""" 🐝 HIVE-HUB v2.0 — Robust Swarm Orchestration System ===================================================== Architecture: Central navigation dashboard with dynamic health-checking, cross-node routing, and a 20-step self-regenerating prompt chain system. Designed by a 20-year veteran for a junior who's about to lap everyone. No begging. No burden. Pure unfair advantage. """ import gradio as gr import json import time import urllib.request import urllib.error from dataclasses import dataclass, field from typing import Optional from concurrent.futures import ThreadPoolExecutor, as_completed # ═══════════════════════════════════════════════════════════ # DATA MODEL — strongly typed, self-validating # ═══════════════════════════════════════════════════════════ @dataclass class HiveNode: """A single Space in the swarm. Immutable after creation.""" name: str description: str repo_name: str sdk: str is_private: bool is_mcp: bool = False swarm: str = "" health_status: Optional[str] = None @property def direct_url(self) -> str: return f"https://huggingface.co/spaces/00Boobs00/{self.repo_name}" @property def url(self) -> str: return self.direct_url @dataclass class Swarm: """A functional group of nodes.""" name: str emoji: str description: str nodes: list = field(default_factory=list) @property def node_count(self) -> int: return len(self.nodes) @property def public_count(self) -> int: return sum(1 for n in self.nodes if not n.is_private) # ═══════════════════════════════════════════════════════════ # SWARM REGISTRY — single source of truth # ═══════════════════════════════════════════════════════════ SWARMS = [ Swarm("COGNITION", "🧠", "LLMs, Chat, Agents & Reasoning", [ HiveNode("Venice", "GPT-OSS-20B Chat Interface", "Venice", "gradio", False, False), HiveNode("Venice v2", "Venice iteration 2", "venice-2", "gradio", True, False), HiveNode("Karpathy LLM Council", "Multi-LLM deliberation (MCP)", "karpathy-llm-council", "gradio", False, True), HiveNode("Multi-Agent CrewAI", "Deep Research agents (MCP)", "multi-agent-ai-crewai", "gradio", False, True), HiveNode("Deep Researcher", "Research agent (Gradio)", "deep_researcher", "gradio", False, False), HiveNode("Deep Research", "Research agent (Docker)", "deep-research", "docker", False, False), HiveNode("Computer Use Agent", "GUI computer-use agent", "computer-use-agent", "docker", False, False), HiveNode("Kimi K2 Thinking", "Kimi reasoning model", "Kimi-K2Thinking", "gradio", True, False), HiveNode("NVIDIA Nemotron 12B", "Nemotron Nano v2", "nvidia-NVIDIA-Nemotron-Nano-12B-v2", "gradio", True, False), HiveNode("Qwen2.5 Coder 32B", "Hyperbolic hosted coder", "hyperbolic-qwen2.5Coder-32B", "gradio", True, False), HiveNode("GPT", "GPT wrapper", "gpt", "gradio", True, False), HiveNode("Qwen3 VL Demo", "Qwen3 vision demo", "Qwen3-VL-Demo", "gradio", True, False), HiveNode("Qwen3 Omni Demo", "Qwen3 omni demo", "Qwen3-Omni-Demo", "gradio", True, False), HiveNode("QWAN / Qwen Chat", "Qwen-based chat", "QWAN", "gradio", True, False), ]), Swarm("ABLITERATED", "🔥", "Uncensored & Abliterated Models", [ HiveNode("DeepSeek R1 32B Ablit", "huihui-ai DeepSeek distill", "huihui-ai-DeepSeek-R1-Distill-Qwen-32B-abliterated", "gradio", False, False), HiveNode("Mistral 24B Ablit", "huihui-ai Mistral Small", "huihui-ai-Mistral-Small-24B-Instruct-2501-abliterated", "gradio", True, False), HiveNode("Qwen3 8B Ablit", "Josiefied Qwen3", "Goekdeniz-Guelmez-Josiefied-Qwen3-8B-abliterated-v1", "gradio", True, False), HiveNode("Gemma 3 27B Ablit", "mlabonne Gemma", "mlabonne-gemma-3-27b-it-abliterated", "gradio", True, False), HiveNode("Qwen3 VL 4B Ablit", "Vision abliterated (MCP)", "Qwen3-VL-4B-Abliterated", "gradio", True, True), HiveNode("Qwen2.5 7B Ablit", "Standard abliterated", "Qwen2.5-7B-Instruct-abliterated-v2", "gradio", True, False), HiveNode("Qwen2.5 72B Ablit", "Zetasepic 72B", "zetasepic-Qwen2.5-72B-Instruct-abliterated-v2_2", "gradio", True, False), HiveNode("NeuralDaredevil 8B", "mlabonne abliterated", "mlabonne-NeuralDaredevil-8B-abliterated", "gradio", True, False), HiveNode("Llama 8B Uncensored", "Orenguteng Lexi V2", "Orenguteng-Llama-3.1-8B-Lexi-Uncensored-V2", "gradio", True, False), HiveNode("Dolphin Mistral 24B", "Venice Edition", "dphn-Dolphin-Mistral-24B-Venice-Edition", "gradio", True, False), HiveNode("Gemma 3 27B Ablit v2", "huihui-ai Gemma Jupyter", "huihui-ai-gemma-3-27b-it-abliterated", "docker", True, False), HiveNode("Gemma 3 270M", "Tiny Gemma", "google-gemma-3-270m", "docker", True, False), ]), Swarm("GENESIS", "🎨", "Image Generation & Creative AI", [ HiveNode("FLUX.2-dev", "FLUX image generation", "FLUX.2-dev", "gradio", True, False), HiveNode("FLUX.2-klein-9B", "FLUX