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deviantecho/querylens

Local AI analytics workbench with text-to-SQL, a trained intent classifier, declarative business models, and execution-based evaluation.

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Python
1 contributors
MIT License
website
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Can AI agents reverse-engineer real websites?

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Versioned graph database on object storage. Git-style workflows and multimodal retrieval

Momentum

How fast deviantecho/querylens is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Nov 14 – Nov 15, 2026

Stars +0 in 2 days

01

Forks +0 in 2 days

01

Merged PRs 0 in 2 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Nov 14Nov 15
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Sat, Nov 14, 2026+0+00
Sun, Nov 15, 2026+0+00
Total+0+00
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Nov 153 posts
    • Would a frontier model do better raw? Probably. But local means private, cheap, and always available. For many internal tools, that tradeoff is worth it.

      Read full on X, posted by @deviantecho

      @deviantecho · x.com

    • Why this works: most errors are local. One wrong identifier. One bad cast. One missing qualifier. Repair fixes the local error. Regenerating from scratch often creates a new one.

      Read full on X, posted by @deviantecho

      @deviantecho · x.com

    • None of this is clever. That is the point. Compiler style checks are deterministic. LLM output is not. Put the deterministic layer around the uncertain one.

      Read full on X, posted by @deviantecho

      @deviantecho · x.com