r/MachineLearning • u/AutoModerator • 2d ago
Discussion [D] Self-Promotion Thread
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u/0RootShell 2d ago
I’ve started an AI Infrastructure Knowledge Base https://ai-infrastructure.net/ covering GPU clusters, distributed training, inference, RL/post-training, performance engineering and agent infrastructure.
Still very much a work in progress. Feedback and contributions welcome.
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u/reivblaze 2d ago
Interesting! Are you experienced in the field or looking to learn along the way? Would you add cuda programming?
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u/0RootShell 1d ago
I am, honestly, most of the content is a distillation of my day to day work and research. And I thought a knowledge base of such content might be useful to others as well.
I'm not dealing with CUDA development much, my focus is mainly agentic (developing custom harnesses), and the infrastructure behind RL and inference.
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u/Ali-WAIL 2d ago
I've been working on WAIL, a runtime control layer for AI applications.
The problem I was trying to deal with is when an AI request technically succeeds, but the execution itself isn't healthy — latency suddenly gets much worse, token delivery becomes unstable, repeated failures start showing up, etc.
WAIL wraps the provider client and watches the runtime behavior. If things degrade enough, it can make a decision to retry or reroute the next request, and it records signed evidence of what it observed and why the decision was made.
It currently supports OpenAI, Anthropic, Gemini, OpenRouter, Ollama and OpenAI-compatible runtimes. It runs in your own environment, so prompts, responses and API keys aren't sent to WAIL.
There's a free Developer plan. Pro is $249/month, and Enterprise is custom.
GitHub: https://github.com/wailinfra/wail-runtime
PyPI: pip install wail-runtime
I'm the founder, so obviously this is self-promotion — but that's what this thread is for :)
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u/www3cam 2d ago
I’d love to talk to others at the cutting edge of ML applying it to other fields. I’m mainly interested in economics, but have interest across other social sciences and even math but my pure math is not good enough to publish there atm. Would be interested in like computational physics, biology and chemistry as well.
Edit: Or even humanities like English, history and philosophy although computational work in those areas seem more nascent.
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u/Putrid_Bee_4840 2d ago
Solo project: non-autoregressive knowledge-graph extraction from sentence embeddings, no LLM in the extraction loop, so malformed output is structurally impossible, and it runs at ~0.013ms/sentence on a consumer GPU.
Any feedback is appreciated.
Link to the repo: https://github.com/DeliVali/cogito-estella
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u/Charming_Group_2950 2d ago
Gray Box: https://github.com/Aaryanverma/graybox (Open-Source)
A local-first tool where you can capture your thoughts instantly and it can auto-organize them and create a connected graph out of them which you can query or chat later. It will give you cited answers from the organized notes. Also there is a read only dashboard where you can see and search all your notes which ones to be focused, which ones are pending to do (if your notes are tasks) etc.
Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.
Video demo: https://youtu.be/Xdj1GCQoFNs
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u/Old_Rock_9457 2d ago
Hi all,
I just want to share my selfhostable, free and opensource project AudioMuse-AI:
It’s all about Music Information Retrieval (MIR) based on different approach. The final goals is creating automatic playlist on the major Music Server like Navidrome, Jellyfin, Plex, Emby and Lyrion.
The basic idea is getting embbeding model from different model like Musicnn, DCLAP (my distilled version of LAION CLAP) and GTE to work over lyrics. And then use different similarity way to create playlist. Also path discovery and so on.
As I said it’s all free and opensource so if you are interested you can just give a look. Also the model are light enough to run also on CPU.
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u/ivan_digital 1d ago
Free and open source update—speech-core is Apache-2.0, the Smart Turn model is BSD-2-Clause, and neither requires an account.
I added an optional audio end-of-turn classifier after VAD pauses in speech-core’s voice pipeline. The classifier examines the last eight seconds of a turn and either confirms the endpoint or keeps accumulating audio, with a maximum-silence fallback. The same state machine is now available through Core ML on macOS/iOS and int8 ONNX on Android.
The Core ML conversion matched the upstream predictions on 1,000 test clips at 92.9% accuracy and ran in about 3.5 ms per window on Apple Silicon. Android device-wide latency is not benchmarked yet.
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u/Genie-Tickle-007 23h ago
Teams can see what their LLM calls cost, but "which of these workloads can move to a cheaper model without a quality drop" usually stays a guess.
ARBR sits as one OpenAI-compatible endpoint in front of your existing providers, classifies each request (task type, difficulty), and lets you set routing rules, budgets, and rate limits in the request path rather than after the fact.
Self-hosted, MIT-licensed, fully free, with no paid or hosted tier.
The part I'd actually want feedback on: model swaps go through eval on sampled live traffic before rollout, then shadow or canary deployment with rollback if quality regresses. Routing decisions stay rule-based and reversible. Nothing auto-promotes without human approval.
Repo's below if anyone wants to poke at it.
https://github.com/project-arbr/arbr-control-plane
Just today we launched on PH - https://www.producthunt.com/products/arbr
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u/Consistent-Ruin1868 5h ago
I’m building an open-source experimental AI operating system where agents are modeled more like OS processes than chat sessions.
The part I’d particularly like feedback on is the security model.
The system uses:
- Time-bounded capabilities/leases
- Agent scopes and token budgets
- Explicit process state
- A kill mechanism for agents
- An action ledger for tool calls
- File changes associated with the operations that caused them
- A system digest that can be snapshotted, diffed and attested
The same concepts are intended to exist at both the kernel and desktop levels.
The kernel itself is built from scratch around capability security and targets x86_64, ARM64 and RISC-V. The desktop currently communicates with it through a QEMU/serial bridge.
The AI layer is BYOK and supports provider APIs as well as OpenAI-compatible local endpoints such as Ollama, LM Studio, vLLM and llama.cpp.
One important limitation: this is still experimental. The desktop is currently running in the browser and communicating with the kernel. The desktop is not yet running inside the kernel.
The question I’m exploring is whether OS-style capability security is actually a useful abstraction for AI agents.
In particular, I’m interested in opinions on:
- Expiring authority
- Capability delegation
- Agent self-renewal
- Auditable agent actions
- Token/resource budgets
- Treating agents as processes
Repository: https://github.com/ni-sh-a-char/RESENTMENT
Browser demo: https://ni-sh-a-char.github.io/RESENTMENT/os/
Apache-2.0.
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u/Lazy_Signature_9886 2d ago
Built Dejavu, a local memory layer for coding agents (Claude Code, Cursor, etc). Free, open source, MIT licensed. No pricing, no signup, nothing to buy.
The problem: agents lose everything between sessions. You end up re-explaining the same architecture decisions and known pitfalls every time you start fresh. Dejavu lets an agent write short typed notes ("slips") as it works, tagged as decisions, pitfalls, preferences, or work-in-progress, scoped automatically to the current git repo so unrelated projects never leak into each other.
The part I expect this sub to have opinions on: no embeddings, no vector DB. Recall runs on SQLite's FTS5 with BM25 ranking, fully deterministic, and auditable as plain SQL rather than a cosine similarity you have to trust. Every recall is capped to a fixed token budget so it can't flood the agent's context window. Trust is tracked separately from relevance too, based on how often a note gets confirmed useful vs. marked wrong, so a note matching your query text isn't automatically treated as correct.
The tradeoff is real. BM25 misses paraphrase gaps an embedding model would catch. For a repo-scoped corpus of a few hundred notes, written by agents in fairly consistent technical shorthand, I think it's the right bet for now, but I'm open to being told otherwise.
Repo: github.com/sanjayrohith/Dejavu