Public notes from activescott tagged with #open-source
FunctionGemma is a specialized version of our Gemma 3 270M model tuned for function calling. It is designed as a strong base for further training into custom, fast, private, local agents that translate natural language into executable API actions.
Connecting the most prolific open source maintainers & contributors with their next jobs
This is a Container Storage Interface (CSI) for S3 (or S3 compatible) storage. This can dynamically allocate buckets and mount them via a fuse mount into any container.
GeeseFS allows you to mount an S3 bucket as a file system. GeeseFS is a high-performance, POSIX-ish S3 (Yandex, Amazon) file system written in Go
An open-source distributed object storage service tailored for self-hosting
Garage implements the Amazon S3 API and thus is already compatible with many applications.
The main goal of Garage is to provide an object storage service that is compatible with the S3 API from Amazon Web Services. We try to adhere as strictly as possible to the semantics of the API as implemented by Amazon and other vendors such as Minio or CEPH.
Useful links:
In December 2025, MinIO officially entered “maintenance mode” for its open-source edition, effectively ending active development. Combined with earlier moves like removing the admin UI, discontinuing Docker images, and pushing users toward their $96,000+ AIStor paid product, the writing was on the wall: MinIO’s open-source days were over.
Garage: What I chose. Lightweight, Rust-based, genuinely open source. SeaweedFS: Go-based, active development, designed for large-scale deployments but works at small scale. Ceph RGW: If you’re already running Ceph, the RADOS Gateway provides S3 compatibility.
The AI Gateway is designed for fast, reliable & secure routing to 1600+ language, vision, audio, and image models. It is a lightweight, open-source, and enterprise-ready solution that allows you to integrate with any language model in under 2 minutes.
Blazing fast (<1ms latency) with a tiny footprint (122kb) Battle tested, with over 10B tokens processed everyday Enterprise-ready with enhanced security, scale, and custom deploymentsWhat can you do with the AI Gateway?
Integrate with any LLM in under 2 minutes - Quickstart Prevent downtimes through automatic retries and fallbacks Scale AI apps with load balancing and conditional routing Protect your AI deployments with guardrails Go beyond text with multi-modal capabilities Explore agentic workflow integrations Manage MCP servers with enterprise auth & observability using MCP Gateway
Arize Phoenix: Open-source LLM tracing and evaluation Evaluate, experiment, and optimize AI products in real time.
Claude’s constitution is the foundational document that both expresses and shapes who Claude is. It contains detailed explanations of the values we would like Claude to embody and the reasons why. In it, we explain what we think it means for Claude to be helpful while remaining broadly safe, ethical, and compliant with our guidelines. The constitution gives Claude information about its situation and offers advice for how to deal with difficult situations and tradeoffs, like balancing honesty with compassion and the protection of sensitive information. Although it might sound surprising, the constitution is written primarily for Claude. It is intended to give Claude the knowledge and understanding it needs to act well in the world.
Claude itself also uses the constitution to construct many kinds of synthetic training data, including data that helps it learn and understand the constitution, conversations where the constitution might be relevant, responses that are in line with its values, and rankings of possible responses. All of these can be used to train future versions of Claude to become the kind of entity the constitution describes. This practical function has shaped how we’ve written the constitution: it needs to work both as a statement of abstract ideals and a useful artifact for training.
This program generates images from text prompts (and optionally from other images) using the FLUX.2-klein-4B model from Black Forest Labs. It can be used as a library as well, and is implemented entirely in C, with zero external dependencies beyond the C standard library. MPS and BLAS acceleration are optional but recommended.
the short version is that it’s now possible to point a coding agent at some other open source project and effectively tell it “port this to language X and make sure the tests still pass” and have it do exactly that.
the short version is that it’s now possible to point a coding agent at some other open source project and effectively tell it “port this to language X and make sure the tests still pass” and have it do exactly that.
Does this library represent a legal violation of copyright of either the Rust library or the Python one? #
I decided that the right thing to do here was to keep the open source license and copyright statement from the Python library author and treat what I had built as a derivative work, which is the entire point of open source.
Even if this is legal, is it ethical to build a library in this way? #
After sitting on this for a while I’ve come down on yes, provided full credit is given and the license is carefully considered. Open source allows and encourages further derivative works! I never got upset at some university student forking one of my projects on GitHub and hacking in a new feature that they used. I don’t think this is materially different, although a port to another language entirely does feel like a slightly different shape.
The much bigger concern for me is the impact of generative AI on demand for open source. The recent Tailwind story is a visible example of this—while Tailwind blamed LLMs for reduced traffic to their documentation resulting in fewer conversions to their paid component library, I’m suspicious that the reduced demand there is because LLMs make building good-enough versions of those components for free easy enough that people do that instead.
SearXNG is a free internet metasearch engine which aggregates results from up to 244 search services. Users are neither tracked nor profiled. Additionally, SearXNG can be used over Tor for online anonymity.
Get started with SearXNG by using one of the instances listed at searx.space. If you don’t trust anyone, you can set up your own, see Installation.
Lemmy is similar to sites like Reddit, Lobste.rs, or Hacker News: you subscribe to forums you're interested in, post links and discussions, then vote, and comment on them. Behind the scenes, it is very different; anyone can easily run a server, and all these servers are federated (think email), and connected to the same universe, called the Fediverse.
For a link aggregator, this means a user registered on one server can subscribe to forums on any other server, and can have discussions with users registered elsewhere.
It is an easily self-hostable, decentralized alternative to Reddit and other link aggregators, outside of their corporate control and meddling.
Each Lemmy server can set its own moderation policy; appointing site-wide admins, and community moderators to keep out the trolls, and foster a healthy, non-toxic environment where all can feel comfortable contributing.
Embed charts anywhere. Our chart API generates chart images, QR codes, and more.
Highly customizable. We're built on Chart.js, the most popular open-source charting library. We'll render any Chart.js configuration.
Easy to use. Start by putting your Chart.js definition in a URL: https://quickchart.io/chart?c={your chart here}
No-code support. Not technical? No problem. Design your chart using the Chart Maker, Zapier, or Make.
NVTOP stands for Neat Videocard TOP, a (h)top like task monitor for GPUs and accelerators. It can handle multiple GPUs and print information about them in a htop-familiar way.
Currently supported vendors are AMD (Linux amdgpu driver), Apple (limited M1 & M2 support), Huawei (Ascend), Intel (Linux i915/Xe drivers), NVIDIA (Linux proprietary divers), Qualcomm Adreno (Linux MSM driver), Broadcom VideoCore (Linux v3d driver).
RDMA lets the Macs all act like they have one giant pool of RAM, which speeds up things like massive AI models.
exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices.