Show HN: 我搭建了一个每年只需 3 美元的 AI 工作流程，用来阻止我在 Twitter 上刷科技新闻
1 分•作者: JustinLee-DEV•4 个月前
我一直以来都有一个简单但令人烦恼的问题：我想了解最新的开源项目和技术趋势，但我真的不想再依赖刷推特来获取这些信息了。
现在，推特上的信息质量越来越差。为了找到几个真正有价值的项目，我大部分时间都浪费在无意义的噪音、煽动性内容和广告上。
我尝试过各种聚合器和订阅源，但最终还是需要手动点击和筛选信息。核心问题并没有得到解决。
所以我构建了一个名为 TechDistill 的小型自动化工作流程。目标很简单：
完全停止浏览信息源，每天只获取一个经过整理的结果。
现在，这个流程每天生成四个 Markdown 文件：
一个总览，用于快速了解今天值得关注的内容。
三个特定来源的报告（GitHub Trending、Hugging Face、Product Hunt），包含更详细的信息。
我的日常流程是这样的：我首先浏览总览，快速判断是否有任何值得深入研究的内容。如果有，我会查看特定来源的报告以获取详细信息。每个项目都附带一个简短的介绍，告诉我它“到底做什么”，所以大多数时候我甚至不需要点击 README 就能理解它。
整个过程是完全自动化的：直接从来源抓取数据 → 清理数据 → 让模型提炼信息。我基本上不再访问原始网站了。
它每天在 GitHub Actions 上运行一次，耗时不到 4 分钟，每次运行的成本约为 0.008 美元（OpenRouter + DeepSeek v3.2）。
我已经使用了几天，最直接的变化是：我实际上已经停止了下意识地刷推特来获取科技新闻。
对我来说，这个项目的重点不是“AI 摘要”。它在于将分散的、高噪音的信息转化为一个稳定、低噪音的输入流，我可以随着时间的推移积累这些信息。
目前，它主要是一个个人工具，但我很好奇：还有其他人尝试用类似的方式解决这个问题吗，或者您有更好的方法？
GitHub: <a href="https://github.com/JunstinLee/TechDistill" rel="nofollow">https://github.com/JunstinLee/TechDistill</a>
查看原文
I’ve always had a simple but annoying problem: I want to stay updated on the latest open-source projects and tech trends, but I really don’t want to rely on scrolling Twitter to get them anymore.
The signal-to-noise ratio is getting worse. To find a few genuinely valuable projects, most of my time is wasted on meaningless noise, outrage bait, and ads.
I tried various aggregators and subscription feeds, but I still ended up having to click through and filter things manually. The core issue wasn't solved.
So I built a small automated workflow called TechDistill. The goal is simple:
Stop browsing the feeds entirely, and just get a curated result every day.
Now, this pipeline generates four Markdown files daily:
One overall Overview, to quickly catch up on what's worth paying attention to today.
Three Source-specific reports (GitHub Trending, Hugging Face, Product Hunt) with more complete details.
My daily routine looks like this: I scan the overview first to quickly judge if there's anything worth diving into. If there is, I check the specific source report for the details. Every project comes with a short blurb telling me exactly "what it actually does," so most of the time I don't even need to click into the README to understand it.
The whole process is fully automated: scrape data directly from the sources → clean it up → let the model distill it. I basically don't visit the original sites anymore.
It runs on GitHub Actions once a day, takes under 4 minutes, and costs ~$0.008 per run (OpenRouter + DeepSeek v3.2).
I’ve been using it for a few days, and the most immediate change is this: I've actually stopped reflexively checking Twitter for tech news.
For me, the point of this project isn’t "AI summarization." It’s about turning scattered, high-noise information into a stable, low-noise input stream that I can actually accumulate over time.
Right now, it's mostly a personal tool, but I’m curious: is anyone else trying to solve this in a similar way, or do you have a better approach?
GitHub: <a href="https://github.com/JunstinLee/TechDistill" rel="nofollow">https://github.com/JunstinLee/TechDistill</a>