VerbaGPT

What it does

VerbaGPT turns natural language into Python code for data analysis—not just SQL queries, but machine learning and custom visualizations. Two modes: • Virgo (cloud): Fast, cost-efficient browser-based analytics. • Taurus (local): Agentic workflows powered by Claude Code Key features: • Data Notes give LLMs context about your schema (no more hallucinated queries) • Prompt Libraries let you package expertise as licensed IP Built for teams who want non-technical users analyzing data.

VerbaGPT - AI-powered data analysis. Turn your data into insights with natural language.

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  I used Claude Code to discover connections between 100 booksJan 2026 · trails.pieterma.es · ▲524

  I think LLMs are overused to summarise and underused to help us read deeper. I built a system for Claude Code to browse 100 non-fiction books and find interesting connections between them. I started out with a pipeline in stages, chaining together LLM calls to build up a context of the library. I was mainly getting back the insight that I was baking into the prompts, and the results weren't particularly surprising. On a whim, I gave CC access to my debug CLI tools and found that it wiped the floor with that approach. It gave actually interesting results and required very little orchestration…

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  VerbaGPTAug 2026 · app.verbagpt.com · ▲8

  VerbaGPT - AI-powered data analysis. Turn your data into insights with natural language.

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  Context GatewayMar 2026 · github.com · ▲220

  We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…

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  I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…

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