Morlex
@0xMorlex • 3,087 subscribers
AI researcher & builder | Building agents with @Claudeai + @Grok Exploring graphs, loops & agent systems | Sharing what works
Videos
ex-Apple engineer gave Grok 4.6 two jobs inside Cursor, went to sleep, and opened the results live the next morning: no babysitting, no checking every generation, just a model left running on real work for hours. • 00:42 - reveal the website Grok redesigned overnight • 23:21 - go from a voice prompt to a full software stack • 45:43 - inspect the generated code + architecture • 01:21:26 - Grok 4.6 vs Opus 5 • 01:46:31 - live PR + Cloud Agent workflow Most coding demos test an AI for 5 minutes. This tests the thing that matters for agents: can the model keep working when you stop watching it? SpaceXAI built Grok 4.6 specifically around longer-running agent tasks, coding and more ambitious visual work. The endgame isn’t prompting faster. It’s giving an agent a job at night and reviewing finished work in the morning. Worth watching before you choose which model runs your overnight agent loops.
Morlex5,270,291 次观看 • 1 个月前
Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.
Morlex74,134 次观看 • 1 个月前
Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below
Morlex51,952 次观看 • 1 个月前
SpaceXAI just released a free 1-hour Grok Bot Agents course that goes far beyond prompting: build one Bot → give it a job → add a Chief of Staff → connect the team → let the whole system run 24/7 2:15 - build your first Grok Bot 6:52 - turn Bots into specialized workers 16:51 - create routines that run without you 31:50 - make multiple agents work together 52:18 - assemble the full 24/7 agent system Most AI agent tutorials teach you how to build one assistant that still waits for instructions This course shows the opposite: specialized Bots, a manager above them, persistent routines, app connections and agents handing work to other agents Watch it, build your first Bot, then turn it into an entire team Then read the full Grok Bot architecture below ↓
Morlex26,449 次观看 • 1 个月前
Andrej Karpathy explained the 5 shifts that turned LLMs from chatbots into agentic systems: 00:00 - Memory turns chat into personal AI 6:41- Multimodal AI can read the world 16:58 - Thinking models solve harder tasks 24:51 - Search makes LLMs live 30:58 - Tools turn LLMs into workers This is not another video about “prompt engineering.” It is a 40-minute roadmap for the next AI workflow: memory / vision / reasoning / search / tools. Watch today, then read the article below on how to turn LLMs into self-improving agent loops.
Morlex37,456 次观看 • 2 个月前
A SpaceXAI engineer just released a free 30-min course on Grok Bot Agents: from your first persistent agent to multi-agent chains that research, ship software, and hand work off to Cursor 05:26 - what actually lives inside a Grok agent 08:49 - the 3 ways to run agents 12:04 - chaining multiple agents into one workflow 16:06 - turning X followers into structured research inside Notion 23:08 - handing coding work from Grok straight to Cursor Most people still treat Grok like a chat window. It answers, you copy, you move on. The shift happens when the agents stop waiting for you: one finds the information, one structures it in Notion, one turns it into working code, and they pass the work down the line on their own. Thirty minutes here beats assembling your setup one agent at a time.
Morlex16,710 次观看 • 1 个月前
Andrew Ng at Stanford: “Stop waiting for the perfect model. Build the iteration "loop" and "graphs" around it.” move from prompt → loop → graph → self-improving system • 00:05 - why every AI project needs an iteration loop • 04:08 - the full cycle: data → model → deploy → monitor • 16:02 - why faster iteration can decide who wins • 26:09 - using error analysis to improve the right data • 39:39 - why shipping a simple system beats waiting for perfection • 54:26 - monitoring drift and improving models after deployment 67-minute Stanford lecture, and it’s one of the clearest playbooks on why experienced AI engineers optimize the loop around the model, not just the model itself. It's watch today, then read the full step by step roadmap in the article below
Morlex22,861 次观看 • 1 个月前
IBM just turned five of its best graph lectures into a 50-minute course on building AI systems around connected knowledge: • 00:00 - How knowledge graphs represent entities and relationships • 05:36 - Why vector-based RAG misses connections between facts • 09:53 - GraphRAG, precision retrieval and context engineering • 20:43 - How graph neural networks learn from connected data • 37:30 - Building GraphRAG with knowledge graphs and Cypher The progression is simple: Text chunks → entities → relationships → graph retrieval → graph intelligence Vector search finds information that looks similar. Graphs recover the structure behind that information. This 50-minute course will teach you more about modern graph engineering than most paid AI programs. Watch it today, then read how to become a knowledge graph engineer in the article below.
Morlex12,053 次观看 • 2 个月前
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