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Step-by-step guide to a tiny hybrid prototype: run small AI models locally on a laptop, fall back to a cloud API for heavy requests, and measure latency, fallback rate, and cost.
New assistant AIs like Google's Spark speed scheduling, email drafts, and lookups for assistants and support teams. Read which tasks are automatable and what managers should change.
Guide to prototype a minimal Android agent gadget: wake on camera or biometric unlock, on-device auth, and route queries to local or cloud LLMs — informed by Microsoft's Project Solara.
Trump's executive order creates a voluntary channel for firms to share AI models pre-release with the US government. Learn how to build a one-page release-readiness checklist and POC.
A hands-on checklist to make AI agents auditable and controllable: short-lived per-instance credentials, chain-of-custody logs, and an external policy gate for tool calls.
A concise Mac-focused walkthrough to clone a-streetcoder/agent-deck and run one staging agent (issue label suggestions). Shows safety checks, secrets handling, timing, and a 7-day pilot.
Facing a WHO-projected 11M staffing gap, providers are piloting agentic AI. This brief shows how small shadow pilots and audit logs can cut clerical load while keeping clinicians in charge.
Practical checklist for teams responding to MIT Technology Review's 'world models' signal: decide real-world grounding, run sims or log tests, and name a safety/rollback owner.
How to run NVIDIA Cosmos 3 to prototype vision-to-action demos: give an image or short clip plus a prompt and get text reasoning or pixel-space robot trajectories. Includes code.
CoinSignal's public leaderboard compares 13 crypto prediction models with verified samples, accuracy, hit rate and calibration—see which meet practical thresholds for pilots.
How to use or build mgranados/screenshotter on macOS to compress screenshots and copy the result to the clipboard—reducing upload bytes and token costs when pasting into AI coding UIs.
Learn how Conductor uses YAML and Jinja2 to make multi-agent AI workflows deterministic and reproducible, reducing latency and making routing, branching, and testing explicit.