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Ask HN: What startups are working on hard, technically challenging problems?

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Re: Ask HN: What startups are working on hard, technically challenging problems?

#111

Post by multiklout »

kzhahou wrote: post #61
multiklout wrote: post #36

Oscar Health in NYC is fixing the health insurance industry by building a tech and data driven insurance company from the ground up. It's an extremely complex, highly regulated industry that is in need of fixing. There are many startups that try to address pieces of the broken health care system, but Oscar is the only startup trying to solve it by doing it all. Engineering talent from Facebook, Google, Tumblr, Spotif…

No doubt health insurance is hard problem, but it's not technically hard . OP asked for examples of company's pushing the limits of advanced technology, and Oscar is not one.

It's not? Why do you feel that way?

Re: Ask HN: What startups are working on hard, technically challenging problems?

#113

Post by semi-extrinsic »

lmeyerov wrote: post #105
semi-extrinsic wrote: post #83

Earlier quoted context omitted.

What do you mean, real-time GPUs? And: what interconnect are you running on? How does your scaling look? Is this just for embarrasingly parallel stuff? Just curious; I'm running multi-GPUs myself for molecular dynamics.

Imagine moving a slider in your viz to change a filter, physics setting, etc. and have cluster start to immediately feed back new results. We started on building fast-start multitenant access to single GPUs and approaching peak on those (full-GPU barnes hut, 10X over Keshav's work). We're now focusing on distributing, and as we are more interested on running on many GPUs for scale out, focusing on communication avoid…

Ok, I see, so your approach makes sense for problems where throwing some GPUs at it gives you a solution in O(10) seconds? Sounds nice if you know you hav problems that fit into that category.

I found the Graphistry webpage lacking in answering the question "which specific problem does this solve?" Infoviz is too broad/vague.

Re: Ask HN: What startups are working on hard, technically challenging problems?

#114

Post by semi-extrinsic »

IgorPartola wrote: post #43
yen223 wrote: post #22

Earlier quoted context omitted.

Is Kuka really a 'startup'? They are very well established among the industrial robotics scene.

What a name!..

Yes, they're obviously not Scandinavian (where the name means male genitalia, plural).

Funfact: in the northern parts of Norway, calling a policeman a "horses penis" is not illegal, as that is a somewhat common thing to call another person. Yes, this was tested in court in 2008.

Re: Ask HN: What startups are working on hard, technically challenging problems?

#115

Post by diafygi »

https://UtilityAPI.com/ is making the Twilio for energy.

Humans needs to switch 87% of our energy sources (~450 Quadrillion BTU/yr) from fossil fuels to other source in our lifetimes[1]. That's an unbelievably huge transition, and one of the current bottlenecks is energy data communication. Solar and energy efficiency companies can't communicate efficiently with utilities and vice versa because each utility has a unique, antiquated, manual system for handling energy data. We are trying to fix that by making a universal API that wraps utilities so energy innovation can happen efficiently on both sides of the meter.

Yes, we are hiring: https://angel.co/utilityapi/jobs

[1]: http://www.pvsolarreport.com/the-next-internet/

Re: Ask HN: What startups are working on hard, technically challenging problems?

#116

Post by kentonv »

Sandstorm.io (https://sandstorm.io) is working on turning server infrastructure and security upside-down without the need to rewrite applications. The goal is for distributed infrastructure and security to be as easy as possible for end users to manage, and as hard as possible for applications to screw up. The model we're building is capability-based; previous capability-based research systems have usually required rewriting apps to fit the model, but ours does not. This leads to a lot of interesting computer science, systems engineering, security, and UX problems, both in designing a new model that is practical and just-in-time reverse-engineering of application intent in order to seamlessly fit it into the new model.

Possibly-interesting technical posts:

https://blog.sandstorm.io/news/2014-08-19-why-not-run-docker...

https://blog.sandstorm.io/news/2014-08-13-sandbox-security.h...

https://blog.sandstorm.io/news/2014-07-24-tinytinyrss-plus-s...

https://blog.sandstorm.io/news/2014-05-12-easy-port.html

https://blog.sandstorm.io/news/2014-12-15-capnproto-0.5.html

https://blog.sandstorm.io/news/2014-07-21-open-source-web-ap...

https://blog.sandstorm.io/news/2015-01-14-compute-units.html

Sandstorm is not currently actively hiring. But it is an open source project if you're interested in contributing. See:

https://github.com/sandstorm-io/sandstorm/wiki/Get-Involved

Re: Ask HN: What startups are working on hard, technically challenging problems?

#117

Post by fsniper »

josh2600 wrote: post #52

Terminal.com is writing next generation virtualization. This includes live-migration without hypervisors (already in production) and live-resizing (also in production). I think we'd like to think we're working on the ugly bits of infrastructure people don't care about. This includes a distributed file system optimized for speed and storing machine state like Github, software defined networking for IP migration across…

This is really interesting. I have never heard of Terminal.com before and I'm now deeply interested. Do you hire remote or sponsor visa?

Re: Ask HN: What startups are working on hard, technically challenging problems?

#118

Post by semi-extrinsic »

joelrunyon wrote: post #67
damnusername wrote: post #6

I submitted this to slashdot a few weeks ago, but it got declined: "For some time now, I have been following the "startup scene" and frankly, I am left with a sense of dismay. How many of the startups actually do anything of any real value to mankind? It seems to me that the startup ideas just keep getting more ridiculous and stupid by the day and I think I would go as far as to call the whole thing deeply broken. I…

I think a lot of startups are solving important problems that might not be obvious. Any Musk startup - Tesla (electric vs. oil consumption), SpaceX (building space "colonies"), SolarCity (alternate energy), Paypal (payments). You also have Uber (limiting cars on the street & oil consumption), AirBnb (better utilization of current buildings rather than building additional hotels). Even stuff like meerkat, etc could po…

Oh, FFS. Tesla has done a fine job at showing electric cars are feasible, for sure. But without real innovation and progress in emission-free baseload electricity generation (i.e. fission or fusion) it has negligible effect on reducing GHG emissions.

Re: Ask HN: What startups are working on hard, technically challenging problems?

#119

Post by bobosha »

At Netra (http://www.netra.io) we are building true visual search. Imagine being able to search video/images using other image(s) or video clips. Yep we are doing that, and eventually Google scale.

We see immense benefits to humanity - from medicine, to environmental science, crime prevention/monitoring....down to the trivial - search millions of your photos for your pet dog, look for all photos where you & Aunt Millie were together etc.

p.s : and yes we are hiring.

Re: Ask HN: What startups are working on hard, technically challenging problems?

#120

Post by tmostak »

MapD (http://www.mapd.com) is building a hyper-interactive big data analytics and visualization platform running on multiple GPUs/CPUs. We basically do every performance trick in the book (like compiling our queries via LLVM and caching hot data on the GPU) to do this. The end result is the ability to scan billions of rows of data in milliseconds at rates greater than 2 terabytes/second per server when running on 8 Nvidia K80s (although we also get great results on laptops and even ARM cores.) And not only can you query the data with SQL but you can visualize it or feed it into machine learning algorithms without copies because it is already distributed across all available compute devices.
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