Pierre-Eric Marchandet 🇫🇷🇪🇺

/dev/random

[LearnKube @learnk8s]

InfraLens is a zero-instrumentation observability tool that uses eBPF to automatically discover and visualize service-to-service communication in Kubernetes clusters without requiring code changes or sidecars

➜ https://t.co/ipQfIAsAFJ

[Pierre-Eric Marchandet 🇫🇷🇪🇺 @PEM___]

@python_spaces ML

[Python Space @python_spaces]

Intro to Machine Learning Systems book for FREE!

This book covers ML Systems, AI Workflows, Data Engineering and more.

To get your copy, do the following.
- Like and Repost
- Comment ML
- Follow me so that I can DM you.

[Tom Dörr @tom_doerr]

Streaming-first language for model-generated UI

https://t.co/B8crPruCiS

[Pierre-Eric Marchandet 🇫🇷🇪🇺 @PEM___]

@knafo_sarah Magnifique.

[elvis @omarsar0]

Enhancing RAG with Application-Aware Reasoning

Neat trick to improve RAG systems: give it the relevant knowledge and show it how to apply it.

Very simple and effective!

This approach also works well with AI agents.

Pay attention, AI devs.

Here are my notes:

[Avi Chawla @_avichawla]

A RAG engine for deep document understanding!

RAGFlow lets you build enterprise-grade RAG workflows on complex docs with well-founded citations.

Supports multimodal data understanding, web search, deep research, etc.

100% local & open-source with 55k+ stars!

[Poonam Soni @CodeByPoonam]

OpenAI, Google, and Anthropic just dropped the guides on:

- Prompt Engineering
- Building effective Agents
- AI in Business
- 601 AI use cases

and so much more...

9 best guides you don’t want to miss:

[Jaynit Makwana @JaynitMakwana]

BREAKING: Email platforms are officially obsolete.

Mailchimp charges $300+ for what this open-source tool gives you free.

BillionMail gives you unlimited sending, full control, & no monthly fees.

Here’s how to set it up in 5 minutes:

[Daily Dose of Data Science @DailyDoseOfDS_]

SQLite-Vec: a tiny, portable vectorDB built on SQLite!

It's lightning fast & lightweight, perfect for on-device RAG solutions!

🔥 Key features:

- Matryoshka embedding slicing
- 32x storage reduction with binary quantization
- L2, cosine, and Hamming distance support
- Works with Python Lists and NumPy Arrays

🌐 Universal Integration:

SDKs for Python, JavaScript, Go, Rust, Wasm and more...

Best part? It's MIT/Apache-2.0 dual licensed. Use it, modify it, make it yours.

[Quant Science @quantscience_]

🚨BREAKING: A new Python library for algorithmic trading.

Introducing TensorTrade: An open-source Python framework for trading using Reinforcement Learning (AI)

[Victor M @victormustar]

Crazy what you can do on Hugging Face in 40 seconds

[Vaibhav (VB) Srivastav @reach_vb]

LETS GOO: Hugging Face just dropped the MCP Course everything you need to know about Model Context Protocol AND how to use it!! 💥

Sign up today - totally free and open source course!

From understanding what MCP is to connecting LLMs, to deploying your own MCP servers - we've got you covered! 🤗

[Brett @BrettFromDJ]

Animated Airbnb-style icons with AI. ☕️

ChatGPT + Runway (full tutorial below).

[Anand Butani @AnandButani]

LangChain just dropped the Figma of AI agents — fully open-source, no code, and insanely powerful.

This could be the game-changer for building custom AI workflows without touching a line of code. 🧠⚡

[• nanou • @NanouuSymeon]

All Python Libraries in one sheet!

[Aadit Sheth @aaditsh]

This guy literally collected 300+ MCP servers and open-sourced all of them

[Santiago @svpino]

A conversational agent with no hallucinations! This is huge!

Here is the $1M problem:

Build a customer-facing AI agent that works consistently, doesn't hallucinate, and is fully aligned with the business rules.

Check out Parlant, a FREE, open-source library. I've talked about them before, and here is the TL;DR:

1. Parlant gives you 10x more control over what your agent says
2. It helps you control how agents conduct conversations
3. It helps you control the service protocols and rules agents follow
4. It helps you control what agents can and can't say

They do something pretty clever:

1. They have a set of predefined potential responses that the agent can use to reply to the customer.

2. When a customer sends a message, the agent generates a fluid response draft using an LLM and matches and renders the closest pre-approved reply template.

3. The agent returns the rendered pre-approved reply, not the one generated by the LLM. This maintains fluidity while avoiding unpredictability.

Parlant also uses your domain terminology, invokes your tools, and implements self-critique to craft/evaluate its response iteratively.

This pretty much solves any hallucination issues with your agent.

[0xDesigner @0xDesigner]

These MCPs are cheat codes that make Cursor "just work."

Here's how they eliminate errors, save time, and improve your app:

(Bookmark this for later)

1. Magic MCP

Not only does it upgrade your UI and animations, it shows you a preview of variations before coding it.

[Python Space @python_spaces]

Intro to Machine Learning Systems book for FREE!

This book covers ML Systems, AI Workflows, Data Engineering and more.

To get your copy, do the following.

- Like and Repost
- Comment ML
- Follow me so that I can DM you.