Alternatives
Products that do what LUML – an open source (Apache 2.0) MLOps/LLMOps platform does
Hi HN, We built LUML (https://github.com/luml-ai/luml), an open-source (Apache 2.0) MLOps/LLMOps platform that covers experiments, registry, LLM tracing, deployments and so on. It separates the control plane from your data and compute. Artifacts are self-contained. Each model artifact includes all metadata (including the experiment snapshots, dependencies, etc.), and it stays in your storage (S3-compatible or Azure). File transfers go directly between your machine and storage, and execution happens on compute nodes you host and connect to LUML. We’d love you to try…
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OrchestraML▲82From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app
- 5LO
We just open-sourced Lume - a tool we built after hitting walls with existing virtualization options on Apple Silicon. No GUI, no complex stacks - just a single binary that lets you spin up macOS or Linux VMs via CLI or API. Why we built Lume: - Run native macOS VMs in 1 command, using Apple Virtualization.Framework: `lume run macos-sequoia-vanilla:latest` - Prebuilt images on https://ghcr.io/trycua (macOS, Ubuntu on ARM) - API server to manage VMs programmatically `POST /lume/vms` - A python SDK on github.com/trycua/pylume Run prebuilt macOS images in just…
2025 · github.com
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Hey HN, we’re Robert, Din and Temirlan from Laminar (https://www.lmnr.ai), an open-source observability and analytics platform for complex LLM apps. It’s designed to be fast, reliable, and scalable. The stack is RabbitMQ for message queues, Postgres for storage, Clickhouse for analytics, Qdrant for semantic search - all powered by Rust. How is Laminar different from the swarm of other “LLM observability” platforms? On the observability part, we’re focused on handling full execution traces, not just LLM calls. We built a Rust ingestor for OpenTelemetry (Otel) spans with GenAI…
2024 · github.com
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Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…
2023 · github.com
- 8OO
Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
2023 · github.com
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Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai
- 12LA
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
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Hey HN, Lume is an open-source CLI for running macOS and Linux VMs on Apple Silicon. Since launch (https://news.ycombinator.com/item?id=42908061), we've been using it to run AI agents in isolated macOS environments. We needed VMs that could set themselves up, so we built that. Here's what's new in 0.2: *Unattended Setup* – Go from IPSW to a fully configured VM without touching the keyboard. We built a VNC + OCR system that clicks through macOS Setup Assistant automatically. No more manual setup before pushing to a registry: lume create my-vm --os macos --ipsw latest…
Jan 2026 · cua.ai
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Built this for my LLM workflows - needed searchable, persistent memory that wouldn't blow up storage costs. I also wanted to use it locally for my research. It's a content-addressed storage system with block-level deduplication (saves 30-40% on typical codebases). I have integrated the CLI tool into most of my workflows in Zed, Claude Code, and Cursor, and I provide the prompt I'm currently using in the repo. The project is in C++ and the build system is rough around the edges but is tested on macOS and Ubuntu 24.04.
2025 · github.com
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If you've built any web-based app in the last 15 years, you probably used something like Datadog, New Relic, Sentry, etc. to monitor and trace your app, right? Why should it be different when the app you're building happens to be using LLMs? So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js A few months ago we launched…
2024 · github.com
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I've built this to make it easy to host your own infra for lightweight VMs at large scale. Intended for exec of AI-generated code, for CICD runners, or for off-chain AI DApps. Mainly to avoid Docker-in-Docker dangers and mess. Super easy to use with CLI / Python SDK, friendly to AI engs who usually don't like to mess with VM orchestration and networking too much. Defense-in-depth philosophy. Would love to get feedback (and contributors: clear & exciting roadmap!), thx
Oct 2025 · github.com
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Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com
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Hello HN, I've been working on this project for a while, and it has been in an "open" beta for some time. I finally believe it's ready for its first release. I hope you like it. Here are some potential questions that may arise: 1. How does it compare to LM Studio? It's likely that if you're already using LM Studio, you'll continue to do so. This project is designed to be more user-friendly. 2. Is it open-source? No, it is not. 3. Does it use any open-source libraries? Yes, it uses llama.cpp and a few others, as indicated in the license information included with the application. 4. Why is not…
2023 · avapls.com
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I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com
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Some time ago, I was implementing saves for my LOVE2D game. I wanted to do a full dump of the game state -- which included closures (AI), complex graphs, sets with tables as keys and also fundamentally non-serializable data (coroutines and userdata), that require user-defined serialization/deserialization logic. I went through every Lua serialization library -- none covered all data types/cases. So I wrote my own. It is a polished version, thoroughly annotated, tested and documented. It is made to be as functional and customizable as possible (or at least I did everything I could…
2025 · github.com
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Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024
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Hey Hacker News, We're the maintainers of docker/model-runner and wanted to share some major updates we're excited about. Link: https://github.com/docker/model-runner We are rebooting the community: https://www.docker.com/blog/rebooting-model-runner-community... At its core, model-runner is a simple, backend-agnostic tool for downloading and running local large language models. Think of it as a consistent interface to interact with different model backends. One of our main backends is llama.cpp, and we make it a point to contribute any…
Oct 2025 · github.com
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Hey HN! We built mlop (https://mlop.ai), a fully open source (https://github.com/mlop-ai) ML experiment tracking platform, much like Weights & Biases. Unlike the existing competitors we focus heavily on performance (yes our ingestion backend is in Rust), and top tier user experience, and we are fully open sourced with easy self hosting using docker. Why did we build this? WandB was misleading about their performance, they say they are non blocking, but in fact, they block user code (see video https://docs.mlop.ai/docs/demo), our logger tries to be…
2025 · github.com
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