Kala

HUMAIN just inked a deal with NVIDIA to spark AI factories in Saudi Arabia, cranking up to 500 megawatts via a colossal sea of GPUs. Picture 18,000 NVIDIA GB300 Grace Blackwell AI supercomputers flexing their muscles, crafting massive sovereign AI models. Saudi's digital metamorphosis and Industry 4.0 ambitions just got a turbo boost.

Guoxing Aerospace plots a sky full of satellites—2,800 of them, each packing a mind-blowing 744 TOPS. They’ll chat with lasers, zipping data at 100Gbps while an 8-billion parameter AI juggles the load.

Meanwhile, South Korea's HPE supercomputer is no slouch, flexing 8,496 GPUs and stashing 205 petabytes of data. They're throwing down $1 billion on GPUs to turbocharge the science scene.

On the chip front, India's HCL and Foxconn buddy up, pooling $300 million to power a fab plant. They'll crank out 36 million chips a month, easy.

Then there's Japan, flipping the script with a new cyber law. Offensive operations and surveillance are now on the table—eye-opening moves for a nation known for its post-war peace narrative.

AWS Lambda for multi-agent systems: Deploy a team of serverless agents that can collaborate, powered by Bedrock's CLAUDE 3.5 Sonnet v2. When token limits play hard to get, charm AWS into behaving.

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AGI's just around the corner? Hold your horses.

Altman, Amodei, and Musk see it coming soon, but some experts roll their eyes. AGI needs more than just souped-up neural networks—a revelation no one's unearthed yet. Real intelligence? It thrives in chaos, pivots on a dime. Today’s AI prefers its patterns neat and tidy. Reinforcement learning teases promise but won't mend fences. 🧠 Machines dominate in recognizing patterns, but true smarts? That’s the mix of creativity and wrestling the world—still eluding them. 🏃‍♂️ Skeptics wave their banners: without that next big eureka, true AGI remains a pipe dream.

Anthropic's Kyle Fish tosses around a bold 15% chance that chatbots might be conscious. Meanwhile, neuroscientists raise an eyebrow and point out our shaky grasp of how intelligence relates to consciousness.

Google's Agent Development Kit (ADK) cranks up agent creation with LLMs. It dishes out unique agent types, slick orchestration patterns, and a debugging process that's anything but flimsy. Thanks to ADK's open-source framework, you can engineer intricate systems that thrive on transparency and auditability. Build agents that aren't just reliable—make them worth trusting.

Ray-Ban Meta glasses tap into the power of multimodal AI. They scan the world, serving up instant translations or dropping landmark trivia, courtesy of AnyMAL.

Amazon SageMaker AI slaps on the safety gear with gusto. It layers up, merging runtime shields with pre-deployment tactics. Imagine Amazon Bedrock and Llama Guard as the dynamic duo—they neutralize the wild risks of LLMs.

xGen-small flips the script. It slashes model size yet juggles 256K tokens like a caffeinated ninja. So much for the old bigger-faster-better mantra. By mixing precise data curation, scalable pre-training, and ironclad privacy, this Salesforce gem rolls out enterprise-ready AI that’s as budget-friendly as it is brilliant.

Pydantic wrestles unwieldy AI output into neat, structured data. Its magic? Slashing error rates by over 90% using JSON validation with type annotations and auto-coercion. Ditch the endless if-statements. Let Pydantic tame AI's creativity into reliable forms and grab a hammock.

Lovable.dev chops down app-building to mere hours with its knack for connecting Azure APIs through natural language. Forget the weeks-long slog. GPT-4 Omni and Azure OCR tackle everything from expense reporting to advanced voice solutions. AI turns mundane tasks into innovation arenas.

AGI aims for true, independent consciousness and comprehension beyond imitation. Experts predict arrival by 2059, but Ray Kurzweil thinks it's closer by 2029.

Semantic Kernel is a developer's best friend, an open-source dynamo for crafting AI apps with large language models (LLMs). It cuts through complexity like a hot knife through butter.

At Kingfisher, GCP Vertex AI Pipelines and Kubernetes dance together, tackling AI scaling issues with grace. Serverless sounds dreamy until your budget cries uncle under traffic spikes. Kubernetes, though, delivers predictability, a perfect match for Kingfisher's consistent AI tasks.

From a side project in Amsterdam to powering AI at the world’s biggest companies - this is the story of Python. Featuring Guido van Rossum, Travis Oliphant, Barry Warsaw, and many more, our upcoming full-length documentary traces Python’s slow-but-steady rise, its community-driven evolution, and the language’s impact on... well… everything. Coming soon.

The world's best AI personal assistant for email. Open source app to help you reach inbox zero fast.

An open agentic framework that uses computers like a human

A collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying GenAI agents.

An open protocol enabling communication and interoperability between opaque agentic applications.

Collection of leaked system prompts

Specification and documentation for the Model Context Protocol

Did you know that Stack Overflow runs on a surprisingly lean tech stack powered by C#, .NET, and SQL Server? Despite handling over 100 million visits a month, the site is so well-optimized that it runs on fewer than 10 web servers. The engineering team focuses heavily on performance tuning and efficient database queries—some pages render in under 15 milliseconds. It’s a prime example of how deep expertise in optimization can outpace sheer infrastructure scale.

It always takes longer than you expect, even when you take into account Hofstadter's Law ~ Hofstadter's Law