Thi.ng – open-source building blocks for computational design

Hacker News

The first stage of the new http://thi.ng website is live, an up-to-date & hopefully more useful springboard to the 250+ projects and overview of my opensource activity since 2006... Work is ongoing, collecting & re-organizing projects, assets, documentation and extracting interrelationships (incl. older work & music projects (e.g. http://thi.ng/synstack related), clients/people...). Aim is a proper archive of the past 20+ years of outputs & encounters. This is the first website update since 2015. As the timeline visualizations show, building(http://thi.ng) > marketing(http://thi.ng) - maybe unusual these days, but hey... :) The entire site is generated w/ tools from the http://thi.ng/umbrella collection, e.g. http://thi.ng/egf (graph file format), http://thi.ng/csv (semantic CSV parsing), http://thi.ng/transducers (functional data transformations), http://thi.ng/defmulti (multiple dispatch for template engine), http://thi.ng/hiccup (HTML/SVG generation), http://thi.ng/viz (timeline visualizations), http://thi.ng/color (heatmap gradients) There's also a massive spreadsheet (148k cells adjacency matrix) to centrally define & manage the 570+ tags used to categorize the 257 projects/repositories and to batch update their package files (edits ongoing, much fun!). The tag/project relationships are compiled into an HTML list with project IDs stored as base90-encoded data attributes for each tag in the tag cloud (http://thi.ng/#tags) The timeline visualizations are generated directly from local Git repos, package files and associated metadata stored in a number of linked http://thi.ng/egf graphs. The site toolchain will be released in due course... Last but not least, any constructive feedback (good or bad) is very welcome! The next phase will include more individual project details, images, making-of docs, videos, xrefs etc. Currently, just a step in the right direction...

Visit product View on Hacker NewsNov 28, 2020

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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