The article announces the release of datasette-ports version 0.4. This plugin now works with the latest Datasette version and can detect and list all Datasette instances running on the current machine, displaying their process IDs (PIDs). Knowing the PID allows users to attach the new Python 3.15 profiler to the running instances.
The article announces the release of Datasette version 0.65.4, noting that it includes security updates. It references the Datasette blog for more details on the 1.0a39 and 0.65.4 security releases. The release is tagged as a security update for the Datasette project.
The article announces the release of Datasette version 1.0a39, noting that it includes security updates. It references the Datasette blog for details on the 1.0a39 release and the 0.65.4 security releases. The release is tagged with security and datasette.
The article discusses the new profiler introduced in Python 3.15, highlighting its capabilities compared to the existing cProfile tool. It explains what the new profiler can detect that cProfile misses, providing insights into performance analysis improvements. The post was originally published on Towards Data Science.
The release of datasette-llm 0.1a9 introduces a small user‑experience improvement: the plugin now automatically detects the default LLM model, eliminating the need for users to manually configure a default model when running Datasette Agent. This change simplifies the setup process for users who previously had to specify a model with the `-s plugins.datasette-agent.default_model` option.
The article describes how the author used Parseable, an observability platform, with Datasette to handle OpenTelemetry traces. Parseable offers an open‑source Rust implementation and an enterprise version, while Datasette 1.0a41 added OpenTelemetry support. The author demonstrates the setup and shows a screenshot of a Datasette trace displayed in Parseable’s local web interface.
The article announces the release of Datasette 1.0a40, which includes a security fix identical to that in version 0.65.5, new features such as the ability for plugins to launch and manage background tasks via the datasette.add_background_task() method, and a migration to httpx2 to support features like datasette.client.get(). The update also contains numerous bug fixes, many of which were addressed during a recent triage effort for the upcoming 1.0 stable release.
The article announces the release of llm-tools-datasette version 0.1.1. It explains that updating LLM to use httpx2 caused compatibility issues with plugins that relied on httpx, including llm-tools-datasette, which has now been fixed. The release notes highlight the resolution of this plugin breakage.
The article announces the release of web-edit version 0.1a1. It highlights performance optimizations that were written by Claude Opus 5.5, which were generated after feeding it a Python 3.15 profiling report created by GPT‑6 Astra. The release is tagged with "profiling" and "projects".
The article describes a test of the new Python 3.15 profiler using GPT-6 Astra Extra High in Codex to run it against a web-edit application from PyPI. It includes a flame graph and source heatmap generated from this profiling effort. The author emphasizes the use of these visual tools to analyze the performance of the web-edit app.
A technical overview and some benchmarks The post Python 3. 14 and its New JIT Compiler appeared first on Towards Data Science .
The article announces the release of datasette-explain version 0.2.2, noting that the plugin’s plan explanations now work on read‑only stored‑query pages. The author upgraded the demo site to Datasette 1.0a40, which motivated the new plugin version. The release is tagged for sqlite and datasette users.
arXiv:2607. 04030v1 Announce Type: cross Abstract: Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data.