January 2026
38 posts: 5 entries, 21 links, 9 quotes, 3 notes
Jan. 16, 2026
Our approach to advertising and expanding access to ChatGPT. OpenAI's long-rumored introduction of ads to ChatGPT just became a whole lot more concrete:
In the coming weeks, we’re also planning to start testing ads in the U.S. for the free and Go tiers, so more people can benefit from our tools with fewer usage limits or without having to pay. Plus, Pro, Business, and Enterprise subscriptions will not include ads.
What's "Go" tier, you might ask? That's a new $8/month tier that launched today in the USA, see Introducing ChatGPT Go, now available worldwide. It's a tier that they first trialed in India in August 2025 (here's a mention in their release notes from August listing a price of ₹399/month, which converts to around $4.40).
I'm finding the new plan comparison grid on chatgpt.com/pricing pretty confusing. It lists all accounts as having access to GPT-5.2 Thinking, but doesn't clarify the limits that the free and Go plans have to conform to. It also lists different context windows for the different plans - 16K for free, 32K for Go and Plus and 128K for Pro. I had assumed that the 400,000 token window on the GPT-5.2 model page applied to ChatGPT as well, but apparently I was mistaken.
Update: I've apparently not been paying attention: here's the Internet Archive ChatGPT pricing page from September 2025 showing those context limit differences as well.
Back to advertising: my biggest concern has always been whether ads will influence the output of the chat directly. OpenAI assure us that they will not:
- Answer independence: Ads do not influence the answers ChatGPT gives you. Answers are optimized based on what's most helpful to you. Ads are always separate and clearly labeled.
- Conversation privacy: We keep your conversations with ChatGPT private from advertisers, and we never sell your data to advertisers.
So what will they look like then? This screenshot from the announcement offers a useful hint:
The user asks about trips to Santa Fe, and an ad shows up for a cottage rental business there. This particular example imagines an option to start a direct chat with a bot aligned with that advertiser, at which point presumably the advertiser can influence the answers all they like!
Jan. 17, 2026
[On agents using CLI tools in place of REST APIs] To save on context window, yes, but moreso to improve accuracy and success rate when multiple tool calls are involved, particularly when calls must be correctly chained e.g. for pagination, rate-limit backoff, and recognizing authentication failures.
Other major factor: which models can wield the skill? Using the CLI lowers the bar so cheap, fast models (gpt-5-nano, haiku-4.5) can reliably succeed. Using the raw APl is something only the costly "strong" models (gpt-5.2, opus-4.5) can manage, and it squeezes a ton of thinking/reasoning out of them, which means multiple turns/iterations, which means accumulating a ton of context, which means burning loads of expensive tokens. For one-off API requests and ad hoc usage driven by a developer, this is reasonable and even helpful, but for an autonomous agent doing repetitive work, it's a disaster.
— Jeremy Daer, 37signals
Jan. 18, 2026
FLUX.2-klein-4B Pure C Implementation (via) On 15th January Black Forest Labs, a lab formed by the creators of the original Stable Diffusion, released black-forest-labs/FLUX.2-klein-4B - an Apache 2.0 licensed 4 billion parameter version of their FLUX.2 family.
Salvatore Sanfilippo (antirez) decided to build a pure C and dependency-free implementation to run the model, with assistance from Claude Code and Claude Opus 4.5.
Salvatore shared this note on Hacker News:
Something that may be interesting for the reader of this thread: this project was possible only once I started to tell Opus that it needed to take a file with all the implementation notes, and also accumulating all the things we discovered during the development process. And also, the file had clear instructions to be taken updated, and to be processed ASAP after context compaction. This kinda enabled Opus to do such a big coding task in a reasonable amount of time without loosing track. Check the file IMPLEMENTATION_NOTES.md in the GitHub repo for more info.
Here's that IMPLEMENTATION_NOTES.md file.
Jan. 19, 2026
Scaling long-running autonomous coding. Wilson Lin at Cursor has been doing some experiments to see how far you can push a large fleet of "autonomous" coding agents:
This post describes what we've learned from running hundreds of concurrent agents on a single project, coordinating their work, and watching them write over a million lines of code and trillions of tokens.
They ended up running planners and sub-planners to create tasks, then having workers execute on those tasks - similar to how Claude Code uses sub-agents. Each cycle ended with a judge agent deciding if the project was completed or not.
In my predictions for 2026 the other day I said that by 2029:
I think somebody will have built a full web browser mostly using AI assistance, and it won’t even be surprising. Rolling a new web browser is one of the most complicated software projects I can imagine[...] the cheat code is the conformance suites. If there are existing tests that it’ll get so much easier.
I may have been off by three years, because Cursor chose "building a web browser from scratch" as their test case for their agent swarm approach:
To test this system, we pointed it at an ambitious goal: building a web browser from scratch. The agents ran for close to a week, writing over 1 million lines of code across 1,000 files. You can explore the source code on GitHub.
