What I Require From Vendors Before an Enterprise AI Pilot
I share the non-negotiable questions I ask before green-lighting an enterprise AI pilot—data handling, eval ownership, exit ramps, and who is on the hook when it fails.
Browse editorial essays, hands-on tutorials, and analysis across models, agents, media, industry, and compute policy.
I share the non-negotiable questions I ask before green-lighting an enterprise AI pilot—data handling, eval ownership, exit ramps, and who is on the hook when it fails.
I outline a practical preflight for generative image pipelines—rights, consistency, safety, provenance, and human review—so creative work survives contact with users.
I document the cost controls I use on real teams: per-feature budgets, cache discipline, model tiers, and kill switches that engineers actually respect.
I share the practical rules I use to choose between stuffing a repo into a long context window and building retrieval—without treating either as a religion.
I walk through the stop-condition checklist I use before giving coding agents autonomy—report-only mode, independent checks, and hard spend caps.
I explain loop engineering—the shift from hand-prompting coding agents to designing systems that prompt, verify, and iterate on their own—with a step-by-step tutorial, maturity ladder, and honest limits.
Tsinghua & BAAI's Brainμ model hits Science, mapping memory-sleep links. I see academic rigor, but commercial viability remains unproven.
China launches a full-stack embodied AI sim using domestic GPUs, advancing the 2025-2026 agenda. Ops take: Local silicon cuts latency but risks supply chain fragility.
I analyze Qwen3.6-27B vs 35B-A3B specs to guide open-source adoption. I think benchmarking methodology remains opaque.
Tashi Zhixang unveiled AWE3.0, a non-simulated, non-VLA embodied model. I read the filing; it claims general-purpose capability without teleoperation or simulation tricks.