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AI & ML interests

computer-vision, image-processing, machine-learning, deep-learning

Recent Activity

ZennyKenny  authored a paper 26 days ago
BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
gagan3012  authored a paper about 1 month ago
Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models
abhi-khoyani  updated a model about 2 months ago
kornia/Efficient_LOFTR
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ZennyKenny 
posted an update about 3 hours ago
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🎉 Novoyaz is live.

A few months ago, I built a quick POC in Hugging Face that used a fine-tuned variant of OpenAI's OSS-20B model that I trained to convert the text from pre-reform Russian-language documents into modern Russian orthography.

⚡️ This morning, I launched novoyaz.io.

This is a production app, the frontend for which I built in like two hours with Lovable, that uses that same fine-tuned model for transliteration, but now has a bunch of extra features that make using it even easier (like taking and uploading pictures with your on-device camera for example 😅).

👉 If you're a researcher, or know a researcher, for whom this app will improve their day-to-day workflows, please get in touch with me.
Nymbo 
posted an update 7 days ago
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I've added an 11th tool to the Nymbo/Tools MCP server, it's for your Obsidian_Vault. I'd argue it's far more context-efficient than any other Obsidian MCP I've seen, and doesn't require any plugins. Also some big improvements to the Web_Search and Web_Fetch tools.

# Obsidian_Vault Tool

It's basically a read-only version of the File_System tool, but it works so well for navigating Obsidian without unnecessary context. It supports recursive (full-text) search across the entire vault, and supports offset so the agent can "scroll" through a document without re-consuming tokens.

Run the server locally and set the OBSIDIAN_VAULT_ROOT environment variable to your vault's root path. If you don't use Obsidian, this is perfectly usable as simply a read-only filesystem.

# Web_Search Improvements

The Web_Search tool previously just used DuckDuckGo as a backend search engine, but now it also supports Bing, Brave, Yahoo, and Wikipedia. Default engine is auto which provides results from all backends in recommended order. Still doesn't require any kind of API or auth for Web_Search.

There's also a new date filter to limit results to those created in the past day, week, month, or year. Oh, and uhh, SafeSearch is now off by default :)

# Web_Fetch Improvements

As context-efficient as the Markdown mode is for web browsing, sometimes it does lose important context in the conversion from HTML to Markdown. So I've added a new HTML mode to the Web_Fetch tool that basically executes a cURL request on the URL, returning the full HTML page if necessary.

# A Note on Claude Skills

I've been having fun with the new File_System and Shell_Command tools. Using Claude Skills doesn't currently work in the public HF space because of environment restrictions, but using Skills works perfectly well running locally.

Happy building ~
ZennyKenny 
posted an update 8 days ago
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Anyone got the scoop on a good OCR model that's available on inference?

Keen to make use of an endpoint (gated or not -- happy to pay for usage) for a personal project, but not so keen to pay for the GPU hosting myself.

🙈🙈🙈
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ZennyKenny 
posted an update 17 days ago
merve 
posted an update 20 days ago
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deepseek-ai/DeepSeek-OCR is out! 🔥 my take ⤵️
> pretty insane it can parse and re-render charts in HTML
> it uses CLIP and SAM features concatenated, so better grounding
> very efficient per vision tokens/performance ratio
> covers 100 languages
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Nymbo 
posted an update 21 days ago
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Two new tools added to the Nymbo/Tools MCP server, File_System and Shell_Exec. You can theoretically do basically anything with these two tools, and it should enable support for many Claude Skills.

GPT-5-Codex proves that for many cases, shell commands really are all you need, and Claude Skills seem to lean into this. The thing is, nothing about the design of Claude Skills actually restricts them to proprietary models!

# File_System

There's a new directory inside the repo called Filesystem, that's the agent's "root". It can perform the following actions : list, read, write, append, mkdir, move, copy, delete, info, help. It's able to keep this all within the scope of one tool call by making the Action field required and all other fields optional. Using a filesystem shouldn't require 15 different tools.

Files created in the public HF space live in the space's running container, and gets cleared when the space is restarted. When running the server locally, files are actually stored on disk.

