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Frankly, the idea was born of my own lawn care struggles. Endless lawn care company fees without any actual improvement. Googling problems and finding generic solutions without regional considerations. One time I overseeded my grass not realizing I had to actually put soil down too.

One day, I decided to run lawn pictures through AI and found some pretty helpful information. So I decided with my clinical background (the idea of treating the cause, not just the symptoms), as well as tech savvy, I would create an AI tool where homeowners can upload pictures of their lawn, enter their ZIP code, and get a diagnosis tailored to their location with actionable next steps in just 15 seconds.

Completely free. The platform is monetized with affiliate sales (if a user elects to purchase through one of our Amazon or other links) and by selling exclusive rights to individual ZIP codes to lawn care companies seeking warm leads. Users can pursue their own DIY plan, purchase a lawn care subscription service, or contact a local lawn care system.

I'd love if you'd test it out, toy with it, try to break it, and give me your feedback. Any feature requests would be super helpful.

Thanks! Excited to hear your thoughts.

Andrew

https://www.drafted.ai). We’re training models that generate residential architecture from structured design constraints.

Product demo: https://www.youtube.com/watch?v=8QkJ7jNU9y4

Residential architecture is still one of the most expensive, slow, and inaccessible creative processes in the world. Designing a custom home typically costs $10,000–$50,000 or more, takes months, and requires making major decisions before most people can even visualize the outcome. As a result, the vast majority of homes are built without direct architectural involvement.

Our goal is to teach computers how the built environment works so anyone can imagine, explore, and eventually create physical spaces tailored to them.

Today, users can design homes using simple inputs such as: - Square footage targets - Footprint shapes - Lot boundaries - Room placement preferences - Spatial relationships and constraints.

Our models generate complete floor plans and matching exterior elevations in seconds. Users can explore designs in both 2D and 3D, iterate instantly, furnish interiors, experiment with materials, and export CAD, PDF, and other files for the rest of the pre-construction process.

One of our newest capabilities allows users to draw any footprint shape and generate a complete home layout inside it: https://www.youtube.com/watch?v=wZJhBm7-OHI.

Over the past month, more than 120,000 people have used Drafted, generating over 325,000 home designs.

If you're building a home, developing property, working in architecture, construction, or AI, we'd love to hear your feedback!

Has anyone here fully swapped Claude/GPT for a local model as their main coding tool, not just for side experiments? If so, please share your setup and performance (e.g tok/s)

https://www.hetzner.com/pressroom/standardization-and-price-...

It's been amazing to vibe code prototypes in any stack, but when it comes to building something reliable/scalable, I couldn't effectively guide the agent unless I knew the technology. And the scariest part is that I'm seeing a lot of my technical skills decreasing due to AI coding.

Reflecting on my journey, I also worry about how the new "AI native" generation of software developer is going to acquire technical depth.

So I built fata.dev: short daily spaced-repetition sessions for programming skills (Rust, CSS, React, Python, TypeScript, Architecture).

You can try it in the browser with no signup: https://fata.app/courses

It's an offline-first mobile app built with Capacitor, RxDB and Firebase. The first courses were painfully written by hand, but most content is now AI-generated. It takes about 3000 LLM calls to generate a course, and every code samples goes through compilation, linting, unit testing, AI and a final manual review.

Would very much appreciate any feedback on the product & website, what works and what could be better. Thanks!