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Most teams build backends by stitching together five or six different systems. Motia shows how simple it can be when one primitive powers everything. APIs, events, background jobs, workflows, AI agents, and real time streams all built with Steps. Works in JavaScript, TypeScript, and Python.

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13,121 görüntüleme • 7 ay önce •via X (Twitter)

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Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Santiago

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Our latest LangChain Academy course – Building Ambient Agents with LangGraph – is now available! Most agents today handle one request at a time through chat interfaces. But as models have improved, agents can now run in the background – and take on long-running, complex tasks. LangGraph is built for these “ambient agents,” with support for human-in-the-loop workflows and memory. LangGraph Platform provides the infrastructure to run these agents at scale, and LangSmith helps you observe, evaluate, and improve your agents. Together, they make it easier to build reliable, production-ready agents. In this course, you’ll: 📩 Learn the fundamentals of LangGraph and how to build your own email agent 📊 Evaluate your agent using LangSmith ✅ Add human-in-the-loop for reviews, and memory so your agent adapts over time 🚀 Deploy your agent with LangGraph Platform and connect it to Gmail

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Just watched a demo of Merge Agent Handler and this actually looks pretty useful if you’re building AI agents. Most agents can generate responses, but getting them to actually take actions across real tools is still messy. Integrations are one thing, but handling auth, permissions, and security usually makes it way more complicated than it should be. What I liked is that it feels built for real workflows — you can actually see what your agent is doing instead of just hoping it works. Feels like a practical layer between AI agents and real work getting done. Worth checking out:

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Kawsar

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Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

Most people will spend 4 hours tonight consuming. Zero creating. Then wonder why nothing changes. 1) Scroll until you fall asleep. 2) Or watch this. A Stanford lecture on Agentic AI that teaches you more about how AI automation actually works than 6 months of copying Make and n8n tutorials. Tool calling. Multi-step workflows. Planning. Reflection. The foundations behind every automation system that actually works. Most people learn this by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Watch the lecture first. Then go through 9 real build projects across 2 weeks: APIs. Webhooks. LLM integration. AI workflows. No theory. Just build. The people who do this weekend will understand AI automation at a level that makes everything they build after faster, cleaner, and more powerful. The people who skip it will still be copying tutorials in 2027 wondering why nothing they build actually works. Completely free. Bookmark this before you open Netflix.

CyrilXBT

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How One Man Is Using Hebrew, Arabic, and English To Heal The Middle East How much good can you do by speaking 3 languages fluently?! Yirmiyahu speaks Arabic, Hebrew, and English - and is determined to heal the divide between Jews and Arabs, one conversation at a time. He dives into how using his background can be used to build trust, and shows us all how we can lead by example. Share this story with anyone who will feel inspired by using the powers of language for good! Follow buildersofmideast for more 🙌

How One Man Is Using Hebrew, Arabic, and English To Heal The Middle East How much good can you do by speaking 3 languages fluently?! Yirmiyahu speaks Arabic, Hebrew, and English - and is determined to heal the divide between Jews and Arabs, one conversation at a time. He dives into how using his background can be used to build trust, and shows us all how we can lead by example. Share this story with anyone who will feel inspired by using the powers of language for good! Follow buildersofmideast for more 🙌

buildersofmideast

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Unitree G1 now has a cluster control system that lets many robots work together with precise coordination. It connects multiple robots on one network, even across different systems and hardware. AI plans and executes actions in real time, making all robots move in sync like one system.

Unitree G1 now has a cluster control system that lets many robots work together with precise coordination. It connects multiple robots on one network, even across different systems and hardware. AI plans and executes actions in real time, making all robots move in sync like one system.

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