NVIDIA’s controversial research argues that tiny language models outperform giant LLMs for agentic tasks and they’re about to flip the AI industry on its head
The brutal math behind software team scaling and why throwing bodies at deadlines backfires every time
Why treating docs as growth strategy separates thriving projects from forgotten GitHub repos
Cutting through the buzzword fog to reveal why these patterns are more similar than different and how semantic diffusion corrupts good ideas.
An analysis of AI scaling challenges, economic constraints, and emerging research directions in foundation model development.
Microsoft’s new open-source TTS model can synthesize feature-length audio with multiple speakers, but comes with audible disclaimers and watermarking to prevent misuse.
A 9B-parameter model achieving six times the throughput of a 70B-parameter competitor raises questions about architectural innovation versus hardware dependency.
Practical strategies to cut transcription expenses by 50-60% while maintaining accuracy in AI-powered systems processing extended meeting recordings
How a new attention mechanism enables 8x longer context lengths while cutting VRAM requirements in half for LLM training on consumer hardware.
DeepSeek V3.1 hits 71.6% on Aider and cuts Claude 4 costs by 32x, shifting open-source vs proprietary balance.
What a twenty-minute dev-stretch can teach you about AI video metrics, workflow design, and the myth of endless quality.
Examining the factors behind the high failure rate of enterprise AI initiatives and their impact on corporate strategy and financial markets.
