
Startup School Guide: Growing Technologies - coingig
After the first couple lectures from &#x27;How To Start A Startup&#x27; I came up with some ideas of how to go about my next startup.<p>The points I came up with are:<p>1) Find out a side project to work on instead of a startup idea<p>2) Just learn<p>3) Choose a growing sector<p>My question to everyone is if we could compile a list of any growing technologies that others as well as myself can focus on and start our own side projects. Some that come to mind are:<p>-P2P<p>-Virtual Reality (Oculus Rift)<p>-Secure messaging smartphones (Cyber Dust)<p>-3D Printing (MakerBot)<p>-Wearables (Apple Watch)<p>-Internet of Things<p>-NFC (iPhone 6)<p>The technologies don&#x27;t have to be new but ones that are growing.
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switch33
[https://en.wikipedia.org/wiki/List_of_emerging_technologies](https://en.wikipedia.org/wiki/List_of_emerging_technologies)
Also pretty relevant here, wikipedia has a list of emerging technologies.

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switch33
My list is somewhat of the same but also different as well. It is focused on
specialization within those trends rather than just a broad overview. The
difference is specialization or niche in these markets can mean the difference
from surviving as a business or failing.

Something else to be aware of is if you develop something now you are most
likely to see it's production use within 3-5 years of development and then you
are most likely to see it's market either growing or not in 10 years. This is
an interesting dichotomy because it means picking things that may be hard but
boring, or fun depending on how you look at them.

I am not affiliated with any of these companies. I just think some of them are
well-placed in the market.

-Devops (mostly containers and virtualization software, but also fast/easy build software, customizable configs that adapt better and searchable/informative apis)

-Devops massive ssl/managing keys/passwords deployment software (there are a few automated tools that may help with this but there is in general not too many companies currently handling it)

-Devops massive worker model software or software that helps automation in newer/interesting ways like stackengine ([http://stackengine.com/blog/](http://stackengine.com/blog/)) just got $1 million in funding and provides a way to convert vagrant images to docker images and back again in a fast fashion

-Devops with companies that provide continuous integration at scale or preconfigured devops things that require too much learning to integrate etc, or alternatively lease integration tools. Also managed data worflow between many integrative models. [https://jidoteki.com/](https://jidoteki.com/)

-Saas, PaaS, etc software as a service especially in devops looks to be going in several directions or so: 1)safe php applications using sandstorm sandbox and type-safe php llvm type stuff (integrating some of parse: [https://parse.com/docs/php_guide](https://parse.com/docs/php_guide) which makes traveling from one php application to another seamless, and Hoa which makes dsls more manageable [http://hoa-project.net/En/](http://hoa-project.net/En/) ) 2)devops delivered through docker + automated binary releases 3)devops in nodejs that controls docker and other things or just low latency/fast productive js (dart could be a good useful bet) 4)determinable execution type programming mostly ocaml and haskell which are considered 90% correct and safer languages 5)clojure immutable data that can be shared between large amounts of web online applications

-safe low latency/low requirement distributed services

-large data crunching (think distributed versus not distributed, purpose of what it is doing, competition)

-the coming large amount of "internet of things" in the next few years will be tons of small robots because of finally cheap enough robotics that they will be buyable + good software markets from many different companies working together The internet of things opens a new dimension for SaaS products that interact seamlessly this is an idealistic idea for how to structure something like that as many multiple agents: [http://eve.almende.com/](http://eve.almende.com/) Also training an AI in a massive simulation with cloud/distributed tech with machine learning algorithms for real usage is a definite possibility

-privacy software (anonymous logins like facebook did, garbled circuits and homomorphic encryption, encrypted multiparty applications like auctions for bidding etc and cloud computing)

-artificial intelligence (not just "deep learning" on it's own like convnets which are doing good performance wise but lack overall structural use, but intelligent software that can solve problems using many other algorithmic components like bloom filters, markov chains, dynamic time warping etc that focuses on specific problem solving interactive on-work jobs, data cleaning, vision, speech recognition and NLP)As a side-note if someone was able to improve data cleaning they can reduce a lot of errors by a large % and is under-utilized at the moment. Although it is problematic that most active learning algorithms ask for a lot more quesitons: [https://amplab.cs.berkeley.edu/publication/scaling-up-crowd-...](https://amplab.cs.berkeley.edu/publication/scaling-up-crowd-sourcing-to-very-large-datasets-a-case-for-active-learning/) . Crowd answering has been a better use for data cleaning.)

-Specifically Artificial Intelligence in vision through point cloud library needs to be more useful. Vision will need to be a very automated and big process and is not talked about as much on how hard it is to determine vision constraints like object detection and avoiding using edge detection etc. Simply learning to move is not the only problem associated with AI since vision is hard: [http://pointclouds.org/](http://pointclouds.org/)

-Specifically Artifical Intelligence using networking to off-load some of the heavy load in computing lots of difficult/complicated tasks by automating large chunks of activity as computer signals this is known as macroprogramming: [http://fiji.eecs.harvard.edu/Macroprogramming](http://fiji.eecs.harvard.edu/Macroprogramming) )

-3D Printing (I think metal 3d printing or harder material like construction from cement is more successful than regular plastic 3d printing as it can lead to more things getting done. 3d printing could be improved wholesale from some re-imaging by making it from adaptable molds rather than a dripple of material that moves (different concept than most 3d printing and may provide different results). Cheaper materials that are durable integration into actual electronics or support for holding computer chips in the printed models)

-Better safer faster payment gateways

-Anything security that helps solve the massive cybercrime problem that still plagues every major company (using AI for reviewing security control policies, account policies, and more CYBR IPO does this)

-Also related in security Intrusion detection systems have risen over 30% in sales lately, and despite defense being a lot better upgraded recently there are still many evident attack vectors. A key factor in this is companies by policy are almost always forced to use older technology, people enjoy renting out windows computers and windows computers have better virtualization server balancing support because they are the most tested)

-data content management systems that make data more available and easily constructable/displayable

-I like how you mentioned "wearables" as a seperate category. There are many things that could be wearables that are not watches most likely that people could use. A swiss army knife type thing (something that can solve many problems) that is electronic yet still carryable on a normal day basis or maybe job specific could take off.

-Using constraint provers to reason around problems in the human domain (math etc) or in computer optimization/security

-Generated business reports using machine learning + formatting (abuse free services like ipython, ihaskell, etc for output)

-Tools that keep people productive (extremely wide category there is lots of old software in it, but mostly is just old, any new way with better UI and more useful features can surpass tons of old software.)

-A "sensor world." Sensors cost near to nothing now (just a few cents under a dollar) and they can be placed basically everywhere. There needs to be a better hive-mind like use for them. Also pretty soon phone apps or simple vision type apps could be able to quickly identify objects around them using web-apps and machine learning that could provide specs or information on them as well as interact with them.

-Just simply "tech of tommorrow." Things that "can scale" massively or improve performance tech wise with speciality in that domain, though this isn't likely a first startup idea.

Note: I can list more if people want or I could expand with more specific
examples of software or companies that do things in these fields etc. Feel
free to reply.

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switch33
A simpler way: Look at what big tech company x is doing that looks to be
selling. Do they use an open source solution to it if it is software based? Do
they provide a api or something that could be useful? Is there a market for an
addition built on their framework or is it based off of an open source
implementation which can be improved?

If yes to all above then you can maybe turn that into a product. A great site
for seeing what is available for frameworks is yeoman:
[http://yeoman.io/generators/](http://yeoman.io/generators/) .

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coryl
Cryptocurrencies, drones