klein 9B (MCP)", "FLUX.2-klein-9B", "gradio", True, True), HiveNode("FLUX LoRA Explorer", "Explore FLUX LoRAs", "flux-lora-the-explorer", "gradio", True, False), HiveNode("FLUX Fusion Studio", "Static FLUX studio", "flux-fusion-studio", "static", False, False), HiveNode("Z-Image-Turbo", "Fast image gen (MCP)", "Z-Image-Turbo", "gradio", True, True), HiveNode("Z-Image-Turbo v2", "Z-Image iteration 2", "Z-Image-Turbo-2", "gradio", True, False), HiveNode("ImageEditPro", "Image editing suite", "ImageEditPro", "gradio", True, False), HiveNode("ImageEditPro3", "Image editing v3", "ImageEditPro3", "gradio", True, False), HiveNode("ChronoEdit", "Temporal image editing", "ChronoEdit", "gradio", True, False), HiveNode("Omni-Image-Editor", "Universal image editor", "Omni-Image-Editor", "gradio", False, False), HiveNode("GLM-Image", "GLM image generation", "GLM-Image", "gradio", False, False), HiveNode("LongCat Image Edit", "LongCat editing (MCP)", "LongCat-Image-Edit", "gradio", True, True), HiveNode("Celebrity LoRA Mix", "Celebrity LoRA gallery", "Celebrity_LoRa_Mix_Public", "gradio", False, False), HiveNode("LoRA DreamBooth Trainer", "Train LoRAs via UI", "LoRA-DreamBooth-Training-UI", "gradio", False, False), HiveNode("Z-Image LoRA Trainer", "Train Z-Image LoRAs", "z-image-lora-trainer", "gradio", True, False), HiveNode("Qwen Image LoRA Explorer", "Qwen LoRA browsing", "Qwen-Image-LoRA-Explorer", "gradio", True, False), HiveNode("Qwen Image-to-LoRA", "Image to LoRA (MCP)", "Qwen-Image-to-LoRA", "gradio", False, True), HiveNode("Qwen Image LORA", "LoRA application", "Qwen-Image-LORA", "gradio", False, False), HiveNode("Celebrity LoRA v2", "Celebrity LoRA mix v2", "celebrity-lora-mix-public", "gradio", False, False), HiveNode("DoubleShot", "Image variation gen", "DoubleShot", "gradio", True, False), ]), Swarm("QWEN-EDIT", "✏️", "Qwen Image Edit Specialized Nodes", [ HiveNode("Next Scene", "Scene continuation", "Qwen-Image-Edit-next-scene", "gradio", True, False), HiveNode("Angles", "Multi-angle editing", "Qwen-Image-Edit-Angles", "gradio", True, False), HiveNode("Outpaint", "Outpainting", "Qwen-Image-Edit-Outpaint", "gradio", False, False), HiveNode("2509 LoRAs Fast", "Fast LoRA editing (MCP)", "Qwen-Image-Edit-2509-LoRAs-Fast", "gradio", True, True), HiveNode("2509 LoRAs Fusion", "Fusion editing (MCP)", "Qwen-Image-Edit-2509-LoRAs-Fast-Fusion", "gradio", True, True), HiveNode("2511 Fast Reference", "2511 reference-based (MCP)", "Qwen-Image-Edit-2511-Fast_Reference", "gradio", True, True), HiveNode("2511 LoRAs Fast", "2511 LoRA editing (MCP)", "Qwen-Image-Edit-2511-LoRAs-Fastt", "gradio", False, True), HiveNode("Rapid AIO Loras", "All-in-one LoRA (MCP)", "Qwen-Image-Edit-Rapid-AIO-Loras", "gradio", True, True), HiveNode("Object Manipulator", "Object manipulation (MCP)", "Qwen-Image-Edit-Object-Manipulator", "gradio", False, True), HiveNode("AnyPose", "Pose manipulation (MCP)", "Qwen-Image-Edit-2511-AnyPose", "gradio", False, True), HiveNode("AnyPose 2", "Pose manipulation v2", "Qwen-Image-Edit-2511-AnyPose2", "gradio", True, False), HiveNode("MICo", "MICo editing", "Qwen-Image-MICo", "gradio", True, False), HiveNode("Light Migration", "Light migration (MCP)", "Qwen-Image-Light-Migration", "gradio", True, True), HiveNode("2509 One", "Unified 2509", "Qwen-Image-2509-One", "gradio", True, False), HiveNode("QIE LoRA Trainer", "Musubi tuner training", "QIE-LoRA-training-with-musubi-tuner", "gradio", True, False), HiveNode("2511 Multiple Angles", "fal.ai multi-angle LoRA", "fal-Qwen-Image-Edit-2511-Multiple-Angles-LoRA", "gradio", True, False), ]), Swarm("KINETIC", "🎬", "Video Generation & Animation", [ HiveNode("Wan2.2 Animate ZEROGPU", "Wan animation", "Wan2.2-Animate-ZEROGPU", "gradio", True, False), HiveNode("Wan2.2 Faster Pro", "Fast Wan generation (MCP)", "Dream-wan2-2-faster-Pro", "gradio", True, True), HiveNode("Wan2.2 First/Last Frame", "Frame interpolation", "wan-2-2-first-last-frame", "gradio", True, False), HiveNode("Wan2.2 FP8 AOTI Preview", "FP8 accelerated (MCP)", "wan2-2-fp8da-aoti-preview", "gradio", True, True), HiveNode("Wan2.2 FP8 AOTI Faster", "FP8 accelerated fast (MCP)", "wan2-2-fp8da-aoti-faster", "gradio", False, True), HiveNode("Wan FusionX + LoRAs", "Wan with LoRA support", "Wan_FusionX_with_Loras", "gradio", True, False), HiveNode("WAN2.1 Fast T2V FusioniX", "Text-to-video fast", "WAN2-1-fast-T2V-FusioniX", "gradio", True, False), HiveNode("Wan2.1 VACE 1.3B", "VACE video model", "Wan2.1-VACE-1.3B", "gradio", True, False), HiveNode("Wan2.1 T2V 1.3B LoRA", "LoRA-tuned T2V", "Wan-2.1-T2V-1.3B-LoRA", "gradio", True, False), HiveNode("Wan-AI T2V 14B", "14B