Today, I broke up with my boyfriend... Suddenly, he said to me: “Do you want me to give you my Instagram password?”
I told him: No need, I trust you...!
And yet, he gave it to me anyway.
Anyway, later I logged into the account.
The poor guy thought I’d go in to check the messages or something like that... as if I’m an idiot or whatever...!
But I went into the archive, and started looking at the bio edits and stuff like that, and then came the shock....
This is one of the best beginner-friendly Claude Code course I have seen.
And it is only 36 minutes.
You can master unto 95% of Claude Code with this.
It is by Nate Herk
Most AI products stop at generating content.
The bigger opportunity is helping teams make better decisions with their data.
If Mora can reliably turn natural language into trustworthy analytics, that’s a category worth watching.
Analytics is overdue for its ChatGPT moment.
Try it today : https://t.co/895FeRvarF
Most AI engineers know how to use MCP.
Very few understand the server patterns that make production AI systems actually scalable. ⚡
This breakdown of the top 5 MCP server architectures is pure gold for anyone building serious AI agents in 2026. 👇
1️⃣ Tool Server
Lets AI agents perform actions using APIs & external tools.
Think:
• sending emails
• database queries
• triggering workflows
• automation tasks
2️⃣ Resource Server
Feeds structured context into the LLM.
Perfect for:
📂 files
🗄️ databases
📑 documents
📚 knowledge systems
3️⃣ Prompt Server
Reusable prompts as infrastructure.
Versioned. Parameterized. Shareable.
This is where prompt engineering starts turning into software engineering.
4️⃣ Gateway Server
One endpoint controlling multiple MCP servers.
Handles:
✅ routing
✅ auth
✅ rate limiting
✅ orchestration
5️⃣ Proxy / Bridge Server
Connects legacy systems to modern AI agents without rewriting everything.
Huge for enterprise AI adoption. 🚀
The biggest shift happening right now:
AI systems are moving from:
“single chatbot apps”
to
“modular AI infrastructure.”
The engineers who understand MCP architecture early will have a massive edge building:
• AI copilots
• autonomous agents
• enterprise AI systems
• multi-agent workflows
Bookmark this.
One of the cleanest MCP architecture references I’ve seen so far.
Code generation is no longer the bottleneck.
Validation is.
AI can ship hundreds of lines of code in minutes. But PR reviewers haven’t magically become 10x faster. Bugs slip through. Verification debt piles up.
That’s why TestSprite caught my attention.
Instead of reading code and guessing, it opens your app and uses it.
A fleet of agents explores your product, generates test plans, runs tests in parallel, and surfaces real issues with clear traces, causes, and fixes.
The new TestSprite 3.0 web portal makes the whole process visible.
You can watch agents explore your app in real time, replay failures step by step, inspect API data flows, and debug issues visually instead of digging through logs.
The result isn’t more AI reviewing AI.
It’s a validation layer that actually closes the loop.
For teams shipping AI-generated code faster than ever, that’s becoming a necessity, not a nice-to-have.
Try it here: https://t.co/gwweHOz5cp