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Context for your AI agents
Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.
Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
Social Network Analysis and Visualization software
Visit our new site: http://socnetv.org
Social Network Visualizer (SocNetV) is a social network analysis and visualization application. You can draw a social network (graph/digraph) or load an existing one (GraphML, UCINET, Pajek, etc), compute cohesion, centrality, community and structural equivalence metrics and apply various layout algorithms based on actor centrality or prestige scores (i.e. Eigenvector, Betweenness) or on dynamic models (i.e. Kamada-Kawai spring-embedder)
A dynamic flair for Stack Exchange sites inspired by Stack Exchange's native User Flairs (http://stackoverflow.com/users/flair/) with advanced features:
• including sites with more than 101 reputation (compared to 200)
• detailed reputation summary (compared to K and M abbreviations)
• detailed statistics about Questions, Answers, Reputation, Gold, Silver and Bronze badges for each included site (compared to <null>)
• updated in real-time (compared to 24–36 hrs)
Please note the...
estimates transition probabilities and their standard deviation
BOOTPM is a MatLab script that estimates transition probabilities and their standard deviations both theoretically and using the bootstrap technique.
It creates N samples sampling -with replacement- M observations and then computes the relative N TPM's.
Then, it simply computes the standard deviation of each transition probability using the distribution across all the N TPM's.
It also compares the boostrapped results to results estimated thoeretically, to detect "strange" results....