I'm guessing those visualizations wouldn't be in this post if it weren't for AI. The interesting question is what happens when ed-tech ships this pattern at scale. Exciting future.

We actually made the course ourselves, following James Stewart's Calculus and our own approach to teaching these topics (we're two PhD students). We wrote everything in English first, then translated it into other languages.

AI accelerated our workflow, we prompted the visuals and explanations we wanted, then polished the results ourselves. So we're confident in the teaching quality and correctness of these videos, they're not pure AI artifacts :)

We're an early-stage edtech startup. Our goal: use AI to build high-quality, computer-animated versions of every STEM course in the world, in every language, and bring them together on a platform like Coursera, but built for the AI age, with deep LLM integration. We want that literally. Every course. Every language.

This project simply couldn't exist without generative AI. We're two co-founders, and we don't speak five of the languages we just published in. There may be mistakes, but these courses wouldn't exist in those languages otherwise.

We produce lectures fast with AI. But once a lecture is done, adding another language is even faster, it's O(1). So we just made this in six languages. We could keep going.

These videos aren't static MP4s, they're open for improvement. If you have ideas, suggestions, or find mistakes, please open an issue on GitHub: https://github.com/academa-dev/multivariable-calculus

Would love your feedback. Happy to answer questions.

Join the waitlist for upcoming courses at https://academa.ai.

Tech stack: We forked 3Blue1Brown's Manim, synced TTS with animations using OpenAI Whisper, and used Inworld AI for text-to-speech.

Comments URL: https://news.ycombinator.com/item?id=46754651

Points: 5

# Comments: 4

See the PR: https://github.com/rendercv/rendercv/pull/528

To give some context: I hadn't worked on RenderCV for about six months, and when I came back, I had grown technically and my design taste had changed. I decided to do a significant overhaul rather than preserve backwards compatibility. It felt necessary to maintain my enthusiasm for this project long-term.

You're right about semantic versioning. I used a two-number scheme (MAJOR.MINOR) back in 2023 when I didn't know much about releasing software. By the time I understood the benefits of MAJOR.MINOR.PATCH, I didn't want to change it mid-stream. I'm planning to switch after v3. It will give me more flexibility to tag updates appropriately.

I'm also adding migration documentation to my list. You're right that I should have done this. Going forward, there will be clear migration guides.

Regarding the Markdown issue where sections show only titles, that sounds like a bug. If you could open an issue with details, I'd appreciate it.

Thank you for using RenderCV!

We wanted to define 9 different entry schemas that could be used under any section title. In our schema, each section, regardless of its title, contains a list composed of one of these nine entry types. This is different from the JSON Resume approach, where specific entry schemas are tied to specific sections (work schema for work section, etc.).

In RenderCV, users can choose any section title they want and use any of the 9 entry types within it. Each entry type is rendered differently in the generated PDF.

Here are a couple of reasons, just a few that come to mind, why using YAML + RenderCV can be more appealing than working directly in Typst:

- Reusing the same content across multiple themes. YAML + RenderCV allows you to experiment with different themes and design options to the exact same content with no changes to the data itself. This is difficult to achieve in pure Typst, where switching templates typically means adapting your content to a different set of Typst commands.

- Centralized control over entry layouts. You can adjust how entire classes of entries are rendered from a single place—for example, changing the layout of all education entries at once, rather than editing each entry individually.

Run rendercv render cv.yaml → get a perfectly typeset PDF.

Highlights:

1. Version-controllable: Your CV is just text. Diff it, tag it.

2. LLM-friendly: Paste into ChatGPT, tailor to a job description, paste back, render. Batch-produce variants with terminal AI agents.

3. Perfect typography: Typst under the hood handles pixel-perfect alignment and spacing.

4. Full design control: Margins, fonts, colors, and more; tweak everything in YAML.

5. Comes with JSON Schema: Autocompletion and inline docs in your editor.

Battle-tested for 2+ years, thousands of users, 120k+ total PyPI downloads, 100% test coverage, actively maintained.

GitHub: https://github.com/rendercv/rendercv

Docs: https://docs.rendercv.com

Overview on RenderCV's software design (Pydantic + Jinja2 + Typst): https://docs.rendercv.com/developer_guide/understanding_rend...

I also wrote up the internals as an educational resource on maintaining Python projects (GitHub Actions, packaging, Docker, JSON Schema, deploying docs, etc.): https://docs.rendercv.com/developer_guide/

Comments URL: https://news.ycombinator.com/item?id=46344616

Points: 100

# Comments: 41

Open-source engine of the app: https://github.com/sinaatalay/rendercv

Comments URL: https://news.ycombinator.com/item?id=41496550

Points: 6

# Comments: 3