Ideas Weave Every Narrative with AI.
AI agent skills aren’t just markdown files. They’re a dynamic discovery and context injection protocol, like a PATH variable for AI. The format is trivial; the mechanism is a genuine architectural shift that will define how agents retrieve and apply knowledge autonomously.
OpenAI is quietly merging ChatGPT and Codex into a cloud-based AI workstation. Your browser now runs Skills, executes code, and manages tasks in a remote sandbox. The era of the chatbot is over. The era of the cloud AI computer has begun.
When AI makes implementation cheap, the product manager’s job shifts from managing resources to making high-quality judgments: what to build, when, and to what degree. Forget learning to code—become a judgment architect. This article breaks down 7 strategies from OpenAI’s Codex lead on how to thrive when building is abundant and taste is the only moat.
The age of prompting is over. The smartest engineers are no longer typing commands into AI agents. They’re designing autonomous loops that prompt the agents for them. This is Loop Engineering, a shift from human operator to system architect. The future belongs not to those who can write the best prompts, but to those who can build the systems that make prompts obsolete.
An open-source desktop alternative to OpenAI Codex challenges the assumption that proprietary AI is necessary for high-quality coding assistance. It feels like Codex, runs on Linux, and signals that the real barrier to adoption is psychological, not technical. Developers should stop paying—and start cloning.
OpenAI silently removed thinking summaries from the Codex endpoint, turning the model into a black box. This isn’t a bug — it’s a strategic move to protect proprietary reasoning at the cost of developer trust. If you build on AI, you just lost your only window into the machine’s logic.
Most developers think the bottleneck for AI coding agents is model quality. It’s not. The real barrier is trust: we’re afraid to let them run unsupervised because they can wreck our systems. Code Airlock solves this by running Claude Code and Codex inside disposable microVMs—giving AI freedom without the fallout. This is the missing piece for enterprise adoption.
Codex’s token consumption is riddled with hidden drains — auto-review, failed task retries, merged quotas, and memory preview. This article reveals the five systemic inefficiencies that cost researchers thousands of dollars, and how to reclaim control.
Two small UX tweaks—one hidden feature, one shrunken popup—reveal a critical truth about AI products: users don’t re-discover features on their own. Most teams treat onboarding as a one-time event, but in fast-changing products, every update needs a re-discovery mechanism. Otherwise, your best features quietly rot.