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The Who and Why of the Web – Q&A Format on Web Technologies and Culture

Hacker News

The Who and Why of the Web – Q&A Format on Web Technologies and Culture

Hey Hackernews! I wrote a small rambling piece on my 'Why' of the web, and the assumed roles we play when we use the web, and how we can subvert these roles and use the web in our own prescribed way. http://blog.higg.so/2015/11/08//the-why-of-the-web/ It talks about the noise surrounding tech startups and disregards 'The What' which tech startups so feverishly try to answer. 'Why' tends to go deeper, and it's better to question our why than look at the What. Feel free to give constructive feedback on my post, and let me know your why, if you have any? -Enginnr

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% 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
18%18% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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