r/opensource

I didn't want to pay a monthly subscription for App Store keyword data, so I wrote an open source CLI that gets it straight from Apple.

What it does

• Popularity from Apple Search Ads (free Apple Ads account, no campaigns or billing needed)
• Difficulty score, keyword suggestions, current top apps for a term
• Rank tracking with history in a local SQLite file
• Metadata linter for title, subtitle and keyword field

Install

npm i -g hristo2612/aso-cli && aso setup

Try it

aso keywords "white noise,sleep sounds"
aso suggest meditation
aso search "habit tracker"

Plain Node, one dependency, MIT licensed. Runs locally, no account, no telemetry. The only network requests go to Apple.

Repo: github.com/hristo2612/aso-cli

Issues and PRs welcome.

I know there’s already a bunch of open source game engines (i had to change this one’s name to avoid trademarks because of that)

Abyss (and its programming language) are self hosted and self compiled

(and written in Hash)

abyss has the unique feature of you being able to edit it’s source code within the engine itself.

Abyss uses the custom function based programming language Hash

Hash is designed to be simple and easy to learn. The goal is that over time you change stuff about it and make it better for you specifically.

(Hash documentation is built into abyss, so I won’t explain any more yet)

let me know what y'all think of my first open source project. I’m sure happy with it

Github link: https://github.com/thecodegangster/abyss-engine/tree/main
(the html file is the source code, I like standalone html apps, I think they’re neat)

Hey,

I wanted a faster chunking library for my system without affecting the overall accuracy. Did not find many options. So I've build https://github.com/d1pankarmedhi/chunkr

It has most of the chunking strategies like Character, Recursive, Markdown header, Late chunking, Hierarchical chunking, etc. It also supports native PDF loader, and other additional file types.

Some stats (MBA M4 16GB):

Test Case (matched parameters)	Chunkr	LangChain	LlamaIndex	Chonkie	semchunk	text-splitter
Recursive (1 MB, 1000/200)	2,264 MB/s	769 MB/s	10 MB/s	225 MB/s	42 MB/s	175 MB/s
Recursive (5 MB, 1000/200)	2,039 MB/s	696 MB/s	—	201 MB/s	40 MB/s	46 MB/s
Fixed Char (1 MB, 1000/200)	750 MB/s	1.7 MB/s	—	22 MB/s	—	—
Markdown (500 KB, 1000/150)	819 MB/s	67 MB/s	19 MB/s	—	—	40 MB/s
Python Code (200 KB, 1500/200)	3,232 MB/s	622 MB/s	—	—	—	5.7 MB/s
Sentence (500 KB)	622 MB/s	—	10 MB/s	20 MB/s	—	—
BPE Tokens (200 KB, cl100k_base, 512/50)	38 MB/s	43 MB/s	2.0 MB/s	151 MB/s	—	7.2 MB/s
100 docs x 50 KB (parallel batch)	3,224 MB/s	679 MB/s	—	213 MB/s	—	—

---

Extractor / Pipeline	Latency	Throughput	Speedup vs PyPDF
Chunkr PDFLoader (Full Text)	747.9 ms	2,762 pgs/s	15.9x Faster
Chunkr PDFLoader (Page Documents)	721.0 ms	2,865 pgs/s	16.5x Faster
PyMuPDF (fitz)	2,616.8 ms	789.5 pgs/s	4.5x Faster
pypdf (pure Python)	11,900.5 ms	173.6 pgs/s	1.0x (baseline)
Chunkr End-to-End (PDF + Recursive)	798.1 ms	2,589 pgs/s	14.9x Faster
PyMuPDF + LangChain RecursiveTextSplitter	2,659.3 ms	776.9 pgs/s	4.5x Faster
pypdf + LangChain RecursiveTextSplitter	12,054.5 ms	171.4 pgs/s	1.0x (baseline)

Do check it out and share your feedback. Thanks!