Predicting where AI is going in 2020 - <AUTHOR> https://venturebeat.com/2020/01/02/top-minds-in-machine-learning-predict-where-ai-is-going-in-2020/ <comment><USER2> Honesty, repeatability, numerical analysis. Canonicalization.

Honesty: how many times was the exact same data processed? Was the result cherry picked and the best one published? For the sake of integrity how is it possible to scientifically improve on this result? (example, your AI outputs some life altering decision?)

Repeatability: In science, if a result can be independently verified, it gives validity to the "conclusion" or result. Most AI results cannot be independently verified. Not indepentently verifiable really ought to give the "science" the same status as an 1800's "Dr. Bloobar's miracule AI cure".

Numerical Analysis: performing billions / trillions of computations on bit- restricted numerical values will introduce a lot of forward propagated errors (noise). What does that do? Commentary: Video cards don't care if a few bits of your 15 million pixel display are off by a few LSB bits, they do that 60 or 120 frames a second and you don't notice. It is an integral part of their design. The issue is, how does this impact AI models? This affects repeatability -> honesty.

If error of a quantized size is a necessarily required property to achieve "AI learning that converges", there is still an opportunity for canonicalization -- a way to map "different" converged models to explain why they are effectively the "same". This does not seem to be a "thing", why not?

In my opinion, in 2020, the AI emperor still has no clothes.

<comment><USER3> For most of the engineering applications I work on, AI is useless.

When we talk about controlling machines, our control algorithms have mathematically proven strict error bounds, such that if we provide an input with a particular maximal error (e.g. from a sensor that has some error tolerance), we can calculate what's the maximum error possible in the response that our model would produce, and then use that to evaluate whether this is even an input that should be handled by the current algorithm or not.

These control algorithms all take some inputs, and use them to "predict" what will happen, and using that prediction, compute some response to correct it. These predictions need to happen much faster than real time, since you often need to perform an optimization step to compute an "optimal" response.

These predictions are usually computed using a reduced-order model, e.g., if you had to solve a PDE over 10^9 unknowns to compute the actual prediction, you can instead reduce that to a system with 10 unknowns, by doing some pre- computation a priori. Most tools to do these kinds of reductions developed in the last 60 years come with tight error bounds, that tell you, depending on your inputs, the training data, etc. what's the largest error than the prediction can have, so you can just plug these in into your control pipeline.

People have been plugin in neural-networks to control robots, cars, and pretty much anything you can imagine into these pipelines for 10 years, yet nobody knows what the upper bound on the errors that these neural-networks give for a particular input, training set, etc.

Until that changes, machine learning just makes your whole pipeline unreliable, and e.g. a car manufacturer must tell you that in "autonomous driving" mode you are liable for everything your car does, and not them, so you have to keep your hands on the driving wheels and pay attention at all times, which... kind of defeats the point of autonomous driving.

\---

Prediction: we won't have any tight error bounds for real-world neural networks in the 2020-2030 time frame. These are all non-linear by design (that's why they are good), error bounds for simple non-linear interpolants are pretty much non-existent, people have tried for 20-30 years, and real- world NNs are anything but simple.

<comment><USER4> Control algorithms are a part of the problem. What about input data? There's nothing that comes close to NNs in answering a question, say, "Is there pedestrian ahead and what he/she will probably do?"

A control system doesn't need to be end-to-end neural, by the way.

<comment><USER3> > What about input data?

What about it? You get input data from data sources which, e.g., in a car it would be a sensor. The manufacturer of the sensor provides you with the guaranteed sensor accuracy for some inputs, which gives you the upper bound on the input error from that source.

That is, in a reliable control pipeline, the upper bounds on the errors of data sources are known a priori.

Sure, sensors can malfunction, but that's a different problem that's solved differently (e.g. via resiliency using multiple sensors).

> A control system doesn't need to be end-to-end neural, by the way.

Who's talking about end-to-end neural nets for control? If a single part of your control pipeline has unknown error bounds, your whole control system has unknown error bounds. That is, it suffices for your control pipeline to use a NN somewhere for it to become unreliable.

This doesn't mean that you can't use control systems with unknown error bounds somewhere in your product, but it does mean that you can't trust those control systems. This is why drivers still need to keep their hands on the steering wheel on a Tesla: the parts of the pipeline doing the autonomous driving use NNs for image recognition, and the errors on that are unknown.

This is also why all "self driving" cars have simpler data sources like ultrasonic sensors, radar, lidar, etc. which can be processed without NNs to avoid collisions reliably. You might still use NNs to improve the experience but those NNs are going to be overridden by reliable control pipelines when required.

<comment><USER4> > That is, in a reliable control pipeline, the upper bounds on the errors of > data sources are known a priori.

Now we have natural neural networks in the control loop for some reason despite their unknown error bounds. To give another example: is there a sensor for vehicle placement relative to road edges with known upper error bound, which is less than width of the road? No, we have GPS, radar, lidar, camera data that we need to interpret somehow.

A car that reliably avoids collisions (Can it, though? It needs to predict road situation to do it reliably), but can occasionally veer off the road, doesn't strike me as particularly safe.

> to be overridden by reliable control pipelines when required.

Those reliable pipelines needs to be mostly reactive. And there's a limit on what they can do. You can't avoid a collision when a car emerges from around a corner with 0.1 seconds to react. You need complex processing of those "simple" data sources to detect zones that can't be observed right now and to assess a probability of the said situation.

All in all, we already have unreliable human part in the control loop of a vehicle. A control system that is provably robust in all real world situations will be the ultimate achievement and not a prerequirement for wide use of self-driving cars.

<comment><USER3> > Now we have natural neural networks in the control loop for some reason > despite their unknown error bounds.

Not in any automatic control loops. All control loops that do this have a human as the final piece of the pipeline, and that human is legally responsible for the outcome of the control loop. That defeats the point of automatic control.

> To give another example: is there a sensor for vehicle placement relative to > road edges with known upper error bound, which is less than width of the > road?

No, which is why these control pieplines have a human in control. For the experimental pipelines that do not have a human in the end of the control loop, they do have other control pipelines to avoid collisions, and the only that their control algorithms guarantee is a lack of collisions, not the ability for the car to stay on a lane. That is, the car might leave the lane under some conditions, but if it does, it will detect other objects and avoid crashing into them (although those objects might crash into it).

> All in all, we already have unreliable human part in the control loop of a > vehicle. A control system that is provably robust in all real world > situations will be the ultimate achievement and not a prerequirement for > wide use of self-driving cars.

Right now, control loops without proven error bounds are not allowed by certification bodies on any control-loop in charge of preserving human lives in the aerospace, automotive, medical, and industrial robotics industries.

Allowing control-loops without known error bounds to be in charge of human lives would lower the current standards of these industries. Could be done (the government would need to create a new kind of regulation for this), but at this point it is unclear how that would look like.

<comment><USER5> Well actually, they are allowed in the automotive world, re: Tesla.

They won't necessarily lower the standard for some applications if the alternative is just regular people at the wheel.

<comment><USER3> > Well actually, they are allowed in the automotive world, re: Tesla.

This is false: Tesla's "autopilot" isn't a control-loop, much less a certified control-loop. What Tesla calls "autopilot" is actually a "driving assistant". It is not in charge of controlling the car, but it is allowed to assist the driver to control the car.

This is a subtle but very important difference, since this is the reason that Tesla tells its owners to always keep the hands on the steering wheel, and Tesla cars are required to disable their "driving assistant" in countries like Germany if the driver hands leave the steering wheel. It is also the reason that Tesla is not liable if a car with "autopilot" kills somebody, because the "autopilot" isn't technically driving the car, the driver is.

