chromedp VS star-history

There are a billion things that you need to consider when building a decent web crawler, especially interacting with pages in the modern web. For example, a lot of content is dynamically loaded by the browser nowadays, and won't show up if you make a simple HTTP request. Open your browser devtools and look at the network tab after you make a request, and you'll see it makes loads of auxiliary requests. Some content is also only loaded after you interact with it (e.g. hover, click). For that reason I'd recommend using something like chromedp and do browser based crawling, even if it's much slower.

You could use something like the chrome dev tools (https://github.com/chromedp/chromedp) or look into JS runtimes in Go and try to handle the I/O yourself.

Thanks. I've found Chromedp [0] to be a good Selenium replacement when programming in Go. Used in conjunction with HeadlessShell [1] you can deploy a Go app into a container and do the testing all within the same container with low overhead.

[0] https://github.com/chromedp/chromedp

Is it node specific? Could you just use https://github.com/chromedp/chromedp and run it as an actually webpage and get the value from that?

It looks like https://github.com/chromedp/chromedp is the most popular option. There's also https://github.com/playwright-community/playwright-go. Any thoughts or advice about using these tools?

The alternative is to drive, for example, Chrome with the DevTools protocol (using something like https://github.com/chromedp/chromedp or https://github.com/raff/godet (this is my version of a DevTools driver).

That’s a great project you pulled off. From the time I starred it (10-12h ago I think), and upon re-checking this post, you gained 500+ stars lol.

Visualized in a chart with star-history: https://star-history.com/#nilsherzig/LLocalSearch

You can actively see a fresh "hype curve" in the transformer-debugger repo that was posted a couple days ago (https://github.com/openai/transformer-debugger) (star history https://star-history.com/#openai/transformer-debugger&Date).

Regardless of the repo's stars or how valuable it really is, at the time I saw it posted to HN, it had 1.6k stars/16 hours. What channel are people listening to to star it so quickly. I'm not implying any nefariousness, mind you, I'm only wondering where all the stargazers were referred from so fast and in such volume.

I’ve been releasing open-source projects for years now, and I’ve always wanted to see how fast my Github repo is growing compared to other successful repositories on https://star-history.com/. The problem is that on Star History, I’m unable to zoom into the graph, so a new repo that has 1,000 stars cannot be compared with a big repo that has 50,000 because you can’t see how the bigger repo does in its beginning. So, I asked GPT Pilot to build this functionality. It scrapes Github repos for stargazers, saves them into the database, plots them on a graph, and enables the graph to be zoomed in and out.

i'm a one man shop in montana, competing w/ Google, Vercel & Facebook for dev mindshare

if i did what everyone else does you never would have heard of htmx

https://star-history.com/#bigskysoftware/htmx&Date

also: https://star-history.com/#bigskysoftware/htmx&facebook/react...

Similar to http://star-history.com for GitHub repos, http://like-history.ai is a tool to help generate the like history of projects on HuggingFace.co

More details: https://twitter.com/Tim_Qian/status/1730245069259575485

yeah, he was the one that really started the madness:

https://star-history.com/#bigskysoftware/htmx&bigskysoftware...

his video posted on july 7th

Github star history graph of the batch:

https://star-history.com/#trpc/trpc&termux/termux-app&respon...