venkii

Related thread I wrote a bit ago: https://www.reddit.com/r/Lighting/comments/1po7sxb/are_there...

I do think it's actually quite hard to beat the Brighter lamp on all of: Lumens, $, QoL (ie: Google Home integration + temp control), Form Factor (ie: not looking ugly), CRI.

I personally noticed issues w/ CRI & Form Factor quite a lot with my previous options.

OP here. I read a lot of Wuxia novels, some of them are well translated, and I usually end up giving up on the poorly translated ones.

I tried a few models (Gemini-2.5, Gemini-3.0-pro, Claude Opus 4.5), and found that they all improved the quality quite noticeably, even without being provided the source. The trick was mostly asking them to be super conservative in the changes they made.

Here's a diff if you're curious: https://www.diffchecker.com/DKgmzLdM/

You might enjoy Asara's Personality Basins, it's on a similar vibe: https://near.blog/personality-basins/

One of my all-time favorite essays, pairs very well with pg's Cities And Ambition (https://www.paulgraham.com/cities.html), as I wonder about what forms of transcendence various cities encourage you to aim towards.

Thanks! I'll look into adding custom palettes & the heightmap method. Might not get to it tbh - but I'll take a look.

Mmm interesting.

I'm not sure if it's possible in pure CSS, maybe it is? It relies on creating new SVG elements.

I think including the SVG in whatever website would usually be the simplest way to use it and it's super lightweight.

Feel free to take a look at the SVG generation function here [1] and lmk if you have any ideas to make it happen in pure CSS.

[1] https://github.com/venkr/gradient-gen/blob/main/src/EllipseG...

Done - thanks :)

Hey HN!

Check out this fun tool I built to make cover images for my blog. The main goal: interesting looking random gradients, that all draw from a single color palette.

I stumbled upon this post from Justin Jay Wang [1], who designed for OpenAI - describing methods for random gradients, including the one that shipped on OpenAI’s homepage from 2020-2022.

Since the post included ~10 SVGs, I was able to reverse engineer the SVG parameters, and decided to put up a generator. You can see the exact deets in my Github README [2].

I also use it to generate cover images for my Substack [3] - if you want to see what using a bunch of images generated by it looks like.

[1] https://justinjay.wang/methods-for-random-gradients/

[2] https://github.com/venkr/gradient-gen

[3] https://venkii.substack.com/

Really like the thesis implied by the title - kinda unfortunate the post tangents & doesn’t spend more time on that explicit claim.

I’ve briefly written about issues in the past: https://venki.dev/notes/google-gemini-is-bad

My first thought would be, re:

>It’s baffling that a tech giant like Google can’t seem to deliver the same level of reliability

It’s actually not that baffling to me! Shipping simple features gets harder and harder as scale, esp as the features must interact well with more and more existing features + get through bureaucracy.

For example, the regions bit you mention: Anthropic/DeepSeek can just pool all their regions together into a single API and mostly vibe it. But since Google already offers their regular compute by region, it’d be a little weird to not do that for LLMs only.

GCP probably just has a lot of complexity due to being enterprise-oriented, and unfortunately this carries over. Unencumbered startups get to start from a blank state though.

Any sense if applying later will be detrimental?

The main reason to delay: I've started writing relatively recently, and expect I might have more posts I'd showcase in my application by then.

Ooh very cool - I've been looking for something like this.

Top feature I'd care about before using this would be the ability to read the actual 10K inline and jump to sources, perhaps as an iframe or so.