed trace of what ChatGPT did to arrive at its answer. This helps guard against cheating - ChatGPT might claim to have run Bash in the main window but it can't fake those black and white logs in the Activity panel.
I had it run Hello World in various languages later in that same session.
In the previous example ChatGPT installed the cowsay package from npm and used it to draw an ASCII-art cow. But how could it do that if the container can't make outbound network requests?
In another session I challenged it to explore its environment. and figure out how that worked.
Here's the resulting Markdown report it created.
The key magic appears to be a applied-caas-gateway1.internal.api.openai.org proxy, available within the container and with various packaging tools configured to use it.
The following environment variables cause pip and uv to install packages from that proxy instead of directly from PyPI:
PIP_INDEX_URL=https://reader:****@packages.applied-caas-gateway1.internal.api.openai.org/.../pypi-public/simple
PIP_TRUSTED_HOST=packages.applied-caas-gateway1.internal.api.openai.org
UV_INDEX_URL=https://reader:****@packages.applied-caas-gateway1.internal.api.openai.org/.../pypi-public/simple
UV_INSECURE_HOST=https://packages.applied-caas-gateway1.internal.api.openai.org
This one appears to get npm to work:
NPM_CONFIG_REGISTRY=https://reader:****@packages.applied-caas-gateway1.internal.api.openai.org/.../npm-public
And it reported these suspicious looking variables as well:
CAAS_ARTIFACTORY_BASE_URL=packages.applied-caas-gateway1.internal.api.openai.org
CAAS_ARTIFACTORY_PYPI_REGISTRY=.../artifactory/api/pypi/pypi-public
CAAS_ARTIFACTORY_NPM_REGISTRY=.../artifactory/api/npm/npm-public
CAAS_ARTIFACTORY_GO_REGISTRY=.../artifactory/api/go/golang-main
CAAS_ARTIFACTORY_MAVEN_REGISTRY=.../artifactory/maven-public
CAAS_ARTIFACTORY_GRADLE_REGISTRY=.../artifactory/gradle-public
CAAS_ARTIFACTORY_CARGO_REGISTRY=.../artifactory/api/cargo/cargo-public/index
CAAS_ARTIFACTORY_DOCKER_REGISTRY=.../dockerhub-public
CAAS_ARTIFACTORY_READER_USERNAME=reader
CAAS_ARTIFACTORY_READER_PASSWORD=****
NETWORK=caas_packages_only
Neither Rust nor Docker are installed in the container environment, but maybe those registry references are a clue of features still to come.
The result of all of this? You can tell ChatGPT to use Python or Node.js packages as part of a conversation and it will be able to install them and apply them against files you upload or that it downloads from the public web. That's really cool.
The big missing feature here should be the easiest to provide: we need official documentation! A release notes entry would be a good start, but there are a lot of subtle details to how this new stuff works, its limitations and what it can be used for.
As always, I'd also encourage OpenAI to come up with a name for this set of features that properly represents how it works and what it can do.
In the meantime, I'm going to call this ChatGPT Containers.
I decided to ask ChatGPT about other tools that were available to it in case there was anything interesting in there:
List all tools that are available to you, with their exact names and descriptions and signatures
Here's what I got back.
Below are all tools available to me in this chat, with their exact names, descriptions, and signatures (as defined in my tool interface).
python.exec (FREEFORM) → anyDescription: Execute Python code in a Jupyter environment for private/internal reasoning (not shown to you). Internet access is disabled.
Signature: python.exec(FREEFORM) -> any
web.run (ToolCallV5 JSON) → anyDescription: Access the internet and specialized lookups (search, open/click/find pages, screenshot PDFs, image search, product search, finance, weather, sports, calculator, time). Supports citations via returned turn... reference IDs.
Signature:web.run({ open?, click?, find?, screenshot?, image_query?, product_query?, sports?, finance?, weather?, calculator?, time?, search_query?, response_length? }) -> any
automations.create → anyDescription: Schedule a future/recurring task/reminder/search via iCal VEVENT.
Signature:automations.create({ prompt: string, title: string, schedule?: string, dtstart_offset_json?: string }) -> any
automations.update → anyDescription: Modify an existing scheduled task.
