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posted an update about 22 hours ago

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2203

Hey Hugging Face community 👋

My name is M. I'm a solo founder and self-taught developer based in Houston, TX. I build AI-powered apps — I have an iOS app called DeFilter currently in App Store review, a security scanning platform called Sentinel, and a job marketplace called HireHuman.fyi for connecting humans with companies that prefer non-AI workers.

I'm also a poker dealer by night, which means I think a lot about reading situations in real time — and that's exactly what sparked this idea.

I'm not the most technical person in the room. But I have a vision, I have drive, and I believe the best projects get built when people with different skills come together around a shared idea.

That's why I'm posting here. I want to build this with the community.

— M (@dealermatt )

1 reply

anakin87

posted an update about 23 hours ago

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How LLM training with RL Environments works?

It all starts with 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗥𝗲𝘄𝗮𝗿𝗱𝘀
- question asked
- model generates reasoning + answer
- answer checked against ground truth
- reward drives RL training

In this setup, the environment is simple: fixed questions and answers, rollout logic, reward(s)

Consider a more complex tic-tac-toe env ❌⭕
It adds:
- dynamic game generation/handling
- tunable opponent skill
- multi-turn interactions

(envs can also include tools)

---

What happens at training?

We use 𝗚𝗿𝗼𝘂𝗽 𝗥𝗲𝗹𝗮𝘁𝗶𝘃𝗲 𝗣𝗼𝗹𝗶𝗰𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 with a tic-tac-toe env

No critic model needed, the group is the baseline
Simpler than PPO

1️⃣ Rollout generation: from the same board, model plays N games via sampling
2️⃣ Each game scored with deterministic rewards (win, format, ...)
3️⃣ Mean score computed across the group
4️⃣ Each rollout's advantage = its score minus the group mean
5️⃣ Model updated to favor trajectories above baseline

🔁 Repeat

For a deep dive, check out
🌱 https://github.com/anakin87/llm-rl-environments-lil-course
a free hands-on course on RL environments for LLMs

2 replies

ajibawa-2023

posted an update 3 days ago

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Go-Code-Large
Dataset: ajibawa-2023/Go-Code-Large

Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.

By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.

Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.

2 replies

ajibawa-2023

posted an update 2 days ago

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Ruby-Code-Large
Dataset : ajibawa-2023/Ruby-Code-Large

Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.

By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.

Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.

mrmanna

posted an update 3 days ago

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𝗔𝗜 & 𝗦𝗧𝗔𝗧𝗘 𝗠𝗔𝗖𝗛𝗜𝗡𝗘
𝘞𝘩𝘺 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘉𝘦𝘨𝘪𝘯𝘴 𝘞𝘩𝘦𝘳𝘦 𝘛𝘰𝘺 𝘈𝘨𝘦𝘯𝘵𝘴 𝘌𝘯𝘥
Published: 18 Apr 2026 | Towards AI Publication | Medium
Open Link: https://medium.com/towards-artificial-intelligence/ai-state-machine-106387406c5a?sk=047b2f064c673a0095a9e8cc011b6a92

We talk a lot about governance, accuracy, and auditability in AI agents.
But I keep seeing a gap between the words and the engineering behind them.
Many agents have tools, orchestration, memory, graphs, and impressive demos. But when you ask how governance is actually enforced, the answer is often weak.
Prompt-level control is not production governance.
A production agent needs explicit state design: legal transitions, controlled progression, recovery paths, approval boundaries, and separation between memory, decision, policy, and execution.
This article explores the silent crisis unfolding in modern AI development: the urgent need to resurrect the disciplined architecture of state machines

1 reply

prithivMLmods

posted an update 3 days ago

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3709

HY-World-2.0 — A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.

> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo

🤗To learn more, visit the app page or the respective model pages.

consome2

posted an update 1 day ago

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684

Built a small site for tracking speech-to-speech, full-duplex, and audio foundation model work.
It covers models, benchmarks, datasets, and some blog posts to organize the landscape in one place.

Still early, but sharing in case it is useful:
https://www.fullduplex.ai/

If you spot missing entries or mistakes, I would really appreciate corrections.

sequelbox

posted an update 1 day ago

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NEW RELEASE: Esper 3.1 for Qwen 3.6!

- Your dedicated DevOps expert: Esper 3.1 maximizes DevOps and architecture helpfulness, powered by high-difficulty DevOps and architecture data generated with DeepSeek-V3.1-Terminus!
- Improved coding performance: challenging code-reasoning datasets stretch DeepSeek-V3.1-Terminus and DeepSeek-V3.2 to the limits, allowing Esper 3.1 to tackle harder coding tasks!
- AI to build AI: our high-difficulty AI expertise data boosts Esper 3.1's MLOps, AI architecture, AI research, and general reasoning skills.

Get it now: ValiantLabs/Qwen3.6-35B-A3B-Esper3.1

We're working on more finetunes for the newest Qwen and Gemma models, and we've also started working on the agentic-first datasets for Esper 4 :) we're going to make open source better and better for your work!

Please note that real life financial and family concerns have popped up and have imposed unfortunate limitations on our ability to devote time to our open-source work :( If you would like to see Esper 4 and our other releases speed up instead of slowing down, this is the best way you can help us: sequelbox/SupportOpenSource

No matter what, we'll keep fighting and we won't give up!

with love,
allegra

aufklarer

posted an update 2 days ago

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694

After running extensive benchmarks across ASR, TTS, and VAD on Apple Silicon, we found some results that weren't documented anywhere.

The most counterintuitive: INT8 runs 3.3x faster than INT4 on the Neural Engine. A 332 MB CoreML model allocates 1,677 MB at runtime. And the right architecture uses both MLX and CoreML simultaneously — not one or the other.

MLX talks to the GPU — programmable, fast for large transformer inference. CoreML talks to the Neural Engine — fixed-function silicon, 135x real-time for small feedforward models like VAD, near-zero power draw.

All benchmarks are from speech-swift, our open-source Swift library for on-device speech AI: ASR, TTS, VAD, diarization, speech-to-speech — everything running locally on Apple Silicon with no API, no cloud, no data leaving the device.

Models on HF: aufklarer/Qwen3-ASR-0.6B-MLX-4bit · aufklarer/parakeet-tdt-0.6b-coreml-int8 · aufklarer/PersonaPlex-7B-MLX-4bit

Full article: https://blog.ivan.digital
Library: https://github.com/soniqo/speech-swift

omarkamali

posted an update 2 days ago

Post

690

Just sharing a little breakthrough with Gherbal LID where we managed to distinguish the 15 variants of Arabic with 6 variants above 90%, 10 variants above 85% accuracy, practically distinguishing Moroccan and Algerian (which overlap massively).

It also embraces the duality of MSA and arabic variants pioneered in ALDi by @AMR-KELEG et al.

Now we're only bottlenecked by the availability of high quality data for the low scoring variants such as Iraqi, Libyan, Sudanese, Adeni ...

More on Gherbal at:
https://omneitylabs.com/models/gherbal

1 reply

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