Stop Conditions First: How I Design Agent Loops That Know When to Quit
I walk through the stop-condition checklist I use before giving coding agents autonomy—report-only mode, independent checks, and hard spend caps.
Copilots, MCP, developer tools
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.
I read Hassabis & Dean’s 2025 AI review. They predict extended industry challenges through 2026. This signals a tough year ahead for creators facing shifting platforms and licensing friction.
OpenAI's Atlas browser debuts at DevDay, bundling AgentKit and Codex GA. I see this as a strategic push to lock users into their ecosystem rather than offering genuine innovation.
I read the claim that PKU and Amazon unveiled a graph-centric RDB foundation model to solve AI data management issues. I note this lacks reproducibility details.
I track how MediaTek’s MDDC 2025 pushes local agent AI adoption while Google Cloud Next defines global interoperability standards via Ironwood TPUs.
I read the East China Normal & Donghua University review on 2025-26 agent tech. My read: Frameworks clarify hype, but governance remains unaddressed.
I read that the Yao Class team just surpassed Devin's benchmark for large model programming. This timing suggests a significant shift in autonomous coding capabilities ahead of the 2025-2026 cycle.