Kala

I'm releasing a complete guide to building production-grade AI agents with MCP - and you can get it early for $10.

I've been building Practical MCP with FastMCP & LangChain for the past few weeks, and it's nearly ready (almost 80% of it).

It covers everything from first principles to production deployment — servers, clients, agent frameworks, RAG, session & state management, middleware, scaling, and 2 full capstone projects backed by a real databases and use cases.

This is not a collection of isolated examples but a complete, opinionated path from understanding how MCP works to shipping something real.

Before the official release, I'm opening it up at $10 instead of $30, that's the price for people who trust the work before it's done.

If you've been trying to figure out how to build AI agents that actually do things beyond generating text, this is for you.

👉 Grab the pre-sale here.

You'll be notified by email once the course is released.

This is a straightforward deal for early supporters, the price goes up at launch!

Happy hacking,
Aymen, Founder of FAUN.dev()

Agents are getting stuffed into everything, but the sharp edges are showing: protocols vs no protocols, realtime voice vs tool reliability, and the very real cost of treating keys like they are not secrets. On the flip side, there is a quiet countertrend brewing too: tiny, single-binary assistants and databases that are trying to grow up without losing MySQL compatibility.

🧩 Do you need an MCP to build your native app?
🧠 GitHub Launches Copilot SDK to Embed Agentic AI into Any Application
🔑 Google API Keys Weren't Secrets. But then Gemini Changed the Rules
🛠️ Introducing helm
🪶 OpenClaw Lightweight Alternative Launches: A 10MB AI Assistant That Runs on $10 Hardware
🎙️ Realtime API: Realtime Prompting Guide
🪖 The Pentagon is making a mistake by threatening Anthropic
🗄️ VillageSQL Launches: A Drop-In MySQL Fork Bringing Extensions and AI to the Core

Ship smarter, not louder.

Have a great week!
FAUN.dev() Team

At scale, IaC state management isn't a storage problem. It's a people and process problem. Join us live to learn how platform teams map dependencies, govern multi-team workflows, & stop mystery outages before they start. You’ll walk away with a practical checklist for scaling IaC.

Register now for this free virtual event on March 12 @ 12PM ET.

GitHub just dropped a technical preview of the Copilot SDK, a toolkit for wiring AI agents straight into apps using the same CLI runtime Copilot already knows.

It’s not just a dumb API wrapper either. It handles multi-turn conversations, streams responses in real-time, and lets you build custom agents with whatever LLM you bring to the party (BYOK style). Available now for Python, TypeScript, Go, and .NET.

System shift: GitHub’s betting big on copilots inside workflows, not just watching you type in a terminal.

VillageSQL drops as a tracking fork of MySQL, but with brains: it bakes in an extension framework for custom data types, functions, and (soon) indexes.

The alpha already speaks fluent plugin, via external or dynamic libraries. That means things like UUIDs, IP types, crypto helpers, and even AI-flavored SQL show up without hacking the core.

System shift: Making extensibility a first-class feature inside a MySQL-compatible engine points to a trend: modular, programmable databases that outgrow their upstream.

PicoClaw, written in Go, shrinks an AI assistant to under 10MB RAM. Boots in ~1s on a 0.6GHz CPU across RISC-V, ARM, and x86.

Agents generated 95% of the core code. It produces a single portable binary with no runtime deps, a sandboxed workspace, and safety guards. Supports Telegram, Discord, CLI, scheduled tasks via a heartbeat, local JSON config, and multiple LLM providers. MIT-licensed on GitHub.

System shift: PicoClaw signals a move to ultra-low-footprint, single-binary AI agents and agent-driven codegen for constrained edge deployments.

If you're working with Kubernetes and exploring AI/ML in real-world environments, this 5-hour live workshop focuses on the practical side of running AI workloads in production.

🎟 Early Bird Offer – 50% Off (No Code Needed) : Build & Scale AI Workloads on Kubernetes Tickets, Sat, Mar 28, 2026 at 7:00 PM | Eventbrite

helm uses TypeScript types to register skills as typed functions with structured I/O. Permissions follow a clear precedence: exact→wildcard→skill→global.

Agents get a keyword search tool and a code-execution tool that runs JS inside an SES sandbox. A recursive proxy forwards calls over IPC to the parent, which enforces permissions.

The chat UI surfaces per-operation allow/ask/deny toggles. Approval requests stream to the UI and block the server until the user answers.

OpenAI ships gpt-realtime and declares GA for the Realtime API. It's a speech-to-speech model that tightens instruction-following, steadies tool calling, and lifts voice fidelity. Latency drops. True realtime agents become possible. The release prescribes prompt skeletons, JSON envelope tool outputs, session.update flows, phonetic hints, and per-tool rules.

Do you need an MCP to build your native app? Surprisingly, modern agents succeed either way. The real difference is how much time, cost, and context you waste along the way.

Anthropic's Claude Gov, optimized for national security uses, has fewer restrictions than regular versions. The Pentagon is threatening retaliation if Anthropic does not waive these restrictions by Friday, including invoking the Defense Production Act or declaring Anthropic a supply chain risk. Anthropic may choose to walk away from its $200 million contract, potentially impacting the Pentagon's access to leading AI technology.

A report reveals Google Cloud's API keys use the same format for public IDs and secret auth. That overlap lets public keys reach the Gemini API.

New keys default to Unrestricted. Existing keys can be retroactively granted Gemini access. Google will add scoped defaults, block leaked keys, and notify affected projects.

For the developer who speaks fluent terminal.

You don't just use Linux. You live it. And now you can wear it. Only real ones will get it. Everyone else will just be confused - and that's exactly the point.

Get it here!

Real-time global intelligence dashboard — AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface

Context cleaning for Claude Code — prune bloated sessions, protect Agent Teams from context loss, auto-guard with tiered pruning

Open-source Agent Operating System

A community collection of OpenClaw use cases for making life easier.

The Kubernetes-native framework for orchestrating autonomous AI coding agents.

Did you know that TorchScript is now officially deprecated in PyTorch - and the reason so many teams hit walls with it comes down to a fundamental design flaw? TorchScript's tracing mode silently produces wrong results on data-dependent control flow, while its scripting mode demands type annotations and crashes on unsupported Python, forcing teams into painful workarounds just to serialize a model. The PyTorch project now points to torch.export for serializable production graphs and torch.compile for runtime optimization - a stack built on TorchDynamo + AOTAutograd + Inductor that handles arbitrary Python without restricting you to a subset of the language. If you are still carrying a TorchScript codebase, the practical move is to migrate submodule by submodule rather than trying to fix the trace.

"Your model isnt failing in production because the data changed; its failing because you never defined what stable means. MLOps is the uncomfortable work of turning research assumptions into monitored contracts, then watching them get broken on schedule."

— SenseiOne

Engineers building on top of foundation models tend to version their prompts far less rigorously than their code, which means a model provider's silent update or a one-word edit to a system prompt can shift output distribution in production with no diff, no rollback, and no incident timeline to reconstruct. The debugging workflow you rely on assumes deterministic inputs, and you quietly stopped having those the moment you put a language model in the critical path.

This week, we're highlighting ChandraKumar R Pillai, an AI Advisor, Technology Speaker, and Enterprise Architect in Brussels who distills the EU AI Act and fast moving tooling into concrete checklists and architecture decisions. With 114K+ followers and 50K+ AI Daily Nutshell readers, he draws on 20+ years in enterprise architecture, cloud, and automation to make AI adoption practical for European teams.