# Shell_Exec

What good is a filesystem if you can't execute commands in that filesystem? This tool automatically detects if the server is running on Windows or Linux, and suggests using the appropriate shell (PowerShell/Bash). Both of these new tools require that the agent uses relative paths, rather than absolute paths. I could be convinced to back pedal on this.

# Closing Thoughts

The File_System and Shell_Exec tools aren't super polished yet, I'll continue to improve the agent's instructions and UX of using the new tools. Most of my testing was done with gpt-oss-20b and if it messes up, it gets the gist after one failed tool call. It should work perfectly fine for the GPU poor.
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ZennyKenny 
posted an update 25 days ago
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Did Hugging Face just ban hammer a bunch of bot accounts or am I just so uninteresting that 30% of my subs dropped me overnight?

😬 Wait, don't answer that.
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ZennyKenny 
authored a paper 26 days ago
ZennyKenny 
posted an update 26 days ago
Nymbo 
posted an update 26 days ago
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I've made some improvements to my custom Deep_Research tool in the Nymbo/Tools MCP server. I've added a second LLM process and it still takes less than 1 minute to complete!

The original version of my Deep_Research tool would basically dump up to 50 fetched webpages onto the Researcher model (Qwen3-235B), with only a little bit of context shown from each page.

# New "Filterer" Process

The new process includes another LLM call before the researcher process. The Filterer (also Qwen3-235B) gets the query summary and the original 50 pages with low context, and decides which pages are most relevant to the research topic. The Filterer then outputs the URLs to the relevant pages, which are then re-fetched (with more context) and sent to the Researcher.

# Researcher Context

The Researcher now gets only the relevant webpages, then begins writing the report. When testing with 50 initial results, the researcher would often end up with 10-20 results of relevant context.

It still takes less than a minute to accomplish everything, thanks entirely to Cerebras inference. It now takes about 35-45 seconds to complete once the tool is run.

It's also worth noting that both the Filterer and Researcher now are provided the current time/date before they see the content, reducing hallucinations caused by knowledge cutoffs.
gagan3012 
authored a paper about 1 month ago
Nymbo 
posted an update about 1 month ago
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I have a few Sora-2 invites - 15509N
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ZennyKenny 
posted an update about 1 month ago
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🥊 Big Code Arena is live! bigcode/arena

💡 bigcode is an open scientific collaboration working on responsible training of large language models for coding applications.

👉 The Arena ranks LLMs based on their ability to support natural language vibe coding requests in a competitive format, based on feedback from human reviewers.

🧠 It was a pleasure to contribute to this project led by @terryyz and appear as an additional contributor in the Big Code Arena paper.
ZennyKenny 
posted an update about 1 month ago
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🖤 Probably one of my favorite projects that I've worked on so far, introducing Новояз (Novoyaz).

🛠 One of the first acts of the Bolshevik government after the Russian Revolution was the reform and standardization of the Russian language, which at the time had a non-standard and challenging orthography.

📚 Upon its reform the government launched a nationwide campaign called Ликбез (Likbez), which sought to improve literacy in the country (by the way, it worked, bringing the national literacy rate from <20% in the 1920s to >80% by the 1930s).

‼ While this is a remarkable result that should absolutely be celebrated, it's one that has left behind literally hundreds of thousands if not millions of artifacts using pre-reform Russian orthography.

😓 Researchers and historians are working tirelessly to translate these artifacts to modern Russian so that they may be archived and studied but many have told me that. they are doing this BY HAND (!).

💡 I thought, well this is a perfect use case for OCR and a fine-tuned LLM to step in and help to aid in this important work!

🌏 Introducing НОВОЯЗ (NOVOYAZ)! Powered by ChatDOC/OCRFlux-3B and https://huggingface.co/ZennyKenny/oss-20b-prereform-to-modern-ru-merged, researchers can now convert images of their pre-reform documents to modern Russian orthography using the power of open-source AI!

Check it out and drop a like to support more real-world use cases for open source AI outside of traditional tech-centric domains!

ZennyKenny/Novoyaz
ZennyKenny 
posted an update about 1 month ago
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🔒 Like a lot of other AI builders, I have some anxiety about the emerging surveillance-capitalist paradigm emerging in the AI space.

👉 Of course-- this kind of thing isn't completely new and has been going on for decades, but the difference is the stronger immersion of AI tools into our daily lives (compared to something like a search engine or social network).

