2026-07-11

Sqlite-Utils, Python, Sqlite, Ai-Assisted Programming

Simon Willison — 2026-07-11#

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The release of sqlite-utils 4.1 showcases how Simon is aggressively integrating AI into his open-source workflow, using GPT-5.6 Codex not just to write code, but to triage issues and manually exercise edge cases in the terminal. It also introduces a clever workaround to migrate existing databases to SQLite’s STRICT mode by utilizing the tool’s built-in table transformation mechanism.

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sqlite-utils 4.1 · Source Just days after shipping version 4.0, Simon released sqlite-utils 4.1 with a batch of minor but highly useful CLI features. Highlights include a --code option to generate rows for insertion directly via Python snippets, and a --type override to prevent CSV/TSV data like ZIP codes from being incorrectly parsed as integers. Inspired by an Evan Hahn post on Hacker News, the release adds strict=True/False toggles to the transform command, seamlessly migrating data to enforce SQLite’s strict table schemas. Interestingly, Simon relied heavily on AI-assisted programming for this release: he had Codex scan his repository to find the easiest open issues, and used an advanced prompt instructing the model to use uv run python -c to manually test its own work and uncover edge cases.

2026-04-05

Blogs, AI, Tech

Ai-Assisted Programming, Sqlite, Security, Agentic-Engineering, Llms

Simon Willison — 2026-04-05#

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Simon highlights a deep-dive post by Lalit Maganti on the realities of “agentic engineering” when building a robust SQLite parser. The piece beautifully articulates a crucial lesson for our space: while AI is incredible at plowing through tedious low-level implementation details, it struggles significantly with high-level design and architectural decisions where there isn’t an objectively right answer.

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Eight years of wanting, three months of building with AI Simon shares a standout piece of long-form writing by Lalit Maganti on the process of building syntaqlite, a parser and formatter for SQLite. Claude Code was instrumental in overcoming the initial hurdle of implementing 400+ tedious grammar rules, allowing Lalit to rapidly vibe-code a working prototype. However, the post cautions that relying on AI for architectural design led to deferred decisions and a confusing codebase, ultimately requiring a complete rewrite with more human-in-the-loop decision making. The core takeaway is that while AI excels at tasks with objectively checkable answers, it remains weak at subjective design and system architecture.

2026-04-08

Blogs, AI, Tech

Docker, Sqlite, Llms, Code-Interpreter, Meta

Simon Willison — 2026-04-08#

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The most substantial piece today is a deep-dive into Meta’s new Muse Spark model and its chat harness, where Simon successfully extracts the platform’s system tool definitions via direct prompting. His exploration of Meta’s built-in Python Code Interpreter and visual_grounding capabilities highlights a powerful, sandbox-driven approach to combining generative AI with programmatic image analysis and exact object localization.

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Meta’s new model is Muse Spark, and meta.ai chat has some interesting tools Meta has launched Muse Spark, a new hosted model currently accessible as a private API preview and directly via the meta.ai chat interface. By simply asking the chat harness to list its internal tools and their exact parameters, Simon documented 16 different built-in tools. Standouts include a Python Code Interpreter (container.python_execution) running Python 3.9 and SQLite 3.34.1, mechanisms for creating web artifacts, and a highly capable container.visual_grounding tool. He ran hands-on experiments generating images of a raccoon wearing trash, then used the platform’s Python sandbox and grounding tools to extract precise, nested bounding boxes and perform object counts (like counting whiskers or his classic pelicans). Although the model is closed for now, infrastructure scaling and comments from Alexandr Wang suggest future versions could be open-sourced.

2026-04-11

Blogs, AI, Tech

Sqlite, Sql, Tools, Webassembly

Simon Willison — 2026-04-11#

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The standout update today centers on the release of SQLite 3.53.0, where Simon highlights highly anticipated native ALTER TABLE constraint improvements and showcases his classic rapid-prototyping workflow by using Claude Code on his phone to build a WebAssembly-powered playground for the database’s new Query Result Formatter.

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SQLite 3.53.0 · Source This is a substantial release following the withdrawal of SQLite 3.52.0, packed with accumulated user-facing and internal improvements. Simon specifically highlights that ALTER TABLE can now directly add and remove NOT NULL and CHECK constraints, a workflow he previously had to manage using his own sqlite-utils transform() method. The update also introduces json_array_insert() (alongside its jsonb equivalent) and brings significant upgrades to the CLI mode’s result formatting via a new Query Results Formatter library. True to form, Simon leveraged AI assistance—specifically Claude Code on his phone—to compile this new C library into WebAssembly to build a custom playground interface.

2026-05-10

Blogs, AI, Tech

Hallucinations, Journalism, Careers, Sqlite

Simon Willison — 2026-05-10#

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Simon highlights a stark example of AI hallucination making its way into mainstream journalism, serving as a critical warning for anyone relying on LLMs for factual summarization.

