engblogs

An in-depth technical exploration of designing AI systems that augment human decision-making and autonomy through governance, reliability, and measurable accountability.

An exploration of building a scalable business by leveraging intelligent systems, data-driven decision making, and automated AI-powered processes.

Unveiling D4RT, a unified Transformer-based encoder-decoder model for real-time 4D reconstruction and tracking of dynamic scenes from 2D video, delivering efficient, pixel-accurate 3D+time understanding.

Technical guide to auditing Elon Musk's court filing for omitted information using NLP-based evidence synthesis to surface gaps, verify claims, and improve transparency.

Practical guidance on building a phased AI strategy for business, from opportunity discovery and tool selection to adoption roadmaps, governance, and measurable ROI.

Explores how NRF 2026 signals the rapid maturation of agentic commerce, detailing the shift to scalable AI-powered shopping experiences, agent-ready product catalogs, and how Stripe, ACP, and UCP enable first- and third-party agent interactions.

A technical recap of reinforcement learning–driven fine-tuning on TPUs, covering when to fine-tune, the post-training lifecycle (SFT and RL), RL’s role in alignment and tool use, and a hands-on MaxText/Pathways demo for scalable agent development.

A technical roadmap detailing advertising strategies and scalable, accessible deployment of ChatGPT.

A concise technical overview of ChatGPT Go and its worldwide availability, highlighting deployment considerations and potential use cases.

Practical guidance for security leaders on implementing Google's Secure AI Framework (SAIF) in Google Cloud to build AI responsibly, focusing on data as the perimeter, treating prompts as code, and identity propagation for agentic AI.

Palo Alto Networks transitions from 30,000 separate one-tenant pipelines to a unified multi-tenant data platform powered by Dataflow, Pub/Sub and BigQuery, enabling autoscaling for real-time processing, simplified operations, and significant cost efficiency.

A critical re-evaluation of classifier-based quality filtering for LLM pretraining, highlighting the paradox that CQF can improve downstream tasks while potentially harming language modeling on high-quality data, and proposing a rethink of data-quality notions via synthetic-data comparisons.