saturn

@RuujSs good share

MIT will teach you how Wall Street measures risk, for free. This lecture covers Value at Risk (VaR): the variance covariance method, Monte Carlo simulation, historical simulation, and how banks actually use these models in practice. No $200K tuition required. Just your curiosity. If you're in finance and haven't watched this, add it to your list.

@velesxbt absolutely man.

@SatOnchain Essential watch for anyone building production AI agents today

An Anthropic engineer: "Over 90% of our engineers build with self-improving loops. In 4-6 months, it'll be 100%. My agentic loops run for days without spending hundreds of dollars." In this 40-min podcast, he reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent. This one video replaces 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

@RitOnchain gng to watch this interview now. insane share.

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.

@velesxbt this is one of the best articles read so far.

@velesxbt yes, he is og.

@SatOnchain I've seen this guy before, he's the master at explaining complex things

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.

@aiweb3rk nice

@SatOnchain bookmarked it

@hyperkquant welcome man

@SatOnchain Thanks for sharing man

@RitOnchain yeah it is.

@SatOnchain this is so cool course.

@RitOnchain this is pretty good road map man.

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.

@RohOnChain this video on agents is must watch. I am bookmarking.

My friend applied to 450 tech job applications over three years. No MIT. No Stanford. Last week Anthropic offered him $750,000. I asked him how he broke in with zero background. He sent me the exact video that helped him to get in. Anthropic's 2 hour course on landing an AI career in 2026. Anthropic's core team breaks down exactly how to build AI agents from scratch. I watched it last night. Halfway through, I realized breaking into an AI lab takes weeks, not years. Bookmark this and read the article below. • 00:00 - building AI agents • 28:51 - AI agent workflow • 54:04 - memory for AI agents • 1:28:40 - production AI agent • 1:55:30 - anthropic hiring process

@RuujSs thanks man

@SatOnchain nice share mate

A senior Anthropic engineer's 12-page PDF on Graph Engineering changed how I build multi-agent systems. The insight: agent memory dies with context. A knowledge graph makes it permanent. 5-stage loop: Extract (Haiku → S-P-O triples) → Resolve (Sonnet clusters aliases by context, not string match) → Assemble (canonical nodes, typed edges, provenance) → Query (subgraph → cited answers) → Repeat. Result: shared memory for multi-agent systems — workers write, evaluators fact-check, loops persist overnight. Read it, then the article below.

@RuujSs crazy share man.

this quant pipeline is f*cking insane 10 rules from the engineering side that reveal exactly why most optimizers blow up and how to survive production it routes around matrix inversion with clustering, sizes every view to actual conviction, and forces you to measure diversification instead of assuming it. effectively replacing naive allocation with a feedback loop that monitors decay and triggers cuts before damage compounds bookmark before the timeline buries it

@velesxbt one of the best videos seen this week. good share veles

A math professor at Oxford spent 15 years proving one uncomfortable thing. Every safety rule regulators wrote after 2008 engineered the exact fragility that will cause the next crash. Central banks now stress-test the global system against his equations. His name is Rama Cont. The paper is free. Cont holds the Chair of Mathematical Finance at Oxford. He built the fire-sale models the Bank of England, the ECB, and the Federal Reserve use to run systemic scenarios. He is not a trader. He is the man regulators call when they want to know what breaks next. The result is called the regulators paradox. It goes like this. If every bank diversifies into the same benchmark index, each individual portfolio looks safe. Concentration in any one asset is low. Risk at the desk level is small. Now step back and look at the system. Every bank holds the same paper. When one bank has to sell, the price move hits every other bank's book at the same instant. The margin call is synchronous. The fire sale is synchronous. The deleveraging is synchronous. Diversification at the individual level becomes correlation at the system level. Beale, May, Nowak and co-authors modeled it in 2011. Cont extended the framework into a working fire-sale engine used by central banks. The result is non-monotonic. A little diversification helps. Too much diversification collapses the whole system into one trade that unwinds together. "Diversification through benchmarks increases portfolio overlaps and increases the magnitude of contagion through fire sales." The paradox has a strange twist inside it. The parts of the market that hold un-benchmarked positions actually stabilize the system. Hedge funds. Family offices. Anyone whose book does not look like the index. When the fire sale starts, they are the only buyers left. The system engineered for safety needs the outsiders to survive. Look at your own book. If your top ten names are the same top ten every retail account in the country owns, you are not diversified. You are hedged against small moves and fully exposed to the one move that matters. The tail is the trade that clears everyone at once. Mandelbrot pointed at this in the sixties. He called it wild randomness. Losses are not spread evenly across time. They arrive in a single afternoon and take out twenty percent of a portfolio built over twenty years. Cont built the machinery to measure it. Rama Cont is still at Oxford. His lectures are on YouTube. Central banks read his papers. Retail argues about position sizing in the same three tickers everyone else owns. The math is free. The regulators paradox is on the syllabus. The next fire sale is what the whole street pays for the lesson.

Pdf here : drive.google.com/file/d/1zoBfq1…

Moonshot AI engineer just showed how Kimi K3 + MCP builds quantitative trading strategies with a 1.94 Sharpe ratio. 15-minutes. free. By Algo-trading expert Saleh. here's what they cover: • setting up Kimi K3 CLI inside ZED editor with Jesse Trade MCP • automated strategy development, backtesting, & optimization • multi-timeframe trend filtering (30m strategy with 4h anchor) • 1.94 Sharpe ratio with 62% win rate on ETH/USDT Watch full video then read article below.

@MAXdeg0 absolutely

@SatOnchain Graph memory is the real unlock here.

Neo4j VP Graph Engineer himself just proved why your AI agents burn $1,000s in token costs with standard memory. 20-minutes. free. By Stephen Chin. here's what they cover: • limits of OpenClaw and Goose markdown memory loops • why Vector DB lookups miss multihop reasoning chains • CrabRAG: hybrid vector search + Neo4j graph traversal • live demo comparing Vector vs Graph agent on home network security watch full video then read article below

@RitOnchain infinite hmm is crazy thing.

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.