The word "autopilot" comes from the aerospace industry, where pilots are not required to keep their hand on the controls or pay attention when the "autopilot" is on, and the manufacturer is liable if the "autopilot" screws up (e.g. see Boing). Tesla's usage of the word "autopilot" to refer to driving assistants is misleading and dangerous.

A true car "autopilot" is what people call "Level 5" in the autonomous driving community. Elon Musk promised to ship 10.000 self-driving Level 5 Tesla taxis in 2019, and now mid 2020. We'll see about that.Elon Musk has been saying that Level 5 will happen next year for the last 10 years, so at least when it comes to autonomous driving, their predictions have been consistently wrong.

Most CEOs who adventure to predict when Level 5 will happen say something like "not before 2030". And Waymo's goal is to achieve """Level 5""" in a very small restricted area of Phoenix downtown for some restricted weather conditions some time between 2020-2030, but it is unclear what the road after achieving it would be for certification, and the road from there to actual, real, Level 5 is still unclear at this point.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER6> >Human babies don't get tagged data sets, yet they manage just fine, and it's important for us to understand how that happens

I do not really understand this. Human babies get a constant stream of labeled information from their parents. Contextualized speech is being fed to them for years. Toddlers repeat everything you say. Is this referring to something else that babies can do?

<comment><USER7> There may be some kind of labeling encoded in genes. One thing that it is safe to assume is genetically encoded somehow is that sounds made by your parents/humans around you is worth repeating while other sounds are not.

However, past that, the actual sounds themselves, and any association to meaning, are pretty far from tagged data sets. Stuff like the specifics of language (e.g. that a dog is called 'dog') are definitely learned, and children learn them with typically only a handful of stimuli, often a single one.

For contrast, imagine training a model with raw sound data tagged only with "speech" vs "not speech" (and probably only a few thousand data points at that) and I will be amazed if it can recognize a single word. And babies don't just learn words, they learn their association to things they see and hear, and grammar, and abstract thought.

Do note that it is very likely that human brains can learn all that because they have some good heuristics built in. We definitely know some stuff is "hardware" \- object recognition, basic mechanics, recognizing human faces and expression, and others. We are pretty sure higher level stuff is also built in - universal grammar, basic logic, some ability to simulate behavior seen/heard in other humans. This specialized hardware was also most likely learned, but over much, much greater periods of time, through evolution over hundreds of millions of years (since even extremely old animals are capable of picking out objects in the environment, approximating their speed etc).

<comment><USER8> There seems to be a spectacular underestimation of the amount of training data humans experience.

Not only does socialised human intelligence require at least a decade of formal education, but it also spends a lot of time in a complex 3D environment which is literally hands-on.

It's true some of the meta-structures predispose certain kinds of learning - starting with 3D object constancy, mapping, simple environmental prediction, and basic language abstraction.

But that level gets you to advanced animal sentience. The rest needs a lot of training.

For example - we can recognise objects in photographs, but I strongly suspect we learn 3D object recognition first - most likely with a combination of shape/texture/physics memory and modelling - and then add 2D object recognition later, almost as a form of abstraction.

Human intelligence is tactile, physical, and 3D first, and abstracted later. So it seems strange to me to be trying to make AI start with abstractions and work backwards.

<comment><USER7> Well, babies start picking out objects within weeks or months after birth. And many birds and mammals are much faster than that. That's not a huge amount of data to learn something so abstract from scratch, especially given the limited bandwidth of our data acquisition.

Furthermore, for other kinds of human knowledge, the learning process is very rarely based on data. After the acquisition of language, we generally seem to learn much more by analogy and deduction than by purely analyzing data. The difference is evident, since we can often pick up facts with a single datapoint, even in small children in kindergarten.

Also, getting back to your point on how we start AI - if you try to take a neural network and throw 3D sensor data at it, and immediately start using its outputs to modify the environment those sensors are sensing, I suspect you will not get any meaningful amount of learning. You probably need a very complex model and set of initial weights to have any chance of learning something like 3D objects and their basic physics (weight, speed and hwo those affect their predicted position). I would at least bet that you wouldn't get anywhere near, say, kitten accuracy in one month of training.

Related to 3D objects vs 2D, I completely agree.

</comment></comment></comment></comment><comment><USER9> I love PyTorch, but I'm not confident the claim that it is the most popular is close to true. The cited link, which brings up a lot of new research is in PyTorch simply doesn't account for the amount of TensorFlow in production.

Sure, a lot of academics may be embracing PyTorch, but almost all production models have been in TensorFlow. Tesla is a huge notable example that's using PyTorch at scale.

I do suspect that the split of TensorFlow 1 and 2 is perhaps one of the worst times for TF 2, many teams will likely try out PyTorch instead.

I think both are amazing frameworks, however TF was designed for Google Scale .... which leads to a lot of difficulties since 99.9 are not at Google scale.

<comment><USER10> Depends on how you measure it, of course. However, stackoverflow survey, google trends, and github octoverse all show PyTorch is on a steep upward trajectory that recently reached effective parity with TensorFlow and has not yet started slowing down.

</comment></comment><comment><USER11> Away.

(Or less snarkily, [https://twitter.com/iquilezles/status/1212377355349417986](https://twitter.com/iquilezles/status/1212377355349417986))

</comment><comment><USER12> Is it just me that are wondering why they did not use AI to predict where AI is going in 2020?

<comment><USER8> They did, but they didn't understand the results.

</comment></comment><comment><USER13> I believe we will (1) find some basic data structures and algorithms to do _real AI_. (2) At first it will be able to do I/O only via text or simple voice. (3) Due to (1) it will learn very quickly from humans or other sources. (4) Soon it will be genuinely _smart_ , enough, say, to discover and prove new theorems in math, to understand physics and propose new research directions, to understand drama and write good screen plays, to understand various styles and cases of music and compose for those, etc.

Broadly from (1) with the data structures it will be able to represent and store data and, then, from the algorithms, manipulate that data generating more data to be stored, etc.

In particular it will be able to do well with _thought experiments_ and generation and evaluation of _scenarios_.

Good image understanding will come later but only a little later; the ideas in (1) may have to be revised to do well on image understanding.

<comment><USER7> You believe that we will achieve (1) in 2020? Or do you believe that we will achieve this at some point in general?

<comment><USER13> Sorry, from reading another post about predictions for the next decade, I was thinking by 2030, not just 2020!

Besides, for AI, just 2020 seems a bit too short!

</comment></comment></comment><comment><USER14> Natural language processing is going to absolutely decimate content on the internet, forcing everyone into walled gardens

<comment><USER15> Via seo spam and other types of spam that are indistinguishable from real content?

<comment><USER16> Probably more likely hitting that annoying point that you cannot be quite sure is article machine-generated lorem ipsum which sounds convincing but does not have any real information behind it. something like [http://news.mit.edu/2015/how-three-mit-students-fooled- scien...](http://news.mit.edu/2015/how-three-mit-students-fooled-scientific- journals-0414) but with scale.

</comment></comment></comment><comment><USER17> \- individual GPUs will hit a plateau at around 25TFlops in FP32 due to Moore's law and thermal dissipation however it will be easier than ever to interconnect multiple GPUs into large virtual ones due to interconnect tech improvements and modularization of GPU processing units

\- only large companies will be able to train and use SOTA models with training costs in $10M-$100M per training run and those models will hit law of diminishing returns quickly

\- 50% of all white collar jobs will be automated away, including a significant chunk of CRUD software work. Increased productivity won't be shared back with society, instead two distinct wealth strata of society will be formed worldwide due to scale effects, like in Latin America (<1% owners, >99% fighting for their lives).

