Apar

let’s try something new

[Apar @limlee23]

@uttdkola Ozil

[rari @0xwhrrari]

Google just dropped a 94-minute workshop

On moving AI systems from basic RAG to graph-based memory:

15:32 - Set up the production agent stack
28:00 - Turn disconnected data into a knowledge graph
41:00 - Build GraphRAG with semantic and hybrid search
58:00 - Extract graph context from images, text, and video
1:09:00 - Orchestrate specialized agents with Google ADK
1:20:00 - Give agents memory across sessions

Most teams are still stopping at RAG

Google is already teaching what comes next:

RAG → GraphRAG → Memory → Multimodal Agent Graphs

Built with Spanner Graph, Gemini, ADK, and Memory Bank

This workshop is worth more than most paid GraphRAG courses

Watch it today

Then read the full From RAG to Context Graphs roadmap below ↓

[Norvex @norvex1029]

Google Brain founder, Andrew Ng: “Prompting will be dead in 6 months. Loops and Graphs are what’s replacing it.”

In 1 hour at Stanford, he shows what the best engineers are building instead — and how you can start building it today.

Everything a $500 course sells you is in the first 10 minutes.

Free.

Watch it, then read the guide below on how to build a system that improves itself.

[Md Ismail Šojal 🕷️ @0x0SojalSec]

This is how ML should be Taught,
Actually seeing the models learn is insanely satisfying.

- https://t.co/cCv04zJEOX

[Apar @limlee23]

@UTDIMole Kaka

[Oscar Arias @OACerebro]

Enjoy!

https://t.co/rRgPMXvXCa

[Apar @limlee23]

@ThatG4m

[Akshay 🚀 @akshay_pachaar]

Another blow to Anthropic!

They spent months building what's now fully open-source.

Anthropic recently put Claude inside Slack, where you can tag it in a channel. It reads the thread, breaks the task into steps, and posts the result back.

The problem is that it only runs Claude and only in the channels Anthropic supports.

Running your own agent there is harder.

The reasoning, tool calls, and state management are mostly handled by the framework. Connecting that agent to a messaging platform is not.

Moreover, each platform has a different integration:

- Slack renders messages with Block Kit
- Teams uses Adaptive Cards
- and each has its own SDK, auth flow, and delivery model.

If an agent needs to run on three platforms, one must write three separate integrations against the same agent logic.

That overhead explains why most custom agents never get deployed to Slack, and why the ones that do are usually a single vendor's hosted assistant.

The alternative is to keep the agent in one place and add a per-platform adapter that translates its output into each platform's native format.

The agent is written once, and each channel requires just another output target instead of a separate build.

CopilotKit open-sourced this full implementation in the Channels SDK.

Essentially, any agent that implements AG-UI can run in a messaging platform in a few lines of code, like Slack, Teams, Discord, WhatsApp, and many more.

Because the agent runs inside the thread, it has that conversation's context, so it can summarize the discussion, open a ticket, or route to the right person.

It works with any backend, so LangGraph, CrewAI, Mastra, Google ADK, or a plain HTTP agent can connect through an existing endpoint.

The same message can render as a Block Kit in Slack and as Adaptive Cards in Teams.

In practice, the model and orchestration stay the same; it requires no migration or rewrite.

It also handles human-in-the-loop approvals, persistence, and transcripts that carry state across platforms, so a thread started in Teams can continue in Slack.

CopilotKit is open-source, and AG-UI is supported across every major agent framework, including LangGraph, CrewAI, Mastra, and Google ADK.

Here's the repo: https://t.co/czi4hE192C

(don't forget to star it ⭐)

The agent running in Slack no longer has to be a vendor's. It can be the one you already built.

The video below shows this in action.

Thanks to CopilotKit for working with me on this launch.

[Vivek Galatage @vivekgalatage]

CS188: Introduction to Artificial Intelligence from UC Berkeley

https://t.co/tjoruPaCmL

[Xudong Han @Xudong07452910]

这篇普林斯顿博士论文，几乎把强化学习过去十年的两条主线串在了一起：

从博弈算法，一路走到世界模型。

作者 Zihan Ding，导师是 Chi Jin。整篇论文超过 300 页，汇集了博士期间八项研究工作，其中多项成果发表于 NeurIPS、ICML、ICLR 和 ICCV。

前半部分研究多智能体博弈：如何学习接近纳什均衡、难以被对手利用的策略，并把这些方法扩展到格斗游戏、多人博弈和真实网络负载均衡。

后半部分转向 Foundation Model 时代：扩散世界模型、生成模型策略、少步视频生成、可交互视频世界模型，以及支持长时预测的记忆机制。

这篇论文最有价值的地方是它记录了强化学习研究重心的一次迁移。

过去，RL 更关注如何在给定环境中学会行动。

现在，Agent 还需要借助生成模型建立对环境的理解，预测不同动作会带来什么未来，再据此规划和决策。

这里收录的也不只是零散的想法，而是一条经过多年研究和多篇顶会论文逐步铺开的路线。

很多论文告诉你某个方法如何提升一个指标。

这篇博士论文更像一张地图，让人看清强化学习如何从策略优化，走向对复杂世界的建模。

想系统理解 RL、世界模型和生成式 AI 之间的关系，这篇很值得完整读一遍。

📎 arxiv: https://t.co/M7rIJ6SKwO

[MIT CSAIL @MIT_CSAIL]

This repository on ML operations has free talks, books, papers and more: https://t.co/CX7l01eoiQ

Developed by @visenger

[Stiles @stilesishim]

He’s the symbol of everything

[kayne @kayn3_]

VAMOS SPAIN

[ItzCarlos @Balatreado]

El fin de la mejor generación del fútbol 💔.

[Apar @limlee23]

@UTDTrey What a game. Absolute banger 🫡

[Aya 🦉 @aya__R7]

لويس فان خال مدرب منتخب هولندا في كأس العالم 2022:

كان هناك تعمد لـ جعل ليونيل ميسي بطل كأس العالم!!!!

تصريح للتاريخ اقسم بالله.

[MP10 @MusicPills10]

Oasis - Wonderwall
(credits to: thedigitalassembly)

[isHaHaHa @hajarkagalwa]

Thank you Ronaldo
https://t.co/sYLHsRmYpu

[NENDU @NENDU__]

From a small kid in Madeira to the greatest player football has ever seen.
This 7-minute Cristiano Ronaldo career compilation is pure emotion. 🥹🐐