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Forth – Rethinking (and improving) News

Hacker News

Forth – Rethinking (and improving) News

Hi HN -- After a thread a couple of weeks ago (https://news.ycombinator.com/item?id=28866242) got more attention than I expected, and someone even suggested doing a Show HN (thanks wanderingmind)-- so, here goes: We're building a network of local journalists, along with others in national verticals. Our incentive structure exists in a way to promote news people want to come back to read, not clickbait headlines, divisive or anger driving hot takes, or lowest common denominator coverage. All geared to 18-34 year olds, who are less likely to consume local news in the traditional forms, like subscribing to a local paper or watching broadcast TV. It's still early stages; but we're proving out the concept. Newsrooms are infamously understaffed, so asking them to take on another platform is usually a nonstarter. Instead, we offer free, proprietary communication tools to help the staff communicate (https://www.nillium.com/newsrooms) and that syndicates updates out automatically. As we're onboarding traditional newsroom partners, we're also pushing forward with college students (see Ithaca, NY: https://www.forthapp.com/locale/6174d42a63b3f0d8dbef92dc) and also automatically publishing White House Pool Reports (https://www.forthapp.com/dl/whitehouse) which are public, but difficult to obtain if you aren't in the media. I'd love to hear what you think. And as an aside/shameless plug, if you work for a newsroom or cover local news, please reach out -- we'd love to have you on board -- jared at nillium dot com.

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Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
56%56% 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
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
21%21% predicted probability of success on Product Hunt, 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
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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