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timestretch

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Mistral Small 3 1 year ago

Their models have been great, but I wish they'd include the number of parameters in the model name, like every other model.

I'm surprised to see an example from Apple Music. That app is an perfect example of design neglect too. The ability to filter your song list for a specific track is now hidden in Apple Music.

To reveal the filter field, you must select the "View" menu, and "Show Filter Field". Worse each time you relaunch Music, this field is again hidden, and you have to select the menu item again.

MacOS allows me to run any software I want. It has a terminal, and allows me to run scripts. It has a shared file system and offers true multi-tasking.

You can already run some iOS apps in MacOS. I'd like a single computer where I can switch between mobile / pencil usage and desktop keyboard / mouse usage.

I love BBEdit and use it daily as my main text editor, and have since classic MacOS days! I'm hoping they add a couple of features:

- Ability to auto-format using Prettier.

- AI Large Language Model auto-complete for code. The field is moving fast, but I'd love to be able to use open models here. I don't want to send all my code to GitHub co-pilot.

BBedit has an excellent built in text-filter functionality and I've written scripts to send selected text as a prompt to various LLMs, but built-in would be ideal!

There is a lot of ongoing research into making language models that can run well on a wider variety of hardware. It seems VRAM is the main limitation at this point.

You can already run smaller language models on your own hardware if you have a GPU with sufficient VRAM. For example, with quantization, you can run gpt-neox-20b (512 token context window) or gpt-pythia-13b (full context window) on an RTX 3090 with 24GB VRAM. Quantization allows you to run the model with less memory, where each parameter utilizes 8 bits or 4 bits instead of 16 or 32 bits.

Another possibility is to use reinforcement learning with human feedback to tune smaller models to give results comparable to larger models.

I've also been using RWKV with good results. It is a language model that uses an RNN and only needs matrix-vector multiplication instead of matrix-matrix, so inference runs much faster. The 7B model uses about 14GB VRAM without quantization. A 14B model is currently in training, but progress checkpoints are available. You can also do inference on a CPU, although it is much slower than GPU.

A hybrid approach where you could "sketch" with SolveSpace, but then generate OpenSCAD style code that you could later edit would be amazing.

The main advantage of OpenSCAD is that it doesn't "blow up" when you change a variable to be out of some range where things work. In OpenSCAD, you can put it back if it does cause a problem.

Changing a dimension in SolveSpace sometimes throws off the whole design. SolveSpace's precision, constraints, and great keyboard user interface make it a fun program to design parts in. It is among the best open source tools out there.

If you enjoy this, check out www.fatbitsapp.com

It's an iOS app I made that lets you make pixel art and see artwork other people are making at the same time. There's also a little websockets powered view on the website of what people are making.

Turing Drawings 13 years ago

This is pretty clever, and made me smile.

I'm not wild about the idea of weeding through bot generated pull requests. But, I like the idea of making tools like this available through GitHub.

It would be cool if GitHub provided a static analysis tools to find bugs, leaks, and security issues in uploaded code. Clang is updated pretty frequently and it would be cool to see new results against all repos when it is updated. Generating pull requests would make it easy to fix issues discovered too.

http://projecteuler.net/ is fun. You write programs that yield an specific answer. Paste the answer in to find out if your solution is correct. I clicked around and worked on the questions that seemed the most interesting.

In terms of books, TAOCP may be a bit more general than what I was thinking of. I assumed you were getting back into programming to make something specific. What sort of software do you plan on working on? Answering that question first may help you spend your coding time more effectively.

I'd recommend reading a programming book on a topic that interests you. You'll write better code after reading it, and may be inspired too. Any simple metric such as character count or lines of code are useless. It is much more important to find something interesting to work on, then commit to learning everything necessary to complete it.