hoomjac

物外之趣

[hoomjac @linhoomjac]

@bluebird0605 请问为什么Pro访问不到完整的AI Insider文章，有些是 Premium 专属的。按照图片中的信息，Pro 和 Premium 都有完整的 AI Insider权益才对？

[hoomjac @linhoomjac]

@shengxj1 哈哈，真羡慕

[Bindu Reddy @bindureddy]

Chat With PDF Is Hard and ChatGPT Sucks At It - Here's Why

The most common GPT-4 wrapper is a "chat with a doc/pdf" app. It is one of the killer applications of AI chatbots, as reading a dense document can be tiresome -- it's much simpler to ask the LLM to parse and summarize it for you.

Unfortunately, ChatGPT doesn't do a good job, especially regarding PDFS that are> 10 pages. It produces sparse generic summaries and flat-out refuses to elaborate further.

One of the reasons this is a non-trivial use case is as follows.

OCR - you need a really good OCR that can parse tables and images well. There is no free or commercial OCR tech that does this well. A lot of business and research PDFs have a lot of tables and images.

Context - While we currently have 128K context-length LLMs, it's unclear what is deployed as part of ChatGPT. ChatGPT often throws an error if you run OCR on the paper and then feed it the paper text. I suspect a much smaller context length model serves ChatGPT requests.

Quick RAG - Implementing a naive RAG that chunks the doc, embeds it, retrieves results, and then passes it to the LLM will likely do the trick, but current ChatBots don't have that feature.

Highlighting Doc Sections - The ideal solution should ideally showcase parts of the document where the response is retrieved from. This makes verification super simple.

Ideally, chat with PDF should have all these features. It seems like a standalone app in the app store can still make decent revenue if it does a good job of all these features. That being said, I don't think this is a venture-backed start-up. More like a 1-2 person mom-pop thing that can be a good lifestyle business.

TLDR: Doing something as simple as "Chat with PDF" is non-trivial and hard to do well

[Haihao Shen @HaihaoShen]

🧩No GPU but wanna create your own LLM on laptop?
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🎯Code: https://t.co/oWYnKP7mG1
#IAmIntel #intelai @intel @huggingface

[Viking @vikingmute]

最近看到最棒的一篇技术文章：
Draggable objects

https://t.co/2bKbEGxkDJ

在前端开发中，拖动（Drag）一个图层是非常常见的需求，我相信大家大部分都能写个皮毛，但是这篇文章循序渐进，从最简单的拖动，到兼容手机和 PC，再到各种 edge cases 的修复，最后再到抽象成一个 vue 的组件，我从中学习到非常多。

每一步都配有 demo，讲解的非常详细，是一篇非常好的交互式文章，如果想把拖动完全弄懂，这篇文章就足够了。

[宝玉 @dotey]

Mr. Ranedeer是我最近看到过的最强大的ChatGPT的Prompt！也适用于GPT的API，但都需要GPT-4。它可以让GPT成为你的导师，指导你学习！

它的用法很简单，在它的项目首页🔗https://t.co/WiuvNFxcZ3🔗有详细说明。

拷贝它的JSON文件内容或者Markdown文件内容，推荐Markdown文件，体积最小。

它有若干选项：
- Depth：学习的深度或难度，1代表小学生水平，10代表博士水平
- Learning Styles：学习风格，有： 感知型，视觉型*（需要插件），归纳型，积极型，顺序型，直觉型，语言型，演绎型，反思型，全局型
- Communication：沟通方式，有：随机的、正式的、教科书的、外行的、讲故事的、苏格拉底的、幽默的
- Tone Styles：语气风格，辩论、鼓励、中立、告知、友好
- Reasoning Frameworks：推理框架，演绎法、归纳法、归纳法、类比法、随意法
- Language：语言，默认英语，你可以改成GPT-4支持的任意语言。

你可以略作修改或者直接复制粘贴到ChatGPT，然后你就可以用命令跟GPT对话让它指导你学习了。

它有以下命令：

/test ：对你当前学习的结果进行测试
/config : 更新AI导师的配置选项
/plan ：根据你的喜好，创建一个学习计划，比如你要学英语
/search : 搜索特定信息，需要插件支持
/start : 开始学习
/continue : 如果输出中断或没有结束，可以通过这个命令继续让GPT输出
/self-eval : 让AI导师评估自己的课程
/language : 改变AI导师输出的语言，比如简体中文
/visualize : 使用插件（例如Wolfram）来实现内容的可视化

比如一个参考指令如下：

给我一个中国学术高中生的随机配置
/language 简体中文
/plan 我想学习高级英语语法

测试下来非常强大，唯一的问题是初始的Tokens占用太多，会话消息一多就要重新开始了。

项目地址：https://t.co/WiuvNFxcZ3

[hoomjac @linhoomjac]

@tufook WOW，有意思，Superforecasting 这本书里有提到类似的预测实验。不妨参考一下👉 https://t.co/Osrui7eUsv

[hoomjac @linhoomjac]

有些库的用法，看官方文档集成 30 分钟，从开源库里 copy 别人的用法，30秒。优秀的使用范例胜过千言万语😂

[hoomjac @linhoomjac]

查看文章：https://t.co/PaWJ2ZCcq9

[hoomjac @linhoomjac]

我们部门从去年开始全面使用 Vue3 开发，响应式导致的 bug 一直困扰着日常编码。最常见的：「解构导致的响应式失效」，即便搞懂了其中的门门道道，这个问题也总会以意料之外的形式出现。比如：

[hoomjac @linhoomjac]

每当这个时刻，我就很想念 React 的 Immutable Data 的简洁。虽然可能多渲染几次，但数据就是数据，no wrap，no shit。

[hoomjac @linhoomjac]

@ghosTM55 @op7418 @kevinzhow @haoel RIP

[hoomjac @linhoomjac]

@kettanaito great post! I can feel the some struggles while using Nextjs recently, particularly the magical Router naming conventions. It really make the codebase confusing, and I often forgetting APIs over time. at times, I do miss the simplicity,not so powerful maybe,of the good old days..

[Sully @SullyOmarr]

Vector databases & embeddings are the current hot thing in AI.

Pinecone, a vector DB company, just raised $100M at ~1b valuation.

Shopify, Brex, Hubspot and others use them for their AI apps

But what are they, how do they work and why are they SO crucial in AI? Let's find out