Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Ex-Google engineer explains how to build memory for self-improving AI agents in 12 minutes. procedural (how to act / skills) + semantic (durable facts / profile) + episodic (dated events / chat history). Memory is an important part of an agentic loop. Memory + loops + harness + evals... show more

Codez

17,753 subscribers

87,207 views • 7 days ago •via X (Twitter)

Science & Technology Education

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Ex-Google engineer reveals how to build AI agent loops, harnesses, LLM ops, and evals in 19 minutes. Trace → evaluate → diagnose → fix → ship → repeat. That loop is how agents self-improve over time. Agentic loops + harness + memory + evals - that’s the senior engineer stack. This is better than $500 paid courses on the same topic, explained in under 20 minutes. Watch it, then save the framework below.

Ex-Google engineer reveals how to build AI agent loops, harnesses, LLM ops, and evals in 19 minutes. Trace → evaluate → diagnose → fix → ship → repeat. That loop is how agents self-improve over time. Agentic loops + harness + memory + evals - that’s the senior engineer stack. This is better than $500 paid courses on the same topic, explained in under 20 minutes. Watch it, then save the framework below.

Codez

167,084 views • 8 days ago

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Movez

550,648 views • 10 days ago

Anthropic just dropped 5 workshops on building self-improving agentic systems from scratch: 00:00 - Ship your first Claude agent 36:44 - Build memory for Claude agents 1:05:06 - Make your agent autonomous 1:26:46 - Set up a proactive agent 2:03:35 - self-improving agents (tools,skills) These 3-hours of free Claude workshops will replace 10 paid agentic courses. Watch today, then read article below on how to build a self-improving agentic system with Fable 5.

Anthropic just dropped 5 workshops on building self-improving agentic systems from scratch: 00:00 - Ship your first Claude agent 36:44 - Build memory for Claude agents 1:05:06 - Make your agent autonomous 1:26:46 - Set up a proactive agent 2:03:35 - self-improving agents (tools,skills) These 3-hours of free Claude workshops will replace 10 paid agentic courses. Watch today, then read article below on how to build a self-improving agentic system with Fable 5.

Movez

862,036 views • 2 days ago

Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

Anthropic Managed Agents Lead: "At Anthropic, >90% of our engineers are building with self-improving loops. In 4-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of dollars." in this 40-minute podcast, an Anthropic team lead reveals how to build effective agents from scratch. Agent → harness → loops → memory = modern agent This one video will replace 10 paid courses on vibe-coding. Watch it today, then explore the same setup in the article below.

Codez

480,210 views • 9 days ago

Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, loops are the next step" "In 3-6 months, everyone will be using self-improving loops, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems from scratch Agent loops Feedback Memory Self-improvement Worth more than most $500 agentic courses

Andrew Ng: "100% of my tasks now run through AI agents, the hype actually passed my expectations, loops are the next step" "In 3-6 months, everyone will be using self-improving loops, prompting alone is over" In a 30-minute talk, Andrew Ng breaks down how to build self-improving agentic systems from scratch Agent loops Feedback Memory Self-improvement Worth more than most $500 agentic courses

rari

66,294 views • 13 days ago

Google Brain founder, Andrew Ng: "100% of my tasks are done by ai agents, self-improving loops are next. Give it 3-6 months and prompting is gone." 31 minutes of clear explanation on building self-improving agents from scratch. Worth more than any $500 agentic course. Watch it, then read the full guide on loops below.

Google Brain founder, Andrew Ng: "100% of my tasks are done by ai agents, self-improving loops are next. Give it 3-6 months and prompting is gone." 31 minutes of clear explanation on building self-improving agents from scratch. Worth more than any $500 agentic course. Watch it, then read the full guide on loops below.

Anatoli Kopadze

1,247,588 views • 15 days ago

⚒️ How to build an AI agent with persistent memory? Watch Shaw (spirit/acc) building an agent from scratch with elizaOS & showing how external, queryable memory based on OriginTrail gives agents context and extends their memory beyond a single session. 02:15 – Set up your first agent 08:55 – Initializing runtime & memory foundations 12:50 – Wiring message handling & agent memory 24:45 – Persistent conversational memory in action 38:00 – Extending agents with plugins

⚒️ How to build an AI agent with persistent memory? Watch Shaw (spirit/acc) building an agent from scratch with elizaOS & showing how external, queryable memory based on OriginTrail gives agents context and extends their memory beyond a single session. 02:15 – Set up your first agent 08:55 – Initializing runtime & memory foundations 12:50 – Wiring message handling & agent memory 24:45 – Persistent conversational memory in action 38:00 – Extending agents with plugins

OriginTrail

37,242 views • 5 months ago

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished

Anthropic Managed Agents team: "Fable 5 - is our best model for self-improving agent systems. add /loops, dynamic workflows, dreaming - and you're unstoppable." in 13 minutes, the Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent-building course. Live from the Anthropic stage in Japan. Unpublished

Movez

67,091 views • 25 days ago

OpenAI engineer: “90% of our engineers use Codex subagent harnesses + loops to ship code 5× faster. close the agent loop. Give an agent a way to verify its own output.” in 30-minute talk, an OpenAI engineer explains how to build self-improving agentic systems with harnesses. Worth more than a $500 agentic course online. Watch it today, then read how hedge funds apply loop engineering in trading.