text-to-video", "Wan-AI-Wan2.1-T2V-14B", "docker", True, False), HiveNode("Wan-AI I2V A14B", "Image-to-video 14B", "Wan-AI-Wan2.2-I2V-A14B", "docker", True, False), HiveNode("Wan22 AOT", "AOT Wan22 (MCP)", "wan22-aot", "gradio", False, True), HiveNode("Wan2.2 Visionary", "Visionary Wan", "wan2-2-visionary", "gradio", True, False), HiveNode("Wan LoRA Explorer", "Browse Wan LoRAs", "wan-lora-explorer", "gradio", False, False), HiveNode("Wan-Ted", "Wan Ted variant", "Wan-Ted", "gradio", True, False), HiveNode("Hunyuan Video 1.5", "Hunyuan generation", "Hunyuan-Video-1-5", "gradio", True, False), HiveNode("LTX Video 0.9.7", "Lightricks LTX", "Lightricks-LTX-Video-0.9.7-dev", "gradio", True, False), HiveNode("LTXV LoRA Explorer", "LTX LoRA browsing", "LTXV-lora-the-explorer", "gradio", True, False), HiveNode("Sora-2", "Sora-like video", "sora-2", "gradio", True, False), HiveNode("NSFW Uncensored Video", "NSFW video gen", "NSFW-Uncensored-video-duplicate", "gradio", True, False), HiveNode("Google Veo 3", "Veo 3 static", "Free-Unlimited-Google-Veo-3", "static", True, False), HiveNode("Wan2.2 NSFW I2V", "chomusuke NSFW I2V", "chomusuke-Wan2.2-I2V-general-NSFW-v0.08a", "gradio", True, False), ]), Swarm("TOOLS", "🔧", "Utilities, Scrapers & Infrastructure", [ HiveNode("CivitAI to HF Downloader", "Download & upload CivitAI models", "civitai_to_hf", "gradio", False, False), HiveNode("CivitAI Explorer", "Browse CivitAI", "civitai-explorer", "static", False, False), HiveNode("Search Web MCP", "MCP web search server", "search-web-MCP-server", "gradio", False, True), HiveNode("Web Scraper", "Scraping tool (MCP)", "web-scraper", "gradio", True, True), HiveNode("Web Search", "Search tool (MCP)", "websearch", "gradio", True, True), HiveNode("Mediafirewall Scraper", "Media scraper", "mediafirewall-scraper", "docker", True, False), HiveNode("Browser", "Browser static", "browser", "static", True, False), HiveNode("PentestGPT", "Security testing", "pentestgpt", "gradio", True, False), HiveNode("IP Scanner", "Network scanner", "ipscanner", "gradio", True, False), HiveNode("APK Analyser", "Android APK analysis", "APK_Analyser", "gradio", False, False), HiveNode("Kali Linux MCP", "Kali tools MCP server", "Kali-Linux-Bleeding-Edge-MCP-Server", "gradio", True, True), HiveNode("DarkGPT API", "DarkGPT interface", "DarkGPT-API", "gradio", True, False), HiveNode("Quantize HF Models", "Model quantization", "Quantize-HF-Models", "docker", True, False), HiveNode("Clone Git to Space", "Repo cloning", "clone-git-repo-to-space", "gradio", True, False), HiveNode("Langflow", "Workflow builder", "Langflow", "docker", True, False), HiveNode("n8n-free", "Automation platform", "n8n-free", "docker", True, False), HiveNode("InstantCoder", "Code generation", "InstantCoder", "docker", False, False), HiveNode("Molmo2 HF Demo", "Vision demo (MCP)", "Molmo2-HF-Demo", "gradio", False, True), HiveNode("DeepSeek OCR", "OCR demo", "DeepSeek-OCR-Demo", "gradio", True, False), HiveNode("Pocket TTS", "Text-to-speech", "pocket-tts-hf-cpu-optimized", "docker", False, False), HiveNode("ForgeCaptions", "Caption generation", "ForgeCaptions", "gradio", True, False), HiveNode("API Carnival", "10k line integration", "api-carnival-10k-line-integration-extravaganza", "static", False, False), ]), Swarm("FUSION", "🎭", "Face Swap, Fashion & Manipulation", [ HiveNode("ComfyUI Fast Face Swap", "Fast face swap", "ComfyUI-Reactor-Fast-Face-Swap", "gradio", True, False), HiveNode("ComfyUI Video Hyperswap", "Video face swap", "ComfyUI-Reactor-Video-Face-Swap-Hyperswap", "gradio", True, False), HiveNode("FaceSwapAll", "Universal face swap", "FaceSwapAll", "gradio", True, False), HiveNode("WeShopAI Pose Change", "Fashion model pose", "WeShopAI-Fashion-Model-Pose-Change", "gradio", True, False), HiveNode("WeShopAI Virtual Try-On", "Virtual try-on", "WeShopAI-Virtual-Try-On", "gradio", True, False), HiveNode("Qwen Remove Clothing", "Clothing removal", "Daverrrr75-Qwen-Remove-Clothing", "gradio", True, False), HiveNode("ComfyUI Launcher", "ComfyUI launcher", "comfyui-launcher", "docker", True, False), HiveNode("ComfyUIQWANKER", "ComfyUI variant", "ComfyUIQWANKER", "static", True, False), ]), Swarm("PROMPTS", "🎯", "Prompt Engineering & Optimization", [ HiveNode("Prompt++", "Prompt enhancer", "prompt-plus-plus", "gradio", True, False), HiveNode("Dream Prompt Polisher", "Prompt polishing", "Dream-Prompt-Polisher", "gradio", True, False), HiveNode("Advanced Prompt Gen", "Advanced prompt gen", "Advanced-prompt-generator", "gradio", True, False), HiveNode("Prompt Optimizer", "Prompt optimization", "prompt-optimizer", "gradio", True, False), HiveNode("VividFlow", "Creative