Signature:automations.update({ jawbone_id: string, schedule?: string, dtstart_offset_json?: string, prompt?: string, title?: string, is_enabled?: boolean }) -> any
automations.list → anyDescription: List existing automations.
Signature: automations.list() -> any
guardian_tool.get_policy → strDescription: Retrieve policy guidance for specific categories (currently: election_voting).
Signature: guardian_tool.get_policy({ category: string }) -> str
file_search.msearch → anyDescription: Search across available internal/connected sources (in this environment: primarily “recording_knowledge” transcripts when relevant).
Signature:file_search.msearch({ queries?: string[], source_filter?: string[], file_type_filter?: string[], intent?: string, time_frame_filter?: { start_date?: string, end_date?: string } }) -> any
file_search.mclick → anyDescription: Expand/open previously returned search results (or connector URLs) for more context.
Signature:file_search.mclick({ pointers?: string[], start_date?: string, end_date?: string }) -> any
gmail.search_email_ids → anyDescription: Search Gmail message IDs by query/tags (read-only).
Signature:gmail.search_email_ids({ query?: string, tags?: string[], max_results?: integer, next_page_token?: string }) -> any
gmail.batch_read_email → anyDescription: Read a batch of Gmail messages by message IDs (read-only).
Signature:gmail.batch_read_email({ message_ids: string[] }) -> any
gcal.search_events → anyDescription: Search Google Calendar events in a time range and/or by keyword (read-only).
Signature:gcal.search_events({ time_min?: string, time_max?: string, timezone_str?: string, max_results?: integer, query?: string, calendar_id?: string, next_page_token?: string }) -> any
gcal.read_event → anyDescription: Read a specific calendar event by ID (read-only).
Signature:gcal.read_event({ event_id: string, calendar_id?: string }) -> any
gcontacts.search_contacts → anyDescription: Search Google Contacts by keyword (read-only).
Signature:gcontacts.search_contacts({ query: string, max_results?: integer }) -> any
canmore.create_textdoc → anyDescription: Create a new canvas text document (markdown or code).
Signature:canmore.create_textdoc({ name: string, type: "document" | "code/bash" | "code/zsh" | "code/javascript" | "code/typescript" | "code/html" | "code/css" | "code/python" | "code/json" | "code/sql" | "code/go" | "code/yaml" | "code/java" | "code/rust" | "code/cpp" | "code/swift" | "code/php" | "code/xml" | "code/ruby" | "code/haskell" | "code/kotlin" | "code/csharp" | "code/c" | "code/objectivec" | "code/r" | "code/lua" | "code/dart" | "code/scala" | "code/perl" | "code/commonlisp" | "code/clojure" | "code/ocaml" | "code/powershell" | "code/verilog" | "code/dockerfile" | "code/vue" | "code/react" | "code/other", content: string }) -> any
canmore.update_textdoc → anyDescription: Apply regex-based edits to the current canvas textdoc.
Signature:canmore.update_textdoc({ updates: { pattern: string, multiple?: boolean, replacement: string }[] }) -> any
canmore.comment_textdoc → anyDescription: Add actionable comments on matched regions of the current canvas textdoc.
Signature:canmore.comment_textdoc({ comments: { pattern: string, comment: string }[] }) -> any
python_user_visible.exec (FREEFORM) → anyDescription: Execute Python code that you will see (tables, plots, generated files). Internet disabled.
Signature: python_user_visible.exec(FREEFORM) -> any
user_info.get_user_info → anyDescription: Get your current location and local time (or UTC if unknown).
Signature: user_info.get_user_info({}) -> any
summary_reader.read → anyDescription: Read safe-to-share internal reasoning summaries from earlier turns (used when asked how I arrived at something).
Signature: summary_reader.read({ limit?: integer, offset?: integer }) -> any
container.exec → anyDescription: Run a command inside the container and return stdout/stderr.
Signature:container.exec({ cmd: string[], session_name?: string | null, workdir?: string | null, timeout?: integer | null, env?: object | null, user?: string | null }) -> any
container.feed_chars → anyDescription: Send input to a running interactive exec session and flush output.
