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Version 1.5.4 of Nerve is now available, introducing a powerful new agent called Search. This agent leverages the Brave Search API to find information based on user queries and generates comprehensive markdown reports.
Installation and Usage
To get started with the new Search agent, run the following commands:
pip install --upgrade nerve-adk nerve install evilsocket/search
Execute a search query with:
nerve run search --query "what is the best LLM for tool calling I can run on 24GB of GPU vRAM?"
You Should Know:
1. Brave Search API Integration
The Search agent uses Brave Search, a privacy-focused search engine. To manually query Brave Search via API, you can use:
curl "https://api.brave.com/search?q=your_query&key=YOUR_API_KEY"
2. Markdown Report Generation
Nerve converts search results into structured Markdown files. To view the report in a formatted way, use:
glow report.md Requires glow (Markdown viewer)
3. GPU Optimization for LLMs
If you’re working with LLMs on limited GPU memory (24GB), consider these optimizations:
Use quantization to reduce model size python -m transformers.onnx --model=model_name --feature=seq2seq-lm --quantize
4. Nerve Agent Management
List all installed Nerve agents:
nerve list
Remove an agent:
nerve uninstall agent_name
5. Automating Searches with Nerve
For automated security research, you can script Nerve searches:
!/bin/bash
queries=("best LLM for cybersecurity" "latest CVE exploits")
for q in "${queries[@]}"; do
nerve run search --query "$q"
done
6. Monitoring GPU Usage
Ensure your GPU isn’t overloaded when running LLMs:
nvidia-smi Check GPU memory usage
7. Extracting Data from Reports
Use `grep` and `awk` to filter Nerve-generated reports:
grep -i "vulnerability" report.md | awk -F'|' '{print $2}'
What Undercode Say:
Nerve’s new Search agent enhances offensive security workflows by automating threat intelligence gathering. Combining this with Brave Search’s privacy focus makes it ideal for OSINT and vulnerability research.
For AI security researchers, optimizing LLMs on constrained hardware remains critical—quantization, mixed precision, and model pruning are key techniques.
Automating Nerve agents in bash or Python scripts can streamline penetration testing and bug bounty hunting.
Expected Output:
Search Report Query: "best LLM for tool calling on 24GB GPU" - Result 1: LLaMA-2-70B with 4-bit quantization (via GPTQ) - Result 2: Falcon-40B-Instruct (optimized with FlashAttention) - Source: <a href="https://search.brave.com">Brave Search</a>
Relevant URLs:
References:
Reported By: Simonemargaritelli Version – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