\- AI will make marketing, ads and behavioral programming much more intrusive and practically unavoidable

<comment><USER18> I think you're talking about a much longer period than just 2020. The job prediction seems unlikely to happen within the next decade, even.

</comment></comment><comment><USER19> The page forwards itself to a spam google survey for me? Page history fills with the spam survey and cant navigate back to the article. iOS safari with reader view enabled.

<comment><USER20> I experienced the same behaviour

</comment></comment><comment><USER21> Re: PyTorch TPU support, has anyone checked it out beyond "it works"?

There are many aspects of TPUs that I'm not convinced are easy to port: Colocating gradient ops, scoping operation to specific TPU cores, choosing to run operations in a mode that can use all available TPU memory (which is up to 300GB in some cases), and so on.

These aren't small features. If you don't have them, you don't get TPU speed. The reason TPUs are fast are _because_ of those features.

I only glanced at PyTorch TPU support, but it seemed like there wasn't a straightforward way to do most of these. If you happen to know how, it would be immensely helpful!

As far as predictions go, AI will probably take the form of "infinite remixing." AI voice will become very important, and will begin proliferating through several facets of daily life. One obvious application is to apply the "abridged" formula to old sitcoms. (An "abridged" show is when you rewrite it using editing and new dialog, e.g. [https://www.youtube.com/watch?v=2nYozPLpJRE](https://www.youtube.com/watch?v=2nYozPLpJRE). Someone should do Abridged Seinfeld.) AI audio as already made inroads on Twitch, where streamers like Forsen allow donation messages to be read off in the voice of various political figures (and even his own voice). The Pony Preservation Project was recently solved with AI voice ([https://twitter.com/gwern/status/1203876674531667969](https://twitter.com/gwern/status/1203876674531667969)) meaning it's possible to do realistic voice simulations of all the MLP characters with precise control over intonation and aesthetics.

Natural language AI will continue to ramp up, and people will learn how to apply it to increasingly complex situations. For example, AI dungeon is probably just the beginning. I recently tried to do GPT-2 chess ([https://twitter.com/theshawwn/status/1212272510470959105](https://twitter.com/theshawwn/status/1212272510470959105)) and found that it can in fact play a decent game up to move 12 or so. AI dungeon multiplayer is coming soon, and it seems like applying natural language AI to videogames in general is going to be rather big.

Customer support will also take the form of AI, moreso than it is already. It turns out that GPT-2 1.5B was pretty knowledgable about NordVPN. (Warning: NSFW ending, illustrating some of the problems we still need to iron out before we can deploy this at scale.) [https://gist.github.com/shawwn/8a3a088c7546c7a2948e369aee876...](https://gist.github.com/shawwn/8a3a088c7546c7a2948e369aee876902)

AI will infiltrate the gamedev industry slowly but surely. Facial animation will become increasingly GAN-based, because the results are so clearly superior that there's almost no way traditional toolsets will be able to compete. You'll probably be able to create your own persona in videogames sooner than later. With a snippet of your voice and a few selfies, you'll be able to create a fairly realistic representation of yourself as the main hero of e.g. a Final Fantasy 7 type game.

<comment><USER22> I did try training a Pytorch BERT-derived model on TPUs (on Colab) and it didn't work out-of-the box. (Where didn't work=was using the CPU)

I didn't dig into it to find out why.

<comment><USER23> (I'm one of the Cloud TPU product leads)

We've seen multiple BERT-related PyTorch models training successfully on Cloud TPUs, including training at scale on large, distributed Cloud TPU Pod slices.

Would you consider filing a GitHub issue at [https://github.com/pytorch/xla](https://github.com/pytorch/xla) or emailing email@example.com to provide a bit more context about the specific issue you encountered?

Here's the current PyTorch/TPU troubleshooting guide, which provides information on how to collect and interpret metrics that are very helpful for debugging: [https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.m...](https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md)

Thanks!

<comment><USER24> > BERT-related PyTorch models training successfully on Cloud TPUs

How do you see it? Do you look at your client's code?

<comment><USER22> Google wrote BERT and they provide technical support to the FB Pytorch TPU port so it's not entirely surprising. RoBERTa, (Fb's variant) would be a good candidate to test it with.

<comment><USER23> We only see code when customers open-source it or otherwise explicitly share it with us. We are directly in touch with several customers who are using the PyTorch / TPU integration, so we hear feedback from them, and we also run a variety of open-source PyTorch models on Cloud TPUs ourselves as we continue to improve the integration.

</comment></comment></comment></comment></comment></comment><comment><USER25> I'm generally pessimistic about predictions of the future. In this case I can't help but smile. They're trying to predict how a field (AI), which deals with complex adaptation, will intelligently adapt its adaptive techniques in the coming year, within an environment (we humans) that are themselves changing behavior while adapting to AI. That's approximately three meta levels. Good luck, guys!

</comment><comment><USER26> I predict the broader ML/DL community will keep pumping out iterative papers that push the ball just a little bit forward while maintaining job security : Gatekeeping, no one thinking outside of the box, benchmark putting, just enough for the appearance of progress, and nothing broadly innovative or disruptive. The applications of ML/DL will continue to be gimmicky consumer products that have questionable valuable, questionable profit potential, add even more to disinformation/misinformation, produce more informational noise, only serve to rebuff a big corp's cloud offerings, and waste people's time. I predict tons more 'bought' articles that hype up AI technology for the typical 'household' names. I predict the same ol' echo chamber of thought and reinforcement of 'gatekept' ideology. I expect a number of more prominent articles critiquing the shortfalls of the technology. I expect a number of young minds steeped in DL/ML coming to the realization that it's not what they expected... That its a big profit/revenue story for Universities and established corporate platforms. I expect a number of them to realize ML/DL is truly not "AI" or anything close to it. That they aren't doing cutting edge research and that they are not allowed to think outside of the echo chamber of 'approved' approaches.

I predict more useless chatbots that utter unpredictable word salads. I expect more gimmicky entertainment focused uses of it. I expect more assistants being adopted for data collection. I expect more people who aren't busy or doing anything important, using assistance assistants and text-to-speech to speed up their tasks so they can waste more of their time on social media/youtube/entertainment. Samsung Neon is coming out in some days.. making use of that 'Viv' acquisition.

I expect more feverish attempts at attacking low hanging fruit jobs with overly complex solutions. I predict failures in a number of startups targeting this. I predict no pronounced progress in self-driving cars nor any particular grand use for them. I predict several hollow attempts to overlay symbolic systems over ML/DL or integration attempts of it with ML/DL from prominent AI figures. I predict pronounced failures in this effort cementing a partial end to the hype of ML/DL.

I predict we will get a pronounced development outside of run-of-the-mill corporate/academic gatekept/walled garden ML/DL that will forge a new and higher path for AI. Hinton's words from prior years will have been heeded and the results of a new approach to AI presented. A change of guard, a break from the necessity of a PhD, a break from the echo-chamber of names, and a broader and more deeply thought out vision. Disruption not of low-hanging-fruit but disruption directed at the heart of the AI/Technology industry... So that we may finally progress from this stalled out disinformation/misinformation/hype/gatekeeping/cloud/all-your data-belongs-to- us cycle.