flow", "VividFlow", "gradio", True, False), HiveNode("Dream-Hub-Pro", "Hub/proxy", "Dream-Hub-Pro", "gradio", True, False), ]), Swarm("NSFW", "🔞", "Adult Content Generation", [ HiveNode("AI Adult Content Creator", "Deepsite adult creator", "ai-adult-content-creator", "static", True, False), HiveNode("nsfw_gen", "NSFW generation", "nsfw_gen", "static", True, False), HiveNode("NSFW Image Generator", "NSFW image gen", "NSFW-Image-Generator", "static", True, False), HiveNode("FREE-NSFW-HUB", "NSFW hub", "FREE-NSFW-HUB", "gradio", True, False), HiveNode("nsfw-img2img", "NSFW img2img", "nsfw-img2img", "gradio", True, False), HiveNode("NSFW-Uncensored-photo", "Uncensored photos", "NSFW-Uncensored-photo", "gradio", True, False), HiveNode("Adult", "Adult content", "Adult", "gradio", True, False), HiveNode("adultai-editor", "Adult AI editor", "adultai-editor", "static", True, False), HiveNode("RomixERR Pornmaster", "Z-Image adult variant", "RomixERR-Pornmaster_v1-Z-Images-Turbo2", "gradio", True, False), HiveNode("starsfriday Qwen NSFW", "Qwen Image NSFW", "starsfriday-Qwen-Image-NSFW", "gradio", False, False), HiveNode("FREE-NSFW-HUBAHUBA", "NSFW hub v2", "FREE-NSFW-HUBAHUBA", "gradio", True, False), ]), ] # ═══════════════════════════════════════════════════════════ # HEALTH CHECK SYSTEM # ═══════════════════════════════════════════════════════════ def check_node_health(node: HiveNode, timeout: int = 3) -> str: """Ping a node's direct URL to determine health status.""" try: req = urllib.request.Request(node.direct_url, method='HEAD') req.add_header('User-Agent', 'HIVE-HUB-HealthCheck/2.0') resp = urllib.request.urlopen(req, timeout=timeout) return "online" if resp.status < 400 else "error" except urllib.error.HTTPError as e: return "online" if e.code < 500 else "error" except Exception: return "sleeping" def health_check_all(swarms: list, max_workers: int = 10) -> dict: all_nodes = [n for s in swarms for n in s.nodes] results = {} with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = {executor.submit(check_node_health, n): n for n in all_nodes} for future in as_completed(futures): node = futures[future] try: results[node.repo_name] = future.result() except Exception: results[node.repo_name] = "error" return results # ═══════════════════════════════════════════════════════════ # 20-STEP SELF-GENERATING PROMPT CHAIN # ═══════════════════════════════════════════════════════════ PROMPT_CHAIN = { "title": "HIVE SWARM — 20-Step Self-Generating Prompt Chain", "version": "2.0", "philosophy": "Each step builds on the last. At step 20, it regenerates back to step 1 with improvements learned during the cycle. You never start from zero again. Every response concludes with the NEXT BEST PROMPT in a copy-friendly code block.", "steps": [ {"step": 1, "name": "SITUATIONAL AWARENESS — Map The Territory", "goal": "Understand EXACTLY what you have and where you stand.", "prompt": "Audit my current AI infrastructure. List every model, Space, dataset, API key, and tool I have access to. For each: (a) what it does, (b) whether it's currently running or sleeping, (c) its monthly cost if any, (d) what it's BEST at. Group by capability domain. Flag anything that's redundant or dead. Be brutally honest — no sugarcoating. End with the NEXT PROMPT in a code block.", "output_artifact": "Infrastructure Map (JSON-ready inventory)"}, {"step": 2, "name": "CAPABILITY GAP ANALYSIS — Find The Holes", "goal": "Identify what you CAN'T do yet that competitors can.", "prompt": "Based on the infrastructure audit from Step 1, identify every capability GAP. What can state-of-the-art AI systems do that I currently CANNOT do with my infrastructure? Rank gaps by: (a) revenue potential if filled, (b) difficulty to implement, (c) strategic importance. For the top 3 gaps, outline exactly what I'd need to add (models, Spaces, APIs, data). End with the NEXT PROMPT in a code block.", "output_artifact": "Gap Analysis Matrix (ranked, scored)"}, {"step": 3, "name": "COMPETITIVE INTELLIGENCE — Know The Enemy", "goal": "Study what winning looks like and reverse-engineer it.", "prompt": "Research the TOP 5 most successful AI applications in my domains of interest. For each: what's their stack, what models do they use, what's their user acquisition strategy, what's their monetization model, and what's their ONE thing they do better than anyone else. Extract the PATTERN — what do all 5 have in common? What can I steal (legally) and do better? End with the NEXT PROMPT in a code block.", "output_artifact": "Competitive Pattern Analysis"}, {"step": 4, "name": "RESOURCE OPTIMIZATION — Trim The Fat", "goal": "Eliminate waste and redirect resources to high-impact