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Introducing PAl. Your Personal Super-Intelligence 🌀 > The World's 1st Floating AI Agent. Revolutionary User Experience > Always On Accessible, Maintain Your Existing Workflow > No Prompt Needed. Just One Click > Proactive Multi-Agent Tasks > The Evolution of Input. From Typing to Approving > Personal Memory > Mandatory... show more

Eon (Yulheon) Seung

1,586 subscribers

154,650 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

15 条评论

blaz 的头像

blaz3 个月前

why would I need this sorry not trying to be rude

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

No worries at all, it wasn't rude! It was just a great question. Since the latest showcase focused mainly on productivity and business features, it's totally understandable why it didn't click right away. We have more diverse use cases coming up, but out of curiosity, what do you usually spend most of your computer time on? Or what features do you need the most?

Shabareesh Shetty 的头像

Shabareesh Shetty3 个月前

That sounds futuristic

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

Sure we need to enter a new era!🪐

Yh 的头像

Yh3 个月前

🔥🔥

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

🔥🔥

Yh 的头像

Yh3 个月前

Looks amazing!

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

Thx

NEWRUNIO 的头像

NEWRUNIO3 个月前

Enter a new era🌀

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

Let's go!

DongYeon Go 的头像

DongYeon Go3 个月前

Looks nice 👍

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

Really appreciate! This piece of work is made by an artist 🎨🧑‍🎨✨

ijo 的头像

ijo3 个月前

Looks really good! 🔥

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

So much appreciate ijo 🔥🗽

Eon (Yulheon) Seung 的头像

Eon (Yulheon) Seung3 个月前

🪐Join the waitlist: Comment for a chance to get the early access code!

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1,583,434 次观看 • 6 个月前

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

132,058 次观看 • 1 年前