HckrWeb, elegant readable HN sorted chronologically

Hacker News

HckrWeb combines HackerWeb's elegant readability with hckerweb's chronological list of HN homepage items. Originally just a mashup of cheeaun's HackerWeb and wvl's hckr news. I've updated it so I can read Hacker News just the way I want to. ## Major changes/innovations: - Default news feed is from hckrnews.com (all items that have made the HN homepage, sorted by date). - Direct "More..." links (no scrolling required) to dates as far back as June 9, 2010 (No hckernews data beyond this date: https://hw.leftium.com/#/date/2010.06.09 ) - https://hw.leftium.com/#/show combines HN's show and shownew pages. (Removes "did I already read up to here?" confusion when "Show HN" items are in both lists.) - https://hw.leftium.com/#/ask shows the first two pages of Ask HN - Displays last time you visited. - Renders individual comment items. This is useful for sharing, or reading a child comment tree. - "Pagedown" icon to scroll past items you've already seen. Tapping an item's icon scrolls it to the very top. More precise and less finger movement! - [dead] items show more info and link directly to news.ycominbinator.com for viewing (if logged in).

Visit product View on Hacker NewsOct 17, 2022

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Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, io · Missing: https docs, excited, just released

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos

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

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

32%32% 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.

Correct prediction on native model

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