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Article URL: https://sny.sh/hypha/blog/scp

Comments URL: https://news.ycombinator.com/item?id=47177675

Points: 1

# Comments: 0

Article URL: https://unfoldingdiagrams.leaflet.pub/3mft6olldos26

Comments URL: https://news.ycombinator.com/item?id=47177674

Points: 2

# Comments: 0

Article URL: https://architectelevator.com/strategy/economics-technical-speaking/

Comments URL: https://news.ycombinator.com/item?id=47177668

Points: 1

# Comments: 0

Article URL: https://johnnysswlab.com/exposing-more-parallelism-is-the-hidden-reason-why-some-vectorized-loops-are-faster-not-vectorization-per-se/

Comments URL: https://news.ycombinator.com/item?id=47177651

Points: 2

# Comments: 0

Article URL: https://www.araju.dev/blog/ai-for-social-impact

Comments URL: https://news.ycombinator.com/item?id=47177635

Points: 1

# Comments: 0

Hi HN, I’m the creator of ORBTX.

I built this because most tools in astrodynamics are either overly simplistic web calculators or heavyweight, expensive legacy suites that look like they were made in the 90s. I wanted to create a "middle ground": professional-grade orbital computation that is fast, visual, and API-first.

The Tech Stack:

Frontend: Next.js & React.

3D Engine: Three.js (React Three Fiber) for real-time trajectory rendering.

Physics: Custom engine handling classical two-body mechanics (Hohmann and Bi-elliptic transfers).

Key Challenges: One of the main hurdles was maintaining floating-point precision for astronomical distances within a browser environment while keeping the 3D visualization smooth at 60fps. I’m currently refining the propagator to handle more complex perturbations in future updates.

The Goal: To move orbital mechanics away from "Excel-engineering" into a modern, developer-ready infrastructure. Think of it as a step toward "Figma for orbital mechanics."

No sign-up required, it's open for testing. I’d love to hear your thoughts on the physics implementation, the API structure, or any edge cases you find!

Comments URL: https://news.ycombinator.com/item?id=47177432

Points: 1

# Comments: 0

Article URL: https://pydantic.dev/articles/the-human-in-the-loop-is-tired

Comments URL: https://news.ycombinator.com/item?id=47177411

Points: 2

# Comments: 0

Article URL: https://adelbordbari.github.io/code/2025-02-01-manage-netplan-with-django/

Comments URL: https://news.ycombinator.com/item?id=47177401

Points: 1

# Comments: 0

Article URL: https://www.quasarui.com

Comments URL: https://news.ycombinator.com/item?id=47177396

Points: 1

# Comments: 0

Article URL: https://far.chickenkiller.com/computing/in-the-search-for-an-e-paper-tablet-in-2026/

Comments URL: https://news.ycombinator.com/item?id=47177388

Points: 1

# Comments: 0

Article URL: https://github.com/vaijab/flint

Comments URL: https://news.ycombinator.com/item?id=47177375

Points: 1

# Comments: 1

I made a read-it-later app because clipping directly into my Obsidian vault was creating a lot of noise.

Workflow is simple: capture links, read in a clean reader view, highlight, then export highlights/notes as clean Markdown. Unread items auto-archive after a few days to keep the queue small.

What’s your approach to web clipping and keeping the vault clean?

Comments URL: https://news.ycombinator.com/item?id=47177359

Points: 1

# Comments: 1

Article URL: https://www.bbc.com/travel/article/20260225-the-nostalgic-winter-drink-dividing-germany

Comments URL: https://news.ycombinator.com/item?id=47177351

Points: 1

# Comments: 0

Article URL: https://luther.io/articles/personal-growth-in-the-age-of-ai/

Comments URL: https://news.ycombinator.com/item?id=47177317

Points: 2

# Comments: 0

Everything I see today is only about LLMs. I personally use LLMs in my daily activities and they are doing great job. But I wonder what happened to traditional machine learning methods all of a sudden! Those hamspam classifiers, sentiment analysis models, word2vecs, RNNs, CNNs, LSTMs, FFNNs, where are they now? What is a typical data scientist or a ML engineer of late 2010s doing now? The decision trees they trained, the neural nets they architected, the accuracy evaluations, hyperparameter tunings where are they all now? It feels like it is all about LLMs suddenly. The traditional ML models which served specific problem with light weight and easily deployable loads are all obsolate now?

Comments URL: https://news.ycombinator.com/item?id=47177313

Points: 1

# Comments: 1

Article URL: https://tildehacker.com/hyping-an-editor-in-the-age-of-ai

Comments URL: https://news.ycombinator.com/item?id=47177273

Points: 1

# Comments: 0

Article URL: https://www.tomshardware.com/tech-industry/semiconductors/intel-foundry-boss-leaves-for-qualcomm-naga-chandrasekaran-takes-charge-of-the-unit

Comments URL: https://news.ycombinator.com/item?id=47177260

Points: 6

# Comments: 0

Article URL: https://chenyu-li.info/leviathan

Comments URL: https://news.ycombinator.com/item?id=47177195

Points: 1

# Comments: 1

Article URL: https://www.swift.org/blog/swift-system-metrics-1.0-released/

Comments URL: https://news.ycombinator.com/item?id=47177189

Points: 2

# Comments: 0

Article URL: https://gwern.net/doc/sociology/2003-ashforth.pdf

Comments URL: https://news.ycombinator.com/item?id=47177186

Points: 2

# Comments: 0