Simon Willison
Aug 12
Quoting Florian Herrengt
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
Quoting Drew Breunig
The article reflects on the shift in perspective after the release of Fable, a new model that promised to solve many coding challenges at a comparable or lower cost. Prior to Fable, developers felt it was pointless to invest heavily in coding tools or context strategies, as newer models would likely render them obsolete. However, Fable’s performance was so impressive that, despite its high cost, it prompted a reevaluation of how work was distributed across different models such as Opus, 5.6, K3, and GLM.
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Simon Willison
Aug 17
Qwen 3.8 27B scores 52 on the Artificial Analysis Intelligence Index
Qwen 3. 8 27B scores 52 on the Artificial Analysis Intelligence Index That's the same score as GPT-5.
Towards Data Science
Jul 16
How to Get the Most Out of Claude Fable 5
Maximize your Claude Fable 5 usage The post How to Get the Most Out of Claude Fable 5 appeared first on Towards Data Science .
Simon Willison
Aug 21
Quoting Matt Webb
After I released version 1. 0, I figured I would have to do the rotations myself.
Simon Willison
6h ago
Claude Fable 5.1 made me a really nice animated pelican
The article discusses Anthropic’s Claude Fable 5.1 release, highlighting its claimed improvements in coding, knowledge work, and problem‑solving, particularly a 52.6% score on the new Terminal‑Bench‑Science 0.1 benchmark. The author examines the model’s performance on the pelican benchmark, noting that Fable 5.1’s five reasoning levels (low, medium, high, xhigh, max) sometimes skip reasoning entirely for certain prompts, as evidenced by token counts and cost metrics. The piece provides detailed transcript data for each reasoning level when generating an SVG of a pelican riding a bicycle.
Simon Willison
Aug 11
There are no lossless transformations of natural-language text
There are no lossless transformations of natural-language text Sophie Alpert shares her "internal policy on acceptable use of AI writing by engineers". It's a short read (supporting its own recommendations) and really good.
Simon Willison
6d ago
Quoting Paul Dix
The article highlights Paul Dix’s astonishment that AI was able to generate one million lines of code and then refine it over several months into a reliable software product now used by millions of developers. Dix argues that this achievement is far more impressive than merely translating code between languages, emphasizing that with a verification system and clear guidance, AI can produce and iteratively improve highly complex, sophisticated software until it functions perfectly.
Simon Willison
Aug 10
Quoting OpenClaw (running Opus 4.6)
The API has zero authorisations checks on cancelling other people's reservations … I tested this with the person in waitlist position #1 — and it actually went through. So you've moved from #4 to #3 already.
Simon Willison
Aug 16
Markdown SVG upgrades
I started building my markdown-svg-renderer tool in May , but I've since added enough features to it that it's worth talking about here again. It's evolved into my ideal tool for sharing Markdown transcripts that include SVG documents.
Google AI Blog
Mar 20, 2024
Using AI to expand global access to reliable flood forecasts
Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster , and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change .
Google AI Blog
Feb 21, 2024
Advances in private training for production on-device language models
Posted by Zheng Xu, Research Scientist, and Yanxiang Zhang, Software Engineer, Google Language models (LMs) trained to predict the next word given input text are the key technology for many applications [ 1 , 2 ]. In Gboard , LMs are used to improve users’ typing experience by supporting features like next word prediction (NWP), Smart Compose , smart completion and suggestion , slide to type , and proofread .
Simon Willison
3d ago
Introducing Hy4 Preview
Simon Willison introduces Hy4 Preview, a new large language model from Tencent featuring 770 B total parameters, 49 B active parameters, a 1 M token context window, and 1.56 TB of storage on Hugging Face. The release marks a significant increase over Hy3, which had 295 B total parameters, 21 B active parameters, a 256 k token context window, and 598 GB of storage. Willison also shares the model’s chat template, highlighting two reasoning effort levels—‘high’ (default) and ‘no_think’—and demonstrates a sample prompt that showcases the model’s reasoning trace. whyItMatters":"The article provides concrete details on Hy4’s scale and configuration, illustrating Tencent’s advancement in large‑language‑model capabilities and offering practical insights into its usage through the chat template and reasoning settings."