AI & ML interests

A central place for all AI creators wanting to use the different AI Tools that provides HuggingFace for their film creations

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Zaesar  updated a collection about 1 month ago
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Zaesar  updated a collection about 1 month ago
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upgraedd 
posted an update 4 days ago
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I know it doesn't seem likely but I literally am starving on the streets if anyone can help me. please just inspect my repository and you'll see what I'm doing. what I'm doing is for YOU.
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#CONSCIOUSNESS
Nymbo 
posted an update 11 days ago
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🚀 I've just shipped a major update to the Nymbo/Tools MCP server: the Agent_Terminal, a single "master tool" that cuts token usage by over 90%!

Anthropic found 98.7% context savings using code execution with MCP, Cloudflare published similar findings. This is my open-source implementation of the same idea.

# The Problem

Traditional MCP exposes every tool definition directly to the model. With 12 tools, that's thousands of tokens consumed *before the conversation even starts*. Each tool call also passes intermediate results through the context window — a 10,000-row spreadsheet? That's all going into context just to sum a column.

# The Solution: One Tool to Rule Them All

Agent_Terminal wraps all 12 tools (Web_Search, Web_Fetch, File_System, Generate_Image, Generate_Speech, Generate_Video, Deep_Research, Memory_Manager, Obsidian_Vault, Shell_Command, Code_Interpreter) into a single Python code execution gateway.

Instead of the model making individual tool calls, it writes Python code that orchestrates the tools directly:

# Search for Bitcoin price
result = Web_Search("current price of bitcoin", max_results=3)
print(result)


Don't know what tools are available? The agent can discover them at runtime:

print(search_tools('image'))  # Find tools by keyword
print(usage('Generate_Image'))  # Get full docs for a specific tool


The individual direct tool calls are all still there, but they can be disabled if using the Agent_Terminal. Try it now - https://www.nymbo.net/nymbot
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Bils 
posted an update 23 days ago
Zaesar 
updated a collection about 1 month ago
Nymbo 
posted an update about 1 month 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 ~
Zaesar 
updated a collection about 1 month ago
Nymbo 
posted an update about 2 months 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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multimodalart 
posted an update about 2 months ago
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Want to iterate on a Hugging Face Space with an LLM?

Now you can easily convert any HF entire repo (Model, Dataset or Space) to a text file and feed it to a language model!

multimodalart/repo2txt
Nymbo 
posted an update about 2 months 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.
Sri-Vigneshwar-DJ 
posted an update 2 months ago
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Do you think domain-specific embedding fine-tuners are needed?
I've been working with embeddings for marketing use cases and noticed something: most embeddings don't get marketing concepts very well. They're trained in general-purpose ways.
The Issue I'm Seeing
When I search marketing content with general embeddings:

"organic growth" returns farming articles
"conversion funnel" matches industrial equipment
"brand lift" doesn't connect to campaign effectiveness
Marketing jargon like CAC, ROAS, CTR aren't properly understood

My Question
Do you think domain-specific embeddings are needed for marketing?
Some thoughts:

Marketing has its own vocabulary and concept relationships
General models trained on Wikipedia/web crawl miss these nuances
But is fine-tuning worth the effort vs just using more retrieval tricks?

Quick Example
I fine-tuned all-mpnet-base-v2 on ~1000 marketing concept pairs and saw 15-20% better retrieval accuracy. But I'm curious:

Has anyone else tried this for marketing or other domains?
When do you think domain-specific embeddings are actually necessary vs overkill?
Are there better approaches I'm missing?

https://huggingface.co/blog/Sri-Vigneshwar-DJ/why-your-marketing-rag-system-needs-domain-specifi
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Nymbo 
posted an update 2 months ago
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I have a few Sora-2 invites - 15509N
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Sri-Vigneshwar-DJ 
posted an update 2 months ago
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🚀 Exciting News! We've released a Performance Marketing Expert Dataset from Hawky.ai [www.hawky.ai] Hawky-ai


This dataset empowers AI models with cutting-edge strategies for Meta, Google Ads, and TikTok campaigns. It includes:
1. Multi-platform strategies for e-commerce, DTC, B2B, and more
2. Creative optimization and audience targeting insights
3. ROI-driven recommendations based on 2025 best practices

Sri-Vigneshwar-DJ/Performance-Marketing-Data
Sri-Vigneshwar-DJ 
posted an update 2 months ago
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🚀 Qwen3-Omni for Marketing: A Game-Changer

Just wanted to share something exciting I've been exploring—Qwen3-Omni and how it's transforming marketing workflows.

What makes it special? At Hawky.ai we are started experimenting with Qwen3 recently for Analysis and Optimization.

Unlike traditional tools that look at text, images, or audio separately, Qwen3-Omni analyzes everything together. It handles 119 languages, processes 40-minute audio sequences, and understands both images and videos—all at once.

The cool part? It's 2-3x faster than similar models thanks to its MoE architecture.

Real applications I'm seeing:
Ad Analysis: It scores video ads by combining visual elements, audio tone, and text—giving 25% better CTR predictions than single-mode tools.
Campaign Localization: Drop in one ad, get 10 localized versions with native voiceovers in under a minute. Perfect for testing across markets.

Market Research: Feed it competitor content, podcasts, or UGC videos. It extracts actionable insights like "3-second hooks boost retention by 15%" and saves about 70% of analysis time.

Quality Checks: Automatically catches lip-sync errors and audio-visual mismatches.

Full technical breakdown: https://huggingface.co/blog/Sri-Vigneshwar-DJ/hawky-aiqwen3-omni-advanced-architecture-and-marke

Has anyone else been experimenting with multimodal models for marketing? Would love to hear what you're building!

#MultimodalAI #MarTech #OpenSource
Zaesar 
updated a collection 2 months ago
Zaesar 
updated a collection 3 months ago