# Newsletter Pitches

---

## Python Weekly
**Submit to:** rahul@pythonweekly.com
**Subject:** New Python tool: GraQle — knowledge graphs for codebases (v0.31.3 on PyPI)

```
Hi Rahul,

I'd like to suggest GraQle for Python Weekly.

GraQle is a Python SDK + CLI that builds knowledge graphs from codebases, giving AI coding tools architecture-aware reasoning instead of file-by-file reading.

Key highlights:
- Pure Python (pip install graqle), 2,000+ tests, v0.31.3 on PyPI
- Builds dependency graphs from Python/JS/TS codebases
- MCP server with 16 tools for Claude Code, Cursor, VS Code Copilot
- 14 LLM backends (Anthropic, OpenAI, Ollama, local models)
- Fully offline capable
- Two EU patents filed (EP26162901.8, EP26166054.2)

Quick start:
  pip install graqle && graq scan repo . && graq run "what depends on auth?"

GitHub: https://github.com/quantamixsol/graqle
PyPI: https://pypi.org/project/graqle/
Website: https://graqle.com

Thanks for considering it!

Best,
Harish Kumar
harish.kumar@quantamixsolutions.com
```

---

## Pycoder's Weekly
**Submit to:** https://pycoders.com/submissions/new (or form on site)
**Category:** Projects / Libraries

```
Title: GraQle — Architecture-aware code intelligence with knowledge graphs
URL: https://github.com/quantamixsol/graqle
Category: Projects

Description:
GraQle builds a knowledge graph from any codebase so AI tools reason over structure instead of reading raw files. Features dependency analysis, impact analysis, institutional memory, and a 16-tool MCP server for Claude Code, Cursor, and VS Code. 14 LLM backends, fully offline. v0.31.3 on PyPI with 2,000+ tests.
```

---

## TLDR Newsletter
**Submit to:** https://tldr.tech/ (contact/sponsor page or tips@tldr.tech)

```
Subject: Developer tool launch: GraQle — knowledge graphs for AI coding assistants

GraQle (pip install graqle) builds a knowledge graph from any codebase, reducing AI coding assistant context from 50,000 tokens to 500 per question. Ships with an MCP server (16 tools) for Claude Code, Cursor, and VS Code Copilot. 14 LLM backends. Fully offline.

Think of it as "give your AI a map of your codebase instead of making it read every file."

GitHub: https://github.com/quantamixsol/graqle
PyPI: https://pypi.org/project/graqle/
```

---

## Console.dev
**Submit to:** https://console.dev/submit (they review interesting dev tools weekly)

```
Tool name: GraQle
URL: https://github.com/quantamixsol/graqle
What it does: Builds knowledge graphs from codebases for architecture-aware AI reasoning. MCP server for Claude/Cursor/Copilot. 14 LLM backends.
Language: Python
License: Proprietary (free tier: 500 nodes)
```

---

## Changelog
**Submit to:** news@changelog.com

```
Subject: GraQle — knowledge graph layer between AI tools and codebases

GraQle builds a knowledge graph from any codebase, turning file-reading AI tools into architecture-aware reasoning agents. MCP server with 16 tools, 14 LLM backends, fully offline. Python SDK on PyPI.

The core insight: dependency questions cost 500 tokens via graph traversal vs 50,000 tokens via file reading. The graph persists across sessions, so AI assistants retain institutional memory.

GitHub: https://github.com/quantamixsol/graqle
Website: https://graqle.com
```

---

## Python Bytes Podcast
**Submit to:** https://pythonbytes.fm/topics/submit

```
Topic: GraQle — a knowledge graph layer for AI coding assistants
URL: https://github.com/quantamixsol/graqle
Why it's interesting: Instead of AI tools reading 60 files to answer "what depends on auth?", GraQle builds a graph and answers in one traversal. Ships as MCP server with 16 tools. 14 backends including Ollama for fully offline use. 2,000+ tests. Two EU patents.
```

---

## Talk Python to Me Podcast
**Submit to:** https://talkpython.fm/episodes/all (contact via site)

```
Potential episode topic: "Knowledge graphs for code intelligence — how GraQle gives AI tools architectural awareness"
- Graph-based code analysis vs file-based
- MCP protocol for IDE integration
- Multi-agent reasoning over dependency graphs
- Building a Python SDK with 14 LLM backend support
```
