When I bought my home, the big bank I'd been using for years quoted me 7% APR. A local credit union was offering 5.5% for the exact same mortgage.

I was surprised until I learned that mortgages are basically standardized products – the government buys almost all of them (see Bits About Money: https://www.bitsaboutmoney.com/archive/mortgages-are-a-manuf...). So what's the price difference paying for? A recent Bloomberg Odd Lots episode makes the case that it's largely advertising and marketing (https://www.bloomberg.com/news/audio/2025-11-28/odd-lots-thi...). Credit unions are non-profits without big marketing budgets, so they can pass those savings on, but a lot of people don't know about them.

I built this dashboard to make it easier to shop around. I pull public rates from 120+ credit union websites and compares against the weekly FRED national benchmark.

Features:

- Filter by loan type (30Y/15Y/etc.), eligibility (the hardest part tbh), and rate type - Payment calculator with refi mode (CUs can be a bit slower than big lenders, but that makes them great for refi) - Links to each CU's rates page and eligibility requirements - Toggle to show/hide statistical outliers

At the time of writing, the average CU rate is 5.91% vs. 6.23% national average. about $37k difference in total interest on a $500k loan. I actually used seaborn to visualize the rate spread against the four big banks: https://www.reddit.com/r/dataisbeautiful/comments/1pcj9t7/oc...

Stack: Python for the data/backend, Svelte/SvelteKit for the frontend. No signup, no ads, no referral fees.

Happy to answer questions about the methodology or add CUs people suggest.

I built Fresh to challenge the status quo that terminal editing must require a steep learning curve or endless configuration. My goal was to create a fast, resource-efficient TUI editor with the usability and features of a modern GUI editor (like a command palette, mouse support, and LSP integration).

Core Philosophy:

- Ease-of-Use: Fundamentally non-modal. Prioritizes standard keybindings and a minimal learning curve.

- Efficiency: Uses a lazy-loading piece tree to avoid loading huge files into RAM - reads only what's needed for user interactions. Coded in Rust.

- Extensibility: Uses TypeScript (via Deno) for plugins, making it accessible to a large developer base.

The Performance Challenge:

I focused on resource consumption and speed with large file support as a core feature. I did a quick benchmark loading a 2GB log file with ANSI color codes. Here is the comparison against other popular editors:

  - Fresh:   Load Time: *~600ms*     | Memory: *~36 MB*
  - Neovim:  Load Time: ~6.5 seconds | Memory: ~2 GB
  - Emacs:   Load Time: ~10 seconds  | Memory: ~2 GB
  - VS Code: Load Time: ~20 seconds  | Memory: OOM Killed (~4.3 GB available)

(Only Fresh rendered the ansi colors.)

Development process:

I embraced Claude Code and made an effort to get good mileage out of it. I gave it strong specific directions, especially in architecture / code structure / UX-sensitive areas. It required constant supervision and re-alignment, especially in the performance critical areas. Added very extensive tests (compared to my normal standards) to keep it aligned as the code grows. Especially, focused on end-to-end testing where I could easily enforce a specific behavior or user flow.

Fresh is an open-source project (GPL-2) seeking early adopters. You're welcome to send feedback, feature requests, and bug reports.

Website: https://sinelaw.github.io/fresh/

GitHub Repository: https://github.com/sinelaw/fresh

It all started as an experiment to see if I could build a game making heavy use of Deno and its SQLite driver. After sharing an early build in the „What are you working on?“ thread here, I got the encouragement I needed to polish it and make a version 1.0 for Steam.

So here it is, Microlandia, a SimCity Classic-inspired game with parameters from real-life datasets, statistics and research. It also introduces aspects that are conveniently hidden in other games (like homelessness), and my plan is to continue updating, expanding and perfecting the models for an indefinite amount of time.