Alternatives
Products that do what OS3-RNode, an RNode-Compatible LoRa Modem on CH32V003 does
I built OS3-RNode as a tiny RNode-compatible LoRa modem firmware for CH32V003 + E22-900M22S. It is based on a small deterministic event-driven core, with no scheduler, no threads, and bounded interrupt work. The goal was to make a minimal and auditable transport for Reticulum/NomadNet on very constrained hardware. The article has the architecture notes and a firmware link. Feedback on the event model, failure handling, and interoperability tradeoffs would be useful.
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2018 · astrocorp.net
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2019 · github.com
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- 6KP
I thought it'd be interesting to use Linux PSI (Pressure Stall Information) for an LLM runtime to trim the KV cache. This is mainly useful imo for edge devices like the Jetson Orin super nano kit which have unified memory. I haven't benched much, but plan to do so more over time and see if I can make a real use of it as I run local LLMs. Let me know if it makes sense :P (I of course vibed this idea)
Jun 2026 · github.com
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Jan 2026 · docs.arcprize.org
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Jan 2026 · github.com
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May 2026 · github.com
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Nov 2025 · github.com
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I made this after seeing someone posit the idea online yesterday over lunch then spent some time refining it. So far it's pretty impressive IMO! Right now I am running Qwen3-30B-A3B on my 24gb unified memory m4 MacBook Pro at 50 tok/sec and this should definitely not be working for such a large model on my middling hardware. Things are detailed in the README to get up and running and DESIGN.md has details on all the choices and such made along the way.
23d ago · github.com
- 12UA
Hey HN! After using a combination of Unsloth and Axolotl a lot, and finding it generally painful to figure out the right performance tuning for things like batch sizing and multi-GPU sharding, I wrote a small Python lib that sets up known-good LoRA training configurations for Llama 3.1 8B and 70B Instruct, and includes helpers for distilling from larger models or training on serverless finetuning platforms, and includes a walkthrough for distilling DeepSeek-R1 into a Llama 3.1 8B LoRA... But you can use it for pretty much any finetuning task, not just distilling large models!
2025 · github.com
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2025 · github.com
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I built this over the weekend after reading about StrongDM's software factory (their writeup: https://factory.strongdm.ai/, Simon Willison's deep dive: https://simonwillison.net/2026/Feb/7/software-factory/, Dan Shapiro's Five Levels: https://www.danshapiro.com/blog/2026/01/the-five-levels-from...). OctopusGarden is an open-source implementation of the pattern StrongDM described: holdout scenarios, probabilistic satisfaction scoring via LLM-as-judge, and a convergence loop that iterates until the code works; no…
Mar 2026 · github.com
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2023 · github.com
- 16AM
I recently finished a round of tweaks on my barebones mud-server and thought HN might be interested. https://github.com/endgame/MudCore It sits somewhere between socketmud and a full mud like tbamud in that it handles all the socket work and telnet negotiation, but it makes no assumptions about anything else. The other interesting thing is that it can talk to other services using ZeroMQ. I'm interested in reactions, particularly in terms of coding style, documentation quality and so on. It's my main "presentation piece" so far, in that it's some of the first code I actually feel proud to…
2012
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Starting from version 3.0, it comes with a high availabilty (=safe)
2023 · github.com
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Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
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Hi HN, I’m sharing the alpha release of S2C, a state machine replication system built atop S3. The goal is to enable a distributed application to maintain consistent state without needing a quorum of nodes for availability or consistency. The idea came from a side project that was using S3 and where I needed strongly consistent distributed state but wanted to avoid adding a separate consensus dependency. I initially tried to use S3 directly for coordination, but it became messy. Eventually, I realized I need a replicated state machine with a deterministic log, and then it ended up as a…
Jan 2026 · github.com
- 20PS
I was debating when to post this, but I've been using it locally in a variety of situations for several months and I think it's pretty stable (and usable!). There are more features I'd like to add, but I wanted to see if there was much interest before spending much more time on it right now.
2025 · github.com
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hey hn, I built an open-source Perplexity clone that can run local LLMs and cloud LLMs. It's fully self-hostable through Docker and uses ollama to support local LLMs. The demo video in the repository shows me running it locally with llama3 on my M1 Macbook Pro. I'm open to any suggestions or feedback, thanks!
2024 · github.com
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I wanted to share a project I've been working on for the past few weeks: llgtrt. It's a Rust implementation of a HTTP REST server for hosting Large Language Models using llguidance library for constrained output with NVIDIA TensorRT-LLM. The server is compatible with the OpenAI REST API and supports structured JSON schema enforcement as well as full context-free grammars (via Guidance). It's similar in spirit to the Python-based TensorRT-LLM OpenAI server example but written entirely in Rust and built with constraints in mind. No Triton Inference Server involved. This also serves as a demo…
2024 · github.com
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Over the past few months, as we scaled our internal AI Agents, we hit a dead end: Running LLM-generated arbitrary code in Docker is basically running naked on security due to container escape risks. But using full traditional VMs takes minutes to boot and eats too much memory to support high-density concurrency. We loved the developer experience of SaaS sandboxes on the market, but they are closed-source, expensive, and have too high a barrier to entry for self-hosting. So, our team decided to build our own. After months of grinding, using RustVMM and KVM, we built a blazing-fast,…
Apr 2026 · github.com
- 24AT
Mar 2026 · github.com
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