Te

Technavneet.com – An online e-commerce retail store

Hacker News

Technavneet.com – An online e-commerce retail store

It's been some time and I have been willing to share something with you guys! I was very hesitant of doing so as I was being naive, I thought of doing everything with perfection. Anyways I digress. I want to introduce you to our e-commerce/offline retail store Technavneet IT Solutions( https://www.technavneet.com ). We sell IT hardware(PC Parts - CPU, Mobo, GPU, Cases, RAM etc) , Laptops, Servers and provide IT services ranging from Web Development, Design, Under the name of ndesignworks. I am trying to be as resourceful as possible and would love to hear some advice/tips anything. We have forum to have discussions about tech/services/news.(forum.technavneet.com) I started this business because it was really hard for me to find a retailer/e-commerce in India who offered good selection of parts, had good website(atleast usable), had technical support and great pricing. Btw last but not the least. Let me introduce myself. I am Navneethakrishnan and I am 21yo. Working on this for past 8 months. (Had some issues with hosting company previously). And I have experience in this field since I was in 4 standard, Building, breaking stuff is what I do as a hobby. I live in India as you might have guessed. Thanks for taking time to read the post. Edit: You can reach me at hello(@)technavneet.com

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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
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
28%28% 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
19%19% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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