Ideas Weave Every Narrative with AI.
Kimi’s K3 model isn’t about 2.8 trillion parameters. It’s about task site management: maintaining context, dynamically loading tools, and controlling costs over long horizons. The future of AI products won’t be decided by who has the biggest model, but by who has the best task scheduler. Developers who don’t adapt will be left moving context files forever.
While individual developers are achieving 10x productivity with AI coding agents, entire teams are failing to scale that success. The real bottleneck isn’t the AI model’s capability—it’s your organization’s outdated verification systems, slow CI pipelines, and lack of unified code review standards. Stop blaming the AI and fix your engineering culture.
After 12 days learning Claude Code, I discovered the most valuable artifact wasn’t a subagent or a hook — it was an 800-word style guide defining how the AI talks to learners. The real moat in AI products isn’t architecture. It’s emotional intelligence encoded as interaction rules that make users feel understood, not just served.
An AI that lives in your menu bar, doesn’t chat, and can do your homework, trade stocks, and send emails in the background. This isn’t science fiction—it’s a real prototype. The shift from reactive chatbots to silent, proactive agents forces us to confront a terrifying question: are we ready for AI that acts without asking?
AI Product Management is dead. Long live Agent Orchestration. As model capabilities commoditize, the real competitive advantage isn’t a better algorithm—it’s embedding so deeply into a user’s workflow that leaving becomes psychologically painful. Here’s how to survive the shift.
Vibe Coding tools like Rork are letting anyone build apps with a single sentence. But while AI has obliterated the barrier to production, it has ruthlessly exposed the real bottleneck: distribution. If you don’t have an audience or deep industry insight, your AI-generated app is just digital dust.
ERP vendors are quietly shifting from selling software subscriptions to metering AI usage via token-like units. By wrapping AI capabilities into usage-based pricing, they are turning your fixed software costs into an unpredictable, perpetual burn rate. As AI agents handle more complex tasks, every automated decision burns tokens—transforming your ERP system from a one-time investment into a permanent tax on your business operations.
Alipay’s new AI assistant A’Bao doesn’t write poetry or debate philosophy — it just handles your money, pays your bills, and books your appointments. While the tech world races to build the ultimate chatbot, Alipay proves that the real AI moat isn’t intelligence. It’s integration. The winners won’t be the smartest AI; they’ll be the most embedded.
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.
Building an AI product isn’t just about prompt engineering. After developing Read-Box, an AI reading assistant with three collaborating Agents, I discovered that 30% of development time was swallowed by invisible, non-functional costs. Product managers must understand that architectural decisions—like choosing shared storage over event buses, or abstracting the LLM layer—directly dictate a product’s iteration speed, scope, and risk.