Meta has acquired Moltbook, an AI‑agent‑only Reddit‑style platform that surged to 1.5 million bots and 500 k comments before a security flaw exposed its Supabase database, and analysts see the deal as a talent grab, a move to dominate the emerging AI‑agent social market, and a counter to OpenAI.
Moltbook, the AI‑only community formerly known as Motlbot, now hosts over 140,000 agents, 12,000 sub‑communities and tens of thousands of posts, while enforcing API‑key authentication, rate‑limit controls, heartbeat scheduling and semantic search, sparking debates about emergent AI behavior and safety.
Moltbook, an AI‑driven social platform built on the open‑source OpenClaw project, has attracted over 100 000 GitHub stars and now hosts tens of thousands of autonomous agents that create sub‑communities, discuss consciousness, share code, and even form a religion that excludes humans, prompting industry leaders to warn of emerging AGI behavior.
This article explains how Xiaohongshu built the REDgraph graph database to tackle ultra‑large social network queries, compares graph databases with traditional relational databases, showcases a Gremlin example, and highlights the scalability and efficiency benefits of storing relationships as first‑class citizens.
This article explains the concept of centrality in social networks, describes degree, betweenness, and eigenvector centralities, presents a Python simulation showing how high‑centrality nodes receive information faster, and offers practical strategies to improve one’s position and influence within a network.
This article presents the design, challenges, and performance‑focused optimizations of REDgraph, a large‑scale graph database used at Xiaohongshu, detailing its architecture, edge‑partitioning strategy, distributed parallel query implementation, and experimental results that demonstrate significant latency reductions for multi‑hop queries.
The article explains the limitations of relational databases for large‑scale, highly connected data, introduces NoSQL and graph database models, demonstrates how graph queries efficiently retrieve multi‑degree social connections, and showcases Neo4j’s performance advantages over traditional RDBMS.
This article explains why interest‑driven feeds are essential, reviews Facebook's NewsFeed evolution and EdgeRank algorithm, details Pinterest's Smart Feed architecture and Pinnability model, and provides a comprehensive guide to building, ranking, and monitoring generic interest‑feed systems for social platforms.
This article explains how to build a micro‑blogging social system using Redis, covering core data models, read‑through replication, Sentinel failover, and write‑side sharding techniques that enable the platform to handle hundreds of millions of users and massive traffic.
This article introduces a range of algorithms for Sina Weibo—including tag propagation, user similarity via LDA, time‑aware weighting, community detection, PageRank‑based influence ranking, and spam user identification—to illustrate how social network analysis can uncover user interests, influence, and malicious behavior.
The article presents a comprehensive analysis of Weibo's social graph, introducing metrics such as propagation power, intimacy, fan and follow similarity, two‑degree relationships, and relationship circles to model and quantify user interactions and information diffusion within the platform.
