2026-02-21 01:12:55
ggml.ai joins Hugging Face to ensure the long-term progress of Local AI
I don't normally cover acquisition news like this, but I have some thoughts.It's hard to overstate the impact Georgi Gerganov has had on the local model space. Back in March 2023 his release of llama.cpp made it possible to run a local LLM on consumer hardware. The original README said:
The main goal is to run the model using 4-bit quantization on a MacBook. [...] This was hacked in an evening - I have no idea if it works correctly.
I wrote about trying llama.cpp out at the time in Large language models are having their Stable Diffusion moment:
I used it to run the 7B LLaMA model on my laptop last night, and then this morning upgraded to the 13B model—the one that Facebook claim is competitive with GPT-3.
Meta's original LLaMA release depended on PyTorch and their FairScale PyTorch extension for running on multiple GPUs, and required CUDA and NVIDIA hardware. Georgi's work opened that up to a much wider range of hardware and kicked off the local model movement that has continued to grow since then.
Hugging Face are already responsible for the incredibly influential Transformers library used by the majority of LLM releases today. They've proven themselves a good steward for that open source project, which makes me optimistic for the future of llama.cpp and related projects.
This section from the announcement looks particularly promising:
Going forward, our joint efforts will be geared towards the following objectives:
- Towards seamless "single-click" integration with the transformers library. The
transformersframework has established itself as the 'source of truth' for AI model definitions. Improving the compatibility between the transformers and the ggml ecosystems is essential for wider model support and quality control.- Better packaging and user experience of ggml-based software. As we enter the phase in which local inference becomes a meaningful and competitive alternative to cloud inference, it is crucial to improve and simplify the way in which casual users deploy and access local models. We will work towards making llama.cpp ubiquitous and readily available everywhere, and continue partnering with great downstream projects.
Given the influence of Transformers, this closer integration could lead to model releases that are compatible with the GGML ecosystem out of the box. That would be a big win for the local model ecosystem.
I'm also excited to see investment in "packaging and user experience of ggml-based software". This has mostly been left to tools like Ollama and LM Studio. ggml-org released LlamaBarn last year - "a macOS menu bar app for running local LLMs" - and I'm hopeful that further investment in this area will result in more high quality open source tools for running local models from the team best placed to deliver them.
Via @ggerganov
Tags: open-source, transformers, ai, generative-ai, llama, local-llms, llms, hugging-face, llama-cpp
2026-02-20 15:13:19
Long running agentic products like Claude Code are made feasible by prompt caching which allows us to reuse computation from previous roundtrips and significantly decrease latency and cost. [...]
At Claude Code, we build our entire harness around prompt caching. A high prompt cache hit rate decreases costs and helps us create more generous rate limits for our subscription plans, so we run alerts on our prompt cache hit rate and declare SEVs if they're too low.
Tags: prompt-engineering, anthropic, claude-code, ai-agents, generative-ai, ai, llms
2026-02-20 07:48:35
Reached the stage of parallel agent psychosis where I've lost a whole feature - I know I had it yesterday, but I can't seem to find the branch or worktree or cloud instance or checkout with it in.
... found it! Turns out I'd been hacking on a random prototype in /tmp and then my computer crashed and rebooted and I lost the code... but it's all still there in ~/.claude/projects/ session logs and Claude Code can extract it out and spin up the missing feature again.
Tags: parallel-agents, coding-agents, claude-code, generative-ai, ai, llms
2026-02-20 01:58:37
The first in the Gemini 3.1 series, priced the same as Gemini 3 Pro ($2/million input, $12/million output under 200,000 tokens, $4/$18 for 200,000 to 1,000,000). That's less than half the price of Claude Opus 4.6 with very similar benchmark scores to that model.
They boast about its improved SVG animation performance compared to Gemini 3 Pro in the announcement!
I tried "Generate an SVG of a pelican riding a bicycle" in Google AI Studio and it thought for 323.9 seconds (thinking trace here) before producing this one:
It's good to see the legs clearly depicted on both sides of the frame (should satisfy Elon), the fish in the basket is a nice touch and I appreciated this comment in the SVG code:
<!-- Black Flight Feathers on Wing Tip -->
<path d="M 420 175 C 440 182, 460 187, 470 190 C 450 210, 430 208, 410 198 Z" fill="#374151" />
I've added the two new model IDs gemini-3.1-pro-preview and gemini-3.1-pro-preview-customtools to my llm-gemini plugin for LLM. That "custom tools" one is described here - apparently it may provide better tool performance than the default model in some situations.
The model appears to be incredibly slow right now - it took 104s to respond to a simple "hi" and a few of my other tests met "Error: This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later." or "Error: Deadline expired before operation could complete" errors. I'm assuming that's just teething problems on launch day.
It sounds like last week's Deep Think release was our first exposure to the 3.1 family:
Last week, we released a major update to Gemini 3 Deep Think to solve modern challenges across science, research and engineering. Today, we’re releasing the upgraded core intelligence that makes those breakthroughs possible: Gemini 3.1 Pro.
Update: In What happens if AI labs train for pelicans riding bicycles? last November I said:
If a model finally comes out that produces an excellent SVG of a pelican riding a bicycle you can bet I’m going to test it on all manner of creatures riding all sorts of transportation devices.
Google's Gemini Lead Jeff Dean tweeted this video featuring an animated pelican riding a bicycle, plus a frog on a penny-farthing and a giraffe driving a tiny car and an ostrich on roller skates and a turtle kickflipping a skateboard and a dachshund driving a stretch limousine.
