Show HN: Claudoro, Pomodoro timer embedded in the Claude Code statusline https://ift.tt/Kp5WXI1

Show HN: Claudoro, Pomodoro timer embedded in the Claude Code statusline 3 weeks ago I had a nasty accident and fractured my vertebrae. As I lay in bed I needed something to take my mind off it all so built "Claudoro". Claudoro is a pomodoro timer built right into the Claude Code status line, as well as can be directly controlled from Claude Code and the CLI. A few years ago I built "pymodoro" which was great, but recently I felt I needed something embedded in the tools I actually use, and I also wanted something that was flexible, and I could tweak and nudge. Anyway I hope it is useful to you, and I'd love some feedback on how to improve it. Thank you...! PS this is a write up all about how it works etc: https://ift.tt/ClW4gPo... https://ift.tt/uJKUzkw July 1, 2026 at 02:25AM

Show HN: I measured the half-life of 41,301 Show HN launches. It's 7 hours https://ift.tt/aPTtkRW

Show HN: I measured the half-life of 41,301 Show HN launches. It's 7 hours I scraped every Show HN from the last 12 months (41,301 posts) plus the full comment tree of every launch with 10+ comments, ~100k comment timestamps, all from the Algolia HN API. The median launch gets 2 points and 0 comments. For launches that do get traction, half the comments they'll ever get arrive within 7.2 hours and 90% within 26, and the top decile decays on the same clock as everyone else. Vote timestamps aren't public, so comment timing is the attention proxy; caveats are in the post. Everything reproduces from the repo with one command ( https://ift.tt/9udb8kD ), and every number in the post maps to a named function. Keen to hear where the methodology falls short https://ift.tt/CdF8u6T July 2, 2026 at 01:38AM

Show HN: Frond – a frontend runtime for your app's dependency graph https://ift.tt/TxgrGCZ

Show HN: Frond – a frontend runtime for your app's dependency graph https://ift.tt/GTE98QU July 1, 2026 at 02:10AM

Show HN: I computed livability for all of Germany by rent, commute, and QoL https://ift.tt/63c2oiZ

Show HN: 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 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://ift.tt/10n6FtM ), or if you're really curious, peek the source code itself on GitHub ( https://ift.tt/Nub2VkC ). Happy exploring! I'm dying to get some feedback. https://ift.tt/2ht1s6q July 1, 2026 at 02:11AM

Show HN: HackerNows – Native iOS HN Client https://ift.tt/EOQuYfb

Show HN: HackerNows – Native iOS HN Client https://hackernows.app/ July 1, 2026 at 12:45AM

Show HN: Erlangchain – A tiny Erlang client for LLMs https://ift.tt/qirTuM6

Show HN: Erlangchain – A tiny Erlang client for LLMs I made a small Erlang library for calling OpenAI and Anthropic from Erlang without third-party dependencies. Includes a simple chat API, basic tool-use support, multimodal message support, and some JSON utils. Try it out! https://ift.tt/Ja8EUzb June 30, 2026 at 09:57PM

Show HN: Classic Minesweeper https://ift.tt/6PKmAx4

Show HN: Classic Minesweeper https://ift.tt/Prypzkv June 29, 2026 at 10:40PM