Claude Code on desktop can now preview your running apps, review your code, and handle CI failures and PRs in the background. Here’s what's new:

Introducing Claude Code Security, now in limited research preview. It scans codebases for vulnerabilities and suggests targeted software patches for human review, allowing teams to find and fix issues that traditional tools often miss. Learn more: anthropic.com/news/claude-co…

Difference between Machine learning vs Deep learning 💨🫡

Infoblox Threat Intelligence uncovers Evilginx-based SSO phishing using subdomains that mimic university portals, targeting at least 18 US institutions since April 2025, and finds nearly 70 related domains for future tracking. blogs.infoblox.com/threat-in…

We’re a week out from #AgentsInProduction 2025! I’ll be speaking on the AI Workforce track about “Deploying AI Agents for Scalable Immigration Automation.” Register: home.mlops.community/public/… @mlopscommunity @prosus @demetriosbrinkmann #AI #MLOps #Automation #AIWorkforce

Check out my latest article: The Evolution and Battle of Open vs. Closed AI Models: Beyond Imagination and Into Reality linkedin.com/posts/washingto…

💡David Tag from @LinkedIn reveals how they built their first production AI agent for hiring using LangChain and LangGraph. Learn the technical architecture behind LinkedIn Hiring Assistant and the framework that scaled AI development across 20 teams. Watch the full session here: youtu.be/NmblVxyBhi8/?utm_me… Catch up on all the talks from Interrupt: interrupt.langchain.com/vide…

Let's build an MCP server that connects to 200 data sources (100% local):

A complete roadmap to learn how to build AI Agents: 1. Build a strong foundation in Python 2. Learn how to work with RESTful APIs 3. Understand how Large Language Models work 4. Get familiar with the OpenAI API 5. Understand how vector databases work 6. Learn how to create and retrieve embeddings 7. Learn about caching and persistent memory using databases 8. Learn to build APIs with Flask or FastAPI 9. Master prompt engineering techniques 10. Study how to build RAG applications 11. Explore agentic frameworks (LangChain, LangGraph) 12. Learn how to connect agents with external tools (MCP) 13. Learn how to containerize and deploy agents with Docker 14. Understand how to limit agent behavior 15. Learn how to implement safety guardrails For those of you who are looking to learn how to build agents, check out the "AI Agents with LangChain and LangGraph" course on Udacity. (I pasted the link in a reply to this post so I don't get throttled.) This is a course that focuses on the core ideas of building agents. The final lesson of the class is a project where you'll build a complete agent. This course will take you one weekend of work, and will teach you what's probably the hottest skill in the market right now.

Sadly, they ignore even when someone reaches out to help. On 21st of May 2024, I bumped into a bug that can allow someone to fake a transaction into the eCitizen. A search led me to the domain admin where I reported the bug but they’ve never responded.

Anyone working professionally in the data space who can spare 15 minutes to share their insights on the difference between learning, what to learn, and how what they’ve learned is applied in the field? RT.

i almost always ask this question in screening rounds (both ML engg and research positions) about half the applicants fail to answer

4 months of training (7,500 Ksh per month) 2 months internship. Registration ends soon.

At @LuxDevHQ, Feel free to stop by for more information about the course and to undergo an in-person skills assessment. 🚀 Contact: 0798166628 | Info@luxdevhq.com