I made danluu's blog easier to read

Hacker News

Hi, I'm Alex! I believe most of the knowledge is nowadays stored and hidden in personal blogs. I have a list of blogs that I read recurrently, and I have benefited myself a lot from this practice. One of the blogs that are usually recommended is the one by Dan Luu. Dan writes about a variety of topics in his blog, and I usually enjoy what he writes. But besides from the content Dan's blog stands out for the complete lack of CSS. IMHO this makes the content difficult to read (it's like reading text from a windows text editor). I believe more people will read and enjoy his blog if a pinch of CSS was added to the blog, so I've taken the liberty to enhance the reading experience. I wrote a script that periodically checks Dan's blog and publish the content in a static webpage (hosted using GitHub Pages). Regarding the CSS I just copied the CSS proposed in http://bettermotherfuckingwebsite.com/ and changed the font family. Give it a look and see what you think! Enjoy the improved readability. P.S. The code that does all this magic might not win any awards for beauty, but it gets the job done. If time permits, I "promise" to tidy it up in the future. Your understanding is appreciated!

Visit product View on Hacker NewsAug 23, 2023

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

73%73% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

62%62% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

28%28% 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

16%16% 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.

Incorrect prediction on native model

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