🤝 Open to Collab
Andrei Ovcharenko
Kreout
AI & ML interests
Java backend development, Spring Boot, Kafka, infrastructure and automation. Interested in local AI tools and practical developer workflows. Open-source developer tools: https://github.com/krotname.
Recent Activity
reacted to juiceb0xc0de's post with ❤️ about 13 hours ago
You built a model. Wanna know what lights up inside it?💡
I put a little juice in the box so you can find out!🧃
I built Cartographics, a HF ZeroGPU Space for building neural activation atlases of language models.
I wanted to make the tools I use to explore model internals accessible to people building their own LLMs. Punch in a Hugging Face model ID, run the compatibility check, and start a capture. Prompt formatting and the full corpus are handled automatically.
The atlases help you investigate questions like:
- Which layers and channels respond most strongly to different kinds of prompts?
- How do activation patterns vary across code, math, reasoning, and other topics?
- Which features activate together, and where do unusually strong activations appear?
The census contains 8,965 prompts, giving you a shared basis for exploring different models.
GPU capture runs through ZeroGPU using your account’s allowance. I handle the CPU analysis separately. This release supports compatible architectures and models that fit the available GPU memory.
I’m building this independently, and I’d love to see what models builders explore with it.
What would you want to investigate in your own model?
Try Cartographics Beta:
https://huggingface.co/spaces/juiceb0xc0de/cartographics
https://huggingface.co/datasets/juiceb0xc0de/bella-bartender-gemma-e4b-cgs-atlas
If you make an atlas with us, leave the Space a ❤️. You're helping other people discover mechanistic interpretability the way I wish I did.
Prerequisites:
Models under 10B parameters (20GB of weights in bf16)
No trust_remote_code models, for now.
Preflight catches edge cases. Don't be scared to test it out. It's free to check.
Sign in with Hugging Face. The capture runs on your ZeroGPU quota, the atlas build runs on ours. reacted to africanspeech's post with ❤️ about 13 hours ago
Introducing AfricanSpeech FR-CMR — Cameroon French Speech Dataset
How well do ASR systems recognize spontaneous French spoken in Cameroon?
We've released our dataset page featuring:
- 1,172 speech clips | 162 speakers | 4.89 hours
- Human-validated ASR references and demographic metadata
- Audio demos and a benchmark comparing five ASR systems
Available for commercial licensing and evaluation.
Open to research and industry collaborations.
Explore the dataset:
https://huggingface.co/datasets/africanspeech/cameroon-french-speech
Contact: contact@africanspeech.com reacted to javadtaghia's post with ❤️ about 13 hours ago
Hugging face article: https://huggingface.co/blog/javadtaghia/atmem-2310-bounded-evidence-discovery-for-persiste
AtMem 2.3.10 is out; and our full DolphinBench result has more than doubled.
AtMem 2.3.9 passed 192 of 600 tasks (32%).
AtMem 2.3.10 passed 390 of 600 (65%). We doubled it.
What changed?
We focused on how an agent discovers and uses evidence.
AtMem combined lexical and semantic retrieval while keeping related passages together with their original source and provenance and more.
Where do we stand?
This is substantial progress, with a clear tradeoff: median recorded task latency increased from 31.87 to 85.78 seconds. More successful investigation currently takes more time.
The published Hermes/Luna/Mem0 reference scores 424/600 (70.7%).
What comes next?
We want to understand the remaining failures: was evidence missing, poorly ranked, packed incompletely, interpreted with the wrong temporal state, or used incorrectly during an action?
Evidence discovery remains opt-in.
Release: https://github.com/aetna000/atmem/releases/tag/v2.3.10
DolphinBench submission PR: https://github.com/mem0ai/dolphinbench/pull/10
Results and execution evidence: https://huggingface.co/datasets/atmem/atmem-2.3.10b1-dolphinbench
PS: DolphinBench tests whether an agent can use a simulated user’s history to complete actions in applications. Finding a relevant passage isn’t enough—a task passes only when all its required checks pass.Organizations