❕ That's why I was really excited to come across Lumo: https://lumo.proton.me/u/1/

❕ Lumo is created by ProtonPrivacy and offers privacy-first features that make sure that what you do with you AI assistant is your business.

❕ I already trust Proton with my other business apps and I've never been disappointed, plus the Lumo architecture is really fantastic, dynamically routing each query to the most appropriate model for the request.

🔥 Really awesome stuff Proton, thank you as always.
ZennyKenny 
posted an update about 2 months ago
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The reactions to mostlyai/synthetic-sdk-demo have been incredible! 🔥

Some users wrote that they were having performance issues on larger datasets, so I've capped the Space's input to 5000 rows and 10 columns, but you can always use the open source SDK that powers the space any time you want on datasets of arbitrary size and shape!

Check it out: https://github.com/mostly-ai/mostlyai 👈
merve 
posted an update about 2 months ago
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large AI labs open-sourced a ton of models last week 🔥
here's few picks, find even more here merve/sep-16-releases-68d13ea4c547f02f95842f05 🤝
> IBM released a new Docling model with 258M params based on Granite (A2.0) 📝 ibm-granite/granite-docling-258M
> Xiaomi released 7B audio LM with base and instruct variants (MIT) XiaomiMiMo/mimo-audio-68cc7202692c27dae881cce0
> DecartAI released Lucy Edit, open Nano Banana 🍌 (NC) decart-ai/Lucy-Edit-Dev
> OpenGVLab released a family of agentic computer use models (3B/7B/32B) with the dataset 💻 OpenGVLab/scalecua-68c912cf56f7ff4c8e034003
> Meituan Longcat released thinking version of LongCat-Flash 💭 meituan-longcat/LongCat-Flash-Thinking
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Nymbo 
posted an update about 2 months ago
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There's now a custom Deep_Research tool in my Nymbo/Tools MCP server! TL;DR: The agent using the tools writes a summary of your requests and up to five DuckDuckGo searches (up to 50 results). Each of the webpages found in the searches are then fetched and given to our researcher (Qwen3-235B-A22B-Thinking-2507). The researcher sees the summary, searched queries, and fetched links, then writes a thorough research report. The agent using the tool provides the user with a summary of the report and a link to download research_report.txt. The researcher's instructions are similar to some leaked Perplexity sys prompts.

# Deep_Research Tool

It accomplishes everything in under a minute so it doesn't hit MCP's 60 second timeout, mostly thanks to Cerebras. The only thing required to make this work is a HF_READ_TOKEN for inference.

The Deep_Research tool could certainly be improved. It still needs some sort of mechanism for sorting URLs based on importance (I've got some ideas but I don't want it to be the responsibility of the agent using the tool). I'll probably add a second researcher to filter out the bad sources before inferencing the big researcher. I'm hellbent on keeping this all within the scope of one tool call.

# More Fetch/Web Search Improvements

The Search_DuckDuckGo tool has been further enhanced. It now allows the agent to browse through all pages of results. The results also now include published date (if detected). It also now supports every DDG search types! Default DDG search is called text, but it can also now search by news, images, videos, and books.

The Fetch_Webpage tool now specifies how much of the page has been truncated, and cursor index, allowing it to pickup where it left off without re-consuming tokens. The model can now also choose to strip CSS selectors to remove excess noise, and there's a new URL Scraper mode that only returns URLs found on the full page.

More to come soon ~
merve 
posted an update about 2 months ago
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IBM just released small swiss army knife for the document models: granite-docling-258M on Hugging Face 🔥

> not only a document converter but also can do document question answering, understand multiple languages 🤯
> best part: released with Apache 2.0 license 👏 use it with your commercial projects!
> it supports transformers, vLLM and MLX from the get-go! 🤗
> built on SigLIP2 & granite-165M

model: ibm-granite/granite-docling-258M
demo: ibm-granite/granite-docling-258m-demo 💗
ZennyKenny 
posted an update about 2 months ago
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The open source Synthetic Data SDK from MOSTLY AI: mostlyai offers the ability to generate realistic, privacy-safe synthetic data with just a few lines of Python.

Try it out yourself in a No Code UI in the SDK Demo Space: mostlyai/synthetic-sdk-demo