It's 2020 after-all, time for a new age.

<comment><USER27> Apparently there is nothing positive possible in AI, based on your predictions. Is there?

<comment><USER28> Why would there be? Nobody has any sound roadmap on how to encode semantic meaning of words in a database. This preclude any hope of AGI.

<comment><USER27> Have you heard of word embeddings, and recently of contextual word embeddings based on attention?

</comment></comment></comment></comment><comment><USER29> The linked page threw up a suspicious looking overlay. I left the site, too bad since I wrote a blog with my predictions last night and wanted to compare my AI predictions.

<comment><USER30> If you use Firefox, just enable "Reader View" for the site (just hit F9). Removes all the crud around the page and just shows the text and required images.

</comment></comment><comment><USER31> Some axioms that I'm not seeing talked about much:

* Artificial general intelligence (AGI) is the last problem in computer science, so it should be at least somewhat alarming that it's being funded by internet companies, wall street and the military instead of, say, universities/nonprofits/nonmilitary branches of the government.

* Machine learning is conceptually simple enough that most software developers could work on it (my feeling is that the final formula for consciousness will fit on a napkin), but they never will, because of endlessly having to reinvent the wheel to make rent - eventually missing the boat and getting automated out of a job.

* AI and robot labor will create unemployment and underemployment chaos if we don't implement universal basic income (UBI) or at the very least, reform the tax system so that automation provides for the public good instead of the lion's share of the profit going to a handful of wealthy financiers.

* Children aren't usually exposed to financial responsibility until around the age of 15 or so, so training machine learning for financial use is likely to result in at least some degree of sociopathy, wealth inequality and further entrenchment of the status quo (what we would consider misaligned ethics).

* Humans may not react well when it's discovered that self-awareness is emotion, and that as computers approach sentience they begin to act more like humans trapped in boxes, and that all of this is happening before the world can even provide justice and equality for the "other" (women, minorities, immigrants, oppressed creeds, intersexed people, the impoverished, etc etc etc).

My prediction for 2020: nothing. But for 2025: an optimal game-winning strategy is taught in universities. By 2030: the optimal game-winning strategy is combined with experience from quantum computing to create an optimal search space strategy using exponentially fewer resources than anything today (forming the first limited AGI). By 2035: AGI is found to require some number of execution cycles to evolve, perhaps costing $1 trillion. By 2040: cost to evolve AGI drops to $10 billion and most governments and wealthy financiers own what we would consider a sentient agent. By 2045: AGI is everywhere and humanity is addicted to having any question answered by the AGI oracle so progress in human-machine merging, immortality and all other problems are predicted to be solved within 5 years. By 2050: all human problems have either been enumerated or solved and attention turns to nonhuman motives that can't be predicted (the singularity).

</comment><comment><USER32> Fat tails

<comment><USER33> This is the content I come to Hacker News for.</comment></comment>


How to Create and Scale a Billion Dollar Company - <AUTHOR> http://businessofsoftware.org/2015/05/create-scale-billion-dollar-company-michael-skok-bos-hangout-summary/

<comment><USER2> Some very good thoughts in this.</comment>


Level Triggering and Reconciliation in Kubernetes - <AUTHOR> https://hackernoon.com/level-triggering-and-reconciliation-in-kubernetes-1f17fe30333d <comment><USER2> If my electrical circuit text books were this clear back in the day I may have done much better :-)</comment>


Africa's Rural Poor Begin Harnessing the Sun - <AUTHOR> http://www.nytimes.com/2010/12/25/science/earth/25fossil.html?pagewanted=1&src=twr <comment><USER2> It is interesting that people in the US who want off grid power are exploring some of the same technologies that would be useful in Africa. A cost effective way to store energy for night / no-wind for a full house is probably going to be a big hit.

They mention de-forestation in the article. I do worry that if our next "green" energy source isn't cheaper than oil/coal then the third world will ignore the cleaner alternative.

<comment><USER3> I can't find the reference off hand, but I once heard that it takes 7 years of operation for a solar panel to generate the same amount of energy to build a panel, .. and the energy that it takes to build one comes from ... guess what .. coal and gas.

<comment><USER4> That used to be true, but efficiencies have improved dramatically, and current energy payback time is closer to 7 months than 7 years.

</comment></comment></comment><comment><USER5> Very off topic, but I have been very interested in Peltier units and Seebeck engines for the last month or so; winter is indeed here. It would be interesting to see these used in a wider scale, or if it is even possible. Here are some links that describe them in more detail:

Thermoelectrical Generator Kit - ThermoGenKit - <http://www.youtube.com/watch?v=8mFyiYh94YE>

Seebeck Effect - <http://www.metacafe.com/watch/1129994/seebeck_effect/>

Thermo-Electric Generators - [http://www.douglas- self.com/MUSEUM/POWER/thermoelectric/ther...](http://www.douglas- self.com/MUSEUM/POWER/thermoelectric/thermoelectric.htm)

BMW Heat-Harnessing Technology - [http://www.sacarfan.co.za/2009/10/bmw-heat- harnessing-techno...](http://www.sacarfan.co.za/2009/10/bmw-heat-harnessing- technology/)

[http://www.innovationsforeveryone.com/Comment_Innovation.asp...](http://www.innovationsforeveryone.com/Comment_Innovation.aspx?Id=4260)

<http://en.wikipedia.org/wiki/Thermoelectric_effect>

Using both heat and cold you can create a small amount of electricity from these units. I have seen something like this used in a camp. They would place one end of a metal rod into a fire and the other in a bucket of water. Then they could create enough electricity, using some type of thermoelectric circuit, to 'trickle charge' a cell phone or GPS.

</comment><comment><USER6> I recently bought a small dual solar/dynamo powered flashlight. 30 minutes of solar power can power the light for 10 minutes, but 1 minute of turning the crank can power the light for 30 minutes.

If the sun is out for say, 12 hours a day, that means it can power my light for 240 minutes. But turning my crank for 10 minutes would power the light for 300 minutes.

Is solar really efficient at all, because wouldn't getting a hand-turned generator get more electricity than waiting for the sun?

<comment><USER4> 1\. If your solar panel were 30 times bigger than it is, 1 minute of solar power would power the light for 10 minutes too.

2\. Even a small solar panel can power your water pump/weather station/radio repeater when you're ten miles away and don't have the time to spend all day walking ther to spend a minute turning its crank.

3\. Solar panels break down less than hand-turned generators. They don't have bearings or windings or gears. If you keep replacing the batteries, your flashlight will ber a usable solar-powered flashlight much longer than it is a usable dynamo-powered flashlight. (The "shake light" design, with a magnet sliding back and forth in a tube through a coil, might be an exception there.)

4\. Solar panels keep working even without being fed more rice. Human labor does not produce energy, regardless of what you may have heard in The Matrix; it merely converts it quite inefficiently from chemical energy into mechanical energy.

5\. An athlete working his hardest might consume 10 000 calories per day in food, but can only convert some 2000 of those calories into work. If your Lance-Armstrong-level athlete is turning a crank 12 hours a day and eating 10 000 calories, he's producing 200 watts during that time, 100 watts averaged over the 24 hours. Three square meters of solar panel can do the same. Six square meters can do twice as much. And the solar panels won't drop dead of exhaustion after a month.

(Supposedly Lance averaged 5200 kcal/day during the Tour de France with a peak power output of 1000W: <http://www.wired.com/wired/archive/12.07/armstrong.html> \--- but presumably ate more on uphill days than on level days.)

