How to enter AI City Remix: rules, judging weights, the seven starter Skills, and a real Vienna run where MiniMax M3.1 Flash wrote the city script for under one cent.
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We ran three viral 'Fable 5.5' prompts, a VoxelBench probe, a bike-riding-a-pelican SVG and a Last Train Home scene, on shipped Fable 5.1 and Opus 5.5. Results, costs and failures.
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Can Gemini 4 Argon make a playable browser game from one prompt? What 48 launch-week game posts show, plus our own run: 19 keyboard checks, one input bug, one fix, $0.39.
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Gemini 4 Argon Three.js scenes: what 105 launch-week 3D posts show, then our own single-file run on SandBase with tokens, cost, render checks and what broke.
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How to evaluate WorkBuddy and Hy4 for game prototypes across engine state, MCP actions, visual quality, testing, and clean hand-off.
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A practical architecture for using WorkBuddy’s specialists and project spaces on multi-step tasks without losing provenance or control.
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A practical SandBase pattern for combining X discovery with model and search APIs while preserving evidence, source boundaries, and human approval.
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Anthropic uses Claude Tag as a CI/CD first responder. Here is the architecture that separates useful diagnosis from unsafe autonomous remediation.
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How Duolingo built a shared agent platform using Temporal workflows, declarative definitions, and multi-runtime support to stop teams from rebuilding infrastructure for every agent project.
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Pre-action authorization checks every AI agent tool call before it runs. Learn how to gate reads, writes, code execution, and loops.
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MCP execution boundaries help production AI agents use tools safely. Learn what to control after tools are connected and before loops run.
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The agent runtime layer is the production infrastructure between your framework and model. Why it decides durability, isolation, and recovery.
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Real guardrails for AI agents in production: input validation, action allow-lists, sandboxing, cost ceilings, and human-in-the-loop. Patterns you can ship.
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How to build a self-correcting AI agent using the reflection pattern and persistent memory. A runnable Python loop that critiques and fixes its own output.
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Compare the three agent memory architectures in 2026 — vector recall, knowledge graphs, and episodic buffers — with real latency numbers, failure modes, and a decision guide.
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How to build cron-driven AI agents that run autonomously on a schedule. Full Python code, cost analysis, retry logic, and production monitoring patterns.
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How Hermes Agent self-improving loop works: architecture teardown, memory systems, evaluation cycles, and real-world performance data from production.
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Architecture teardown of OpenClaw: three-layer pipeline, code execution sandbox, memory system, and how it achieves top SWE-bench scores with diagrams.
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How to run one AI agent across Slack, Discord, and WhatsApp. Unified message handling, platform adapters, auth patterns, and deployment architecture.
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