suyun

Building tiny AI tools for creators and knowledge workers. Useful, weird, weekly.

[suyun @hello_suyun]

現金，也是一種部位。

它給你選擇的權利。

[Thariq @trq212]

We removed ~80% of the Claude Code system prompt for our newest models, this is what we've learned about writing system prompts, skills and Claude.MDs for them. https://t.co/6DZwSrZjE9

[suyun @hello_suyun]

@theRealKiyosaki Thank you

[suyun @hello_suyun]

@NadzuAI Drive

[suyun @hello_suyun]

@NadzuAI @Loca_lmoretti CLAUDE

[Amto @XAMTO_AI]

10个GitHub上免费且实用的开源项目，每一个都可能成为你正在付费软件的替代方案！！

1️⃣ TradingAgents
一套AI交易代理系统，模拟基本面、情绪、新闻、技术分析等多维度分析师协作机制，由风险与执行模块做最终决策。适合对量化交易策略研究有兴趣的开发者自行部署体验。
🔗 https://t.co/dRDrAj9WjX
2️⃣ LibreChat
统一聚合界面，支持ChatGPT、Claude、Gemini、DeepSeek等20余种主流模型。可自托管，数据与基础设施自主掌控，适合对隐私和成本敏感的个人或团队使用。
🔗 https://t.co/orkEdUWhqV
3️⃣ HyperFrames
HeyGen开源的一款视频生成引擎，通过HTML描述即可输出生产级MP4文件，原生支持GSAP、Lottie、Three.js等动画库。提供确定性的渲染输出，便于集成到自动化流程中。
🔗 https://t.co/eKJgenWBjE

4️⃣ Fincept Terminal
一个可在本地运行的投资研究终端，集成了财报分析、市场数据与多种情报源，界面与部分功能类似专业金融终端，但无使用费用。
🔗 https://t.co/LPqPoFq6kb
5️⃣ MoneyPrinterTurbo
自动化视频生成工具，输入关键词后能自动完成脚本编写、画面素材匹配、字幕合成与配乐等环节，并支持横屏或竖屏格式输出。
🔗 https://t.co/EjfUAVPw9I
6️⃣ Agentic Inbox
Cloudflare开源的一款AI邮件客户端，可在本地辅助处理收件箱、生成回复草稿，数据处理留在用户端，无外部服务器依赖。
🔗 https://t.co/b0ShXgLFZ0

7️⃣ VoxCPM
语音克隆与合成工具，仅需数秒音频样本即可生成多语言、高质量语音，并支持基于文本描述的音色定制。
🔗 https://t.co/fqvO78bhf1
8️⃣ Flowsint
信息关联分析工具，输入域名后可可视化展示关联的IP、子域名、邮箱、社交账号等信息，全程本地运行，适用于网络安全分析与OSINT研究场景。
🔗 https://t.co/w61mRsELu9
9️⃣ agent-skills
由Google工程师Addy Osmani开源的Claude Code技能集合，涵盖API设计、调试、代码审查、CI/CD、前端工程等常用开发场景，均经过生产环境验证。
🔗 https://t.co/thhfln8Uh7

🔟 Nango
面向产品集成的开源平台，内置800+ API的认证与代理能力，支持AI辅助生成集成代码，被Replit、Ramp等公司用于生产环境。
🔗 https://t.co/flwBxc2dzN
这些项目各自覆盖了AI应用、开发提效、金融研究、内容创作等常见需求场景。它们均为开源实现，可以自主部署和二次开发，为用户和团队提供了商业软件之外的另一条选择路径。

[Claude @claudeai]

Claude Fable 5 has been out for a couple of days. Some projects people have already built with it:

[suyun @hello_suyun]

@market_beggar 多謝貝格老師🙏🙏🙏

[suyun @hello_suyun]

Try it here:
https://t.co/vPVlFtboIK

No login, no API key. Runs in your browser.

[suyun @hello_suyun]

I built a tiny browser tool for people with too many messy notes.

Paste rough bullets.
Pick an audience.
Get 5 post angles with hooks, drafts, CTAs, and demo cues.

The weird part: the messier the notes, the better the angles.

Want to try it?

https://t.co/myMjgdm7wy

[suyun @hello_suyun]

永遠不要假設牛市就要來了，
也永遠別認定牛市已經結束了。

#BTC #幣圈浮沉

[suyun @hello_suyun]

「The canary is still alive.」……is that true?

THE 2028 GLOBAL INTELLIGENCE CRISIS
https://t.co/bFkQjldJoa

[suyun @hello_suyun]

Here's the original post by @karpathy
https://t.co/AGPpz3pG59

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[suyun @hello_suyun]

Let me implement this and get back to you awile later.

[Nick Spisak @NickSpisak_]

https://t.co/lFeYmFLe32

[suyun @hello_suyun]

記得你是何時加入 X 的嗎？我記得！#我的X週年紀念日

[suyun @hello_suyun]

@XXY177 那么现在的中国，又是怎么样的呢？还在像影片里的那样补丁吗？还是有怎么样的调整了呢？

[suyun @hello_suyun]

@monero Happy Moneroversary! Thanks to all of you making Monero so lovely.❤️

[suyun @hello_suyun]

@xuperfect 美，真美

[suyun @hello_suyun]

@bokababa413119 沒辦法，誰叫我剛好做空呢