aws-gocljs VS hnrss

i make two kinds of websites:

- static. markdown rendered to html using github’s api[1].

- dynamic. a go binary and an html file with inlined js zipped together and shipped somewhere[2].

it’s nice to never consider the machinery of either of these anymore. instead i think about building interesting things.

1.

https://github.com/nathants/render

https://nathants.com/

2.

https://github.com/nathants/aws-gocljs

https://gocljs.nathants.com/

have you tried cljs and reagent? it’s a different vibe.

my bootstrap: https://github.com/nathants/aws-gocljs

the project: https://reagent-project.github.io/

https://gocljs.nathants.com is 300kb gzipped on deploy in a single html file. setup is here: https://github.com/nathants/aws-gocljs.

aws, go, and clojurescript.

go is notoriously boring.

the reagent api for clojurescript hasn’t changed in a decade, though recent things like shadow-cljs do improve qol.

aws releases services with 2 in their name instead of changing existing ones. the old boring service will plod along forever.

aws apigateway v2 is much better, but i have many deployed projects i will never migrate because they are fine on v1.

i do it like this: https://github.com/nathants/aws-gocljs

lambdas updatecode api takes less than a second. using container instead of a zip for lambda has advantages, but speed is not one of them.

i auto rebuild my go zip and patch aws on every code change. it’s done before i alt tab and curl.

script: https://github.com/nathants/aws-gocljs/blob/master/bin/dev.s...

lambda + s3. add ec2 spot if you need it.

just make sure you understand how billing works. mostly it’s just egress bandwidth is expensive.

do something like this:

https://github.com/nathants/aws-gocljs

or with less opinions:

https://github.com/nathants/libaws/tree/master/examples/simp...

welcome to cloud, glhf!

using remote resources as a part of your local dev flow can be very useful if your local environment is constrained on:

- upload and/or download bandwidth

- cpu/ram/gpu/ssd

this can be as simple as an ephemeral ec2 spot machine that reacts every time files on it’s filesystem change. it then does stuff, like building and shipping.

your local setup then needs to rsync files from local to remote every time you save a file.

i’m on an upload constrained setup right now, and this[1] significantly speeds up my iterations uploading lambda zips.

fancier setups probably are similarly advantageous, but add tradeoffs proportional to their complexity.

1. https://github.com/nathants/aws-gocljs/blob/258ea5bb72d06a50...

go, clojurescript, and aws. all three of these have problems, but like linux are the least bad of the available options. from some angles they are even quite good.

- go, a natural fit for backend with types and compilers and speed

- clojurescript (and react via reagent), a natural fit for frontend with dynamism, flexibility, and data centrism

- aws, a natural fit for infra. like linux, literally everyone is using it. if you avoid architect advice and tape over most of the knobs it’s quite good

example:

https://github.com/nathants/aws-gocljs

on aws as scale to zero services. lambda, dynamo, s3, and ephemeral ec2 spot.

when egress bandwidth is needed i use cloudflare workers + r2 just like i would use s3 presigned urls.

typically i start from a full project template[1][2]. sometimes i start from scratch[3].

1. https://github.com/nathants/aws-gocljs

2. https://github.com/nathants/aws-exec

3. https://github.com/nathants/libaws

That’s interesting.

I have predictive models that can predict if a headline (w/o the rest of the article and not considering the URL) will (a) get more than 10 votes and (b) if it does get more than 10 votes will the votes/comments ratio be more than 2 (which is roughly average)

The first model gets a ROC-AUC (see https://scikit-learn.org/stable/modules/generated/sklearn.me...) in the low 60’s (not good, the second model gets in the low 70’s (actually pretty good though it is a heat seeking missile for clickbait headlines) and my latest content-based recommender for RSS items gets almost 80. (I saw a paper that one system at TikTok gets about 85)

To do all that you need about 10,000 headlines and don’t get a lot of benefit from having more than 100,000. The ceilings on performance have more to do with the nature of the problem rather than my models: the same article can get submitted twice and get 0 votes one time and 200 the other time so it can never be as accurate as “is this an article about galactic astronomy?”

I had it ingest the HN comments firehose and found the amount of articles was overwhelming, my YOShInOn RSS reader now ingests the “best comments” from

https://hnrss.github.io/

together with 110 other feeds and actually I like the comments it picks out a lot. Now that the system is adding about 3000 items per day it might be able to handle a big feed like the comments firehose since now those comments are diluted with so many quality articles. For a problem like that you might want a two-score system with: (i) is it relevant? (something I like) and (ii) is it popular? (like Google’s PageRank)

I think you could make a model that compares comments in the best comments feed with other comments. I have tried formulating the problems above as regression problems where I try to predict the actual score and it does not work well because of the uncertainty problem but formulated as a classification problem for a score over a threshold it is easy to make a well-calibrated model that tells you “this article has a 20% chance of frontpaging” which is about the best anyone can do.

Subscribe via rss, so you can scratch the curiosity itch and each the FOMO, without coming to the site all the time and looking over the same things 20 times?

https://hnrss.github.io/

https://news.ycombinator.com/item?id=9491978

and this https://hnrss.github.io/

ps i’m ok with some % of false positives, but hopefully a sprinkle of OpenAI could keep that magically low?

thanks

It sounds interesting but I use https://hnrss.github.io/

Unless it had most of the features of hnrss.org I would not be able to use it.

Perhaps you could pivot your approach and submit a PR to hnrss for the feature?

Looks like there's an unmerged PR on the third-party hnrss project that would add this: https://github.com/hnrss/hnrss/pull/84

Check out below link to get a more customized, topic wise rss feeds.

https://hnrss.github.io/