LangWHAT?
You've seen names like LangChain, LangGraph, LangFlow or LangSmith – but what’s really behind them?

 LangChain helps us build LLM apps via modular code.

 LangGraph adds branching logic and multi-agent workflows.

 LangFlow lets us create flows with drag & drop.

 LangSmith monitors and evaluates our LLM stack.

LangChain, LangGraph and LangSmith come from the same ecosystem. LangFlow is a visual builder developed independently by DataStax.

Tried both LangChain and Langflow to build the same chatbot — Medium article coming shortly.

#LangChain #LangFlow #LLM #AI #KI #python #OpenSource #LangGraph #LangSmith #technology #chatbot #ollama

What do a baby learning to walk and AlphaGo’s legendary Move 37 have in common?
They both learn by doing — not by being told.
That’s the essence of Reinforcement Learning.

It's great to see that my article on Q-learning & Python agents was helpful to many readers and was featured in this week's Top 5 by Towards Data Science. Thanks!  And make sure to check out the other four great reads too.

-> https://www.linkedin.com/pulse/whats-our-reading-list-week-towards-data-science-dcihe

#Reinforcementlearning #AI #Python #DataScience #KI #alphago #google #googleai #ArtificialIntelligence

When the internet became public in the 90s, Google did not yet exist. Today, just 2 years after ChatGPT, we are again at such a turning point: the companies that will shape our AI future have probably not yet been founded - and we cannot yet imagine them.

#AI #ArtificialIntelligence #KI #Technology #Tech #Techhistory #FutureTech #TechTrends #google #chatgpt

I asked a Harvard postdoc which skills are essential to thrive as a researcher in AI & Biomedicine:

His answer:
 Cultivate abstract thinking.
 Build solid foundations instead of chasing hypes.
 Think independently, embrace a do-it mindset, stay curious and persistent.

Knowledge is accessible. Thinking is up to us.

What skills are you trying to develop?

#career #careergrowth #careeradvice #scientists #research #researcher #AI #ki #computerscience #datascience

Just getting into Reinforcement Learning?
This book helped me a lot. And it's beginner-friendly:
 Reinforcement Learning: An Introduction by Sutton & Barto
http://incompleteideas.net/book/the-book.html

#ai #ki #artificialintelligence #reinforcementlearning #python #technology #agenticai

There are tons of sources on AI, machine learning and deep learning — but not all of them are equally high quality or reliable.

To stay informed about AI without getting overwhelmed, I use a simple system that works for me:

 I group sources into categories: Newsletters, blogs, YouTube, books, courses. Each has its place.
 I stick to a handful of high-quality ones — and review them regularly.
 I check my intent: Am I building knowledge, getting inspired, or just browsing?
 And most importantly: I try to do more than I consume.

It’s not about reading & knowing everything. It’s about learning the key things — and applying it.

#AI #KI #artificialintelligence #ml #machinelearning #deeplearning #datascience #career

Reinforcement Learning doesn’t tell you what’s right.
It only tells you how good your choice was.
No feedback on what to do. Only on how it went.

 Example: A multi-armed bandit (like a slot machine with several levers). You don't know which lever is the best - you can only find out by trying it out. Exploring means giving up a known reward (from exploitation) — in hopes of finding a better one.

This balance between exploration and exploitation is the central dilemma in reinforcement learning.

 A simple strategy is ε-greedy:
→ In 90% of cases you take the best known action
→ In 10% of cases, you try a different one by chance

In simulations, ε-greedy methods perform better in the long term than pure greed (always take the supposedly best) - because they master the “explore-exploit trade-off”.

#ReinforcementLearning #ML #KI #AI #DataScience #MachineLearning #Datascientist

What does a baby learning to walk have in common with AlphaGo’s Move 37?

Both learn by doing — not by being told.

That’s the essence of Reinforcement Learning.

In my latest article, I explain Q-learning with a bit Python and the world’s simplest game: Tic Tac Toe.

-> No neural nets.
-> Just some simple states, actions, rewards.

The result? A learning agent in under 100 lines of code.

Perfect if you are curious about how RL really works, before diving into more complex projects.

Concepts covered:
 ε-greedy policy
 Reward shaping
 Value estimation
 Exploration vs. exploitation

Read the full article on Towards Data Science → https://towardsdatascience.com/reinforcement-learning-made-simple-build-a-q-learning-agent-in-python/

#Python #ReinforcementLearning #ML #KI #Technology #AI #AlphaGo #Google #GoogleAI #DataScience #MachineLearning #Coding #Datascientist #programming #data

Built my first multi-page site with Astro & Tailwind CSS today. Simple structure, fast load times - and by default, it ships zero JS to the client. Love the developer experience.

→ https://sari95.github.io/scalaro/

What are your experiences with astro & tailwind?

#astro #tailwindcss #css #html #webdev #webdevelopment #programming