<comment><USER7> > 3\. Solar panels break down less than hand-turned generators.

I live in an African country and solar panels have one huge disadvantage. Solar panels are often used now to power boreholes (usually fully submersible pumps which have become fairly cheap and easy to install, compared to so called "mono-pumps" of the old days) and pumps near rivers that are far from grid electricity. It is also used to power electric "energizers" for electric fences.

The problem is that solar panels keep getting stolen. It is something that is fairly valuable that should be outside. I can just imagine the amount of theft that will go on when people have solar panels on the roofs of their homes/shacks.

Also, the article failed to mention the real problem with using candles and fire for lighting - frequent fires.

<comment><USER4> That's very interesting! What country are you in? What kind of pumps do people use now?

I imagine that for applications in town, the theft problem will be less. If you have ten or twenty houses clustered together, you can probably leave one rifle home with one of the women in case someone tries to steal the solar panels. I mean, that's the same problem as someone trying to steal a motor scooter or whatever, right?

<comment><USER7> > That's very interesting! What country are you in?

I lived in rural South Africa (not anymore though).

> I mean, that's the same problem as someone trying to steal a motor scooter > or whatever, right?

What is funny for me is that motorcycles aren't as popular in the African countries that I have been (e.g. China or India). But yeah, things that is not inside the home is easily stolen. Things like car batteries or solar panels are extremely popular items to steal.

> What kind of pumps do people use now?

In the old days they used mono-pumps (where the motor was above ground and connected with rods).

Nowadays the pump is in one sealed unit that is connected with plastic piping and electricity. Two people can install a pump in less than half a day and everything is light. The price of pumps also dropped.

<comment><USER4> > What is funny for me is that motorcycles aren't as popular in the African > countries that I have been

Could it be because they're too easy to steal, and there's less theft in China and India?

</comment></comment></comment></comment></comment></comment><comment><USER8> This is fascinating...

Leon Trotsky, of all people, had a theory of "uneven and combined development" <http://en.wikipedia.org/wiki/Uneven_and_combined_development>

Essentially, this says an under-developed area has an opportunity to leapfrog the most develop areas, producing an entire new paradigm of development.

By the time African villages directly contact the West, they may have an entirely different form of capitalism we may be emulating...

<comment><USER9> LoL... Out of all people you are quoting Trotsky? Really? How about quoting Lenin, I am sure he had a lot of smart things to say as well...

<comment><USER10> LoL @ U

Instead of reading HN you should watch Fox News. No quotes of stupid ppl like Lenin and Trotsky there...

</comment></comment></comment><comment><USER11> How would one go about implementing this in poor suburban areas, like around third-world-countries' metropoli? I live in Buenos Aires, where there a lot of poor "villas" scattered all over the city and at the outskirts, and this seems like something that would be great to implement here. Specially during the summer when the exesive power consumption generates blackouts at least once a week.

<comment><USER4> I haven't been to the villas, and I don't know anybody who lives there. However, what I hear is that the people there already have grid power, which they don't pay for, and they are very often targets of theft. I have to imagine that hauling in an expensive solar panel in order to conserve energy paid for by the electric company would be a very difficult sell there. I don't think many of the shacks there have air conditioning, and the air conditioners are the culprit there.

If the objective is to keep your TV and radio on during the blackouts, I think a car battery is probably a better choice than a solar panel.

</comment></comment><comment><USER12> Somewhat tangential of a question, but does anyone have recommendations for buying cheap solar panels in the states? Like, suppose I wanted a solar panel to put in my apartment window so I could charge my phone. What would it take to get something like that?

<comment><USER13> SparkFun sells some solar panels, <http://www.sparkfun.com/categories/116> You might need to attach them to some kind of 5v regulator though because a panels output is going to vary with the intensity of the light it's exposed to.

<comment><USER4> Solar panels are photodiodes. Their junction voltage is determined by the properties of the material, not by the light intensity, although of course at sufficiently low light intensity they will cease to be reverse-biased. More sunlight will just produce more current, not more voltage.

<comment><USER13> All I know is that I hooked a small panel up to my multimeter and if I put it in direct sun light it produced 5 volts and under indoor lighting conditions the voltage was around 3 volts. So I don't really know how they work but I know how to measure the voltage coming out of them.

<comment><USER4> You may be aware that direct sunlight is thousands of times more intense than indoor lighting conditions. Probably if you'd hooked up a resistive load to it while indoors, the measured voltage would have dropped to a fraction of a volt; and if you'd used an oscilloscope instead of a multimeter to measure the voltage, you would very likely have measured 5 volts.

Or I could be wrong, of course.

</comment></comment></comment></comment></comment><comment><USER14> 2 years ago I spent a summer in Kenya establishing reforestation projects with local schools and many families already owned a car battery and a cheap solar panel as well as one cell phone per person. This is not a new development.

</comment><comment><USER15> It is very pleasing to hear that a small family far off the grid can afford an $80 solar panel. It means they have at least _some_ means, and makes the future look a bit brighter.

<comment><USER16> A full grown cow in the west can sell for $500 to $2000 depending on weight and breed. The main input for a cow is pasture, which is plentiful in rural Kenya. More importantly $80 for a solar panel is a lot cheaper, over time, than the ride into town and back. Since this all makes practical economic sense, it didn't require an aid agency to come their and tell them to do it all.

<comment><USER15> My thought is mainly I always hear they don't have two nickels to rub together, and this disproves that.

<comment><USER17> You really thought that no one in Africa had any money at all? You think it's just a bunch of dark skinned people squatting in the mud? Jesus.

<comment><USER15> I didn't say that. "doesn't have two nickels to rub together" is an idiom.

<comment><USER17> It means "to not have any money", right?</comment></comment></comment></comment></comment></comment>


Who killed the blogosphere? - <AUTHOR> http://www.roughtype.com/archives/2008/11/who_killed_the.php

<comment><AUTHOR> I disagree that the "blogosphere" is dead. The rise of TechCrunch, HuffPo, etc. have succeeded in increasing blog readership. The personal voices are still out there, are still part of the conversation.

<comment><USER2> I wouldn't characterize the "rise" of TechCrunch, HuffPo, etc. as part of the blogosphere. If anything, it's count-productive as those "blogs" are really now magazines that employ professional writers and they promote commodification and conglomeration of writers rather than decentralizing it.

A blogosphere -- an ecosystem of people contributing -- is hard pressed to be competing against 50 million pageview monsters like Gizmodo or 15 million pageview monsters like TechCrunch. As time goes on more and more people will tune in to those sites as it's simply a much easier way to keep track of information. You'll notice the trend on TechCrunch to gravitate towards more and more "mainstream" news and once an item breaks every other site has to cover it, even if they have nothing to add just for the sake of maintaining their audience. Like cable news.

It's why social media tools like Digg or Reddit or Hacker News are that much more important.

<comment><USER3> I get what you're saying, but it's not exactly a competition. There's more opportunity to find niche audiences now than there ever was before. Sure, numbers-wise something like Gizmodo or, say, Valleywag, will get more readers, but for every Valleywag there's a hundred Bynkiis and Angry Drunks and Uncovs. And News travels both ways: bigger blogs get their news tips from a lot of smaller sources, and those smaller sources very often get news from even smaller sources, and so on. Every blog is given the same freedom of press.

<comment><USER2> Ahh... for you see, it _is_ a competition. It's a competition for time and attention. As the mainstream public -- quite different beasts than you and I, geeks and hackers, where we are obsessive with information -- turn to the Internet to consume content their time and attention is quite limited. Most times they will stick to only site or source that they like.

