ericchiang

7 ms·

1.
Hacking SAML with Claude Code(oblique.security)
3 points by ericchiang 28d ago|0 comments
2.
by ericchiang 3mo ago
Wait this is awesome. A huge issue with Enterprise OAuth2.0 is managing all the random apps. Each with their own half-baked enterprise controls for managing scopes, token expiry, and no control over device bound sessions. So instead, you ca
3.
The performance bug hiding in our Cloud Run billing settings(oblique.security)
3 points by ericchiang 4mo ago|0 comments
4.
Deterministic Time with Go's Synctest(oblique.security)
1 points by ericchiang 7mo ago|0 comments
5.
Protobuf Generators for Fun and Profit(ericchiang.github.io)
2 points by ericchiang 2y ago|0 comments
6.
Confidential Compute and GPUs(ericchiang.github.io)
1 points by ericchiang 2y ago|0 comments
7.
Writing Shared Libraries in Rust(ericchiang.github.io)
1 points by ericchiang 3y ago|0 comments
8.
Calling C from Go(ericchiang.github.io)
1 points by ericchiang 3y ago|0 comments
9.
Analyzing Spotify Stream History(ericchiang.github.io)
123 points by ericchiang 3y ago|21 comments
10.
Golang Let's Encrypt client(github.com)
2 points by ericchiang 11y ago|0 comments
11.
Go Testing With Docker(ericchiang.github.io)
2 points by ericchiang 11y ago|0 comments
12.
Pigeon Racing, LSD Math and 5 More Datasets for Your Next Analytics Project(blog.yhathq.com)
1 points by ericchiang 11y ago|0 comments
13.
Intro to TLS with Go(ericchiang.github.io)
6 points by ericchiang 11y ago|0 comments
14.
by ericchiang 11y ago
This is a very small amount of boilerplate around the golang.com/x/net/html package. If you need the huge feature set of goquery, use that. But I find this pretty suitable for my day to day problems.
15.
by ericchiang 11y ago
rows := scrape.FindAll(table, scrape.ByTag(atom.Tr)) cols := []*html.Node{} for _, row := range rows { // Find returns the first result col, ok := scrape.Find(row, scrape.ByTag(atom.Td)) if ok {
16.
Scrape: A simple, higher level interface for Go web scraping(github.com)
79 points by ericchiang 11y ago|15 comments
17.
Db.py 0.4: Handlebars Meets SQL(blog.yhathq.com)
2 points by ericchiang 12y ago|0 comments
18.
ML Pitfalls: Measuring Performance(blog.yhathq.com)
10 points by ericchiang 12y ago|0 comments
19.
Yhat (YC S15) Gives Data Science Teams a Head Start(blog.ycombinator.com)
4 points by ericchiang 12y ago|0 comments
20.
Show HN: Ws – A simple WebSocket cli tool(github.com)
5 points by ericchiang 12y ago|0 comments
21.
Putting Data Science Models into Production(speakerdeck.com)
3 points by ericchiang 12y ago|0 comments
22.
Yhat – Currency Portfolio Optimization(blog.yhathq.com)
3 points by ericchiang 12y ago|0 comments
23.
Show HN: Pup – A command-line HTML parser(github.com)
126 points by ericchiang 12y ago|27 comments
24.
by ericchiang 12y ago
> You can wip up REST service very easily that wraps sk-learn predictor and I would bet it's actually much easier to do than writing PMML exporters. So as it turns out I spend my days building the very product you're describing
25.
by ericchiang 12y ago
Probably the best explanation of the intuition behind Benford's law. Worth a watch if you've got the time: https://www.youtube.com/watch?v=XXjlR2OK1kM
26.
by ericchiang 12y ago
Hi there. That ml problem is more for example than for rigor. In fact that particular problem would probably be better suited for other algorithms (eg, random forest). My background's in biomedical imaging, so I'm quite fond of pr