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Source channel @githubtrending · Post #14702 · May 14
#other 独立开发变现周刊每周分享独立开发者如何通过小产品实现收入的真实案例，涵盖AI工具、SaaS、插件等多种类型，展示从几千到百万美元的月收入故事。它不仅提供成功经验和技术细节，还开设变现训练营，帮助你学习产品开发、市场定位和盈利策略。此外，周刊构建了活跃社区，方便开发者交流合作，分享资源和反馈。通过这些内容和支持，你能获得实用指导和灵感，更有效地打造和变现自己的产品，实现持续盈利。 https://github.com/ljinkai/weekly
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@R4marketing · Post #806 · 08/13/2021, 08:00 AM
Совсем лайтовая статья для новичков "10 главных конструкций языка R". Содержание: - Комментарии - Переменные и векторы - Внешние модули - Ввод и вывод - Присваивание и сравнение - Условный оператор if - Цикл for - Функции - Классы, методы и объекты #статьи #easy
@testflightynoti · Post #37780 · 05/07/2026, 08:30 PM
#Futubull#Easy#Investment Join the Futubull-Easy Investment beta on ✈️#TestFlight 🔗 Link: https://testflight.apple.com/join/LXJdcvBn Shared by Dimitri
@WangZhuanZhan · Post #34299 · 10/23/2024, 12:23 PM
X-x喜x乐l长c安a- 喜乐长安 (2016) 直达链接：https://pan.quark.cn/s/4723d3f85101 #喜乐长安 #长安乱 #Easy Life 链接：https://link3.cc/sf_com #电影#喜剧#台湾#10年代
@wangzhuanzhan · Post #33280 · 09/23/2024, 06:10 AM
F-f绯f闻w计j划h- 绯闻计划 Easy A (2010) 直达链接：https://pan.quark.cn/s/1b65684910c6 #绯闻计划#Easy A #破处女王#现代红字 链接：https://link3.cc/sf_com #电影#喜剧#美国#10年代
@WangZhuanZhan · Post #34066 · 10/15/2024, 05:50 AM
T-t通t天t大d盗d- 通天大盗 通天大盜 (1987) 直达链接：https://pan.quark.cn/s/c40ba0abe557 #通天大盗#通天大盜 #妙婿向钱冲#Easy Money 链接：https://link3.cc/sf_com #电影#喜剧#香港#80年代
@wangzhuanzhan · Post #32222 · 09/03/2024, 08:49 AM
S-s水s性x杨y花h- 水性杨花 Easy Virtue (1928) 直达链接：https://pan.quark.cn/s/49ce72903d4a #水性杨花#Easy Virtue #放浪#轻浮的德行 链接：https://link3.cc/sf_com #电影#喜剧#美国#50年代之前
@testflightynoti · Post #37748 · 05/07/2026, 08:21 AM
#Easy#Credit#T#i#ch#nh#s Join the Easy Credit - Tài chính số beta on ✈️#TestFlight 🔗 Link: https://testflight.apple.com/join/B8XYxOWV Shared by Dimitri
@githubtrending · Post #15433 · 01/23/2026, 02:30 PM
#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles. https://github.com/OpenBMB/UltraRAG