BeatStamp
Automate beat tag placement and brand every beat faster!
What it does
BeatStamp is a web tool built for music producers that automates producer tag placement across instrumentals. Instead of manually dragging tags throughout a session, BeatStamp helps creators save time, streamline workflow, protect beats, and strengthen their brand identity. While built for producers, DJs and other creators can also use BeatStamp to automate drops and audio watermarks. BeatStamp offers both free and PRO plans for users with larger workloads and advanced needs.
Does the same job
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- BABeats, a web-based drum machineJan 2026 · beats.lasagna.pizza · ▲160
Hello all! I've been an avid fan of Pocket Operators by Teenage Engineering since I found out about them. I even own an EP-133 K.O. II today, which I love. A couple of months ago, Reddit user andiam03 shared a Google Sheet with some drum patterns [1]. I thought it was a very cool way to share and understand beats. During the weekend I coded a basic version of this app I am sharing today. I iterated over it in my free time, and yesterday I felt like I had a pretty good version to share with y'all. It's not meant to be a sequencer but rather a way to experiment with beats and basic sounds,…
- IMI made a generative online drum machine with ClojureScriptSep 2025 · dopeloop.ai · ▲202
After two years of development, I'm super excited to release Beat Maker! This is my take on what I hope is the best free, web-based drum machine. My goal was to build something that was not only fun and easy to use for beginners but also powerful enough for serious producers. I did extensive research on existing drum machines, analyzed their UX, and tried to build something that adds something new. It's a nearly 100% client-side app, written in ClojureScript, and is a PWA so you can install it to your home screen for an app-like experience. Besides the standard grid editor, Beat Maker has…
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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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