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Quoting New York Times Editors’ Note · Source Simon shares a sobering editors’ note from the New York Times illustrating the dangers of unchecked generative AI in the newsroom. A reporter mistakenly attributed an AI-generated summary of Canadian Conservative leader Pierre Poilievre’s views as a direct, verbatim quote. The hallucinated text falsely claimed he called politicians who changed allegiances “turncoats,” underscoring exactly why LLM outputs must be rigorously verified against primary sources rather than trusted blindly.

2026-05-21

Blogs, AI, Tech

Datasette, Llms, Sqlite, Generative Ai, Sandboxing

Simon Willison — 2026-05-21#

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The major news today is the official announcement of Datasette Agent, merging Simon’s three years of work on the LLM library with Datasette to create an extensible, conversational AI assistant for querying data. It represents a huge milestone for his ecosystem, opening the door for users to naturally interrogate their databases and easily build custom tools using a new plugin architecture.

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Datasette Agent Simon officially announced Datasette Agent, a conversational AI interface that lets users ask questions of the data stored in Datasette. The post features a live demo using Gemini 3.1 Flash-Lite to successfully query a blog database to find a bird-watching record. He highlights a growing plugin ecosystem—including charts, image generation, and sandbox execution—and notes that tools like Claude Code and OpenAI Codex are proving excellent at writing these extensions. Looking ahead, Simon teased a major refactor for his LLM library, a Claude Artifacts-style plugin, and a personal AI assistant named “Claw” built using his older Dogsheep tools.

2026-05-27

Blogs, AI, Tech

Coding Agents, Llms, Openai, Anthropic, Sqlite

Simon Willison — 2026-05-27#

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Simon makes a compelling case that April 2026 marks a new inflection point where frontier AI labs have found true product-market fit with coding agents. By analyzing sudden enterprise pricing pivots, sales hiring sprees, and massive inference compute deals, he illustrates how the enterprise adoption of AI agents is finally turning massive usage into real revenue.

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I think Anthropic and OpenAI have found product-market fit Simon argues that the sudden shift by OpenAI and Anthropic to charge enterprise customers full API token prices for agent usage signals true product-market fit. He notes that heavy coding agent users easily burn thousands of dollars in token equivalents, prompting labs to pivot away from middlemen like Cursor or Copilot to capture this enterprise value directly. The piece features some classic Simon dogfooding—using Claude Code and Datasette Agent to analyze AI lab job listings—and highlights a SpaceX S-1 filing revealing Anthropic’s staggering $1.25 billion monthly compute spend.

2026-05-29

Blogs, AI, Tech

Anthropic, Datasette, Artificial Intelligence, Sql, Sqlite

Simon Willison — 2026-05-29#

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Today’s most significant update is the release of Datasette 1.0a31, a massive paradigm shift for the project that introduces UI support for executing write queries directly against the database.

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datasette 1.0a31 Simon has released a major alpha for Datasette, bringing a highly-requested evolution: users with the right permissions can now execute write queries and save “stored queries” (formerly “canned queries”) directly in the UI. This allows developers to set up templated insert, update, and delete operations against their databases. This release also marks the third post on the recently launched Datasette blog, highlighting his ongoing push for better project documentation.

2026-06-15

Blogs, AI, Tech

Datasette, Sqlite, Llms, Anthropic, Jailbreaking

Simon Willison — 2026-06-15#

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The most exciting update today is the release of datasette-agent 0.3a0, which introduces natural language database modification right from the terminal. By combining the new execute_write_sql tool with an --unsafe auto-approval mode, Simon has made it possible to chat directly with a SQLite database and modify its schema and records on the fly.

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datasette-agent 0.3a0 · Source Simon just shipped a major update to his experimental datasette-agent project, adding an execute_write_sql tool that can prompt for user approval before writing to a database. He also enhanced the CLI chat terminal with options like --yes, --root, and --unsafe to streamline or bypass these permission checks entirely. Using the --unsafe flag alongside a model like gpt-5.5, developers can now converse directly with a specific database to execute structural changes, such as creating tables or inserting records via natural language.

2026-06-21

Blogs, AI, Tech

Sqlite-Utils, Sqlite, Cloudflare, Migrations, Ai Agents

Simon Willison — 2026-06-21#

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The major news today is the first release candidate for sqlite-utils v4, which officially absorbs the battle-tested sqlite-migrate package and introduces nested transactions. It’s a significant maturation for one of Simon’s core data tools, streamlining the developer experience by bringing schema evolution directly into the main library.

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sqlite-utils 4.0rc1 adds migrations and nested transactions Simon dropped the first release candidate for sqlite-utils v4, adding built-in database migrations and a db.atomic() API for nested transactions. The migrations system is deliberately small, offering no reverse migrations, and relies on a design already proven in his LLM CLI project. As a major release, it includes several backwards-incompatible changes—such as defaulting floating-point types to the correct SQLite REAL type, and requiring db.view() instead of db.table() for accessing views—so he is asking the community to test it via uvx or pip.