But how well did they do? Their initial announcement a couple of days ago was met with unsurprising skepticism, especially when it became apparent that their GitHub Actions CI was failing and there were no build instructions in the repo.
It looks like they addressed that within the past 24 hours. The latest README includes build instructions which I followed on macOS like this:
cd /tmp
git clone https://github.com/wilsonzlin/fastrender
cd fastrender
git submodule update --init vendor/ecma-rs
cargo run --release --features browser_ui --bin browser
This got me a working browser window! Here are screenshots I took of google.com and my own website:
Honestly those are very impressive! You can tell they're not just wrapping an existing rendering engine because of those very obvious rendering glitches, but the pages are legible and look mostly correct.
The FastRender repo even uses Git submodules to include various WhatWG and CSS-WG specifications in the repo, which is a smart way to make sure the agents have access to the reference materials that they might need.
This is the second attempt I've seen at building a full web browser using AI-assisted coding in the past two weeks - the first was HiWave browser, a new browser engine in Rust first announced in this Reddit thread.
When I made my 2029 prediction this is more-or-less the quality of result I had in mind. I don't think we'll see projects of this nature compete with Chrome or Firefox or WebKit any time soon but I have to admit I'm very surprised to see something this capable emerge so quickly.
jordanhubbard/nanolang (via) Plenty of people have mused about what a new programming language specifically designed to be used by LLMs might look like. Jordan Hubbard (co-founder of FreeBSD, with serious stints at Apple and NVIDIA) just released exactly that.
A minimal, LLM-friendly programming language with mandatory testing and unambiguous syntax.
NanoLang transpiles to C for native performance while providing a clean, modern syntax optimized for both human readability and AI code generation.
The syntax strikes me as an interesting mix between C, Lisp and Rust.
I decided to see if an LLM could produce working code in it directly, given the necessary context. I started with this MEMORY.md file, which begins:
Purpose: This file is designed specifically for Large Language Model consumption. It contains the essential knowledge needed to generate, debug, and understand NanoLang code. Pair this with
spec.jsonfor complete language coverage.
I ran that using LLM and llm-anthropic like this:
llm -m claude-opus-4.5 \
-s https://raw.githubusercontent.com/jordanhubbard/nanolang/refs/heads/main/MEMORY.md \
'Build me a mandelbrot fractal CLI tool in this language'
> /tmp/fractal.nano
The resulting code... did not compile.
I may have been too optimistic expecting a one-shot working program for a new language like this. So I ran a clone of the actual project, copied in my program and had Claude Code take a look at the failing compiler output.
... and it worked! Claude happily grepped its way through the various examples/ and built me a working program.
Here's the Claude Code transcript - you can see it reading relevant examples here - and here's the finished code plus its output.
I've suspected for a while that LLMs and coding agents might significantly reduce the friction involved in launching a new language. This result reinforces my opinion.
Jan. 20, 2026
Giving University Exams in the Age of Chatbots (via) Detailed and thoughtful description of an open-book and open-chatbot exam run by Ploum at École Polytechnique de Louvain for an "Open Source Strategies" class.
Students were told they could use chatbots during the exam but they had to announce their intention to do so in advance, share their prompts and take full accountability for any mistakes they made.
Only 3 out of 60 students chose to use chatbots. Ploum surveyed half of the class to help understand their motivations.
Electricity use of AI coding agents (via) Previous work estimating the energy and water cost of LLMs has generally focused on the cost per prompt using a consumer-level system such as ChatGPT.
Simon P. Couch notes that coding agents such as Claude Code use way more tokens in response to tasks, often burning through many thousands of tokens of many tool calls.
As a heavy Claude Code user, Simon estimates his own usage at the equivalent of 4,400 "typical queries" to an LLM, for an equivalent of around $15-$20 in daily API token spend. He figures that to be about the same as running a dishwasher once or the daily energy used by a domestic refrigerator.
Jan. 21, 2026
Claude’s new constitution. Late last year Richard Weiss found something interesting while poking around with the just-released Claude Opus 4.5: he was able to talk the model into regurgitating a document which was not part of the system prompt but appeared instead to be baked in during training, and which described Claude's core values at great length.
He called this leak the soul document, and Amanda Askell from Anthropic quickly confirmed that it was indeed part of Claude's training procedures.
Today Anthropic made this official, releasing that full "constitution" document under a CC0 (effectively public domain) license. There's a lot to absorb! It's over 35,000 tokens, more than 10x the length of the published Opus 4.5 system prompt.
One detail that caught my eye is the acknowledgements at the end, which include a list of external contributors who helped review the document. I was intrigued to note that two of the fifteen listed names are Catholic members of the clergy - Father Brendan McGuire is a pastor in Los Altos with a Master’s degree in Computer Science and Math and Bishop Paul Tighe is an Irish Catholic bishop with a background in moral theology.