Beyond ReAct: Building the modern AI agent stack for massive tool ecosystems
Giving an LLM thousands of tools leads to noisy decisions. Learn how to optimize AI agent planning and tool routing without overwhelming the context window.
AI is writing your code, but who’s reviewing it?
As AI coding assistants go mainstream, a silent wave of technical debt is building. Here’s how the industry is fighting back.
Machine learning in space: Building intelligent systems for the harshest environments
A new breed of tiny, hyper-efficient AI is revolutionizing space, extending satellite life and unlocking the next great era of autonomous exploration.
Decoding the brain, inspiring AI: How Rahul Biswas is bridging neuroscience...
The convergence of AI and neuroscience opens exciting possibilities for understanding human cognition and driving innovation in deep learning.
How Nvidia’s ASPIRE framework accelerates robot programming with self-improving AI
ASPIRE and the new era of self-improving AI frameworks are drastically reducing token costs and deployment friction for real-world robotics applications.
How the AI arms race moved from smart models to full-stack...
A breakdown of how OpenAI, Nvidia, Google, and Amazon are shifting their development strategies to capture value across every layer of the tech stack.
Demystifying loop engineering: Get more from AI agents, avoid loopmaxxing
The complete guide to the new loop engineering trend. Write powerful agentic loops while avoiding loopmaxxing.
Why LLMs should stop thinking out loud (and what comes after...
Chain-of-Thought prompting is slow, expensive, and largely an illusion. The future of machine reasoning happens in latent space.
Beyond vibe coding: How Codev 3.0 engineers the AI-powered dev team
Casual AI prompting breaks down as codebases grow. Codev introduces strict protocols and multi-model reviews to help teams ship maintainable software.
Why the future of agentic AI is all about the harness
Scaling LLMs hits limits when dealing with agentic AI tasks. For that, we need to look at the harness and the system built around the model(s).
Applied ML: When ‘perfect’ becomes the enemy of ‘good’
Waiting for perfect data can stall your machine learning project and result in losing opportunities of creating good-enough models.
AI can’t replace software engineers yet, but here is how to...
AI is not a replacement for engineers, but it can be very useful to product managers testing hypotheses and product ideas.
How to turbocharge your product and market research with DeepSearch
If you think in terms of the JBTD framework, Deep Search products can save you a ton of time and effort in finding new product and market opportunities.
