A research-oriented collection to monitor durable AI trends, surface research questions, and turn experiment ideas into strategic insight.
A living, interactive backlog form for capturing trend watches, research questions, hypotheses, and experiment ideas — prioritized and structured so teams can review, assign, and spin up pilots.
Practical guide to when and how to use synthetic data for scarce or sensitive datasets: generation approaches, labeling propagation, quality checks, evaluation strategies against held-out real data, governance, and a compact experimental workflow teams can try.
A practical, team-ready template to capture emerging AI trends, assess potential business impact and uncertainty, define early indicators, and design focused experiments. Includes scoring guidance, example entries, and recommendations for turning this template into an interactive trackable form.
Short, actionable research briefs that describe promising AI opportunities, propose bounded experiments, identify required data and tooling, surface likely risks, and suggest measurements to decide whether to scale.
Practical calculators, planners, and decision inputs to compare foundation model options by cost-per-inference, latency/SLA implications, adaptation tradeoffs (adapter vs fine-tune), and vendor/governance risks. Save your inputs for team review.
Interactive checklist and evaluation worksheet to determine whether and how to deploy AI models on edge and mobile devices. Covers latency, throughput, device constraints, model size, compression strategies, privacy and data minimization, offline behavior, update and rollback plans, monitoring, and a simple feasibility score to guide go/no-go decisions.