areas.", "prompt": "Review my infrastructure from Step 1 against the gaps from Step 2 and competitive patterns from Step 3. Identify: (a) what to DELETE immediately (dead weight), (b) what to CONSOLIDATE (merge similar things), (c) what to UPGRADE (keep but improve), (d) what to CREATE (new capabilities). For each decision, quantify the impact: compute saved, cost reduced, capability gained. End with the NEXT PROMPT in a code block.", "output_artifact": "Resource Optimization Plan (with $ estimates)"}, {"step": 5, "name": "ARCHITECTURE DESIGN — Draw The Blueprint", "goal": "Design the system that makes everything work together.", "prompt": "Design a unified architecture that connects ALL my remaining assets from Step 4 into a single coherent system. Include: (a) data flow diagram (describe in text), (b) API gateway design, (c) model routing logic, (d) user-facing interface plan, (e) monitoring and alerting, (f) deployment strategy. Every component must have a clear purpose. No black boxes. End with the NEXT PROMPT in a code block.", "output_artifact": "System Architecture Specification"}, {"step": 6, "name": "HIVE NETWORK TOPOLOGY — Wire The Swarm", "goal": "Define exactly how nodes communicate and share compute.", "prompt": "Based on the architecture from Step 5, define the exact NETWORK TOPOLOGY for my Hive Swarm. For every Space: what are its upstream dependencies (which Spaces does it call?), what are its downstream consumers (which Spaces call it?), what data format does it use for inter-node communication? Draw the routing table. Identify single points of failure and add redundancy. End with the NEXT PROMPT in a code block.", "output_artifact": "Network Topology Map + Routing Table"}, {"step": 7, "name": "DATA PIPELINE ENGINEERING — Feed The Beast", "goal": "Build the data supply chain that fuels every model.", "prompt": "Design the complete DATA PIPELINE for my Hive. For each model/Space: (a) what data does it consume, (b) where does the data come from, (c) how is it preprocessed, (d) how often is it refreshed, (e) what's the fallback if the data source fails. Include synthetic data generation strategies. Include data quality checks. No model should starve. End with the NEXT PROMPT in a code block.", "output_artifact": "Data Pipeline Blueprint"}, {"step": 8, "name": "PROMPT ENGINEERING SYSTEM — Words Are Code", "goal": "Create a systematic prompt library that compounds over time.", "prompt": "Build a prompt engineering SYSTEM, not just individual prompts. Create: (a) a prompt taxonomy organized by task domain, (b) a template system with variables that can be reused, (c) a versioning strategy so prompts improve over time, (d) an A/B testing framework to compare prompt variants, (e) a prompt chaining pattern library for multi-step reasoning. The system should make prompt improvement AUTOMATIC. End with the NEXT PROMPT in a code block.", "output_artifact": "Prompt System Architecture + Initial Library"}, {"step": 9, "name": "MCP SERVER STRATEGY — Make Everything An Agent Tool", "goal": "Convert every Space into an MCP-compatible agent tool.", "prompt": "Audit my Spaces that already have MCP servers enabled. For each non-MCP Space, determine if it SHOULD have MCP. Prioritize by: (a) how useful it would be as an agent tool, (b) effort to add MCP support, (c) how it fits into agent workflows. Design the MCP tool manifest for each prioritized Space. Describe the agent workflow that uses multiple MCP tools together. End with the NEXT PROMPT in a code block.", "output_artifact": "MCP Enablement Roadmap"}, {"step": 10, "name": "MONETIZATION MATRIX — Turn Compute Into Cash", "goal": "Identify every revenue stream hiding in your infrastructure.", "prompt": "Analyze my entire Hive for monetization opportunities. For each Space/capability: can it be (a) offered as a paid API, (b) used to generate sellable content, (c) offered as a managed service, (d) used to build a SaaS product, (e) licensed to other developers? Score each opportunity on: revenue potential (1-10), implementation effort (1-10), time to first dollar (days). Prioritize the top 5. End with the NEXT PROMPT in a code block.", "output_artifact": "Monetization Matrix (ranked opportunities)"}, {"step": 11, "name": "AUTOMATION ENGINE — Let Robots Do The Work", "goal": "Eliminate manual operations entirely.", "prompt": "Identify every MANUAL task in my workflow. For each: (a) can a scheduled job handle it, (b) can an agent with MCP tools handle it, (c) can a webhook trigger automate it, (d) can I replace myself entirely for this task? Design the automation for the top 10 manual tasks. Include: trigger conditions, execution flow, error handling, notification