Apple Built 3D View Synthesis That Runs in Under a Second
Author(s): Gowtham Boyina Originally published on Towards AI. The View Synthesis Problem Take a single photo and generate realistic views from different camera angles — this is monocular view synthesis. It’s useful for VR/AR, 3D modeling, and spatial computing, but most approaches …
The Three Breakthroughs That Changed How I Think About AI Tool Use
Author(s): Gowtham Boyina Originally published on Towards AI. The Three Breakthroughs That Changed How I Think About AI Tool Use I’ve been building AI agents for a while, and like most developers in this space, I’ve hit the same frustrating wall over …
Someone Built an AI Interface for Industrial Equipment, and It’s Kind of Wild
Author(s): Gowtham Boyina Originally published on Towards AI. The Concept That Made Me Stop Scrolling I was browsing GitHub late last night and came across something called “IoT-Edge-MCP-Server.” The tagline caught me: “MCP server for Industrial IoT, SCADA and PLC systems.” Here’s …
Pipelex: Building Reliable AI Workflows with Business Logic, Not API Calls
Author(s): Gowtham Boyina Originally published on Towards AI. AI Workflows That Agents Build & Run The rise of large language models has unlocked tremendous potential for AI-powered applications. Yet, many developers find themselves trapped in a cycle of prompt engineering, wrestling with …
Chrome DevTools MCP: Empowering AI Coding Agents with Browser Automation
Author(s): Gowtham Boyina Originally published on Towards AI. Introduction The landscape of AI-assisted development is evolving rapidly, and one of the most exciting developments is the ability for AI coding agents to interact directly with web browsers. The Chrome DevTools MCP (Model …
Agent Lightning: Revolutionizing AI Agent Training with Reinforcement Learning
Author(s): Gowtham Boyina Originally published on Towards AI. The Challenge of Training Modern AI Agents In the rapidly evolving landscape of artificial intelligence, AI agents have emerged as powerful tools for tackling complex real-world tasks — from code generation and data analysis …
Less is More: How Tiny Networks Outperform Giant LLMs on Hard Puzzles
Author(s): Gowtham Boyina Originally published on Towards AI. A deep dive into Tiny Recursive Models (TRM) — achieving 45% accuracy on ARC-AGI-1 with 7M parameters, outperforming models 10,000x larger Large Language Models have revolutionized AI, but they struggle on certain types of …
BLAST: Building High-Performance Browser-Augmented LLM Applications
Author(s): Gowtham Boyina Originally published on Towards AI. Revolutionizing Web Browsing AI with Stanford’s Auto-Scaling Technology In the rapidly evolving landscape of AI-powered automation, a new challenge has emerged: how do we efficiently serve browser-augmented Large Language Models at scale? Stanford’s MAST …
Claude Skills: The Game-Changing System That’s Transforming How AI Assistants Learn Specialized Tasks
Author(s): Gowtham Boyina Originally published on Towards AI. A Deep Dive Into Anthropic’s Revolutionary Approach to AI Customization Imagine if your AI assistant could instantly become an expert in your company’s brand guidelines, understand your organization’s specific data analysis workflows, or automate …
Inside ChatGPT: How 700 Million People Actually Use AI
Author(s): Gowtham Boyina Originally published on Towards AI. The Fastest Tech Adoption in Human History By July 2025, ChatGPT had reached a milestone that no technology in history had achieved so quickly: 700 million weekly active users, representing roughly 10% of the …
Ling-1T: A Trillion-Parameter Approach to Efficient AI Reasoning
Author(s): Gowtham Boyina Originally published on Towards AI. Exploring InclusionAI’s Claims of Balancing Scale with Computational Efficiency The AI landscape continues to evolve rapidly in 2025, with new models pushing various boundaries. Ling-1T, released by inclusionAI, represents an ambitious entry into the …
Why Your Software Development Life Cycle Will Not Work for Your AI Agents (And How to Change That)
Author(s): Gowtham Boyina Originally published on Towards AI. Classical software development is deterministic. You code, you test, you deploy, and the result — when provided the same input — is deterministic. The sequence of logic is predictable, and the failure modes. AI-agents …
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