只在Lovart输了一句“双缝干涉实验与背后量子力学理论的动态解释”给了n张图和一个小短片,不过短片效果有待提升。
Prompt "The Double-Slit Interference Experiment and the Dynamic Interpretation of the Underlying Quantum Mechanics Theory"
https://t.co/OS0I0eWK0L
It’s been just a few hours since Google dropped Gemini 2.5 Pro (I/O edition)
And people can't stop being creative with it.
Here are 10 WILD examples so far:
Master ChatGPT Prompts Like a Pro (In Minutes)🤖Struggling with weak AI https://t.co/gKif0btggD are 7 golden rules of Prompt Engineering that instantly improve your results
🧩 Built an MCP that lets Claude talk directly to Blender. It helps you create beautiful 3D scenes using just prompts!
Here’s a demo of me creating a “low-poly dragon guarding treasure” scene in just a few sentences👇
Announcing: Agentic Document Extraction!
PDF files represent information visually - via layout, charts, graphs, etc. - and are more than just text. Unlike traditional OCR and most PDF-to-text approaches, which focus on extracting the text, an agentic approach lets us break a document down into components and reason about them, resulting in more accurate extraction of the underlying meaning for RAG and other applications. Watch the video for details.
AI Agents 101: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆.
In general, the memory for an agent is something that we provide via context in the prompt passed to LLM that helps the agent to better plan and react given past interactions or data not immediately available.
It is useful to group the memory into four types:
𝟭. Episodic - This type of memory contains past interactions and actions performed by the agent. After an action is taken, the application controlling the agent would store the action in some kind of persistent storage so that it can be retrieved later if needed. A good example would be using a vector Database to store semantic meaning of the interactions.
��. Semantic - Any external information that is available to the agent and any knowledge the agent should have about itself. You can think of this as a context similar to one used in RAG applications. It can be internal knowledge only available to the agent or a grounding context to isolate part of the internet scale data for more accurate answers.
𝟯. Procedural - This is systemic information like the structure of the System Prompt, available tools, guardrails etc. It will usually be stored in Git, Prompt and Tool Registries.
𝟰. Occasionally, the agent application would pull information from long-term memory and store it locally if it is needed for the task at hand.
𝟱. All of the information pulled together from the long-term or stored in local memory is called short-term or working memory. Compiling all of it into a prompt will produce the prompt to be passed to the LLM and it will provide further actions to be taken by the system.
We usually label 1. - 3. as Long-Term memory and 5. as Short-Term memory.
A visual explanation of potential implementation details 👇
And that is it! The rest is all about how you architect the topology of your Agentic Systems.
What do you think about memory in AI Agents?
#LLM #AI #MachineLearning