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Side note:

Simon Halvdansson runs "Harmonic", an Android client for Hacker News, I am using it daily for 2+ years and I sincerely recommend it.

I even asked him a feature (mark a story as read), and he implemented it shortly after.

Shout-out to you Simon!

https://github.com/SimonHalvdansson/Harmonic-HN

https://play.google.com/store/apps/details?id=com.simon.harm...


https://vincentandrieu.fr my personal website


[deleted]


Please don't respond to a bad comment by breaking the site guidelines yourself. That only makes things worse.

https://news.ycombinator.com/newsguidelines.html


Seems nice but I'm afraid it would not be compatible with my main work setup: VS Code on my main monitor, my web browser on my external monitor, and my eyes going back and forth between these 2 windows every few seconds to either read code or check the effects on the hot-reloading app. If one of the windows is dimmed, it would be painful.


Then you don't need a focus app, I'd say. But HazeOver has some "rules" that could help you. I agree that it only makes sense for me, on a single big display. If I'm using the MBP 14" one, I'm always maximized...


Carbonyl looked promising but unfortunately is not maintained anymore: https://github.com/fathyb/carbonyl


Here is the Coinbase-hosted version of the email:

https://links.coinbase.com/s/vb/aKIVjNeDcGByosXohhDZZ7ijTHvh...


Running it on a MacBook with M1 Pro chip and 32 GB of RAM is quite slow. I expected to be as fast as phi4 but it's much slower.


With eval rate numbers:

- phi4: 12 tokens/s

- mistral-small: 9 tokens/s

On Nvidia RTX 4090 laptop:

- phi4: 36 tokens/s

- mistral-small: 16 tokens/s


Noob question (I only learned how to use ollama a few days ago): what is the easiest way to run this DeepSeek-R1-Distill-Qwen-32B model that is not listed on ollama (or any other non-listed model) on my computer ?


If you are specifically running it for coding, I'm satisfied with using it via continue.dev in VS Code. You can download a bunch of models with ollama, configure them into continue, and then there is a drop-down to switch models. I find myself swapping to smaller models for syntax reminders, and larger models for beefier questions.

I only use it for chatting about the code - while this setup also lets the AI edit your code, I don't find the code good enough to risk it. I get more value from reading the thought process, evaluating it, and the cherry picking which bits of its code I really want.

In any case, if that sounds like the experience you want and you already run ollama, you would just need to install the continue.dev VS Code extension, and then go to its settings to configure which models you want in the drop-down.


This model is listed on ollama. The 20GB one is this one: https://ollama.com/library/deepseek-r1:32b-qwen-distill-q4_K...


Ok, the "View all" option in the dropdown is what I missed! Thanks!


Search for a GGUF on Hugging Face and look for a "use this model" menu, then click the Ollama option and it should give you something to copy and paste that looks like this:

  ollama run hf.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF:IQ1_M


Got it, thank you!


   ollama run deepseek-r1:32b

They dropped the Qwen/Llama terms from the string

https://ollama.com/library/deepseek-r1


Whenever they have an alias like this, they usually (always?) have a model with the same checksum but a more descriptive name, e.g. the checksum 38056bbcbb2d corresponds with both of these:

https://ollama.com/library/deepseek-r1:32b

https://ollama.com/library/deepseek-r1:32b-qwen-distill-q4_K...

I prefer to use the longer name, so I know which model I'm running. In this particular case, it's confusing that they grouped the qwen and llama fine tunes with R1, because they're not R1.


I'm using it inside of LM Studio (https://lmstudio.ai), which has a "Discovery" tab where you can download models.


Many people here seem impressed about speed/performance. I have been using all sorts of terminals / emulators over the past 20 years and it never occurred to me a terminal can be slow. When I type a command, I just get the result instantaneously, for any terminal. What are the use cases that can make a terminal be slow?


I don‘t get it either. The only use case I can see is for programs that output basically a video feed in text form.

A commenter in this thread mentioned sifting through log / debug output… but why wouldn‘t you pipe it to a file instead?


TESTED:

1. Create a file with 1 million lines:

  for i in {1..1000000}; do echo "Line $i: This is a test of terminal performance."; done > bigfile.txt
2. cat the file and see how much time it takes:

  time cat bigfile.txt
RESULTS:

- iterm2: 3.5s

- Default macOS terminal: 2.3s

- Ghostty: 1.8s


> What are the use cases that can make a terminal be slow?

Rendering huge stdout/stderr can be a bottleneck. Try running a program that writes half a million lines of text to stdout without file redirects and a lot of terminal emulators will struggle to keep up.


I think this mostly plays a role when using modal text editors like vim in your terminal. Speed matters so very much then! Give it a try if you want ;)


Have you ever experienced vim being slow? If so, would you know how I could reproduce this?


This product got me so excited. Who remembers?


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