HN user

teaearlgraycold

3,953 karma

Sup!

Posts24
Comments2,474
View on HN
danangell.com 5d ago

Claude Code is the ceiling on vibe-coded software

teaearlgraycold
2pts4
www.doublethe.fun 12d ago

Show HN: An interactive double pendulum "fractal"

teaearlgraycold
3pts0
danangell.com 2mo ago

Twitch Donations IRL

teaearlgraycold
3pts0
news.ycombinator.com 2y ago

Ask HN: How to cache NFS files across K8s pods on the same node?

teaearlgraycold
1pts0
news.ycombinator.com 2y ago

Ask HN: AWS g6 instances loading into VRAM slowly?

teaearlgraycold
3pts0
news.ycombinator.com 2y ago

Ask HN: What's the right way to scale K8s for GPU workloads?

teaearlgraycold
2pts3
en.wikipedia.org 2y ago

Vavilovian Mimicry

teaearlgraycold
6pts0
news.ycombinator.com 2y ago

Ask HN: I'm abandoning NextJS. What's an alternative full-stack TS solution?

teaearlgraycold
3pts3
danangell.com 2y ago

GPT-4 Understands

teaearlgraycold
4pts3
news.ycombinator.com 2y ago

Ask HN: What early stage startups on the SF peninsula are in-person?

teaearlgraycold
2pts0
news.ycombinator.com 3y ago

Ask HN: Resources on web dev freelancing/contracting

teaearlgraycold
2pts3
danangell.com 3y ago

O(zero) Runtime

teaearlgraycold
1pts0
danangell.com 3y ago

Show HN: My 10 Gb/s Router

teaearlgraycold
6pts2
danangell.com 3y ago

Post Information Scarcity

teaearlgraycold
2pts2
danangell.com 3y ago

Working with GPT

teaearlgraycold
3pts0
github.com 3y ago

OpenAI crowd sources LLM benchmarking datasets by offering advanced GPT-4 access

teaearlgraycold
1pts2
danangell.com 3y ago

More thoughts on ChatGPT's political beliefs

teaearlgraycold
1pts2
news.ycombinator.com 3y ago

Show HN: Python Quine

teaearlgraycold
1pts0
news.ycombinator.com 3y ago

Ask HN: How to handle building a startup for users that need special compliance?

teaearlgraycold
1pts0
danangell.com 4y ago

A Type-Level API Client

teaearlgraycold
3pts1
news.ycombinator.com 4y ago

Ask HN: Interest in a 0-downtime managed Postgres migration tool?

teaearlgraycold
2pts0
github.com 4y ago

Show HN: Terminal-Rendered ASCII Shaders

teaearlgraycold
46pts5
gist.github.com 4y ago

Show HN: Little Busy Beaver Compiler

teaearlgraycold
2pts0
teaearlgraycold.me 8y ago

Visualizing Entropy in Binary Files

teaearlgraycold
1pts0

In my experience GLM 5.2 is a pretty good Opus replacement. But K3 has not given me an experience on par with Sol or Fable. The price/intelligence ratio might still make sense. But it’s not very inspiring when it comes to my real world tasks. I’m doing pretty mundane web stuff.

If we’re talking about human technology available in a few hundred years, don’t discount far more exotic options. I’ve heard people talk of theoretical terrestrial lasers pushing on tiny probes. With an absolutely gigantic laser and magical material at the back of the probe that won’t instantly vaporize there’s enough energy to get something the size of a smartphone up to a reasonable proportion of the speed of light.

I can’t prescribe this theoretical technology to the problem. But I also think it’s unreasonable to set the limit using known technology and then discount the idea altogether. We have no idea what will be possible in 300 years.

For example you cannot have concurrent access. As soon as you need a worker process and a web process SQLite is out. Or if you are trying to use it as a vector db all of those vector searches will block a node event loop.

An acoustic analyzer for a sink basin in a hackerspace. It will allow for automatic detection of dishes/cups left in the sink without cameras and can be handled by an rp2040. So far looks like it has very low false positives but can only successfully detect certain materials or large masses.

Sad to see no mention of @ts-check. I love being able to write native browser JS modules with nearly all of the functionality from TypeScript.

RAG, as far as I understand, is a term that came about before LLM tool-calling was as prevalent. Your options were to have an LLM hallucinate up a response, or instead do a [document -> chunk -> embedding -> vector db -> query -> context window] pipeline. I haven't heard anyone talk of LLMs + web search or other tool calls as RAG, even though if you pull apart the semantics the term is applicable. In fact I don't hear people talk about RAG much at all. I suppose much of what people were trying to solve with document chunking/embedding pipelines has been solved with bigger models and tool calls. And along with that change in tooling we have left behind the term "RAG", which leaves it attached to the concept of those pipelines.

Here’s my attempt:

Assume you have a fan sitting still. You smack it and it’s now rotating with 1m/s angular velocity. If you want it to go faster you can’t smack it at the same speed. You have to hit it faster else you’re just tickling it and it stays the same speed. So you smack your hand twice as hard and now it’s going even faster. Then three times as hard, four times, etc.

If you sum the smack energy it will be 1+2+3+4, which starts to build out a right isosceles triangle if you graph it. Such a triangle is half of a square, ie: 1/2*v^2.