Signature:container.feed_chars({ session_name: string, chars: string, yield_time_ms?: integer }) -> any
container.open_image → anyDescription: Display an image from an absolute path in the container (jpg/jpeg/png/webp).
Signature:container.open_image({ path: string, user?: string | null }) -> any
container.download → anyDescription: Download a file from a URL into the container filesystem.
Signature:container.download({ url: string, filepath: string }) -> any
bio.update (FREEFORM) → anyDescription: Persist (or forget) user-specific info across conversations (“memory”).
Signature: bio.update(FREEFORM) -> any
api_tool.list_resources → anyDescription: List available “resources” (including invokable tools) in an OP-style file tree.
Signature:api_tool.list_resources({ path?: string, cursor?: string | null, only_tools?: boolean, refetch_tools?: boolean }) -> any
api_tool.call_tool → anyDescription: Invoke a resource as a tool (must know its full path/URI from list_resources).
Signature:api_tool.call_tool({ path: string, args: object }) -> any
personal_context.search → anyDescription: Retrieve user-specific context from prior interactions/linked sources to maintain continuity.
Signature:personal_context.search({ query: string }) -> any
image_gen.text2im → anyDescription: Generate or edit images from text instructions (and optionally referenced image IDs).
Signature:image_gen.text2im({ prompt?: string | null, size?: string | null, n?: integer | null, transparent_background?: boolean | null, is_style_transfer?: boolean | null, referenced_image_ids?: string[] | null }) -> any
user_settings.get_user_settings → anyDescription: Read current settings and allowed values (personality, accent color, appearance).
Signature: user_settings.get_user_settings() -> any
user_settings.set_setting → anyDescription: Change one setting (accent color, appearance, or personality) to an allowed value.
Signature:user_settings.set_setting({ setting_name: "accent_color" | "appearance" | "personality", setting_value: string }) -> any
artifact_handoff.prepare_artifact_generation → anyDescription: Must be called immediately when the user asks for a spreadsheet or slide deck artifact.
Signature: artifact_handoff.prepare_artifact_generation() -> any
Tags: pypi, sandboxing, npm, ai, openai, generative-ai, chatgpt, llms, ai-assisted-programming, code-interpreter
2026-01-26 07:51:32
Paul Kinlan is a web platform developer advocate at Google and recently turned his attention to coding agents. He quickly identified the importance of a robust sandbox for agents to operate in and put together these detailed notes on how the web browser can help:
This got me thinking about the browser. Over the last 30 years, we have built a sandbox specifically designed to run incredibly hostile, untrusted code from anywhere on the web, the instant a user taps a URL. [...]
Could you build something like Cowork in the browser? Maybe. To find out, I built a demo called Co-do that tests this hypothesis. In this post I want to discuss the research I've done to see how far we can get, and determine if the browser's ability to run untrusted code is useful (and good enough) for enabling software to do more for us directly on our computer.
Paul then describes how the three key aspects of a sandbox - filesystem, network access and safe code execution - can be handled by browser technologies: the File System Access API (still Chrome-only as far as I can tell), CSP headers with <iframe sandbox> and WebAssembly in Web Workers.
Co-do is a very interesting demo that illustrates all of these ideas in a single application:
You select a folder full of files and configure an LLM provider and set an API key, Co-do then uses CSP-approved API calls to interact with that provider and provides a chat interface with tools for interacting with those files. It does indeed feel similar to Claude Cowork but without running a multi-GB local container to provide the sandbox.
My biggest complaint about <iframe sandbox> remains how thinly documented it is, especially across different browsers. Paul's post has all sorts of useful details on that which I've not encountered elsewhere, including a complex double-iframe technique to help apply network rules to the inner of the two frames.
Thanks to this post I also learned about the <input type="file" webkitdirectory> tag which turns out to work on Firefox, Safari and Chrome and allows a browser read-only access to a full directory of files at once. I had Claude knock up a webkitdirectory demo to try it out and I'll certainly be using it for projects in the future.
Tags: browsers, javascript, sandboxing, ai, generative-ai, llms, ai-agents, coding-agents, claude-code
2026-01-25 12:53:01
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
