BloClaw

What it does

Tired of LLM JSON parsing errors and static plots in cheminformatics? BloClaw ditched JSON for an XML-Regex router (0.2% error rate) and built a Python sandbox that monkey-patches Plotly to render interactive 3D molecules natively in your browser. 100% Open Source.

• LC
  LLMs can generate valid JSON 100% of the time2023 · github.com · ▲854

  Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…

• MA
  Modshim – A new alternative to monkey-patching in PythonOct 2025 · github.com · ▲109

  I've invented a new alternative to forking / vendoring / monkey-patching packages in Python. It's a bit like OverlayFS for Python modules - it allows you write modifications for a target module (lower) in a new module (upper), and have these combined in a new virtual module (mount). It works by rewriting imports using AST transformations, then running both the lower and upper module's code in the new Python module. This prevents polluting the global namespace when monkey-patching, and means if you want to make changes to a third-party package, you don't have to take on the…

• AS
  A Spatial Environment for Python2022 · python.natto.dev · ▲169

  Hi all! A little background: I've been working on natto.dev, a spatial environment for JavaScript. I'm really excited about new interfaces for code (leveraging metaphors we're good at, spatial reasoning, making state visible, design tools, etc). With all the buzz around PyScript, I discovered Pyodide and got it working inside natto. This Python version is a stripped down version of https://natto.dev (eg interactive outputs, multiplayer) so please check that out if this interests you. I'm excited to share this spatial environment for Python. Imagine Jupyter cells arranged on a 2D…

• LS
  LLM Scraper – turn any webpage into structured data2024 · github.com · ▲88
• MC
  MonkeyPatch – Cheap, fast and predictable LLM functions in Python2023 · github.com · ▲95

  Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x…

• RL
  Robust LLM extractor for websites in TypeScriptMar 2026 · github.com · ▲72

  We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…

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• IB
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• BC
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• IB
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  AI · Apr 2026 · text.blogosphere.app