OpenAI engineer: “90% of our engineers use Codex subagent harnesses + loops to ship code 5× faster. close the agent loop. Give an agent a way to verify its own output.” in 30-minute talk, an OpenAI engineer explains how to build self-improving agentic systems with harnesses. Worth more than a $500 agentic course online. Watch it today, then read how hedge funds apply loop engineering in trading.

Movez

39,438 views • 14 days ago

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

unicode

61,445 views • 11 days ago

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,640 views • 1 year ago

Anthropic engineer: "At Antropic, >80% of our engineers are building with self-improving loops. in 3-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of $$$." in 30-minute podcast, an Antropic engineer shows how to build the right spec for an agentic loop. Loop + plan + PRDs + spec + markdown - that’s the secret stack. Worth more than a $500 agentic course on the internet. Watch it now, then read how quants are building trading loops from scratch.

Anthropic engineer: "At Antropic, >80% of our engineers are building with self-improving loops. in 3-6 months, it will be 100%. my agentic loops can run for hours without spending hundreds of $$$." in 30-minute podcast, an Antropic engineer shows how to build the right spec for an agentic loop. Loop + plan + PRDs + spec + markdown - that’s the secret stack. Worth more than a $500 agentic course on the internet. Watch it now, then read how quants are building trading loops from scratch.

Movez

273,928 views • 12 days ago

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.

Anthropic Managed Agents team: "Fable 5 is our best model for running self-improving agent systems. Add /loops, dynamic workflows, dreaming and you are unstoppable" in 13-minutes, Anthropic team shows how to build self-improving agent systems with Fable 5 from scratch. Worth more than a $500 agent building course. Live from the last Anthropic stage in Japan. Unpublished.

Codez

467,843 views • 26 days ago

Google senior engineer just dropped a free 8-minute lesson on building AI agentic systems. This is the clearest explanation of multi-agent AI systems and loops you'll find on the internet. People still paying 500$ for agentic courses, while Google makes it for free. Agent Studio → Loops → Managed Agents → Antigravity → ADK 2.0 - thats the stack Watch it, then read the full guide on loop engineering below.

Google senior engineer just dropped a free 8-minute lesson on building AI agentic systems. This is the clearest explanation of multi-agent AI systems and loops you'll find on the internet. People still paying 500$ for agentic courses, while Google makes it for free. Agent Studio → Loops → Managed Agents → Antigravity → ADK 2.0 - thats the stack Watch it, then read the full guide on loop engineering below.

Codez

31,421 views • 10 days ago

Andrej Karpathy: “Vibe coding is incredible. But agentic engineering is the next level. 90% of my coding routine is automated by AI agents.” In this 30-minute talk, Andrej Karpathy explains how to build an AI agent workflow from scratch. Worth more than 500$ agentic engineering course on the internet. Watch it today, then read the article below.

Andrej Karpathy: “Vibe coding is incredible. But agentic engineering is the next level. 90% of my coding routine is automated by AI agents.” In this 30-minute talk, Andrej Karpathy explains how to build an AI agent workflow from scratch. Worth more than 500$ agentic engineering course on the internet. Watch it today, then read the article below.

Movez

175,409 views • 29 days ago

Anthropic engineers just showed how to build agentic systems that run for days using "loops." "At Anthropic, >30% of our code is already written by loops - that's how we ship so fast. in this 40-minute workshop, they reveal the whole stack: agent loop + harness + memory + sub-agents. Worth more than any $500 vibe-coding course. Watch workshop today, then read article below.

Anthropic engineers just showed how to build agentic systems that run for days using "loops." "At Anthropic, >30% of our code is already written by loops - that's how we ship so fast. in this 40-minute workshop, they reveal the whole stack: agent loop + harness + memory + sub-agents. Worth more than any $500 vibe-coding course. Watch workshop today, then read article below.

Movez

511,464 views • 15 days ago

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. one person + 10 agents with memory = a team that runs 24/7, remembers every customer improves itself. worth than $500 vibe-coding course.

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. one person + 10 agents with memory = a team that runs 24/7, remembers every customer improves itself. worth than $500 vibe-coding course.

Codez

1,605,641 views • 1 month ago

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

158,947 views • 3 months ago

Andrew Ng: "100% of my tasks are now done by AI agents - hype has exceeded my expectations. Loops is next step. in 3-6 months, everyone will be using self-improving loops. No more prompting." In a 30-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch. Worth more than a $500 agentic course.

Andrew Ng: "100% of my tasks are now done by AI agents - hype has exceeded my expectations. Loops is next step. in 3-6 months, everyone will be using self-improving loops. No more prompting." In a 30-minute talk, Andrew Ng explains how to build self-improving agentic systems from scratch. Worth more than a $500 agentic course.

Movez

952,294 views • 16 days ago

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. worth than $500 vibe-coding course. Bookmark & replace one movie today with this course, then read the complete article below.

Anthropic's Claude team just showed how to build an AI agent with real memory in under 30 minutes. 24-minutes. free. by the people who built Claude. worth than $500 vibe-coding course. Bookmark & replace one movie today with this course, then read the complete article below.

Khairallah AL-Awady

173,477 views • 1 month ago