NVIDIA’s latest open 120B-parameter model targets long-horizon, multi-agent workflows with a hybrid Mamba–transformer design, sparse MoE routing, multi-token prediction, and a 1M-token context window — aiming to cut inference costs while boosting accuracy and throughput.
NVIDIA’s Nemotron 3 family introduces a hybrid Mamba‑Transformer + MoE backbone built for efficient, accurate agentic reasoning at million‑token scale—openly licensed and deployable across common platforms.
Introducing Nemotron 3 Super: An Open Hybrid Mamba-Transformer MoE for Agentic Reasoning Read Post »
Microsoft Research proposes PlugMem, a memory layer that converts raw agent interactions into compact facts and skills organized in a retrievable graph—cutting redundancy, reducing tokens, and improving decision relevance.
PlugMem: A plug-and-play memory module for AI agents Read Post »
Engineering and ops leaders are adopting AI to compress detection, diagnosis, and remediation cycles. Here’s how teams can make credible, measured progress toward faster incident resolution while maintaining reliability.
How to reduce MTTR with AI code assistants: Codex-style gains for incident teams Read Post »
AI now scans vast startup signals, accelerates deal screening, and tracks portfolio health in real time. The payoff is speed and scale—along with a trade-off that can dampen outlier wins.
How AI for Venture Capital Is Transforming the Job Read Post »
Explainable AI is advancing fast, but businesses still face gaps in evaluation, trade-offs, and governance. Here’s how to use current methods responsibly—and where to push for better standards.
Model Explainability Techniques: What Works Today and What Still Needs Work Read Post »
