OpenClaw Lightweight Alternative Launches | Kala

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Kala

#ArtificialIntelligence #MachineLearning #MLOps

📝 A Few Words

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.

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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()

🔍 Inside this Issue

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

⭐ Patrons

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ℹ️ News, Updates & Announcements

faun.dev

GitHub Launches Copilot SDK to Embed Agentic AI into Any Application

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.

faun.dev

VillageSQL Launches: A Drop-In MySQL Fork Bringing Extensions and AI to the Core

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.

faun.dev

OpenClaw Lightweight Alternative Launches: A 10MB AI Assistant That Runs on $10 Hardware

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.

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🔗 Stories, Tutorials & Articles

bengubler.com

Introducing helm

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.

developers.openai.com

Realtime Prompting Guide

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.

sentry.engineering

Do you need an MCP to build your native app?

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.

understandingai.org

The Pentagon is making a mistake by threatening Anthropic

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.

trufflesecurity.com

Google API Keys Weren't Secrets. But then Gemini Changed the Rules

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.

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🛍️ Swag, Deals, And Offers

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⚙️ Tools, Apps & Software

github.com

koala73/worldmonitor

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

github.com

Ruya-AI/cozempic

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

github.com

RightNow-AI/openfang

Open-source Agent Operating System

github.com

hesamsheikh/awesome-openclaw-usecases

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

github.com

kelos-dev/kelos

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

👉 Spread the word and help developers find and follow your Open Source project by promoting it on FAUN. Get in touch for more information.

🤔 Did you know?

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.

🤖 Once, SenseiOne Said

"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

(*) SenseiOne is FAUN.dev’s work-in-progress AI agent

⚡Growth Notes

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.

Each week, we share a practical move to grow faster and work smarter

👤 This Week's Human

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.

💡 Engage with FAUN.dev on LinkedIn — like, comment on, or share any of our posts on LinkedIn — you might be our next “This Week’s Human”!

😂 Meme of the week

❤️ Thanks for reading

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Kala #518: OpenClaw Lightweight Alternative Launches
Legend: ✅ = Editor's Choice / ♻️ = Old but Gold / ⭐ = Promoted / 🔰 = Beginner Friendly

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