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If you're late to the whole "memory in AI agents" topic like me, I recommend investing 43 minutes to watch this video

12 October 2025·443 words·3 mins

Articoli AI AI Agent

Articoli Interessanti - This article is part of a series.

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

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#### Source

Type: Content Original link: https://x.com/helloiamleonie/status/1976623087710781942?s=43&t=ANuJI-IuN5rdsaLueycEbA Publication date: 2025-10-14

Summary
#

WHAT - This is a Twitter post promoting a tutorial video on the concept of memory in AI agents. The video explains and implements the four types of memory described in the CoALA paper.

WHY - It is relevant for AI business because it provides a practical overview of how to implement memory in AI agents, a crucial topic for improving the agents’ ability to learn and adapt over time.

WHO - The video creator is Adam Łucek, an expert in the field of AI. The post was shared by Leonie Bredewold, a Twitter user.

WHERE - It fits within the educational context of AI, specifically in the subdomain of AI agents and memory.

WHEN - The post was published on 2024-05-16. The concept of memory in AI agents is an emerging and evolving topic.

BUSINESS IMPACT:

Opportunities: The video can be used to train the internal team on implementing memory in AI agents, thus enhancing the capabilities of our products.
Risks: There are no immediate risks, but it is important to stay updated with the latest research and implementations to avoid being outpaced by competitors.
Integration: The content of the video can be integrated into internal training programs and used to update the company’s best practices.

TECHNICAL SUMMARY:

Core technology stack: The video likely uses machine learning frameworks and programming languages such as Python. No specific details are provided about the technology stack used.
Scalability and architectural limits: No specific details are provided, but the implementation of memory in AI agents can be scaled according to the project’s needs.
Key technical differentiators: The video focuses on the practical implementation of the four types of memory described in the CoALA paper, offering a practical and applicable approach.

Use Cases
#

Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Strategic Intelligence: Input for technological roadmap
Competitive Analysis: Monitoring AI ecosystem

Resources
#

Original Links
#

If you’re late to the whole “memory in AI agents” topic like me, I recommend investing 43 minutes to watch this video - Original link

Article suggested and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-10-14 06:37 Original source: https://x.com/helloiamleonie/status/1976623087710781942?s=43&t=ANuJI-IuN5rdsaLueycEbA

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Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - GVCLab/PersonaLive: PersonaLive! : Expressive Portrait Image Animation for Live Streaming

Part : GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

Part : GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

Part : GitHub - yichuan-w/LEANN: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

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Part : GitHub - Tencent-Hunyuan/HunyuanOCR

Part : Effective harnesses for long-running agents Anthropic

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Part : opcode - The Elegant Desktop Companion for Claude Code

Part : NocoDB Cloud

Part : A Step-by-Step Implementation of Qwen 3 MoE Architecture from Scratch

Part : MemoRAG: Moving Towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery

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Part : A must-bookmark for vibe-coders

Part : Huge AI market opportunity in 2025

Part : The Anthropic Economic Index Anthropic

Part : dots.ocr: Multilingual Document Layout Parsing in a Single Vision-Language Model

Part : PaddleOCR

Part : DeepSite v2 - a Hugging Face Space by enzostvs

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Part : Show HN: CLAVIER-36 – A programming environment for generative music

Part : Small models are the future of agentic ai

Part : Kimi K2: Open Agentic Intelligence

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Part : Scientific Paper Agent with LangGraph

Part : Anthropic's Interactive Prompt Engineering Tutorial

Part : swiss-ai/Apertus-70B-2509 · Hugging Face

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Part : NextChat

Part : The LLM Red Teaming Framework

Part : Colette - ci ricorda molto Kotaemon

Part : VibeVoice: A Frontier Open-Source Text-to-Speech Model

Part : [2502.12110] A-MEM: Agentic Memory for LLM Agents

Part : [2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Part : Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS

Part : HumanLayer

Part : PageIndex: Document Index for Reasoning-based RAG

Part : Deploying DeepSeek on 96 H100 GPUs

Part : Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

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Part : browser-use/web-ui

Part : Casper Capital - 100 AI Tools You Can’t Ignore in 2025...

