PromptCube

If you are looking for a headless CMS setup and see Cloudinary being tossed around in every Sanity or Contentful tutorial, pay close attention to how they actually charge you. It looks straightforward on the surface, but the way they normalize resources i

Building an AI agent or a simple chatbot is easy, but trying to scale that into a production-ready enterprise workflow is where everything falls apart. Most teams hit a wall when they realize that a prompt isn't a strategy. You need a way to manage data,

If your AI assistant forgets your entire project architecture the moment you hit "New Chat," you don't actually have a model problem-you have a memory infrastructure problem. In the current landscape of LLM agents and Claude Code, relying on the context w

很多公司在尝试引入大模型的时候最头疼的不是模型本身不够聪明而是怎么把这些模型真正塞进现有的业务流里而不至于变成一个只会写诗的"玩具"我最近在研究企业级 AI 落地路径时关注到了 BROCS 这个框架它试图解决的就是这种从"实验室 Demo"到"生产环境实战"的断层问题 说白了BROCS 不是在教你如何调优一个模型参数而是在构建一套企业级的 AI 能力支撑体系它把复杂的落地过程拆解成了几个关键维度我总结了一下它的核心逻辑： 企业级 AI 赋能的核心维度 - 治理与合规 (Governance)： 这是企业最

I've been looking into how high-concurrency browser games handle state synchronization without melting the server, and it's a massive departure from the "send everything every time" approach. If you're building a strategy game where a session might stay o

If you have ever tried to run three different LLM agents on a single local machine, you know the pain. You hit a wall where your CPU starts screaming, your RAM is tapped out, and-worst of all-your agents start stepping on each other's toes, overwriting fi

I spent most of last Thursday afternoon oscillating between a raw LangChain script and a Dify workflow, trying to build a simple RAG (Retrieval-Augmented Generation) agent that could query my local documentation. One approach felt like building a car from

Cursor vs Claude Code vs Windsurf: Which AI coding agent actually ships code? I spent four hours last Wednesday trying to refactor a legacy React component using standard Copilot autocomplete, and I felt like I was fighting a ghost. The suggestions were t

最近在翻看关于 Prompt Injection提示词注入检测的论文有一篇关于「潜在诊断分类法」Latent Diagnostic Taxonomy的研究挺有意思大家平时做安全防护习惯性地会训练一个分类器作为第一道防线只要分类器说"这是注入攻击"我们就拦截如果它说"安全"我们就放行但问题是分类器自己也会"一本正经地胡说八道" 这篇论文揭示了一个很扎心的事实：在针对注入攻击的实验中即便分类器表现出极高的置信度其中竟然有大约 77% 的决策在去掉仅仅一个 token 之后就会发生反转这意味着你以为稳如泰山的防

The timeline for a massive-scale AI-driven cyberattack is shrinking much faster than most security teams are preparing for. We aren't talking about a theoretical "what if" scenario anymore; the major players in the LLM space are sounding alarms that the w