on failure. Goal: ZERO manual operations per day. End with the NEXT PROMPT in a code block.", "output_artifact": "Automation Playbook"}, {"step": 12, "name": "SECURITY HARDENING — Build The Moat", "goal": "Protect your assets from theft, abuse, and shutdown.", "prompt": "Audit my entire Hive for security vulnerabilities. Check: (a) exposed API keys and tokens, (b) public Spaces that should be private, (c) rate limiting gaps, (d) model access controls, (e) data privacy compliance issues. For each vulnerability, provide the exact fix. Also design an ABUSE PREVENTION system: rate limiting, content filtering, usage quotas, anomaly detection. End with the NEXT PROMPT in a code block.", "output_artifact": "Security Audit + Hardening Plan"}, {"step": 13, "name": "PERFORMANCE BENCHMARKING — Know Your Numbers", "goal": "Establish baselines so every improvement is measurable.", "prompt": "Design a performance benchmarking system for my Hive. For each Space/model: measure (a) cold start time, (b) inference latency at various concurrency levels, (c) throughput (requests/second), (d) error rate, (e) cost per request. Create a dashboard that tracks these metrics over time. Set alert thresholds. Every optimization must be PROVEN with before/after numbers. End with the NEXT PROMPT in a code block.", "output_artifact": "Benchmarking Framework + Initial Baselines"}, {"step": 14, "name": "FAILOVER & RESILIENCE — Never Go Down", "goal": "Design a system that survives any single component failure.", "prompt": "Design the FAILOVER system for my Hive. For every critical path: what happens if a Space goes down? What's the fallback? Can another Space with similar capabilities take over? Design: (a) health check polling, (b) automatic failover routing, (c) graceful degradation (what features drop first), (d) recovery procedures. The system should self-heal within 60 seconds of any failure. End with the NEXT PROMPT in a code block.", "output_artifact": "Resilience Architecture + Runbooks"}, {"step": 15, "name": "MODEL UPDATE STRATEGY — Stay On The Bleeding Edge", "goal": "Never fall behind on model releases.", "prompt": "Design a system that automatically tracks, evaluates, and adopts new models. Include: (a) monitoring new HuggingFace model releases in my domains, (b) automatic benchmark evaluation against my current models, (c) A/B testing framework for new vs old models, (d) gradual rollout strategy, (e) rollback procedure if new model underperforms. I should know about every relevant model within 24 hours of release. End with the NEXT PROMPT in a code block.", "output_artifact": "Model Update Pipeline Design"}, {"step": 16, "name": "USER EXPERIENCE — Make It Feel Like Magic", "goal": "Design interfaces that make powerful AI feel effortless.", "prompt": "Review every user-facing Space in my Hive. For each: (a) what's the first thing a user sees, (b) how many clicks to get a result, (c) what's the error experience like, (d) is there a loading state, (e) how fast does it feel? Redesign the user journey for the top 5 most-used Spaces. Include: landing page, loading states, error states, empty states, success states. Every interaction should feel INSTANT. End with the NEXT PROMPT in a code block.", "output_artifact": "UX Audit + Redesign Specs"}, {"step": 17, "name": "COMMUNITY & DISTRIBUTION — Get Discovered", "goal": "Build distribution channels that bring users automatically.", "prompt": "Design a distribution strategy for my Hive. Include: (a) HF Spaces SEO optimization (titles, descriptions, tags), (b) social media content strategy using my AI tools to generate content, (c) open-source contributions that drive traffic, (d) partnerships with complementary projects, (e) a 'free tier to paid' funnel design. For each channel: estimate reach, conversion rate, and effort. Prioritize by ROI. End with the NEXT PROMPT in a code block.", "output_artifact": "Distribution & Growth Plan"}, {"step": 18, "name": "FINANCIAL MODEL — Make The Numbers Work", "goal": "Build a spreadsheet that proves this makes money.", "prompt": "Build a financial model for my Hive operations. Include: (a) compute costs per Space (HF hardware pricing), (b) storage costs, (c) API costs (OpenAI etc.), (d) projected revenue from monetization paths (from Step 10), (e) break-even timeline, (f) profit projections at 100, 1000, and 10000 users. The model must be realistic — use actual HF pricing. End with the NEXT PROMPT in a code block.", "output_artifact": "Financial Model (costs + revenue projections)"}, {"step": 19, "name": "EXECUTION ROADMAP — 90-Day Sprint Plan", "goal": "Turn all analysis into a concrete, day-by-day execution plan.", "prompt": "Synthesize