GitHub
Blazing fast tool that appends only unique lines to files.
What it does
xnew is a fast, low-memory CLI that appends only unique lines to files. Built in Go for large datasets, it streams input efficiently and scales cleanly from thousands to hundreds of millions of lines. - jsmonhq/xnew
Does the same job
all alternatives →- WSWe scaled Git to support 1 TB repos2022 · xethub.com · ▲279
I’ve been in the MLOps space for ~10 years, and data is still the hardest unsolved open problem. Code is versioned using Git, data is stored somewhere else, and context often lives in a 3rd location like Slack or GDocs. This is why we built XetHub, a platform that enables teams to treat data like code, using Git. Unlike Git LFS, we don’t just store the files. We use content-defined chunking and Merkle Trees to dedupe against everything in history. This allows small changes in large files to be stored compactly. Read more here:…
- XSX-spreadsheet – A JavaScript canvas spreadsheet for web2019 · github.com · ▲347
- EAEno – A lightning fast, user-friendly YAML/TOML alternative2018 · ▲102
eno [1] - A modern plaintext language w/ libraries [2] for JavaScript, Python, Ruby & soon more! We migrated a big relational research database to a file-based solution - requirements were: - Super fast and easy editability for users - Highest performance for parsing/validating >10K documents on every user change. Our trials with YAML/TOML showed us that we wanted something both faster [3][4] and easier [4], something tailored for file-based content management ... and after months of research & development it's now publicly available (under MIT license) for everyone! Last but…
- LALeaves – A text-UI disk usage treemap visualizerJul 2026 · github.com · ▲93
GUI disk analyzers are great for figuring out what's filling up your laptop/desktop drive. On containers or remote servers, the options are limited to purely text based utilities (e.g. du) or list-centric TUIs (e.g. ncdu) which are usually limited to viewing one directory at a time. I created leaves to fill that gap. Inspired by classic utilities like WinDirStat and KDirStat, it uses a 2-dimensional treemap^1 visualization to show the entire directory hierarchy with proportionally sized rectangles. It's performant enough to handle millions of files, thanks to Rust and multi-threading.…
- PAPbnj – A minimal, self-hosted pastebin you can deploy in 60 secondsDec 2025 · pbnj.sh · ▲69
I'm sure folks here have seen pastebins a thousand times. There's no innovation left in this space – and that's kind of the point. When I wanted to self-host a pastebin, every option I found was too much. Git-based version control, OAuth, elaborate admin panels. I just wanted something I could deploy in under a minute with a CLI that actually works. So I built pbnj (yes, like the sandwich). What it is: - A minimal, beautiful pastebin with syntax highlighting for 100+ languages - One-click deploy to Cloudflare (free tier gives you ~100,000 pastes) - CLI-first: pbnj file.py → get a URL, copied…
- DEDump entire Git repos into a single file for LLM prompts2024 · ▲57
Hey! I wanted to share a tool I've been working on. It's still very early and a work in progress, but I've found it incredibly helpful when working with Claude and OpenAI's models. What it does: I created a Python script that dumps your entire Git repository into a single file. This makes it much easier to use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Key Features: - Respects .gitignore patterns - Generates a tree-like directory structure - Includes file contents for all non-excluded files - Customizable file type filtering Why I find it useful for…
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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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