Posts

The era of the “chatbot” is rapidly evolving into the era of the “agent.” For the past two years, the world has been captivated by Large Language Models (LLMs) that can write essays, debug code, and hold witty conversations. However, the limitation of these models has always been their isolation; they could talk about the world, but they couldn’t do anything in it. Enter Large Action Models (LAMs) and Agentic Workflows. We are currently witnessing a seismic shift from passive text generation to active task execution. With the recent explosion of high-quality, open-source LAMs and agent frameworks, the power to build autonomous systems that navigate the web, manage software, and orchestrate complex business processes is no longer restricted to Big Tech labs. ...

Pushing PostgreSQL Limits: Engineering a Database Backbone for Billions of AI Interactions In the era of generative AI, where platforms like ChatGPT handle hundreds of millions of users generating billions of interactions daily, the database layer must evolve from a mere data store into a resilient, high-throughput powerhouse. PostgreSQL, long revered for its reliability and feature richness, has proven surprisingly capable of scaling to support millions of queries per second (QPS) with a single primary instance and dozens of read replicas—a feat that challenges conventional wisdom about relational database limits.[1][2] This post explores how engineering teams can replicate such scaling strategies, drawing from real-world AI workloads while connecting to broader database engineering principles, cloud architectures, and emerging tools. ...

Introduction The narrative of Artificial Intelligence has, for the last several years, been dominated by the “bigger is better” philosophy. Massive Large Language Models (LLMs) with hundreds of billions of parameters, housed in sprawling data centers and accessed via APIs, have set the standard for what AI can achieve. However, a silent revolution is underway—the shift toward Local Reasoning. As privacy concerns rise, latency requirements tighten, and the cost of cloud inference scales exponentially, the focus is shifting from the cloud to the “edge.” Small Language Models (SLMs) are now proving that they can perform sophisticated reasoning tasks directly on smartphones, laptops, and IoT devices. This post explores the technical breakthroughs, optimization strategies, and architectural shifts making on-device intelligence a reality. ...

OpenClaw Unleashed: Building Your Autonomous AI Sidekick for the Agentic Future In an era where AI assistants are evolving from passive chatbots to proactive agents capable of executing complex tasks independently, OpenClaw emerges as a game-changer. This open-source powerhouse runs locally on your machine, connects seamlessly to your favorite messaging apps, and transforms high-level goals into tangible actions—without relying on cloud subscriptions or vendor lock-in. Unlike traditional tools that merely respond to queries, OpenClaw remembers your preferences, automates workflows, and even extends its own capabilities by writing custom code on the fly.[1][2][5] ...

Decoding AI Startup Pitch Decks: Essential Lessons from 2026’s Hottest Raises In the hyper-competitive world of AI startups, pitch decks are more than slides—they’re battle-tested blueprints revealing how founders convince top investors to bet millions on unproven ideas. Unlike press releases that celebrate wins after the fact, these documents expose raw strategies for defensibility, data moats, and distribution in an era where AI models commoditize overnight. This post dives deep into the patterns from recent pre-seed and seed AI decks, drawing connections to computer science fundamentals, engineering trade-offs, and broader tech trends. Whether you’re a founder crafting your deck, an investor spotting signals, or an engineer curious about AI’s business side, you’ll find actionable insights here.[1][3] ...