Art Dev Lev Build (Ft Special HN Edition)

Hacker News

+ _1_ Special HN Edition “There’s a lot left to discover.” - PG http://paulgraham.com/useful.html //not your average WH + _2_ “always day 1” be os //stream the Amazon + _3_ gpt3 spring / out pandora’s like solstice //samo faithful automattic ink + _4_ æ - a new code - a new coin - a new world - a new build - a new word - a new turn - a new ring //æternal + _5_ it’s a template it’s an art dev. lev. build //it’s a practice + _6_ ranksy rat only cat with a rap that PageRank itself //barz on the stack (another 1) + _7_ r&r reading ranksy like reading rainbow like reading railroad //b&n + _8_ out the underground spray spray! slash slash! quik lines of neon rhymes sharp cut pixel-sign’d the screen w/ mythic digit tech ~ æ a BASIC symbol graffiti’d via modest mau5 that pounced straight out banksy’s stencil, tagged a column in the forum & scrammed — //beyond the forum + _9_ * a new microblog style * a new way to idea-aggregate //what will you build? + _10_ "I don't get no respect" - RN like this cat on HN //cat can't code _______ _______ _______ original HN article / microblog style debut - https://news.ycombinator.com/item?id=26476204 feedback / criticism / suggestions welcome! more at https: // superstrings . substack . com

Visit product View on Hacker NewsMar 20, 2021

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TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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