Kala

Big brains, bigger models—and a few rebels causing trouble. This week’s AI wave blends 600kW GPU monsters, privacy-first LLMs, and a protocol that might just be the USB-C of AI. Oh, and one junior dev is here for revenge. Let’s ride. ⚡

🔥 Nvidia's roadmap shows just how deep Moore’s Law is buried
🚀 DeepSeek-V3 hits 20 t/s on Mac Studio—OpenAI’s worst nightmare
🧠 The Llama 4 herd: Multimodal, massive, and shockingly efficient
🔐 Apple Intelligence runs AI locally, and it’s faster than you think
🎛️ MCP Protocol is becoming AI’s plug-and-play standard
🦾 Agentic AI: From chat tools to full-on coding agents
🛠️ Ray project: Distributed AI compute for mere mortals
🧪 The Power of Asymmetric Experiments @ Meta
🐣 Revenge of the junior developer: Coding’s next revolution
🔍 Exploring Generative AI with Martin Fowler’s steady hand

💡 Stay sharp. It’s all signal, no noise.

Struggling with Kubernetes visibility? Join ManageEngine and DevOps expert Viktor Farcic in this exclusive webinar to uncover strategies for enhancing performance, eliminating blind spots, and optimizing your Kubernetes environment. Register now !

Cloudflare just made it dead simple to build remote MCP servers—accessible over the web, with built-in OAuth, persistent sessions, and tool access control. Unlike local-only setups, remote MCPs let users connect via web apps or agents without installing anything. This is a big leap: from dev-only tools to real AI-powered user experiences for everyone.

Nvidia just dropped its 2028 GPU squad, honoring Richard Feynman. Enter the 600kW behemoth with 576 GPUs. Moore's Law? Toast. Yet AI's appetite swells for more muscle, more density. Nvidia leads the pack, but watch out—AMD and Intel might just dog-pile onto this trend and cook up their own dense chip wonders in sprawling datacenters.

Midjourney's V7 finally rolls up after a year's hiatus, waving its banners of smarter text prompts and crisper image quality. But, don't hold your breath for upscaling—it's MIA for now.

Draft Mode blasts out images at lightning speed—10 times faster, at half the price. It's like a tech-savvy sprinter. Some bells and whistles still wait in the wings, though.

Meet DeepSeek-V3-0324, the renegade of language models. Packing a whopping 641GB into its digital knapsack, it's rocking an MIT license like a badge of rebellion. It buddies up with a Mac Studio's M3 Ultra processor, scoffing at the need for a stuffy datacenter.

The kicker? It flips the switch on just 37B out of a mind-boggling 685B parameters, only when needed. This clever trick cranks up efficiency and speed by a jaw-dropping 80%.

Meet Llama 4 Scout and its whopping 17 billion active parameters, making Llama 3 look like a snail in comparison. It churns through over 460 tokens/s. Maverick ups the ante with 128 experts, setting the stage for AI brilliance.

Meta's bold move to crank up control group sizes—sometimes 21 times larger—while shrinking test groups by half keeps those cherished confidence intervals intact. Asymmetric experiments shine when you've got low experiment bandwidth, recruitment costs peanuts, and test interventions drain the budget. This approach is a lifesaver for long-term impact analysis in those pesky "holdouts."

Model Context Protocol (MCP) is the AI world's version of USB-C. It lets models snag live data and tango with APIs, juicing up their powers like never before. Microsoft's Azure OpenAI Services uses MCP to catapult GPT models out of their static halls of knowledge, mixing in real-time tool hookups for on-the-fly insights.

Apple Intelligence runs a tightly-optimized 3B parameter model directly on Apple Silicon, with extreme quantization and hardware tuning for low-latency, private on-device AI. For heavier tasks, it offloads to Apple’s own encrypted Private Cloud Compute—never logging or training on your data. Compared to open-source giants like Mistral 7B and LLaMA 2, Apple trades scale for speed, privacy, and tight integration—and still competes shockingly well.

Vibe coding—a cheeky term from Dr. Andrej Karpathy—lets LLMs tackle the drudgery, propelling coding's future as manual coding fades into history. By 2025, coding agents are poised to outshine chat-based tools, urging developers to swap their keyboards for AI irony hats. Efficiency gears will shift as these digital minions reshape what productivity looks like. Enter agentic coding, where developers must morph into maestros of managing these digital juggernauts. But beware: this isn't a free ride. It demands cash, lots of it, as budgets groan and sigh. Progress won't just jog forward; it’ll pole-vault, leaving the stubborn ones in the dust.

GenAI tools like Copilot help most with small, repetitive tasks—but only if devs guide and review them carefully. Bigger changes? More risk, more cleanup. Use tests, short prompts, and stay skeptical.

Model Control Protocol (MCP) flips the script on security operations. Picture this: LLMs that juggle tools like circus pros, slashing through technical babble while burying clunky UIs. This week, chatter ascended as three fresh MCP servers popped up, promising to disrupt the security scene with nimble automation and seamless actions fueled by the pulse of community standards.

Dives into the wild ride of emerging tech shaking up culture and rewiring brains. Lifts the curtain on the money machines funding science and the geniuses sparking breakthroughs.

Meet Llama 4 Scout and its wild cousin Maverick. Each struts around with 17 billion parameters. Scout's got 16 experts; Maverick goes big with 128. Together, they outshine GPT-4o in the multimodal spotlight while comfortably riding a lone NVIDIA H100 GPU. Then there’s the heavyweight, Llama 4 Behemoth. With a jaw-dropping 288 billion parameters, it crushes the competition in STEM tests, leaving GPT-4.5 in the dust. This crew isn't just flexing muscles; they're redefining the limits of context and efficiency in AI, leading the charge in tech wizardry.

Get LiteLLM rolling on Azure in no time using the build5nines/azd-litellm template. This wizardry streamlines all your LLMs via a single API. Say farewell to chaos, hello to efficiency. Enjoy savings—and fewer headaches.

A look at what Agentic AI is and then how to create an agentic AI agent using Copilot Studio then Semantic Kernel.

GitHub's official MCP Server

A curated list of awesome projects, resources, and tools for building stateful, multi-actor applications with LangGraph

A browser extension and MCP server that allows you to interact with the browser you are using.

Seamlessly Integrate Any API with AI Agents

A collection of MCP servers

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Did you know that Netflix uses a custom-built tool called Spinnaker for continuous delivery? Originally developed in-house and later open-sourced, Spinnaker helps Netflix deploy code thousands of times per day across its global infrastructure. It supports multi-cloud environments, enabling seamless rollouts on AWS, Google Cloud, and more. One of its key features is automated canary analysis, which deploys new code to a small subset of users and monitors for issues before a full rollout—helping Netflix ship faster while keeping their 200+ million users streaming smoothly.

“If you can’t describe what you are doing as a process, then you don’t know what you are doing.” ― Clayton M. Christensen, Competing Against Luck: The Story of Innovation and Customer Choice