Startups & Ventures

2022-02-04 13:23:43 Join our group Bybit Activities 2022(EN) to see more details to have the chance to get up to $500 bonus!

link: https://t.me/bybit_activities_en Open / Comment

2022-02-03 20:46:01 Finding reliable users to fill out your surveys is too time-consuming
Recruiting people for your customer surveys with a specific demographic and recruiting enough of them to have representative samples is incredibly time-consuming. I really wish that there was a service where I could fill out a couple of parameters and request these users on demand.
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Attract early-stage supporters and contributors for your project Open / Comment

2022-02-03 16:46:08 Attract early-stage supporters and contributors for your project

Today is the launch day of Tokenhunt.club - a tool allowing you to create a dedicated page for your product / project, get a sharable link and attract your first loyal audience. This is a way to ensure early boost and valuable feedback at the beginning of your product journey.

The project is now Open Beta, so you're getting all the features for free and also you can make it to the list of featured projects and get noticed by other community members, incl. engineers, vc's etc.

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Publish your project at: https://tokenhunt.club
Example of a project page: https://tokenhunt.club/project/phygital-flowers-marketplace

In case any questions / feedback or support feel free to contact @yuralisin

Good luck with your ideas Open / Comment

2022-02-03 09:03:31 Common reasons why startups fail by Failory.com:
- Marketing mistakes were by far the most common, and they were generally speaking the most deadly with 69% of all mentioned marketing mistakes being fatal. In fact, the fatal marketing mistakes were more numerous than all other fatal mistakes combined (56% vs 44%), as can be seen in the pie chart below. By far the most common reason for shutdown was lack of product-market-fit which constituted more than half of the marketing mistakes, but more on that below.

- Team problems – friction, lack of experience, lack of motivation, etc., were the second most common. They were some of the least-deadly percentage-wise (only 39% of all mentioned team problems being fatal), but because they are abundant they were still the second most common reason for shutdown.

- Financial problems and mistakes were the third most common. That said, bearing in mind more than 50% of the projects didn’t have any budget to begin with and more than 75% of the projects were self-funded, it’s a surprise that only 16% of the projects point at financial problems as the major reason for failure.

- Tech problems were extremely rare, which is surprising considering almost all projects in the data have a technical side to them. The most common remark was that too much time and effort was spent on tech that proved to be useless in the long run. The most common answer to “a thing you would do differently next time” by far was “I’d talk to customers and validate my assumptions before writing a single line of code”. That said, a big % of tech problems were fatal: e.g. relying on a 3rd party API that changes can ruin a business overnight.

- Operational problems were quite rare and not that deadly, but it’s important to mention that most interviewees ran software projects, so operational problems (e.g. suppliers, distribution) are not as common as in brick-and-mortar and physical product projects by definition.

- Legal problems were rarest, mentioned only four times, but two of those four proved to be lethal. For most early-stage startups the legal side is a non-factor. Yet, there are still industries where you can’t afford to ignore it (food, finance, etc.). Open / Comment

2022-01-30 20:59:51 Open-sourced fitness bracelet
Recently I've got a fitness bracelet(Honor) to track my sleep better and other metrics. I had an idea to collect as much data as I may through API, so I'd build an app to get my(!) data and analyze it in a way I prefer. Why? I'd like to have data for the years I wear the device to make my conclusions and visualizations, rather than just what I see on their app.

Why would anyone care? A bracelet app may show you how well you sleep, but not raw data you or other people would analyze better. You may scan the data through more advanced algorithms(e.g. employing ML) than just doing "if sleepTime < 6 hours { return 'You slept too little' }". There are good data though my bracelet app presents to me I could handle it to make some other aggregations.

The other thing to mention is it'd be nice to have all your raw personal data that the device collects, right? Why can't you upload it all and analyze yourself? Yes, I bought the thing, give me the great API. Instead, there are many obstacles. In my case, I should register in some Huawei(the Honor parent) company development program, then get their development kit available only on Windows. It's ridiculous.

A potential solution? Well, some (nerd?) human-oriented devices and open API to do what you want with your data. Analyze it. Make your server the device will send the data to. Do whatever you want. No closed APIs and unknown policies regarding your data that is being sent to some big company servers, so they can "improve our metrics". Open / Comment

2022-01-29 19:51:45 Cloud video transformation
Many companies build their own solutuions to work with videos. For instance, encoding, decoding, cutting, making previews and so on. For a company which goal isn't providing such functionality, it's resource consuming to make its own solutions to work with video.

The idea is to provide video transformations functionality as API in a cloud. Thus, I can easily, for example, cut a video file, then reduce its size in one or two API calls instead of spending a huge amount of time implementing this myself.

Monetization: users pay for an API call. Or tiers for a specific amount of calls per day, week, or month. Open / Comment

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2022-01-27 19:12:11 Idea: Algorithmic Casting Director
Problem: There about 135,600 actors in the US that are both employed and unemployed. Globally this number is even higher. Given this, it is extremely difficult for casting directors to find high-quality talent.

Solution: An algorithmic casting director that can analyze the audio and video submissions from candidates to determine if they would be a good fit for the role. Right now, casting directors have to watch each individual video in order to determine whether or not to move someone forward in the process. Given that these directors are looking at hundreds or thousands of videos, this leads to implicit bias. Thus, the business would attempt to eliminate bias and increase diversity in casting by being more objective and (hopefully) more accurate in finding high-quality artists. Open / Comment

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2022-01-26 09:15:06 Idea: Releases tracker
A tool that helps me to track when my favorite series, shows, movies will be released. Also, it'd be great to track recent news that mention those movies/series. Open / Comment