Feed aggregator

Article URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9474417/

Comments URL: https://news.ycombinator.com/item?id=41960370

Points: 1

# Comments: 0

Article URL: https://github.com/shayonj/pg_flo

Comments URL: https://news.ycombinator.com/item?id=41960342

Points: 1

# Comments: 0

Made this project in a couple of weekends after playing around with NotebookLM and seeing the hype for more projects like this. Let me know what you think!

Comments URL: https://news.ycombinator.com/item?id=41960318

Points: 1

# Comments: 0

Article URL: https://www.loata.ai/blogs/123e4567-e89b-12d3-a456-426614174002

Comments URL: https://news.ycombinator.com/item?id=41960288

Points: 2

# Comments: 2

Article URL: https://www.nbcnews.com/business/business-news/nvidia-overtakes-apple-worlds-valuable-company-rcna177431

Comments URL: https://news.ycombinator.com/item?id=41960280

Points: 1

# Comments: 1

Hello HN! I have an early work-in-progress Neural Network Framework written on top of JAX.

Simple | Declarative | No need to learn duplicated JAX transforms or specialized manipulation functions

How to build a NN? 1. Make `f(params, x, hyperparameters)` 2. Initialize the params `params = trace(f, key, x, *hyperparameters)` 3. Use the function `f` you made: `f(params, x, hyperparameters)` 4. Optionally: use partial to bake-in the hyperparams: `model = f(_,_, hypermeters)` ; `_` will be explained in the README. You can also use `partial`

Its key difference with other JAX frameworks is its simplicity and straightforwardness. With other frameworks, the network is transformed to an (init, apply) which are pure functions and you basically don't use the actual code you've written and instead use this. With zephyr, neural networks look like neural networks: a function of parameters, input-data, and hyperparameters. It's also patterned for FP use, so partial application will a useful alternative to states in OO.

Lastly, it's meant to be declarative and simple. Neural networks are just functions, not objects that need instantiation or anything. This means code are usually shorter as declaration and usage/computation happens in the same place since those things are highly coupled and so placing them together results in less cognitive load. (No more wondering what the MLP(x)'s output-dim-per-layer is, since it's all bundled together).

It's early stage and so the core nets are few and unpolished, but I want to focus on the core first before moving on to implementing all core nets. Feedback on any part of it is very welcome!

Comments URL: https://news.ycombinator.com/item?id=41960276

Points: 1

# Comments: 0

Article URL: https://djmag.com/features/are-rotary-mixers-actually-better

Comments URL: https://news.ycombinator.com/item?id=41960272

Points: 1

# Comments: 0

Article URL: https://github.com/buttercannfly/AIpex

Comments URL: https://news.ycombinator.com/item?id=41960232

Points: 1

# Comments: 1

Article URL: https://simonwillison.net/2024/Oct/27/llm-jq/

Comments URL: https://news.ycombinator.com/item?id=41960210

Points: 3

# Comments: 0

Article URL: https://www.mentalfloss.com/article/51880/where-did-term-86-come

Comments URL: https://news.ycombinator.com/item?id=41960202

Points: 2

# Comments: 0

I was at my desk, constantly switching between Linear and PowerToys, and it was getting really frustrating. Every time I changed tools, my thoughts felt jumbled, and I often forgot the exact words I wanted to use for issues while waiting for Linear to load. Then I had an idea—what if I used PowerToys Run to create a new issue, similar to how I would on macOS with apps like Things or OmniFocus? I imagined setting it up so I could quickly access Linear directly from PowerToys Run. I could type a specific command like “New Issue” followed by the details, all without switching applications. After doing that, everything became clearer.

I finally felt more in control, able to create tasks efficiently while staying focused, and I could tackle my day without getting lost in the transitions.

You can check out the project here and watch a demonstration of it in action.

Link to Repo: https://github.com/vednig/powertoys-linear

Note: This is my first plugin for any software and first piece of code in C# there may be many discrepancies, I'm open to feedback. And code is also open for contributions.

Comments URL: https://news.ycombinator.com/item?id=41960188

Points: 1

# Comments: 0

Article URL: https://skeptics.stackexchange.com/questions/57440/did-the-manhattan-project-scientists-consider-whether-the-first-nuclear-test-cou

Comments URL: https://news.ycombinator.com/item?id=41960179

Points: 1

# Comments: 0

Article URL: https://github.com/benknoble/frosthaven-manager

Comments URL: https://news.ycombinator.com/item?id=41960178

Points: 1

# Comments: 0

Article URL: https://xmrit.com/articles/gift-exceptional-variation

Comments URL: https://news.ycombinator.com/item?id=41960173

Points: 1

# Comments: 0

Article URL: https://www.projectgus.com/2024/10/simplifying-bench-kona/

Comments URL: https://news.ycombinator.com/item?id=41960166

Points: 1

# Comments: 0

Article URL: https://www.charlespetzold.com/blog/2024/09/Rudimentary-3D-on-the-2D-HTML-Canvas.html

Comments URL: https://news.ycombinator.com/item?id=41960162

Points: 1

# Comments: 0

Article URL: https://phillipspobrien.substack.com/p/weekend-update-104-georgia-shows

Comments URL: https://news.ycombinator.com/item?id=41960149

Points: 4

# Comments: 0

Article URL: https://dragonruby.itch.io/dragonruby-gtk/devlog/809843/dragonruby-hits-v60

Comments URL: https://news.ycombinator.com/item?id=41960124

Points: 1

# Comments: 0

Article URL: https://www.reuters.com/technology/artificial-intelligence/google-develop-ai-that-takes-over-computers-information-reports-2024-10-26/

Comments URL: https://news.ycombinator.com/item?id=41960114

Points: 2

# Comments: 0

Article URL: https://petewarden.com/2024/10/21/introducing-moonshine-the-new-state-of-the-art-for-speech-to-text/

Comments URL: https://news.ycombinator.com/item?id=41960085

Points: 4

# Comments: 0