#open-source

Public notes from activescott tagged with #open-source

Friday, July 31, 2026

The self-hostable alternative to Dropbox and Google Drive. Upload, organize, and share files from your own infrastructure.

Bring your own storage — local disk, S3, R2, or Vercel Blob. One env var to switch. Full type-safe API with tRPC.

Very cool.


Next.js 16 with App Router, PostgreSQL with Drizzle ORM, tRPC for type-safe APIs, BetterAuth for authentication, and Tailwind CSS for the UI. The project uses a Turborepo monorepo with pnpm workspaces.

Oof. This Vercel-based stack is hard for me to swallow.


Multi-Store Replication

Attach multiple storage backends per workspace. Designate a primary store, fan out writable replicas for redundancy, and ingest files from read-only sources.

Neat. Almost no backup needed.


Search Inside Images & PDFs

Find files by what's in them, not just the name. Locker transcribes images and PDFs into searchable text so every document is discoverable.

Smart.

Virtual Bash Shell

Navigate your files with familiar commands. Use ls, cd, find, cat, and grep through a virtual filesystem API.

Love the idea of this, but it's not a CLI per se, it's an API you can use via tRPC.

Sunday, July 26, 2026

Thursday, July 23, 2026

Monday, July 20, 2026

Thursday, June 4, 2026

While many AI open source model providers are pursuing larger and more powerful models, Google is still giving attention to the smaller, more local side of the market. Today, the tech giant released Gemma 4 12B, an 11.95-billion-parameter open-weights model with permissive Apache 2.0 license optimized to execute locally on a standard enterprise laptop using just 16GB of VRAM or unified memory.

Traditional multimodal systems typically utilize discrete, separate encoders to translate audio waveforms and visual data into representations that the core language model can process.

This conventional approach inherently increases both inference latency and total memory consumption.

Gemma 4 12B radically alters this pipeline by functioning entirely without these secondary encoders. Instead, visual patches and raw audio waveforms are projected directly into the core large language model's embedding space through lightweight linear layers.

The vision encoder is replaced by a 35-million-parameter module utilizing a single matrix multiplication, while the audio encoder is eliminated entirely.

For enterprise engineering teams, this unified architecture delivers distinct operational advantages: lower latency for multimodal tasks, reduced VRAM requirements (down to 16GB — typical for laptops), and the ability to fine-tune the entire multimodal system in a single, cohesive pass.

Google has ensured that Gemma 4 12B is not an isolated experiment; it is ready for production. Weights are available on Hugging Face and Kaggle, and the model integrates seamlessly with industry-standard deployment frameworks such as vLLM, SGLang, MLX, and llama.cpp.

Friday, May 29, 2026