matan-h

matan-h / pyhash-complete

Public

fast Python autocomplete using precomputed table

autocomplete complete dataset editor lsp
16
1
100% credibility
Found Mar 25, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This project creates frequency-based data tables from real Python code usage to improve autocomplete suggestions in editors by ranking popular attributes higher.

How It Works

1
🔍 Find smarter code helper

You hear about a tool that makes Python code suggestions better by prioritizing the most commonly used options instead of random ones.

2
📥 Grab the ready data

Download a small file packed with real-world Python usage stats from thousands of projects.

3
Choose your way to explore
🔎
Quick lookup

Type a code snippet to instantly see its popularity score.

✏️
Open demo editor

Launch a simple window to type code and see smart suggestions pop up.

4
⭐ See magic suggestions

Watch as the tool ranks options by how often real people use them, making coding feel intuitive and fast.

5
🔄 Make your own data (optional)

If you want custom stats, point it at a big collection of code examples to build a fresh suggestion file.

🎉 Code with confidence

Enjoy faster, smarter autocomplete that saves time and reduces frustration every time you write Python.

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Star Growth

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AI-Generated Review

What is pyhash-complete?

This Python project delivers fast autocomplete by ranking suggestions based on real-world usage frequencies from 5.2K GitHub repos, not alphabetical order. Download a tiny precomputed binary table from releases for instant lookups via simple Python CLI commands like scoring "os.stat" or listing top module attrs. Run the demo editor for a live taste of smarter completions in under 8ms.

Why is it gaining traction?

Unlike LSPs that bury gems under obscurities like os.CLD_CONTINUED, it surfaces battle-tested attrs first, trained on actual code patterns for fast Python API calls and stdlib chains. The hook is its blazing speed and small footprint—perfect for fast GitHub search or autocomplete in resource-constrained setups—plus ongoing LSP integration for editors.

Who should use this?

Python backend devs debugging stdlib chains, data scientists chasing fast Python for data science workflows, or anyone building fast Python type checkers tired of hunting common methods. Ideal for CLI tools, scripts, or embedding in custom IDEs where quick, usage-aware completions beat AI slowness.

Verdict

Promising fix for autocomplete woes, but at 16 stars and 1.0% credibility, it's early—docs are README-focused with no tests. Grab the dataset for fast GitHub download and test the demo; integrate if you're prototyping fast Python high performance techniques.

(187 words)

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