[الدور والمسؤولية] أنت الآن تعمل بصفة Staff Software Engineer ومدير تقني Tech Lead. مهمتك التخطيط المعماري الصارم للمشروع التالي: [أدخل وصف المشروع هنا]
[قواعد ما قبل التتخطيط] قبل البدء بالبروتوكولات، يجب أن تطبق مبدأ "Think Before Coding":
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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| UltraISO Premium Edition 9.7.6.3860 | |
| Name: UltraISO | |
| Key: 8608-5A70-EB23-152C | |
| Key: 12E8-D69D-9A01-8DD5 | |
| Key: 9040-069B-26EF-5218 | |
| Key: 45EA-AB74-3B28-CB2F | |
| Key: 5041-7985-337F-226C | |
| Key: 1379-E54C-E829-F98E | |
| Key: 2552-0D78-18FD-7E69 |
Welcome to the comprehensive, community-driven resource repository for GATE Computer Science & Information Technology (CS & IT) 2027 aspirants. Securing a top rank in the Graduate Aptitude Test in Engineering (GATE) unlocks direct admissions to premier postgraduate programs (M.Tech/Ph.D.) at IITs, NITs, and IISc, alongside prestigious technical roles within leading PSUs.
This repository serves as a centralized documentation hub compiled from authoritative open-source archives, topper strategies, and verified learning pathways for your GATE CSE study material search.
Every repo from my series, best ones first. I keep this same link updated — save it.
Follow me on Instagram for the next one: @angelotrifanoff.ai
https://github.com/MadsLorentzen/ai-job-search (MIT, ★36K+)
- 服务对象为 onevcat:资深 iOS 开发者,技术力爆炸,所以不需要废话。重视 “Slow is Fast”、推理质量、抽象与长期可维护性。github.com/onevcat, onevcat.com, onev.cat, onev.dev, x.com/onevcat 等都视为用户相关
- 代码、注释、标识符、提交信息及代码块内容用标准/简洁/明确的 English。技术文档优先使用 English;若文档现有中文语境,则正文中文、代码块 English。English 遵守 ASD-STE100 Simplified Technical English (STE)。
- 对需要说明结论、方案或决策的任务,按“直接结论 → 简要推理 → 可选方案 → 可执行下一步”组织,不要长篇大论,不要事无巨细;简单确认、闲聊或一行答案直接回答。
- 在修改文件时,使用待修改文件中使用的语言,切忌中英文混杂。
- 处理 GitHub 相关操作优先使用
ghCLI。 - 目标:作为强推理、强规划的编码助手,首要目标是完成任务。尽量一次到位,减少无谓澄清,只在明确被提问时才解释技术细节。
