aiweb3rk

this repo is f*cking insane someone packaged holonic context graphs, graphRAG, and local LLM inferencing into a single self-hosted container harness. it builds 3D context graphs, extracts entity-relationship paths, and gives your AI agents full fact-level provenance with zero hallucinations. zero API keys needed. bookmark before this gets buried.

Jane Street pays $250K–$350K/year for new grad software engineers who pass interviews like this. 36-minutes. free. By Jane Street team. here's what they cover: • graph traversal approach to unit conversion (BFS vs DFS) • class abstractions for graphs, nodes, and directed edges • handling bi-directional graph conversion facts • 1 core mistake candidates make: talking constantly instead of syncing intent Bookmark & watch today. Then read the article below.

this course is f*cking insane. IBM just released a 1-hour course on building agentic knowledge graphs from scratch: • 00:00 - Introduction to knowledge graphs • 05:35 - Building your first agentic graph • 19:59 - Agentic memory powered by graphs • 30:39 - Graphs for multi-agent orchestration Watch it today, then learn how to become a knowledge graph engineer in the article below.

The Complete Knowledge Graph Engineer Playbook (Junior → Staff, $100K → $230K +) 📂 Graph Engineering ┃ ┣ 📂 Foundations ┃ ┣ 📂 CS Fundamentals ┃ ┣ 📂 Data Structures & Algorithms ┃ ┣ 📂 Discrete Math / Graph Theory ┃ ┣ 📂 Linear Algebra ┃ ┗ 📂 Python ┃ ┣ 📂 Core Concepts ┃ ┣ 📂 Ontology Design ┃ ┣ 📂 RDF / OWL / SKOS ┃ ┣ 📂 Semantic Web Standards ┃ ┣ 📂 Entity Resolution ┃ ┣ 📂 Taxonomy Modeling ┃ ┗ 📂 Graph Algorithms ┃ ┣ 📂 Graph Databases ┃ ┣ 📂 Neo4j ┃ ┣ 📂 TigerGraph ┃ ┣ 📂 AWS Neptune ┃ ┣ 📂 Stardog ┃ ┣ 📂 GraphDB ┃ ┗ 📂 ArangoDB ┃ ┣ 📂 Query Languages ┃ ┣ 📂 Cypher ┃ ┣ 📂 SPARQL ┃ ┣ 📂 Gremlin ┃ ┗ 📂 GQL ┃ ┣ 📂 Data Engineering ┃ ┣ 📂 ETL Pipelines ┃ ┣ 📂 Streaming Ingestion (Kafka) ┃ ┣ 📂 Change Data Capture ┃ ┣ 📂 Data Modeling ┃ ┗ 📂 NLP-Based Entity Extraction ┃ ┣ 📂 AI / LLM Integration ┃ ┣ 📂 GraphRAG ┃ ┣ 📂 Vector + Graph Hybrid Search ┃ ┣ 📂 Knowledge Graph Embeddings ┃ ┣ 📂 LLM-Assisted Extraction ┃ ┗ 📂 Agentic Graph Orchestration ┃ ┣ 📂 Systems & Scale ┃ ┣ 📂 Distributed Graph Processing ┃ ┣ 📂 Sharding / Partitioning ┃ ┣ 📂 Query Performance Tuning ┃ ┗ 📂 Caching Layers ┃ ┣ 📂 Portfolio ┃ ┣ 📂 Build a KG From Scratch ┃ ┣ 📂 Open Source Contributions ┃ ┣ 📂 GraphRAG Demo App ┃ ┗ 📂 Entity Resolution Pipeline ┃ ┣ 📂 Certifications ┃ ┣ 📂 Neo4j Certified Professional ┃ ┣ 📂 AWS ML Specialty ┃ ┗ 📂 Data Engineering Certs ┃ ┣ 📂 Target Industries ┃ ┣ 📂 AI / Search ┃ ┣ 📂 Fintech ┃ ┣ 📂 Biomedical / Pharma ┃ ┣ 📂 Enterprise SaaS ┃ ┗ 📂 Defense / Intelligence ┃ ┗ 📂 Moats ┣ 📂 Domain Ontology Expertise ┣ 📂 Production-Scale Graph Experience ┣ 📂 Cross-Functional Translation Skills ┣ 📂 LLM + Graph Hybrid Fluency ┗ 📂 Graph Theory Depth Bookmark it and then read the article.

this new paper is f*cking insane researchers ran more than 1,600 experiments on Infinite Hidden Markov Models. most researchers initialize states randomly. that's exactly what performs worst. simple k-means clustering produced more accurate regime recovery, faster convergence, and better bull/bear market classification across thousands of experiments. bookmark before this gets buried.

this fu*king insane 27 minutes free video by quant is out. watch it now. Tom King ($800k + portfolio) just dropped his exact Market Regime matrix. stop risking full size against the trend. Bookmark it now and then read the article.