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StreetEYE, all the top financial market news in one click

Hacker News

StreetEYE, all the top financial market news in one click

Some people have called StreetEYE 'HN for investors', but it's a little different. You can go to Bloomberg, Reuters, WSJ, FT and hit a page with the most-read stories, but now everyone shares everything on social networks and blogs, so we can try to make one page with the most popular news across all sites and blogs - find the top people who share news, and combine algorithmic, professional, and crowdsourced curation to make the best front page across financial media. StreetEYE is more like a mashup of HN, Techmeme, Google News, Flipboard, Buzzfeed. For now, no comments or submissions to avoid spam, but will add them. A longer manifesto here - http://blog.streeteye.com/blog/2013/10/the-streeteye-manifesto/ Been around for a couple of years in various iterations, has a bit of traction among financial journos/twitterati. Never submitted before because the typical HN reader is not the target, but just did a relaunch and there was a lot of interest in this thread where it was mentioned - https://news.ycombinator.com/item?id=6844565 Other sites in similar space http://boredbanker.com http://filll.com http://www.realclearmarkets.com/ http://www.businessinsider.com/clusterstock/thehive http://www.usv.com for VC (of course) http://www.trejdify.com for broader business news Would love to hear feedback, suggestions, any issues let me know! Be gentle, still MVP/POC. Platform is Django on back end, Bootstrap + crude jQuery on front end. It's not an x86 emulator/Riemann hypothesis solver in Javascript/Haskell but it's been a fun project and I hope people find it useful. If it has product/market fit, would look to build a team, do a startup accelerator.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
81%81% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
76%76% 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: google, new · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
33%33% 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.

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