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Benhao Tang PRO
benhaotang
AI & ML interests
Physics Master student in theoretical particle physics at Universität Heidelberg, actively looking into the possibilities of integrating AI into future physics research.
Recent Activity
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fastrtc/talk-to-openai
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albertvillanova's
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1 day ago
🚀 New smolagents update: Safer Local Python Execution! 🦾🐍
With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. 🔒
Here's why this matters & what you need to know! 🧵👇
1️⃣ Why is local execution risky? ⚠️
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2️⃣ New Safety Layer in smolagents 🛡️
We now inspect every return value during execution:
✅ Allowed: Safe built-in types (e.g., numbers, strings, lists)
⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3️⃣ Immediate Benefits 💡
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4️⃣ Security Disclaimer ⚠️
🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5️⃣ The Best Practice: Use Sandboxed Execution 🔐
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6️⃣ Upgrade Now & Stay Safe! 🚀
Check out the latest smolagents release and start building safer AI agents today.
🔗 https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Let’s discuss! 👇
#AI #smolagents #Python #Security
liked
a model
4 days ago
deepseek-ai/DeepSeek-R1
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benhaotang's activity
liked
a
Space
about 2 hours ago
reacted to
albertvillanova's
post with 👍
1 day ago
Post
3319
🚀 New smolagents update: Safer Local Python Execution! 🦾🐍
With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. 🔒
Here's why this matters & what you need to know! 🧵👇
1️⃣ Why is local execution risky? ⚠️
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2️⃣ New Safety Layer in smolagents 🛡️
We now inspect every return value during execution:
✅ Allowed: Safe built-in types (e.g., numbers, strings, lists)
⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3️⃣ Immediate Benefits 💡
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4️⃣ Security Disclaimer ⚠️
🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5️⃣ The Best Practice: Use Sandboxed Execution 🔐
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6️⃣ Upgrade Now & Stay Safe! 🚀
Check out the latest smolagents release and start building safer AI agents today.
🔗 https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Let’s discuss! 👇
#AI #smolagents #Python #Security
With the latest release, we've added security checks to the local Python interpreter: every evaluation is now analyzed for dangerous builtins, modules, and functions. 🔒
Here's why this matters & what you need to know! 🧵👇
1️⃣ Why is local execution risky? ⚠️
AI agents that run arbitrary Python code can unintentionally (or maliciously) access system files, run unsafe commands, or exfiltrate data.
2️⃣ New Safety Layer in smolagents 🛡️
We now inspect every return value during execution:
✅ Allowed: Safe built-in types (e.g., numbers, strings, lists)
⛔ Blocked: Dangerous functions/modules (e.g., os.system, subprocess, exec, shutil)
3️⃣ Immediate Benefits 💡
- Prevent agents from accessing unsafe builtins
- Block unauthorized file or network access
- Reduce accidental security vulnerabilities
4️⃣ Security Disclaimer ⚠️
🚨 Despite these improvements, local Python execution is NEVER 100% safe. 🚨
If you need true isolation, use a remote sandboxed executor like Docker or E2B.
5️⃣ The Best Practice: Use Sandboxed Execution 🔐
For production-grade AI agents, we strongly recommend running code in a Docker or E2B sandbox to ensure complete isolation.
6️⃣ Upgrade Now & Stay Safe! 🚀
Check out the latest smolagents release and start building safer AI agents today.
🔗 https://github.com/huggingface/smolagents
What security measures do you take when running AI-generated code? Let’s discuss! 👇
#AI #smolagents #Python #Security
- 2 replies
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18 days ago
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19 days ago
OK, grok 3 deep research also failed on my benchmark...
And this is the final solution it gives me:
Use wsl --shutdown before hibernating; if it fails, try net stop LxssManager.
