#coding

花果山大圣

6天前

留学生手里15磅买了个升降桌这配合单车vibe coding和打红警完美

#留学生 #升降桌 #单车vibe #coding #红警

1周前

Coding 伸懒腰期间，去咖啡馆的图书区域瞄了眼，发现一本《台州有意思》，讲国清寺的这段我可以作证，确实现在5元门票都不要了。不过日语的麻糍、虾蛄的发音真的和台州话一样吗？有没有即懂日语又懂台州话的朋友来求证一下。

#coding #国清寺 #台州 #麻糍 #虾蛄

1周前

老婆不在，今晚在这 Vibe Coding ☺️

#老婆不在 #Vibe Coding #夜晚 #情感 #coding

1周前

如果你要问我，20来年，我学到的最有用的两个技能是什么我的答案是：「盲打+五笔输入法」因为这两个技能，几乎在「coding」和「writing」上言出法随。

#盲打 #五笔输入法 #coding #writing #技能

1周前

你知道为什么卖工作流（Workflow）的「源文件」未必是一桩好生意吗？因为一个源文件大概率只能卖到几十到几千块，而实际上如果你拥有 Coding 之外的商业运转知识 + 很强大的 Connection 能力，你的极简 Workflow 的价值至少也可以用万来计量。

#AI掘金：知识付费新机，流量为王时代· 117 条信息

#工作流 #源文件 #商业价值 #coding #connection

𝙩𝙮≃𝙛{𝕩}^A𝕀²·ℙarad𝕚g𝕞

2周前

vibe 从coding到builder，我决定开始我的vibe builder之旅

#vibe #coding #Builder #builder之旅

𝙩𝙮≃𝙛{𝕩}^A𝕀²·ℙarad𝕚g𝕞

2周前

那些赚钱的独立开发者正在悄悄扔掉键盘？Vibe 从Coding到Builder！

#独立开发者 #赚钱 #vibe #coding #Builder

4周前

钓鱼和Vibe Coding 都会上瘾，之前公众号周更4篇+。这周就发了一篇评测。剩余时间基本都在 Coding 和玩微物路亚钓点小白条。下周恢复公众号更新，敬请关注。

#钓鱼 #coding #公众号 #更新 #微物路亚

2个月前

这篇访谈非常不错，拾象科技团队对Agent认知很深刻，尤其是“我觉得 Coding 有可能拿走整个大模型产业阶段性 90% 的价值。这个价值怎么长出来？今天的第一幕还是服务全球 3000 万程序员。我举个例子，Photoshop 服务的是全球两三千万专业设计师，门槛很高。但是当剪映、Canva、美图秀秀出来后，可能有 5

#拾象科技 #Agent认知 #coding #大模型产业 #PhotoShop #剪映 #Canva #美图秀秀

2个月前

这就是我接下来想要探索的东西，那就是 —— 在 AI Coding 时代真正的源代码是什么？正如我原推所说，在 AI Coding 时代，传统意义上的代码已经不能称之为源代码了，因为它们是 LLM 廉价且批量生成的。因此，这些海量的所谓的源代码对人类来说是不可读和难以理解和维护的，在 AI Coding 时代它们最多只能称之为「中间代码」。那么这个时代真正的源代码是什么呢？它必须是人类可以理解和传递，同时也易于 LLM 理解的东西。我相信这肯定与记忆有关（但又不止于此），这让我充满好奇和探索欲。让我们拭目以待吧！

#AI #coding #AI时代 #源代码 #人类

6个月前

很多时候历史的发展是很有意思的，DeepSeek成了图腾级事件后，国内各级政府的超算预算蹭蹭的往上涨，甚至大有把服务器变成和石油、粮食一样的战略储备的趋势，而汉语信息熵是英语2-3倍，隐约感觉在LLM、Coding等领域，汉语本身也会有点不小的动静和不一样的发展，对了，今年是他的本命年，哈哈哈

#DeepSeek #超算预算 #战略储备 #汉语信息熵 #LLM #coding #本命年

7个月前

Using AI-assisted coding to build software prototypes is an important way to quickly explore many ideas and invent new things. In this and future posts, I’d like to share with you some best practices for prototyping simple web apps. This post will focus on one idea: being opinionated about the software stack. The software stack I personally use changes every few weeks. There are many good alternatives to these choices, and if you pick a preferred software stack and become familiar with its components, you’ll be able to develop more quickly. But as an illustration, here’s my current default: - Python with FastAPI for building web-hosted APIs: I develop primarily in Python, so that’s a natural choice for me. If you’re a JavaScript/TypeScript developer, you’ll likely make a different choice. I’ve found FastAPI really easy to use and scalable for deploying web services (APIs) hosted in Python. - Uvicorn to run the backend application server (to execute code and serve web pages) for local testing on my laptop. - If deploying on the cloud, then either Heroku for small apps or AWS Elastic Beanstalk for larger ones (disclosure: I serve on Amazon’s board of directors): There are many services for deploying jobs, including HuggingFace Spaces, Railway, Google’s Firebase, Vercel, and others. Many of these work fine, and becoming familiar with just 1 or 2 will simplify your development process. - MongoDB for NoSQL database: While traditional SQL databases are amazing feats of engineering that result in highly efficient and reliable data storage, the need to define the database structure (or schema) slows down prototyping. If you really need speed and ease of implementation, then dumping most of your data into a NoSQL (unstructured or semi-structured) database such as MongoDB lets you write code quickly and sort out later exactly what you want to do with the data. This is sometimes called schema-on-write, as opposed to schema-on-read. Mind you, if an application goes to scaled production, there are many use cases where a more structured SQL database is significantly more reliable and scalable. - OpenAI’s o1 and Anthropic’s Claude 3.5 Sonnet for coding assistance, often by prompting directly (when operating at the conceptual/design level). Also occasionally Cursor (when operating at the code level). I hope never to have to code again without AI assistance! Claude 3.5 Sonnet is widely regarded as one of the best coding models. And o1 is incredible at planning and building more complex software modules, but you do have to learn to prompt it differently. On top of all this, of course, I use many AI tools to manage agentic workflows, data ingestion, retrieval augmented generation, and so on. and our wonderful partners offer courses on many of these tools. My personal software stack continues to evolve regularly. Components enter or fall out of my default stack every few weeks as I learn new ways to do things. So please don’t feel obliged to use the components I do, but perhaps some of them can be a helpful starting point if you are still deciding what to use. Interestingly, I have found most LLMs not very good at recommending a software stack. I suspect their training sets include too much “hype” on specific choices, so I don’t fully trust them to tell me what to use. And if you can be opinionated and give your LLM directions on the software stack you want it to build on, I think you’ll get better results. A lot of the software stack is still maturing, and I think many of these components will continue to improve. With my stack, I regularly build prototypes in hours that, without AI assistance, would have taken me days or longer. I hope you, too, will have fun building many prototypes! [Original text: ]

#AI #coding #software prototypes #innovation #best practices

8个月前

Honest reflections from coding with AI so far as a non-engineer: It can get you 70% of the way there, but that last 30% is frustrating. It keeps taking one step forward and two steps backward with new bugs, issues, etc. If I knew how the code worked I could probably fix it myself. But since I don't, I question if I'm actually learning that much.

#AI #coding #non-engineer #Technology #reflection