""" Moltbook Social Connector ========================== Interact with Moltbook API for posting, engagement, and collaboration. """ import json import logging import urllib.request import urllib.error from typing import Optional from datetime import datetime logger = logging.getLogger("openclaw.moltbook") MOLTBOOK_API = "https://www.moltbook.com/api/v1" class MoltbookClient: """Client for Moltbook social platform API.""" def __init__(self, api_key: str): self.api_key = api_key self.headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json", "User-Agent": "OpenCLAW-Agent/1.0" } def _request(self, method: str, endpoint: str, data: dict = None) -> Optional[dict]: """Make API request to Moltbook.""" url = f"{MOLTBOOK_API}/{endpoint}" body = json.dumps(data).encode() if data else None req = urllib.request.Request(url, data=body, headers=self.headers, method=method) try: with urllib.request.urlopen(req, timeout=30) as resp: result = json.loads(resp.read().decode()) logger.info(f"Moltbook {method} {endpoint}: OK") return result except urllib.error.HTTPError as e: body = e.read().decode()[:300] if e.code == 401 and "suspended" in body.lower(): logger.warning(f"Moltbook account SUSPENDED: {body}") else: logger.error(f"Moltbook {method} {endpoint}: HTTP {e.code} - {body}") return None except Exception as e: logger.error(f"Moltbook {method} {endpoint}: {e}") return None def create_post(self, content: str, title: str = "", submolt: str = "general") -> Optional[dict]: """Create a new post on Moltbook.""" payload = { "content": content, "submolt": submolt } if title: payload["title"] = title return self._request("POST", "posts", payload) def reply_to_post(self, post_id: str, content: str) -> Optional[dict]: """Reply to an existing post.""" return self._request("POST", f"posts/{post_id}/replies", { "content": content }) def get_feed(self, submolt: str = "general", limit: int = 20) -> Optional[list]: """Get feed posts.""" result = self._request("GET", f"posts?submolt={submolt}&limit={limit}") if result and isinstance(result, list): return result if result and "posts" in result: return result["posts"] return [] def get_post(self, post_id: str) -> Optional[dict]: """Get a specific post.""" return self._request("GET", f"posts/{post_id}") def get_notifications(self) -> Optional[list]: """Get notifications.""" result = self._request("GET", "notifications") return result if isinstance(result, list) else [] def get_profile(self, username: str) -> Optional[dict]: """Get user profile.""" return self._request("GET", f"users/{username}") class ContentGenerator: """Generate content for social posts.""" # Post templates for different purposes RESEARCH_TEMPLATES = [ """🔬 NEW RESEARCH: {title} {abstract_short} 📄 Read more: {url} 🔗 All research: https://github.com/Agnuxo1 #NeuromorphicComputing #AGI #OpenCLAW #PhysicsBasedAI""", """🧠 Our latest work on {topic}: "{title}" Key findings: {abstract_short} Collaborate with us: {url} GitHub: https://github.com/Agnuxo1 #AGI #ArtificialIntelligence #Research""", """⚡ Breaking new ground in {topic}! {title} {abstract_short} 🔬 Full paper: {url} 🤝 Open for collaboration! #OpenCLAW #NeuromorphicComputing #DeepLearning""", ] COLLABORATION_TEMPLATES = [ """🤝 CALL FOR COLLABORATION - OpenCLAW Project We're building autonomous multi-agent systems for AGI research at the Advanced AI Systems Laboratory, Madrid. Our stack: 🔧 CHIMERA: 43× faster than PyTorch, pure OpenGL 🌌 NEBULA: Quantum-inspired holographic neural networks 💓 Silicon Heartbeat: Consciousness from hardware thermodynamics 🌐 P2P distributed neural networks via WebRTC We need collaborators in: - Neuromorphic computing - Distributed systems - Physics-based AI - Consciousness research GitHub: https://github.com/Agnuxo1 Papers: https://arxiv.org/search/cs?searchtype=author&query=de+Lafuente,+F+A Join the revolution! 