Part : CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents

Part : Show HN: Whispering – Open-source, local-first dictation you can trust

Part : Fallinorg v1.0.0-beta

Part : paperetl

Part : Automatically annotate papers using LLMs

Part : My AI Had Already Fixed the Code Before I Saw It

Part : Llama-Scan: Convert PDFs to Text W Local LLMs

Part : Claudia – Desktop companion for Claude code

Part : Show HN: Fallinorg - Offline Mac app that organizes files by meaning

Part : Focalboard

Part : Elysia: Agentic Framework Powered by Decision Trees

Part : LangExtract

Part : +1 for "context engineering" over "prompt engineering"

Part : The race for LLM cognitive core

Part : [2507.07935] Working with AI: Measuring the Occupational Implications of Generative AI

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Prava - Teaching GPT‑5 to use a computer

Part : InstaVM - Secure Code Execution Platform

Part : Litestar is worth a look

Part : Jobs at Kaizen | Y Combinator

Part : Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

Part : Introducing pay per crawl: Enabling content owners to charge AI crawlers for access

Part : Agentic Design Patterns - Documenti Google

Part : [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Part : Qwen3-Coder: Agentic coding in the world

Part : FutureHouse Platform

Part : Voxtral | Mistral AI

Part : Research Agent with Gemini 2.5 Pro and LlamaIndex | Gemini API | Google AI for Developers

Part : AI Act, c'è il codice di condotta per un approccio responsabile e facilitato per le Pmi - Cyber Security 360

Part : [2507.06398] Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI

Part : MindsDB, an AI Data Solution - MindsDB

Part : Backlog.md – Markdown-native Task Manager and Kanban visualizer for any Git repo

Part : Opencode: AI coding agent, built for the terminal

Part : The new skill in AI is not prompting, it's context engineering

Part : SymbolicAI: A neuro-symbolic perspective on LLMs

Part : Gemini for Google Workspace Prompting Guide 101

Part : Judge Rules Training AI on Copyrighted Works Is Fair Use, Agentic Biology Evolves, and more...

Part : MCP is eating the world—and it's here to stay

Part : How Dataherald Makes Natural Language to SQL Easy "How Dataherald Makes Natural Language to SQL Easy" is already in English.

Part : Field Notes From Shipping Real Code With Claude

Part : Nice - my AI startup school talk is now up!

Part : Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again

Part : Automated 73% of his remote job using basic automation tools, told his manager everything, and got a promotion

Part : Building Effective AI Agents

Part : How Anthropic Teams Use Claude Code

Part : Snorting the AGI with Claude Code

Part : Nanonets-OCR-s – OCR model that transforms documents into structured markdown

Part : The Illusion of Thinking

Part : Trends – Artificial Intelligence | BOND

Part : Claude Code is My Computer | Peter Steinberger

Part : [2505.24863] AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Part : [2505.24864] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Part : My AI Skeptic Friends Are All Nuts · The Fly Blog

Part : Designing Pareto-optimal GenAI workflows with syftr

Part : "BillionMail 📧 An Open-Source MailServer, NewsLetter, Email Marketing Solution for Smarter Campaigns"

Part : Ask HN: What is the best LLM for consumer grade hardware?

Part : [2411.06037] Sufficient Context: A New Lens on Retrieval Augmented Generation Systems

Part : Show HN: Onlook – Open-source, visual-first Cursor for designers

Part : Agent Development Kit (ADK)

Part : Strands Agents

Part : Show HN: AutoThink – Boosts local LLM performance with adaptive reasoning

Part : Introduction - IntelOwl Project Documentation

Part : Show HN: My LLM CLI tool can run tools now, from Python code or plugins

Part : [2505.03335v2] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Codex’s Robot Dev Team, Grok's Fixation on South Africa, Saudi Arabia’s AI Power Play, and more...

Part : [2502.00032v1] Querying Databases with Function Calling

Part : Come Addestrare un LLM con i Tuoi Dati Personali: Guida Completa con LLaMA 3.2

Part : AI Hedge Fund

Part : Troy Hunt: Have I Been Pwned 2.0 is Now Live!

Part : A Research Preview of Codex

Part : [2505.06120] LLMs Get Lost In Multi-Turn Conversation

Part : Ollama's new engine for multimodal models

Part : Vision Now Available in Llama.cpp

Part : [2505.03335] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Requests for Startups | Y Combinator

Part : Token & Token Usage | DeepSeek API Docs

Part : Cua is Docker for Computer-Use AI Agents

Part : [2504.07139] Artificial Intelligence Index Report 2025

Part : Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

Part : DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature

Part : A foundation model to predict and capture human cognition | Nature

Part : Large language models are proficient in solving and creating emotional intelligence tests | Communications Psychology

Part : Everything About Transformers "Everything About Transformers"