I computed livability for all of Germany by rent, commute, and QoL
This project started with me wanting to find a nice place to live with my partner. I'm a data scientist by day so I thought I'd approach it systematically. When I showed it to my friends, many said they wanted to try it too, so I made it into an app and put it online. (-> MIT licensed) The app is German, but it's translator friendly :) Instead of a single "best place" ranking, it scores ~0.5 km2 hexes across Germany based on user preferences on a dozen or so liveability layers like rent, commute time, recreational potential, noise level, and many more. User preferences are reflected on the…
What it does
In the maker’s words, at launch
This project started with me wanting to find a nice place to live with my partner. I'm a data scientist by day so I thought I'd approach it systematically. When I showed it to my friends, many said they wanted to try it too, so I made it into an app and put it online. (-> MIT licensed) The app is German, but it's translator friendly :) Instead of a single "best place" ranking, it scores ~0.5 km2 hexes across Germany based on user preferences on a dozen or so liveability layers like rent, commute time, recreational potential, noise level, and many more. User preferences are reflected on the map by colorizing gradients and hovering tiles, updated live. Some thoughts/challenges along the way: The mental framework: I approached the project with the assumption "the market regulates", i.e. "with enough axes, no place is inherently better than another". I quickly found out that most of the rent can be exlained as a resource negotiation over proximity to job sources and people - rather than the more elusive factors commonly shown in research to lead to a happier life such as low stress signals. The app now has a 'default bundle' of QOL factors today that are underpriced, but it's possible to disable this and differentiate places on just the axes of user preference. Finding good data: It's not enough to find data that's 80% correct, since it might majorly misrepresent a place and you'll immediately stop trusting the site. I've collected data from a large variety of public sources, including various german state departments, OpenStreetMap and WorldCover. Then, some python post processing on the data, and a final round of aggregation client side based on user preferences. I'm fairly confident in it now, but misrepresentations and misunderstandings remain the most likely points of criticism. Rejection of data: I explored and ultimately rejected many data such as crime statistics, user ratings, accident statistics, air quality, radon measurements (and more...). Most rejections were made because of a lack of scientific evidence for their relevance. The axes of "is this place well-received by inhabitants" and "would I feel safe here" are probably the most glaring omissions of the software. Making it fast enough: There are around 500k hexes, so I needed to come up with models that take under a day to compute on my laptop, and below a second to aggregate on the frontend. Some of the solutions include GPU rendering, an mx+b style approximation of reachability, aggregation of data through hot spots found by population peaks, aggressive caches and spatially-local windows for public transit, and so on. I'm amazed at how fast computers are that this is even possible today! You can read more about the layers on the methods page (https://wohnortatlas.de/method.html), or if you're really curious, peek the source code itself on GitHub (https://github.com/Ivorforce/wohnortatlas). Happy exploring! I'm dying to get some feedback.
Does the same job
all alternatives →- AWA website that heatmaps your city based on your housing preferences2025 · theretowhere.com · ▲321
For the past few months, I've been working on a website that answers two different questions: - Where in my city have the best travel times to all the things and people I care about? - Given a listing, how far is it from all the things and people I care about? Personally this was fueled by my own frustrations when I was apartment hunting in NYC. I was frustrating to have to juggle so many Google Maps tabs when I was evaluating a listing, and it was also annoying to not have full confidence that I was even searching in the right places. I wanted to be close to work, a Trader Joe's, and a…
- AWA website for sharing the "Good, Bad, and Why"s of urban spaces2025 · dedede.de · ▲89
Hello HN! We're a small team in Kyoto building a website called dédédé (https://dedede.de/en) that invites people to share the various positives, negatives, oddities, etc. they find in urban spaces. The project grew out of an earlier effort where we'd built an app that assisted participatory urbanism workshops run by local nonprofits. With the new platform, we're trying to build something similar but more casual and hopefully with broader appeal, that'll be fun to use even outside of formal workshop situations. We'd love to hear your thoughts!
- ISI started a repo for sharing algorithm implementations2013 · github.com · ▲52
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
- AWA website for taking a virtual walk, drive or flyover in 130 cities2025 · citywalki.com · ▲5
I've built this as a side project last month. I know there are a few similar sites out there, but I've found them cumbersome to navigate with ads that slow down the experience. So I decided to take a stab at it and build my own version, with a minimalist UI (+ I implemented a custom right-click menu to enable navigation even when the UI is hidden), ad-free, just for fun. There are over 130+ cities that you can explore right now, but I'm constantly adding new ones.
- AHA housesharing vs. affordability map we made for London2014 · splittable.co · ▲15
More work this month
the category →
The app store for voice native apps that lives in your notch
Work · 28d ago · voiceos.com
Mac-native app for screenshots, recordings and collaboration
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Launched alongside, July 2026
the whole month →- IR
I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but…
Life & fun · Jul 2026
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Life & fun · Jul 2026 · john.fun
- 1W18 Words▲1,160
Life & fun · Jul 2026 · 18words.com
- BA
Over the past few months, our team has been building more and more slidedecks using web frontend technologies with coding harnesses like Claude Code, but a common complaint is to make even small edits we need to edit the code either manually or via the harness. To avoid this loop, I ended up creating Bento, a single HTML file with everything you need in a slide tool including animations and shared editing. There's no install or cloud login, everything works offline. The default deck is around 560 KB and it doesn't need to fetch anything once you got it. Open it in a browser and then you can…
Dev tools · Jul 2026 · bento.page
- GG
A few days ago I found myself trying out GLM 5.2 and was really positively impressed. The capabilities and security I was getting from this LLM are similar to those I've gotten from models like Claude or GPT, and this really surprised me. But then I thought, "I wonder how it would work on a normal computer like mine," and above all, "I wonder if it would work without going into OOM on a computer like mine." So I started working with the help of agents to test this possibility. I started converting the model to int4, understanding MTP usage, and if possible implementing DSA for long context.…
AI · Jul 2026 · github.com