I've been saying for a while that I wish AI labs would highlight things that their new models can do that their older models could not, so top marks to the Gemini team for this video.
Update 2: I used llm-gemini to run my more detailed Pelican prompt, with this result:
From the SVG comments:
<!-- Pouch Gradient (Breeding Plumage: Red to Olive/Green) -->
...
<!-- Neck Gradient (Breeding Plumage: Chestnut Nape, White/Yellow Front) -->
Tags: google, svg, ai, generative-ai, llms, llm, gemini, pelican-riding-a-bicycle, llm-release
2026-02-19 13:44:29
I've long been resistant to the idea of accepting sponsorship for my blog. I value my credibility as an independent voice, and I don't want to risk compromising that reputation.
Then I learned about Troy Hunt's approach to sponsorship, which he first wrote about in 2016. Troy runs with a simple text row in the page banner - no JavaScript, no cookies, unobtrusive while providing value to the sponsor. I can live with that!
Accepting sponsorship in this way helps me maintain my independence while offsetting the opportunity cost of not taking a full-time job.
To start with I'm selling sponsorship by the week. Sponsors get that unobtrusive banner across my blog and also their sponsored message at the top of my newsletter.
I will not write content in exchange for sponsorship. I hope the sponsors I work with understand that my credibility as an independent voice is a key reason I have an audience, and compromising that trust would be bad for everyone.
Freeman & Forrest helped me set up and sell my first slots. Thanks also to Theo Browne for helping me think through my approach.
Tags: newsletter, blogging, troy-hunt
2026-02-19 12:48:47
SWE-bench February 2026 leaderboard update
SWE-bench is one of the benchmarks that the labs love to list in their model releases. The official leaderboard is infrequently updated but they just did a full run of it against the current generation of models, which is notable because it's always good to see benchmark results like this that weren't self-reported by the labs.The fresh results are for their "Bash Only" benchmark, which runs their mini-swe-bench agent (~9,000 lines of Python, here are the prompts they use) against the SWE-bench dataset of coding problems - 2,294 real-world examples pulled from 12 open source repos: django/django (850), sympy/sympy (386), scikit-learn/scikit-learn (229), sphinx-doc/sphinx (187), matplotlib/matplotlib (184), pytest-dev/pytest (119), pydata/xarray (110), astropy/astropy (95), pylint-dev/pylint (57), psf/requests (44), mwaskom/seaborn (22), pallets/flask (11).
Correction: The Bash only benchmark runs against SWE-bench Verified, not original SWE-bench. Verified is a manually curated subset of 500 samples described here, funded by OpenAI. Here's SWE-bench Verified on Hugging Face - since it's just 2.1MB of Parquet it's easy to browse using Datasette Lite, which cuts those numbers down to django/django (231), sympy/sympy (75), sphinx-doc/sphinx (44), matplotlib/matplotlib (34), scikit-learn/scikit-learn (32), astropy/astropy (22), pydata/xarray (22), pytest-dev/pytest (19), pylint-dev/pylint (10), psf/requests (8), mwaskom/seaborn (2), pallets/flask (1).
Here's how the top ten models performed:
It's interesting to see Claude Opus 4.5 beat Opus 4.6, though only by about a percentage point. 4.5 Opus is top, then Gemini 3 Flash, then MiniMax M2.5 - a 229B model released last week by Chinese lab MiniMax. GLM-5, Kimi K2.5 and DeepSeek V3.2 are three more Chinese models that make the top ten as well.
OpenAI's GPT-5.2 is their highest performing model at position 6, but it's worth noting that their best coding model, GPT-5.3-Codex, is not represented - maybe because it's not yet available in the OpenAI API.
This benchmark uses the same system prompt for every model, which is important for a fair comparison but does mean that the quality of the different harnesses or optimized prompts is not being measured here.
The chart above is a screenshot from the SWE-bench website, but their charts don't include the actual percentage values visible on the bars. I successfully used Claude for Chrome to add these - transcript here. My prompt sequence included:
Use claude in chrome to open https://www.swebench.com/
Click on "Compare results" and then select "Select top 10"
See those bar charts? I want them to display the percentage on each bar so I can take a better screenshot, modify the page like that
I'm impressed at how well this worked - Claude injected custom JavaScript into the page to draw additional labels on top of the existing chart.
![Screenshot of a Claude AI conversation showing browser automation. A thinking step reads "Pivoted strategy to avoid recursion issues with chart labeling >" followed by the message "Good, the chart is back. Now let me carefully add the labels using an inline plugin on the chart instance to avoid the recursion issue." A collapsed "Browser_evaluate" section shows a browser_evaluate tool call with JavaScript code using Chart.js canvas context to draw percentage labels on bars: meta.data.forEach((bar, index) => { const value = dataset.data[index]; if (value !== undefined && value !== null) { ctx.save(); ctx.textAlign = 'center'; ctx.textBaseline = 'bottom'; ctx.fillStyle = '#333'; ctx.font = 'bold 12px sans-serif'; ctx.fillText(value.toFixed(1) + '%', bar.x, bar.y - 5); A pending step reads "Let me take a screenshot to see if it worked." followed by a completed "Done" step, and the message "Let me take a screenshot to check the result."](https://static.simonwillison.net/static/2026/claude-chrome-draw-on-chart.jpg)
Update: If you look at the transcript Claude claims to have switched to Playwright, which is confusing because I didn't think I had that configured.
Via @KLieret
Tags: benchmarks, django, ai, openai, generative-ai, llms, anthropic, claude, coding-agents, ai-in-china, minimax