What? How about just tell me if WSL have problem, just do not using WSL... How can this be a solution when there is even an official troubleshooting guide that provide more solutions. This is the even worst than gemini and perplexity, at least they read the official guide, just got lost in github issue threads... Now I really want to know how OpenAI's compares to mine, if I have 200 dollars.
upvoted
an
article
19 days ago
Article
Building a Real-Time Video Chat with Gemini 2.0, Gradio, and WebRTC 👀👂
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•
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reacted to
schuler's
post with 😎
20 days ago
Post
3387
🔮 GPT-3 implemented in pure Free Pascal!
https://github.com/joaopauloschuler/gpt-3-for-pascal
This implementation follows the GPT-3 Small architecture from the landmark paper "Language Models are Few-Shot Learners":
Clean Pascal Implementation
https://github.com/joaopauloschuler/gpt-3-for-pascal
This implementation follows the GPT-3 Small architecture from the landmark paper "Language Models are Few-Shot Learners":
┌─────────────────────────┐
│ Input Layer │
├─────────────────────────┤
│ Token & Positional │
│ Embedding │
├─────────────────────────┤
│ 12x Transformer │
│ Blocks │
│ - 12 heads │
│ - 768 hidden dims │
│ - 3072 intermediate │
├─────────────────────────┤
│ Output Layer │
└─────────────────────────┘Clean Pascal Implementation
for CntLayer := 1 to {Layers=}12 do
begin
Result.AddTransformerBlockCAI(
{Heads=}12,
{intermediate dimensions=}4*768,
{NoForward=}true,
{HasNorm=}true,
false
);
end;liked
a
model
21 days ago
upvoted
an
article
21 days ago
Article
Introducing smolagents: simple agents that write actions in code.
•
841
reacted to
louisbrulenaudet's
post with 🤗
21 days ago
Post
3106
I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆
Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆
Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool
posted
an
update
23 days ago
Post
2391
Try out my updated implementation of forked OpenDeepResearcher(link below) as an OpenAI compatible endpoint, but with full control, can be deployed completely free with Gemini api or completely locally with ollama, or pay-as-you-go in BYOK format, the AI agents will think dynamically based on the difficulties of given research, compatible with any OpenAI compatible configurable clients(Msty, Chatbox, even vscode AI Toolkit playground).
If you don't want to pay OpenAI $200 to use or want to take control of your deep research, check out here:
👉 https://github.com/benhaotang/OpenDeepResearcher-via-searxng
**Personal take**
Based on my testing against Perplexity's and Gemini's implementation with some Physics domain questions, mine is comparable and very competent at finding even the most rare articles or methods.
Also a funny benchmark of mine to test all these searching models, is to trouble shot a WSL2 hanging issue I experienced last year, with prompt:
> wsl2 in windows hangs in background with high vmmem cpu usage once in a while, especially after hibernation, no error logs captured in linux, also unable to shutdown in powershell, provide solutions
the final solution that took me a day last year to find is to patch the kernel with some steps documented in carlfriedrich's repo and wait Microsoft to solve it(it is buried deep in wsl issues). Out of the three, only my Deep Research agent has found this solution, Perplexity and Gemini just focus on other force restart or memory management methods. I am very impressed with how it has this kind of obscure and scarce trouble shooting ability.
**Limitations**
Some caveats to be done later:
- Multi-turn conversation is not yet supported, so no follow-up questions
- System message is only extra writing instructions, don't affect on search
- Small local model may have trouble citing source reliably, I am working on a fix to fact check all citation claims
If you don't want to pay OpenAI $200 to use or want to take control of your deep research, check out here:
👉 https://github.com/benhaotang/OpenDeepResearcher-via-searxng
**Personal take**
Based on my testing against Perplexity's and Gemini's implementation with some Physics domain questions, mine is comparable and very competent at finding even the most rare articles or methods.
Also a funny benchmark of mine to test all these searching models, is to trouble shot a WSL2 hanging issue I experienced last year, with prompt:
> wsl2 in windows hangs in background with high vmmem cpu usage once in a while, especially after hibernation, no error logs captured in linux, also unable to shutdown in powershell, provide solutions
the final solution that took me a day last year to find is to patch the kernel with some steps documented in carlfriedrich's repo and wait Microsoft to solve it(it is buried deep in wsl issues). Out of the three, only my Deep Research agent has found this solution, Perplexity and Gemini just focus on other force restart or memory management methods. I am very impressed with how it has this kind of obscure and scarce trouble shooting ability.
**Limitations**
Some caveats to be done later:
- Multi-turn conversation is not yet supported, so no follow-up questions
- System message is only extra writing instructions, don't affect on search
- Small local model may have trouble citing source reliably, I am working on a fix to fact check all citation claims
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