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https://www.tonic-ai.com
josephpollack
Tonic-AI
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AI & ML interests

vision , multimedia , gradio, accessibility & cool demos

Recent Activity

suayptalhaΒ  authored a paper about 1 month ago
Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training
ZennyKennyΒ  authored a paper 2 months ago
BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
TonicΒ  updated a Space 8 months ago
TeamTonic/README
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ZennyKennyΒ 
posted an update about 5 hours ago
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πŸ“ One of the coolest parts about being an early Strawberry user has been the opportunity to build on the app at the ground floor.

The platform already has a ton of great integrations that let you interact with your external apps directly with tools, but I wanted to add the ability to do stuff in Slack as well.

πŸ’ͺ So I took the base Anthropic Slack MCP server, added a whole bunch of new tools, and generalized it as an HTTP-based SSE-server and deployed it in like 2 minutes with Railway so that Strawberry could make use of it (as can Claude or any other MCP client).

Now, you can Chat with your Strawberry Companion (or Claude, or whatever) and do things like:
➑️ Get caught up across all of your Slack channels after a long weekend or noisy incident without having to read 20 threads in 10 different channels
➑️ Create, read, and edit Canvases, Messages, and Channels
➑️ Take any resources or content that you're using in your Chat and inject it directly into Slack without copy / paste

😎 I'm pretty pleased with the results, and I made a short demo video showing the results of the work (link in comments). The best part is, it's available on GitHub for anyone else to use too (link in the comments, instructions in the README). The setup takes about 5-10 minutes.
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ZennyKennyΒ 
posted an update 12 days ago
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What a trip. Just walked through @burtenshaw and @evalstate tutorial on adding Hugging Face Skills to your Claude Code agent so you can fine tune LLMs by chatting with AI.

These are the kinds of innovations that are going to help everyone benefit from the power of Artificial Intelligence. Well done gentlemen and thank you for sharing.
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hesamationΒ 
posted an update 16 days ago
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this is big... 50 AI researchers from Bytedance, Alibaba, Tencent, and other labs/universities just published a 300-page paper with surprising lessons about coding models and agents (data, pre and post-training, etc).

key highlights:

> small LLMs can beat proprietary giants
RL (RLVR specifically) gives small open-source models an edge over big models in reasoning. a 14B model trained with RLVR on high-quality verified problems can match the performance of OpenAI's o3.

> models have a hard time learning Python.
mixing language models during pre-training is good, but Python behaves different from statically typed languages. languages with similar syntax (Java and C#, or JavaScript and TypeScript) creates high positive synergy. mixing Python heavily into the training of statically typed languages can actually hurt because of Python's dynamic typing.

> not all languages are equal (coding scaling laws)
the amount of data required to specialize a model on a language drastically depends on the language. paper argues like C# and Java are easier to learn (less training data required). languages like Python and Javascript are actually more tricky to learn, ironically (you see AI most used for these languages :)

> MoE vs Dense (ability vs stability)
MoE models offer higher capacity, but are much more fragile during SFT than dense models. hyperparams in training have a more drastic effect in MoE models, while dense models are more stable. MoE models also require constant learning rate schedules to avoid routing instability.

> code models are "insecure" by default (duh)
training on public repos makes models learn years of accumulated insecure coding patterns. safety fine-tuning often fails to work much on code. a model might refuse to write a hate speech email but will happily generate a SQL-injection vulnerable function because it "works."

read the full paper:
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence (2511.18538)
ZennyKennyΒ 
posted an update 18 days ago
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😐 I keep seeing takes on LinkedIn from American business influencers melting down about Silicon Valley startup "dependence" on open-source Chinese models.

πŸ€” Can anyone describe a credible scenario where these models can be leveraged by the Chinese government to endanger American security interests or am I right to believe that this is just Red Scare nonsense?
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NymboΒ 
posted an update 22 days ago
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πŸš€ 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 β€” 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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ZennyKennyΒ 
posted an update 28 days ago
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The #feedback channel of app early access Slack Workspaces is some of the best unintentional comedy material I have ever come across tbh.
ZennyKennyΒ 
posted an update about 1 month ago
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πŸŽ‰ Wow. Congratulations @bfirsh and the Replicate team on the CloudFlare acquisition!

✌️ You've really built an incredible ecosystem and product offering and should be super proud.
ZennyKennyΒ 
posted an update about 1 month ago
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πŸŽ‰ 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 πŸ˜…).

πŸ‘‰ 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.
lunarfluΒ 
posted an update about 1 month ago
lunarfluΒ 
posted an update about 1 month ago
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The new King πŸ‘‘has arrived!

Moonshot AI now the top model on Hugging Face πŸ”₯
moonshotai/Kimi-K2-Thinking
lunarfluΒ 
posted an update about 1 month ago
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πŸ’ΈπŸ€‘You don’t need 100 GPUs to train something amazing!

Our Smol Training Playbook teaches you a better path to world-class LLMs, for free!

Check out the #1 trending space on πŸ€— :
HuggingFaceTB/smol-training-playbook
suayptalhaΒ 
authored a paper about 1 month ago
NymboΒ 
posted an update about 2 months 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 ~
ZennyKennyΒ 
posted an update about 2 months 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.

πŸ™ˆπŸ™ˆπŸ™ˆ
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ZennyKennyΒ 
posted an update about 2 months ago
NymboΒ 
posted an update 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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ZennyKennyΒ 
posted an update 2 months 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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ZennyKennyΒ 
authored a paper 2 months ago
ZennyKennyΒ 
posted an update 2 months ago
NymboΒ 
posted an update 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.