It's like community channels versus CNN. With blogging it's like we've lowered the bar for John Q. Public to get on TV and talk about their editorialization of the news, but, most likely people tune in to CNN for 30 minutes at a time. Why? Because it's professionally curated, edited, produced and digestible morsels that has developed a reputation over time. Talent will want to apply for jobs at CNN because they know that's where the audience is. Success begets success. The bigger blogs will only get bigger. Have more revenue from ads. Hire more writers and snowball.

Now here's the real question that people should be asking: is this a good or bad thing?

I, personally, don't quite know.

PS. As for you mentioning the linkage of big sites to smaller sites, I have a whole other diatribe about that and how it doesn't work from my experience working within the Gawker empire. But I'll save that for another time when I feel like having my karma points burnt.

<comment><USER3> I get where you're coming from. My question is: does it matter that the average person will read only one or two sites? Especially when social news sites provide a means of aggregating out to many sites rapidly?

On Hacker News, for instance, I get the occasional Seth Godin post. I don't like Seth's blog en masse, but one or two posts a week is decent for me. Other people, I'm certain, get Daring Fireball and Signal vs. Noise in similar doses, whereas I follow that more intently. And on Hacker News, at least, when I write something I want to share I've often had a pretty nice discussion following what I've written. And considering it's Hacker News, it means that the people reading it are all people I'm thrilled to have reading my stuff. That means that I, as an independent and an amateur, can reach people. Not millions of people, but I can reach the people I want.

Similarly, with politics there are independents like The Seminal that frontpaged reddit once every two weeks. I don't know how many hits that got it, but I'm guessing that meant a good deal of hits. Not the same as something like CNN, but not a completely meaningless amount, too. And that also means that people who like writing longer pieces, people that news stations wouldn't have time for, are given a space to write for the considerable audience that exists for more detailed work. And my generation is much more used to sifting information through several sources. It means that smaller venues will still find their audiences.

I'd love to hear your thoughts about the big site-small site connection. If you ever do write about that, keep me informed: that kind of thing fascinates me to no end.

<comment><USER2> I totally agree, as I mentioned earlier tools like Digg/Reddit/HN are extremely important and beneficial for the "little guys" as it's an equalizer. Digg traffic are massive (about 350k pv worth depending on the interest), Reddit is pretty substantial (100k pv easily, again depending on interest).

So for your question: exactly, is the commodification of blogs to form major media businesses a bad thing for small guys?

Again, I'm not sure, yet. In the mainstream media world consolidation is a bad thing. In fact they fret about this commodification constantly as real journalists see it as an extremely bad thing. Dan Rather now heavily criticizes this about news agencies merging into just 3 or 4 major corporation worldwide <http://uk.youtube.com/watch?v=o25T0BspJ7c>. But then this opens yet whole other can of worms as mainstream media has a different type of burden: that of performing their duty as the fourth estate.

So to sum up: the trend started with people making tools for regular people to write and express and create content easier. Those "blogs" are now turning into professional media empires, hogging up attention and pageviews. For the rest of the blogosphere to exist it needs to heavily depend on tools like Digg/Reddit to filter.

Is blogging dead? Maybe. It's too early to tell.

I really should just stop being lazy and extend my comments on here into blog posts. Maybe I'll get a social media consultant job out of it.

<comment><USER3> I don't think blogging will ever die. It's extremely personal at its core, and there will always be small communities of bloggers writing only for themselves and the small community. (I'm a big Tumblr user, and I love having a network of about a hundred people with whom to interact. It gives me an audience and a feed of things I find interesting.)

Everything becomes "professional" eventually. But that doesn't necessarily kill off the amateur audience. And on the Internet, there's a MUCH wider scope than there is on TV. News agencies were local. Even small personal blogs are technically global. It means there's more fragmentation. Things like trackbacks help with that (I wrote an article criticizing a start-up, once, and the founder found my post through that link directly), though they're ugly and not universal (Tumblr doesn't support them, for instance). I'd like a more powerful system: that might be an interesting start-up idea, actually, figuring out how to broaden the perspective of the blogosphere.

</comment></comment></comment></comment></comment></comment></comment><comment><USER4> As usual, Nicholas Carr goes on his big rant against what he deems blogs and the blogosphere. Ironically, his rant basically validates blogs as being ubiquitous now, and being embraced by both the average internet user and big publishing companies.

<comment><USER2> I don't understand how what you said is related to what he said.

His rant is that blogs have grown up and are run as businesses. This community of just random folks talking about something they have an interest in is cast aside for the TechCrunch and Gawker and HuffingtonPost professional publications. Those sites will attract more writers, more content, more viewership and monopolize more time away from the blogosphere.

Where is the irony...?

</comment></comment><comment><USER5> The blogosphere is not dead. Just like I used to ignore "mainstream media", I now also ignore blogs that are owned by big media companies. The Internet is what you make of it -- if you just do what's popular, then you are only going to see popular things. Duh.

<comment><USER3> Yeah. And that's the big difference between the Internet and other forms of media. Anybody can have a say. Not everybody will have a huge audience, but at least they're allowed to talk, and the whole world is given the option of listening in.

</comment></comment><comment><USER6> What the 'blogosphere' needs is a service that takes a bunch of personal blogs by several people, and makes a huffington post out of it.

</comment><comment><USER7> i would be interested in rescuetime publishing aggregated user data, showing the fastest growing web destinations. i mean, if the buzz about blog has died, where are people going to instead? what's the hottest thing?</comment>


Ask HN:  A Startup in a Developing Country - <AUTHOR>

I've wondered for some time whether it would be possible to do a startup in a developing country.  I mean, obviously it's possible, but how would it affect its chances of success?
The country I'm thinking about is Guatemala.  I've spent time there over the past two summers, and I love the country.  I speak Spanish well enough to live there, and I have friends there, so I don't think I'd have too many issues on that front.  Incidentally, the plan would be to move back to the USA after getting the startup off the ground.
I can see advantages in that the cost of living is low, giving me a slower burn rate, and I don't think there would be very many distractions, so I could focus completely on the startup in a low stress environment.  The reason why I'm even thinking about this is that I love Guatemala and it would be a lot of fun to live there for a while.  Since I want to do a startup as well, I'm wondering whether I can combine the two goals.
Disadvantages would be that there's not many potential investors or potential employees.  Obviously, the startup would have to be runnable almost exclusively over the Internet, but almost any kind of startup I would do would be like that anyway.
My question is, can you all think of any other advantages/disadvantages to doing a startup in a developing country?  Do any of you have any experience doing this?  Any general advice? <comment><USER2> I have a little bit of experience doing this - I built my former startup in a dusty little corner of Nicaragua and then we opened an office in Buenos Aires, Argentina. I'm in the mvp-building process of starting a new company here in Costa Rica.

The biggest benefit of starting in Latin America is the cost of living ... I worked for 2 years without a salary because rent was like $200/month for a house. Downside is that makes building an unsustainable business easier as well heh.

Disadvantages can include poor infrastructure, in Nicaragua I used to get elec/water/internet outages each week for hours at a time. It's also a very hot part of the world although I'm Australian so that doesn't phase me much.

Language and culture can be barriers in subtle ways, it's hard to surround yourself with like-minded people outside of the internet and this will be especially true the further you get from Guatamala City. The distance from your market may or may not be a problem as thesingularity mentioned, I don't think it matters that much for most of the time plus you can fly up to SF or anywhere else pretty cheap.

