{"title": "RedMind AI", "logoImg": "bafkreif322pkrpcnyosb35tcxxvledosyobnlzobsjyxt4qhpibpqe3tuy", "website": "https://github.com/redmindai/redmind-ai", "bannerImg": "bafkreif2angf2lxd6ghhugxzof5hzyg4r4tgjht2lz3dz4iczqaiu7ampq", "createdAt": 1745011069286, "userGithub": "redmindai", "credentials": {}, "description": "RedMind AI is an autonomous neural network for ethical penetration testing. It combines the power of LLMs, Shodan, and Burp Suite to detect security vulnerabilities in web applications and servers.\n\nThe user simply provides a domain or IP address, and the system:\n 1. Gathers technical data (banners, versions, open ports) via Shodan API\n 2. Sends the results to an LLM (DeepSeek-Coder 33B) for semantic analysis\n 3. Optionally simulates HTTP requests via Burp Suite Repeater API or similar\n 4. Generates a report with potential risks, CVSS scores, PoCs, and remediation tips\n\nModel foundation\n\nThe core of the project is built on the open-source DeepSeek-Coder model, which will be:\n • Customized via prompt engineering and fine-tuned for cybersecurity tasks\n • Further trained with CVE datasets, PoCs, Burp traffic, and OSINT data using LoRA / QLoRA\n • Continuously improved to provide more accurate analysis and actionable results\n\nMission\n\nTo make security audits accessible, fast, and understandable — even for developers and teams without deep cybersecurity knowledge.\nKey Features:\n • Shodan API for OSINT scanning\n • LLM (DeepSeek / Mistral) for analysis, classification, PoC generation\n • Burp Suite integration for live or simulated HTTP testing\n • Reports with CVSS-levels and remediation\n • Output formats: PDF / Markdown / Telegram integration\n\nMilestones\n\nMonth 1 – Setup & MVP\n • Project registration, GitHub repo, domain setup\n • Connect Shodan API and test scanning pipeline\n • Deploy DeepSeek-Coder 33B (quantized/FP16) on cloud GPUs\n • Build initial prompt templates for LLM analysis\n • Launch basic UI (HuggingFace Space or Gradio)\n\nOutcome: MVP where user submits a target → receives AI-generated analysis\n\n\nMonth 2 – Burp Integration + PoC Generation\n • Connect Burp Suite (Repeater API or simulated layer)\n • Generate potential attack payloads (SQLi, XSS, etc.)\n • Integrate CVE databases and remediation libraries\n • Improve LLM guidance and structure\n\n\nMonth 3 – Fine-tuning (LoRA / QLoRA)\n • Create and clean training data: CVEs, payloads, HTTP dumps\n • Perform targeted fine-tuning\n • Validate outputs on real-life targets\n\n\nMonth 4 – Full Reporting System\n • Generate structured reports (PDF / Markdown)\n • Include CVSS scoring, risk level, fix recommendations\n • Display reports via web or Telegram\n\nMonth 5–6 – Public Beta Launch\n • Add extra APIs (Wappalyzer, ipinfo, DNS)\n • Open early access version for testers\n • Gather feedback and iterate\n • Publish to Product Hunt, Reddit, Twitter\n\nAlex S – project initiator, technical architect and visionary behind RedMind AI.\n\nMy Role:\n • Architecting the AI + cybersecurity stack\n • Integrating Shodan, Burp, and LLM analysis\n • Building datasets for fine-tuning\n • Designing prompt templates and testing LLM logic\n • Overseeing development and future scaling\n\nOpen to:\n • Collaborators (AI engineers, pentesters, designers)\n • Mentorship from AI/security experts\n • Integration with open-source or commercial tooling\n • Feedback from early adopters and beta testers\n\nVision:\n\nThis is more than a tool — it’s a step toward autonomous cybersecurity assistance for everyone.\n\n\nUse of Funds\n\nWe are requesting $6000 in funding to cover the infrastructure and development needs for launching the MVP of RedMind AI — an autonomous AI-based penetration testing assistant.\n\n1. GPU Infrastructure ($4000)\nWe will rent cloud GPU servers with at least 200 GB of total VRAM to run large-scale LLMs such as DeepSeek-Coder 33B. This will allow us to perform inference and lightweight fine-tuning. Platforms may include RunPod, LambdaLabs, or Vast.ai. This budget will cover 3–4 months of cloud usage.\n\n2. API Integrations and Security Tools ($1200)\nWe will use these funds to subscribe to premium APIs and tools such as Shodan API, Wappalyzer, DNS/IP info services, and potentially Burp Suite Pro or its automation layer.\n\n3. Model Fine-Tuning and Data Preparation ($400)\nWe plan to fine-tune our base model using LoRA/QLoRA on cybersecurity-specific datasets (CVE data, Burp traffic, HTTP requests, and PoC examples). This will increase the model’s accuracy and domain specialization.\n\n4. Web Hosting and Frontend ($300)\nWe will use this budget to build a simple interface (via Gradio or Hugging Face Space), register a custom domain, and host the public-facing demo.\n\n5. Maintenance and Buffer ($100)\nReserved for gas fees, service transactions, and infrastructure adjustments.\n\nThis $6,000 budget will enable us to launch a functional MVP, test it with real users, and prepare for the public beta.", "logoImgData": {}, "bannerImgData": {}}