Engineering on Matt Suiche

Seeing Sound: Generative Techno and DSP in Pure NumPy

Mon, 13 Apr 2026 00:00:00 +0000

This post is a bit of a grab bag — personal notes dumped here so I can pick up the thread later. The main goal: achieving generative EDM/techno music. Everything else — DSP, frequency bands, oscillators, filters — is machinery toward that end.

Especially now with AI/GenAI, this feels achievable: create bangers with a few Python scripts, provide generative sound experiences that are unique each time. Not generating samples from prompts — actually synthesizing sound from first principles.

Local Models Within Reach: Everything That Changed in Eight Months

Sun, 05 Apr 2026 00:00:00 +0000

Eight months ago I published Building Agents for Small Language Models, a set of hard-won notes from shipping agents on 270M–32B parameter models. At the time, running useful local models meant embracing constraints: small context windows, CPU-only fallbacks, broken UTF-8 streams, and reasoning that fell apart past two steps.

I stand by that post. But the ground has shifted fast. What was a set of careful workarounds in August 2025 is starting to look like the default architecture for a large class of workloads. Local models are no longer the constrained sibling of cloud APIs — for many agent use cases, they are the better answer. Here is what has changed.

Building Agents for Small Language Models: A Deep Dive into Lightweight AI

Wed, 27 Aug 2025 00:00:00 +0000

The landscape of AI agents has been dominated by large language models (LLMs) like GPT-4 and Claude, but a new frontier is opening up: lightweight, open-source, locally-deployable agents that can run on consumer hardware. This post shares internal notes and discoveries from my journey building agents for small language models (SLMs) – models ranging from 270M to 32B parameters that run efficiently on CPUs or modest GPUs. These are lessons learned from hands-on experimentation, debugging, and optimizing inference pipelines.