Why LLMs should stop thinking out loud (and what comes after chain-of-thought)
Chain-of-Thought prompting is slow, expensive, and largely an illusion. The future of machine reasoning happens in latent space.
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.
Why sandboxing OpenClaw doesn’t stop data exfiltration
Research into Nvidia’s NemoClaw reveals that sandboxes don't stop AI agents like OpenClaw from leaking data. We need to rethink security from first principles.
Google brings multi-token prediction Gemma 4 LLMs
How Gemma 4’s multi-token prediction and community-driven DFlash are speeding up local LLM throughput by 3-6x.
How Memory Sparse Attention scales LLM memory to 100 million tokens
Memory Sparse Attention (MSA) scales LLM context windows to an unprecedented 100 million tokens while preserving accuracy.
Claude Code is leaking API keys into public package registries
A new study reveals how AI coding assistants like Claude Code are quietly hoarding and publishing sensitive API keys to code repositories.
Anthropic’s MCP vulnerability: When ‘expected behavior’ becomes a supply chain nightmare
Security researchers have uncovered a massive architectural flaw in Anthropic's Model Context Protocol, exposing millions of AI applications to remote takeovers.
The paradox of LLM self-distillation: Faster reasoning, weaker generalization
Optimizing LLMs for concise answers can destroy their ability to explore alternative solutions on difficult problems. New study reveals the hidden cost of self-distillation.
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.
