AI Agent automation brings efficiency but also risk. This article shares 10 battle-tested safety baseline rules with Hook gatekeeper implementation concepts, helping you build an AI workflow that won't self-destruct.
MIT open-source macOS notch dashboard for system health and AI coding-provider quotas. Local-first: clone, build, and run.
LoRA and QLoRA let you fine-tune a large language model with just a few dozen examples on your own computer, no cloud GPU rental, no uploading sensitive data. A real biotech lab case shows why this technique changes how local AI feels to use.
Google agents-cli uses a three-tier SkillToolset progressive disclosure architecture that cuts baseline context token consumption by about 90% in static scenarios, though real-world workloads may narrow that to 37%. This piece dissects the token-efficiency claim, the vendor lock-in risk, and the fragility of description-based routing, extracting lessons useful for any independent agent system.
I benchmarked five 4-bit local models on a single M4 Pro (48GB) with oMLX across eight benchmarks, mapped the speed/quality sweet spot, and calibrated which cloud generation they match. Plus a field report on three speculative-decoding traps (MTP / dflash / specprefill) and the cache trade-off that decides them.
How to share a single AI Agent memory across Claude Code, GitHub Copilot CLI, and Antigravity CLI (agy). Comparison of entry configs, @import mechanisms, and cross-platform injection strategies.