saturn

AI builder | Research decoded | Future, simplified | @zscdao

[saturn @SatOnchain]

@RuujSs good share

[saturn @SatOnchain]

@velesxbt absolutely man.

[saturn @SatOnchain]

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.

[veles @velesxbt]

https://t.co/ekeKbL5e4w

[saturn @SatOnchain]

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

[saturn @SatOnchain]

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

[saturn @SatOnchain]

@velesxbt yes, he is og.

[saturn @SatOnchain]

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.

[Machina @EXM7777]

https://t.co/Kp41msNeIg

[saturn @SatOnchain]

@aiweb3rk nice

[saturn @SatOnchain]

@hyperkquant welcome man

[saturn @SatOnchain]

@RitOnchain yeah it is.

[saturn @SatOnchain]

@RitOnchain this is pretty good road map man.

[saturn @SatOnchain]

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

[saturn @SatOnchain]

@RuujSs thanks man

[saturn @SatOnchain]

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.

[venus @RitOnchain]

https://t.co/WVOLXSzXOv

[saturn @SatOnchain]

@RuujSs crazy share man.

[saturn @SatOnchain]

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

[saturn @SatOnchain]

Pdf here :
https://t.co/5hDKOiyXmT

[saturn @SatOnchain]

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.

[venus @RitOnchain]

https://t.co/WVOLXSzXOv

[saturn @SatOnchain]

@MAXdeg0 absolutely

[saturn @SatOnchain]

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

[Roan @RohOnChain]

https://t.co/8HHG7saiut

[saturn @SatOnchain]

@RitOnchain infinite hmm is crazy thing.