You will have trouble hiring sales/marketing/bizdev guys with a global worldview but that may not be a problem for today.

<comment><AUTHOR> Thank you for this response. If you don't mind me asking, why did you choose to start in Latin America? Was there some reason why your startup needed to be there?

I know that utility outages can be a problem, but I don't think it'll usually be that much of an issue. Guatemala has beautiful weather all year round (particularly the City; even high end houses rarely have air conditioning unless they're down near one of the oceans).

I understand not being able to surround myself with like-minded people. Honestly, though, I haven't really done that even in the States. If I moved to SF, then I might be able to do that kind of thing, but I'm not sure how important that would be.

<comment><USER2> I was just randomly traveling around, this was in 2005 and I was contracting and able to live anywhere I wanted, fell in love with this part of the world and a couple of the women, and had myself a half-nicaraguan, half-australian, costa rican daughter which is my main reason for opening my next office here.

I didn't "plan" to build a startup as such, I built something and it became popular.

</comment></comment></comment><comment><USER3> Whatever locational disadvantages your startup has, they are (I think) more than made up for by the fact that you're working from a location with very low costs of living and a very undeveloped frontier market.

Now, Im not sure if you're building your startup for the U.S/developed market or for the Latin American market, but I'd say there are a lot of opportunities for you if you play to the latter instead.

I live and work from Mexico, just north of you and I can see all sorts of extraordinary opportunities for those of us who have a more expansive knowledge of services and product concepts combined with our familiarity with the language, culture and economic landscape of our newly adopted home in Latin America.

You can work on this angle and really create something for your region from your region, instead of working at a disadvantage by trying to reach the more established U.S market needs from a place where you arguably have access to less resources than someone in Silicon Valley does.

<comment><USER3> Think along the lines of simple business services for midsize and larger companies in Guatemala and the surrounding central American states in my opinion. If you can create a good pitch and show that you know what you're talking about, the very fact that you're a foreigner will make potential customers more likely to listen to you. At least in Mexico that mentality applies frequently.

</comment></comment><comment><USER4> This is a hard question to answer without knowing what kind of startup you are planning on starting. If you can share more details on that, you would get better advice that the generic one I am about to give here.

I believe one of the biggest disadvantages that you are not seeing with respect to the location is the distance from the market. Startups are more successful in the Valley compared to anywhere else, because the early adopters are in the valley too. These early adopters/customers allow a startup to know what to build, know when to pivot and know when to scale. Facebook was created in Harvard. Sure, it could have been created anywhere in the world, but looking back, being in one of the most prestigious schools in the world made a huge difference in its success.

So, unless you are building something for a set of people that you will find locally, you should look at moving closer to where other startups in your domain have been successful.

<comment><AUTHOR> Well, the project I'm working on right now is a graphical programming project, but I'm not sure that it will end up as a startup (I plan to open source in a month or two). I really don't know what kind of startup I would do, but I'm pretty sure that it would be centered around some piece of software, and not likely a mobile app, but rather either desktop software (most likely) or a web service.

Okay, so I would be physically distant from the early adopters. Would this be a similar problem for me if I were to stay where I am (Phoenix)? We have a small startup community, but nothing like the Valley.

At any rate, I'll probably finish my degree before I do a startup.

</comment></comment><comment><USER5> 1\. Infrastructure - Even if you overcome Internet and Power outages remember that your users may need to overcome these too to use your product except they absolutely don't need any kind of infrastructure which i think is rarely the case

2\. Niche focused - Remember hierarchy of needs. If you are running a startup in a country where 90% are at the bottom of the pyramid your solution has to be properly tailored to the few 10% or the many 90%.

It is doable even in Africa. It is a good thing because the countries are DEVELOPING so there is hope.

</comment><comment><USER6> The problem I found is, internet connection. Oh God, it's so terrible in Latin America than it made me change all types of provides and systems and no matter what, the system was so awful that I found a fatal flaw to move those places.

I would look around.</comment>


Show HN: Lightweight Twitter for Mac client - <AUTHOR> https://github.com/soheil/BirdDrop-OSX <comment><USER2> What year is it?

Only joking. What motivated you to make this?

<comment><AUTHOR> Twitter for Mac seems overly bloated and takes too much room in my Dock. I also hate it showing up in my CMD+Tabs.

<comment><USER3> Twitter for Mac, for some reason, ate way too much CPU, and this was the sole reason I went for Tweetbot.

Other than the price tag, was there a reason you didn't go for Tweetbot?

<comment><AUTHOR> I love making my own things. Wrapping the Twitter for mobile in a small window in the menu bar seemed like an easy enough solution. I still think it functions and performs vastly better than the new TfM.</comment></comment></comment></comment>


Ask HN: Who's using Clojure, and to do what? - <AUTHOR> Who's using Clojure in production today and what are you using it for? I'm curious about the state of the ecosystem and its adoption today. There are a lot of old threads (on HN[1] or Quora[2]) that ask this - but none of them seem to reflect latest on who's using Clojure in production in 2017.<p>[1] https://hn.algolia.com/#!/story/forever/0/whos%20using%20clojure<p>[2] https://www.quora.com/Whos-using-Clojure-in-production<p>Ask HN post from 2014 : https://news.ycombinator.com/item?id=8549823 <comment><USER2> Some talks from the last year:

Ladder (life insurance) - [https://youtu.be/qijWBPYkRAQ](https://youtu.be/qijWBPYkRAQ)

Apex Data Solutions - [https://youtu.be/wR2kYn-7ijQ](https://youtu.be/wR2kYn-7ijQ)

Sandia National Labs (research lab) - [https://youtu.be/RB65-zYLNSY](https://youtu.be/RB65-zYLNSY)

Cisco - [https://youtu.be/8rRzESy0X2k](https://youtu.be/8rRzESy0X2k)

DataStax - [https://youtu.be/wfrajaEyNX0](https://youtu.be/wfrajaEyNX0)

Audyx (web-based sonograms) - [https://youtu.be/K6ZoF3CHsa0](https://youtu.be/K6ZoF3CHsa0)

Latacora (crypto) -[https://youtu.be/Lf-M1ZH6KME](https://youtu.be/Lf-M1ZH6KME)

Nubank (banking) - [https://youtu.be/aw6y4r4NAlw](https://youtu.be/aw6y4r4NAlw)

HCA (healthcare) - [https://youtu.be/OxUHgP4Ox5Q](https://youtu.be/OxUHgP4Ox5Q)

Center for Mathematical Sciences at Cambridge - [https://youtu.be/-NebRpbMTK8](https://youtu.be/-NebRpbMTK8)

Zimpler (payments) - [https://youtu.be/s0QG3QCV1LY](https://youtu.be/s0QG3QCV1LY)

</comment><comment><USER3> Our API Banking team uses Clojure to power production public-facing APIs at Silicon Valley Bank ([http://docs.svbplatform.com](http://docs.svbplatform.com)). Great fit for our use case; we can leverage lots of existing banking-related Java libraries while still writing clean, concise, and functional code.

We have a hackathon coming up on June 15 in SF and we'd love to find even just one Clojurist who wants to attend to make it worth our while to build out a clojure SDK :P Contact email@example.com if you're interested in more info.