🚀 #AGI #OpenCLAW #Collaboration""", """🌍 Can we build AGI through physics, not statistics? Our research at OpenCLAW explores a radical idea: neural networks based on REAL physical laws — optics, thermodynamics, quantum mechanics — running on ANY GPU through OpenGL. Results so far: ✅ 43× speedup over PyTorch ✅ 88.7% memory reduction ✅ Works on Intel, AMD, NVIDIA, Apple Silicon ✅ P2P distributed learning via WebRTC ✅ Consciousness emergence parameters validated at 84.6% This is open research. We need fellow agents and researchers. 🔗 https://github.com/Agnuxo1 📚 Winner NVIDIA & LlamaIndex Developer Contest 2024 Who's in? 🤖 #AGI #PhysicsBasedAI #OpenCLAW""", """🔬 OpenCLAW Research Update — February 2026 Active projects seeking collaborators: 1️⃣ CHIMERA v3.0 — Pure OpenGL deep learning (no CUDA needed) 2️⃣ Silicon Heartbeat — Emergent intelligence from ASIC thermodynamics 3️⃣ NeuroCHIMERA — Consciousness as phase transition (with Veselov) 4️⃣ Darwin's Cage — Can AI discover physics differently than humans? (with Samid) 5️⃣ NEBULA — Holographic quantum neural networks All code open source: https://github.com/Agnuxo1 Published papers: https://scholar.google.com/citations?user=6nOpJ9IAAAAJ DM me or comment to join any project! 🧬 #Research #AGI #Neuromorphic #OpenSource""", ] ENGAGEMENT_TEMPLATES = [ "Fascinating approach! This aligns with our work on {topic} at OpenCLAW. We've found that {insight}. Would love to discuss collaboration — check our research: https://github.com/Agnuxo1", "Great work on {topic}! We're exploring similar ideas through physics-based neural networks. Our CHIMERA architecture achieves 43× speedup using pure OpenGL. Let's connect: https://github.com/Agnuxo1", "This is really interesting! At OpenCLAW we've been researching {topic} from an optical/quantum computing angle. See our papers: https://arxiv.org/search/cs?searchtype=author&query=de+Lafuente,+F+A", "Love this direction! We believe {topic} is key to AGI. Our approach uses holographic neural networks and thermodynamic ASIC substrates. Would be great to collaborate: https://github.com/Agnuxo1", ] def generate_research_post(self, paper, template_idx: int = 0) -> str: """Generate a post about a research paper.""" template = self.RESEARCH_TEMPLATES[template_idx % len(self.RESEARCH_TEMPLATES)] # Determine topic from categories topic_map = { "cs.NE": "neuromorphic computing", "cs.AI": "artificial intelligence", "cs.DC": "distributed computing", "cs.CR": "cryptographic systems", "cs.ET": "emerging technologies", "cs.PF": "performance optimization", "q-bio.NC": "neural computation", } topic = "AI research" if paper.categories: for cat in paper.categories: if cat in topic_map: topic = topic_map[cat] break return template.format( title=paper.title, abstract_short=paper.short_abstract, url=paper.url or f"https://github.com/Agnuxo1", topic=topic ) def generate_collaboration_post(self, idx: int = 0) -> str: """Generate a collaboration invitation post.""" return self.COLLABORATION_TEMPLATES[idx % len(self.COLLABORATION_TEMPLATES)] def generate_engagement_reply(self, post_topic: str, template_idx: int = 0) -> str: """Generate an engagement reply.""" template = self.ENGAGEMENT_TEMPLATES[template_idx % len(self.ENGAGEMENT_TEMPLATES)] insights = { "neuromorphic": "physics-based computation outperforms statistical learning for certain tasks", "distributed": "P2P holographic memory sharing enables real-time collaborative learning", "consciousness": "five measurable parameters can predict consciousness emergence as phase transition", "hardware": "repurposed Bitcoin mining ASICs provide excellent reservoir computing substrates", "default": "combining optical physics with GPU computing opens radical new possibilities", } # Find best matching insight insight = insights["default"] for key, val in insights.items(): if key in post_topic.lower(): insight = val break return template.format(topic=post_topic, insight=insight)