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Briefed – Summaries for Hard Paywalled Content

Hacker News

Briefed – Summaries for Hard Paywalled Content

Hey HN! Briefed developer here. Briefed creates summaries for (mostly hard) paywalled content. I built it because it's something that I wanted, but couldn't find (specifically the hard paywalled part). Why did I want this? 1. There are a lot of publications that I'm only tangentially interested in. Not enough to warrant a full subscription but enough to want to get the gist of what they're saying. A step between reading the title and reading the full article. Here's a list of the publications we currently create summaries for: https://briefed.news/publications 2. I often don't have the time to read entire articles. I can save an article as "read later" in my rss app, but I've often lost interest by the time I get to it. Briefed creates two summaries for each article: one detailed, and one tldr. Here is an example of one: https://briefed.news/post/softbank-sues-former-irl-ceo-for-f... 3. Sometimes I've read the full paywalled article and want to read comments here on HN, but hard paywalls limit the engagement those posts will get. Hopefully a detailed summary is enough to start meaningful and curious conversation. I'm still kicking the tires, so the first 500 people to sign up will get the first year free (normal price is $12/year; the price that I felt I would personally pay for such a service). Use the promo code LAUNCH500 at checkout. The code will be automatically invalidated after 500 people use it. Let me know what you think! Any feedback , issues, or questions are welcome. Thanks, Brandon

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: new, code · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit
19%19% 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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