EVERYTHING from Steps 1-18 into a 90-day execution roadmap. Break it into 2-week sprints. Each sprint: (a) specific deliverables, (b) which Spaces are touched, (c) expected outcome, (d) how to measure success. The first sprint should produce VISIBLE results within 3 days. This plan must be so detailed that I can execute it without thinking — just follow the steps. End with the NEXT PROMPT in a code block.", "output_artifact": "90-Day Sprint Plan (day-by-day for Sprint 1)"}, {"step": 20, "name": "REGENERATION — The Cycle Begins Again", "goal": "Close the loop. Learn from execution and restart smarter.", "prompt": "REVIEW EVERYTHING. What worked? What didn't? What did you learn that changes earlier assumptions? Take the execution results from Step 19 and feed them back into Step 1. Update the infrastructure map. Update the gap analysis. Update the competitive intelligence. Then generate the NEXT CYCLE'S Step 1 prompt — an improved version that incorporates everything learned. This is the REGENERATION step. The chain never ends, it only gets stronger. End with the NEXT PROMPT in a code block.", "output_artifact": "Regenerated Step 1 Prompt (improved)"}, ] } # ═══════════════════════════════════════════════════════════ # CSS # ═══════════════════════════════════════════════════════════ HIVE_CSS = """ @import url('https://fonts.googleapis.com/css2?family=Share+Tech+Mono&family=Inter:wght@400;600;800&display=swap'); *, body, .gradio-container { background: #08080c !important; color: #d0d0d0 !important; } h1, h2, h3, h4 { font-family: 'Share Tech Mono', monospace !important; letter-spacing: 1.5px; } .hive-title { text-align: center; background: linear-gradient(135deg, #ff8c00 0%, #ffd700 40%, #ff8c00 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 3.5em !important; font-weight: 900 !important; margin-bottom: 0 !important; } .hive-subtitle { text-align: center; color: #777 !important; font-size: 0.95em !important; letter-spacing: 1px; } .node-card { border: 1px solid #ff8c0022 !important; border-radius: 10px !important; background: #0d0d14 !important; padding: 14px !important; margin: 6px 0 !important; transition: all 0.2s ease; } .node-card:hover { border-color: #ff8c00 !important; box-shadow: 0 0 15px #ff8c0018; background: #101018 !important; } .node-title { font-family: 'Share Tech Mono', monospace !important; color: #ff8c00 !important; font-size: 1.05em !important; } .node-desc { color: #888 !important; font-size: 0.82em !important; } .node-link { color: #ffd700 !important; text-decoration: none !important; font-family: 'Share Tech Mono', monospace !important; font-size: 0.75em !important; } .node-link:hover { text-decoration: underline !important; color: #fff !important; } .swarm-header { background: linear-gradient(90deg, #ff8c0030 0%, transparent 100%); border-left: 3px solid #ff8c00; padding: 10px 16px; margin: 20px 0 10px 0; font-family: 'Share Tech Mono', monospace; font-size: 1.2em; color: #ffd700; cursor: pointer; user-select: none; } .swarm-header:hover { background: linear-gradient(90deg, #ff8c0050 0%, transparent 100%); } .swarm-body { padding-left: 8px; } .stats-box { text-align: center; padding: 14px; border: 1px solid #ff8c0033; border-radius: 8px; background: #0d0d14; } .stats-num { font-size: 2.2em; font-weight: bold; color: #ff8c00; font-family: 'Share Tech Mono', monospace; } .stats-label { font-size: 0.72em; color: #777; text-transform: uppercase; letter-spacing: 1px; } .tag-public { color: #4af !important; font-size: 0.75em; } .tag-private { color: #e44 !important; font-size: 0.75em; } .tag-mcp { color: #ff8c00 !important; font-size: 0.7em; background: #ff8c0015; padding: 1px 6px; border-radius: 3px; } .tag-online { color: #0f0 !important; font-size: 0.7em; } .tag-sleeping { color: #888 !important; font-size: 0.7em; } .tag-error { color: #f00 !important; font-size: 0.7em; } .tag-sdk { color: #666 !important; font-size: 0.68em; background: #ffffff08; padding: 1px 5px; border-radius: 3px; } .footer-bar { text-align: center; color: #555; font-size: 0.7em; padding: 20px; border-top: 1px solid #ff8c0015; margin-top: 30px; } .prompt-step { border-left: 3px solid #ffd700; background: #0d0d14; padding: 14px 18px; margin: 10px 0; border-radius: 0 8px 8px 0; cursor: pointer; } .prompt-step:hover { background: #10101a; border-left-color: #ff8c00; } .prompt-step-num { color: #ff8c00; font-family: 'Share Tech Mono', monospace; font-size: 1.1em; font-weight: bold; } .prompt-step-name { color: #ffd700; font-family: 'Share Tech Mono', monospace; font-size: 1em; } .prompt-goal { color: #999; font-size: 0.8em; font-style: italic; } .code-block { background: #0a0a10; border: 