Can you spec a random dribbble in 60 seconds
http://specthis.parseapp.com this is a simple app to keep spec writers (product managers and designers for apps currently) stay sharp through a fun spec'ing exercise. you are shown a random dribbble image. you have 1 minute* to give: - a 3 word description - a 1 liner - a 1 para description - tags and a name for a hypothetical spec. it currently has 500 designs. and it takes me just a one-liner to add many more. ( PS: is 1 minute too much, too less ? ) once i have enough data, i think i'll be able to build an inverted index. ie: given a design pattern or tag => show me similiar…
What it does
In the maker’s words, at launch
http://specthis.parseapp.com this is a simple app to keep spec writers (product managers and designers for apps currently) stay sharp through a fun spec'ing exercise. you are shown a random dribbble image. you have 1 minute* to give: - a 3 word description - a 1 liner - a 1 para description - tags and a name for a hypothetical spec. it currently has 500 designs. and it takes me just a one-liner to add many more. ( PS: is 1 minute too much, too less ? ) once i have enough data, i think i'll be able to build an inverted index. ie: given a design pattern or tag => show me similiar designs or "specs". i was helping a friend of mine train to think like a product manager. so i built an mvp this weekend http://specthis.parseapp.com flashcards for GRE/GMAT/TOEFL are well known, but even a google search for specs show practically nothing. mind games for product managers/designers/spec writers are unheard of. perhaps this could even become a github for specs. i have a few 6-7 letter domain names i've soft circled as well. PS: a major reason for posting here is that i am looking for a designer to collaborate with. if you like the idea, help spread the word.
Does the same job
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Hi HN, I built Eigendrum, a web tool that solves the 2D wave equation for arbitrary shapes so you can hear what they sound like as drums. How it works: * Solves -∇²u = λu using finite element analysis (Kφ = λMφ) on a triangle mesh. * Validated to <0.1% error against closed-form solutions for circles (Bessel zeros) and rectangles. * Sound model factors in strike location, Rayleigh damping, and mallet width. * Includes Kac drums I & II to demonstrate identical sound spectra from different geometries. * No frameworks, build steps, or dependencies. Repo and tests:…
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Launched alongside, November 2014
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- MM
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