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.
Don't classify. Hallucinate!
Don't classify. Hallucinate!
Related stories
Simon Willison
Aug 23
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.
Google AI Blog
Mar 11, 2024
Chain-of-table: Evolving tables in the reasoning chain for table understanding
Posted by Zilong Wang, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team People use tables every day to organize and interpret complex information in a structured, easily accessible format. Due to the ubiquity of such tables, reasoning over tabular data has long been a central topic in natural language processing (NLP).
Simon Willison
Sep 11
Quoting Boris Cherny
The article discusses how production code generated by Claude, Anthropic’s AI, should meet higher standards than human-written code. Anthropic enforces this through numerous guardrails such as lint rules, extensive testing, Claude-driven end‑to‑end tests, daily fuzzers, automated code and security reviews, and automated refactoring. These measures aim to prevent the code from becoming difficult to maintain.
Simon Willison
Oct 19
My obituary for programming
Simon Willison reflects on how programming used to be a rewarding career, describing it as a job where developers solved complex logic puzzles for high pay and had full control over strategy and timelines. He notes that coding agents and large language models (LLMs) now handle many of these puzzles, offering a "gift that keeps on giving" for those who enjoy building systems without caring deeply about the underlying problems. However, for programmers who found joy in directly solving problems, this shift represents a significant loss.
Simon Willison
Oct 7
Anti-Patterns in Software Blogging
The article "Anti-Patterns in Software Blogging" highlights common pitfalls in technical writing, such as meandering introductions, overestimating readers’ prior knowledge, and overusing links to avoid explanations. It emphasizes the importance of a clear, conversational voice and warns against the blandness that can arise when developers rely too heavily on AI-generated content. The piece urges bloggers to write in a natural, engaging style to better connect with their audience.
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
Oct 16
Quoting Addy Osmani, Morgan Westlee Lunt, and Qing Wang
The article discusses a shift in code review practices, moving from a final gate to continuous review that functions like spellcheck during coding. It highlights how the Claude Code team uses the Claude AI to catch routine mistakes before human reviewers intervene, allowing developers to focus on higher-level issues. The piece references insights from Addy Osmani, Morgan Westlee Lunt, and Qing Wang on this evolving approach.
Google AI Blog
Mar 14, 2024
Cappy: Outperforming and boosting large multi-task language models with a small scorer
Posted by Yun Zhu and Lijuan Liu, Software Engineers, Google Research Large language model (LLM) advancements have led to a new paradigm that unifies various natural language processing (NLP) tasks within an instruction-following framework. This paradigm is exemplified by recent multi-task LLMs, such as T0 , FLAN , and OPT-IML .
Simon Willison
Sep 14
Quoting Laurie Voss
Laurie Voss argues that while the cost of writing code has fallen dramatically, the costs of reviewing, fixing, and operating software are rising and will continue to do so. She emphasizes that the true expense lies in understanding user needs, precisely defining requirements, and ensuring a pleasant user experience—costs that are unique to each software product and do not scale with reuse. As software demand grows without an upper limit, these user‑centric costs will dominate the overall development effort.
Simon Willison
Sep 18
Quoting Thariq Shihipar
The article announces that Claude Code will now support AGENTS.md files starting with version 2.1.277. If a CLAUDE.md file is absent in a folder, Claude will automatically look for and use AGENTS.md, leveraging Claude Code mods to customize the harness. The built‑in mod is available for use, and users can also create their own custom project instructions.
Simon Willison
Aug 29
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."