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Tesletter – Tesla news curated with love delivered to your inbox weekly

Hacker News

Tesletter – Tesla news curated with love delivered to your inbox weekly

I've been a Tesla owner (Model S 75) since December 2017, and since then I've spent a crazy amount of time reading about Tesla (forums, Twitter, Reddit, etc.). I've never spent so much time reading about any other car that I've owned - I don't think I've done that for any other gadget - and I thought it would be of interest for others to get a hand-picked list directly in their inboxes once a week. Not only because of how much time it takes to consume the information but also because some exciting information passes without making "too much noise." I'm collecting up to 10 links per week and sending them out on Thursday mornings. I stick the topic to Tesla (the product) and not to their stock, who is their CEO dating, etc. A couple of examples: - Issue 6 - May 3rd, 2018 - https://tslttr.com/issue-6 - Issue 5 - April 26th, 2018 - https://tslttr.com/issue-5 - Issue 4 - April 19th, 2018 - https://tslttr.com/issue-4 Our homepage https://tesletter.com

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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: io · Missing: https docs, excited, just released
61%61% 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: model, new, inbox · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
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
13%13% 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.

Correct prediction on native model

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