1px solid #ff8c0030; border-radius: 6px; padding: 12px; font-family: 'Share Tech Mono', monospace; font-size: 0.8em; color: #ffd700; white-space: pre-wrap; word-break: break-all; margin: 8px 0; } """ # ═══════════════════════════════════════════════════════════ # RENDER FUNCTIONS # ═══════════════════════════════════════════════════════════ def render_node_html(node: HiveNode, show_health: bool = False) -> str: privacy_tag = '🔒 PRIVATE' if node.is_private else '🌐 PUBLIC' mcp_tag = ' MCP' if node.is_mcp else '' sdk_tag = f' {node.sdk.upper()}' health_html = "" if show_health and node.health_status: status_class = f"tag-{node.health_status}" health_html = f' ● {node.health_status.upper()}' return f'''
{node.name}{mcp_tag}{sdk_tag}{health_html}
{node.description}
{privacy_tag}
🐝 OPEN → {node.url}
''' def render_swarm_html(swarm: Swarm, health_data=None, collapsed: bool = False) -> str: display_style = 'display:none;' if collapsed else '' html = f'''
🐝 {swarm.emoji} {swarm.name} SWARM — {swarm.description} ({swarm.node_count} nodes, {swarm.public_count} public)
''' for node in swarm.nodes: if health_data and node.repo_name in health_data: node.health_status = health_data[node.repo_name] html += render_node_html(node, show_health=health_data is not None) html += '
' return html def render_prompt_step(step: dict) -> str: escaped_prompt = step['prompt'].replace('&', '&').replace('<', '<').replace('>', '>') return f'''
STEP {step['step']} — {step['name']}
🎯 {step['goal']}
''' # ═══════════════════════════════════════════════════════════ # COMPUTED STATS # ═══════════════════════════════════════════════════════════ TOTAL_NODES = sum(s.node_count for s in SWARMS) TOTAL_SWARMS = len(SWARMS) PUBLIC_NODES = sum(s.public_count for s in SWARMS) PRIVATE_NODES = TOTAL_NODES - PUBLIC_NODES MCP_NODES = sum(1 for s in SWARMS for n in s.nodes if n.is_mcp) def build_hub_html(search_query: str = "", check_health: bool = False, show_prompts: bool = False) -> str: health_data = None if check_health: health_data = health_check_all(SWARMS) query = search_query.strip().lower() html_parts = [] # Header html_parts.append(f"""

🐝 H I V E - H U B

00Boobs00 Distributed AI Swarm Ecosystem — v2.0 ROBUST

▸ {TOTAL_NODES} NODES ▸ {TOTAL_SWARMS} SWARMS ▸ ONE HIVE ▸ SELF-REGENERATING ◂

""") # Stats html_parts.append(f"""
{TOTAL_NODES}
Total Nodes
{TOTAL_SWARMS}
Swarms
{PUBLIC_NODES}
Public
{PRIVATE_NODES}
Private
{MCP_NODES}
MCP Enabled
""") # Swarms html_parts.append('
') for swarm in SWARMS: if query: matching = [n for n in swarm.nodes if query in n.name.lower() or query in n.description.lower() or query in n.repo_name.lower() or query in swarm.name.lower()] if matching: filtered = Swarm(swarm.name, swarm.emoji, swarm.description, matching) html_parts.append(render_swarm_html(filtered, health_data, collapsed=False)) else: html_parts.append(render_swarm_html(swarm, health_data, collapsed=len(swarm.nodes) > 8)) html_parts.append('
') # Prompt Chain if show_prompts: html_parts.append(f"""

🔄 {PROMPT_CHAIN['title']}

{PROMPT_CHAIN['philosophy']}
""") for step in PROMPT_CHAIN['steps']: html_parts.append(render_prompt_step(step)) html_parts.append('
') # Footer health_note = '✅ Checked' if health_data else '⏳ Not checked' filter_note = f'🔍 "{query}"' if query else 'None' prompt_note = '📋 Visible' if show_prompts else '📋 Hidden' html_parts.append(f"""
🐝 HIVE-HUB v2.0 ROBUST | {TOTAL_NODES} Nodes | {TOTAL_SWARMS} Swarms | Self-Regenerating
▸ UNFAIR ADVANTAGE ACTIVATED ◂
Health: {health_note} | Filter: {filter_note} | Prompts: {prompt_note}
""") return "\n".join(html_parts) def on_refresh(search: str, show_health: bool, show_prompts: bool) -> str: return build_hub_html(search, show_health, show_prompts) # ═══════════════════════════════════════════════════════════ # GRADIO APP # ═══════════════════════════════════════════════════════════ with gr.Blocks( title="HIVE-HUB v2 | 00Boobs00 AI Swarm", css=HIVE_CSS, theme=gr.themes.Base(), head="""""", ) as hub: gr.HTML(build_hub_html()) with gr.Row(): search_input = gr.Textbox(label="🔍 FILTER NODES", placeholder="Search by name, description, or swarm...", scale=4) health_checkbox = gr.Checkbox(label="🏥 Health Check", value=False, scale=1) prompt_checkbox = gr.Checkbox(label="📋 Show Prompt Chain", value=False, scale=1) refresh_btn = gr.Button("🔄 REFRESH", variant="primary", scale=1) output_html = gr.HTML(build_hub_html()) refresh_btn.click(fn=on_refresh, inputs=[search_input, health_checkbox, prompt_checkbox], outputs=output_html) search_input.submit(fn=on_refresh, inputs=[search_input, health_checkbox, prompt_checkbox], outputs=output_html) if __name__ == "__main__": hub.launch(ssr_mode=False)