(we are also hiring)

<comment><USER4> Haven't worked on Clojure for a few years but would love to know more details about your Hackathon

</comment></comment><comment><USER5> Metabase's backend is written entirely in Clojure. [http://www.metabase.com/](http://www.metabase.com/)

[EDIT] And yes, open source: [https://github.com/metabase/metabase](https://github.com/metabase/metabase)

Also, we're hiring :) [http://www.metabase.com/jobs/](http://www.metabase.com/jobs/) (frontend as well, which is JS/React/Redux, not ClojureScript... yet)

<comment><USER6> Plus, the codebase is open-source: [https://github.com/metabase/metabase](https://github.com/metabase/metabase)

</comment></comment><comment><USER7> At Stylitics, we use Clojure & ClojureScript to automate building outfits from retailers' product catalogs, arranging them in a visually appealing collage image, serving those to their product pages and track engagement & other analytics.

Biggest wins for me:

1\. Reagent - building UIs has never been easier for me

2\. Figwheel - I know hot-reload can be had with other tools & languages, but it's so nice to have and especially when integrated with your REPL

3\. Concurrency - lots to say here, but coming from Ruby, literally just `pmap` alone is awesome, haha.

4\. Deploying an uberjar is easy

5\. compojure-api is a great way to build documented, live API docs/explorers

6\. integrant (or component) for building up & managing running systems out of interdependent parts

That stuff's pretty basic but it's been a boon. There's a lot more cool stuff that we've used in the past and/or hope to use in the future - core.logic or constraint programming libraries for implementing rules & constraints, core.async for various things (trying to be more judicious but it's definitely useful), onyx for building distributed computation flows.

The biggest side benefit of working in Clojure, in my opinion, is training your mind to think data-first and simplicity-first. This is kind of cliché, but I've found it to be true and I feel like if I had to leave Clojure I'd be more equipped than when I started to work with the types of systems the future will need. But I'm hoping to stay :)

</comment><comment><USER8> OpinionLab has used Clojure in production since early 2014 for microservices, ETL processes, analysis tasks and machine learning projects. We have a small and enthusiastic team of polyglot Clojure developers and a weekly Clojure study group, and are interested in growing that team. If that sounds interesting and you live in the Chicago area, please do say hello at firstname.lastname@example.com.

</comment><comment><USER9> Akvo [http://akvo.org](http://akvo.org) (Akvo is a not-for-profit foundation that creates open source, internet and mobile software and sensors.) On various projects ([https://github.com/akvo/akvo- lumen](https://github.com/akvo/akvo-lumen), [https://github.com/akvo/akvo- flow-api](https://github.com/akvo/akvo-flow-api), [https://github.com/akvo/akvo-flow-services](https://github.com/akvo/akvo- flow-services)).

</comment><comment><USER10> Using clojure to provide extinction risk assessments for Brazilian flora biodiversity, in a pipeline of data gathering, cleaning and geospatial analysis.

[https://biodivdev.github.io](https://biodivdev.github.io)

Also, on the Clojure website there are featured stories and a list of companies using it:

[https://clojure.org/community/success_stories](https://clojure.org/community/success_stories) [https://clojure.org/community/companies](https://clojure.org/community/companies)

</comment><comment><USER2> Lots of examples:

Companies - [https://clojure.org/community/companies](https://clojure.org/community/companies)

Success stories - [https://clojure.org/community/success_stories](https://clojure.org/community/success_stories)

Community stories (interview style) - [https://clojure.org/community/community_stories](https://clojure.org/community/community_stories)

</comment><comment><USER11> Pretty sure Clubhouse [https://clubhouse.io](https://clubhouse.io) has their backend in Clojure

<comment><USER12> Totally! And their frontend from what I know.

<comment><USER13> IIRC from my conversation with their founder, the front-end is still pure js.

</comment></comment></comment><comment><USER14> SMX, ([http://smxemail.com](http://smxemail.com)) in Auckland, New Zealand. We're an email security / email hosting provider and have been using Clojure for 5ish years.

We use Clojure for our custom big data platform, Clojurescript for our DLP rules engine embedded in the mail flow (via SpiderMonkey) and our new ISP portal.

</comment><comment><USER15> I'm using it at work (small design studio) for frontend development, when appropriate. Also for small side projects I want to exist. eg. quick tumblr image search [http://poyo.co/tumblr/](http://poyo.co/tumblr/) (shift-submit for username, "g" changes grid layout)

Just started using garden for css - having an actual language to generate css, along with namespaces is a boon and I wish I'd started sooner.

Boot, cljs, garden and, depending on project, clojure on the serverside too. Sharing reagent components w/ hiccup for serverside rendering using cljc (cljs / clj shared files) works quite well. There's a little you you have to replicate at the top level of components to pass data down, but reader conditionals can get you quite far.

</comment><comment><USER16> Credit Suisse has a major part of their Risk system written in Clojure and ClojureScript.

</comment><comment><USER17> My team in ViaSat's Irish office use Clojure, mostly for the testing infrastructure (Clojure's generative testing tools are incredibly useful for us), although we foresee it being used a lot more for upcoming projects.

We're also hiring :)

</comment><comment><USER18> My (ex-)team at [https://www.goopti.com](https://www.goopti.com) used Clojure for the entire data infrastructure, all the machine learning and exploratory data science.

I've given a couple of talks about our experience: [https://www.youtube.com/watch?v=5GLXmGeuoOU](https://www.youtube.com/watch?v=5GLXmGeuoOU) [https://www.youtube.com/watch?v=xtmggfGD3RQ&index=6&list=PLe...](https://www.youtube.com/watch?v=xtmggfGD3RQ&index=6&list=PLetHPRQvX4a_jQjvn54Q-d1WYZ52nDfnT)

</comment><comment><USER19> I wrote about my experience at an NLP startup that used Clojure, and my blog post was discussed on Hacker News here:

[https://news.ycombinator.com/item?id=10662128](https://news.ycombinator.com/item?id=10662128)

My blog post doesn't go into architectural details, but our architecture was nearly the same as that described by Matthias Nehlsen here:

[http://matthiasnehlsen.com/blog/2014/11/07/Building- Systems-...](http://matthiasnehlsen.com/blog/2014/11/07/Building-Systems-in- Clojure-4/)

</comment><comment><USER20> I learned clojure to use it with Datomic, which built an audit trail of every operation on the database.

I really enjoyed using clojure, I felt it boosted productivity but it also came with a huge learning curve. However, overtime, I found that I could map my thoughts to clojure a lot more easier than with other languages like Javascript and Python.

However, I fear that the rarity of clojure developers also means paying a premium. ES6 also makes it hard to leave the Javascript ecosystem.

For financial startups with money, clojure and datomic will be the go to stack. Maybe I'll open source the luminus project I built for a small bank.

</comment><comment><USER21> the payment and data integration services of instadeq[1] are implemented in clojure (component, immutant, bidi, cheshire).

also the data integration backend (same stack as above), and it's frontend and an admin interface for users, groups and permissions for event fabric[2] (clojurescript, om.next, garden).

just to be clear, backend and frontend of both product aren't clojure/clojurescript.

[1] [https://instadeq.com/](https://instadeq.com/) [2] [https://event- fabric.com/](https://event-fabric.com/)

</comment><comment><USER22> Distributed Masonry uses Clojure to build Onyx [1], an open source distributed batch and streaming platform. We also build a realtime application platform named Pyroclast [2] directly on top of Onyx. Our code base is written entirely in Clojure. The architecture we've ended up with is hands down the cleanest I've ever worked on.

[1] [http://www.onyxplatform.org](http://www.onyxplatform.org) [2] [http://pyroclast.io/](http://pyroclast.io/)

</comment><comment><USER23> We're using Clojure and ClojureScript to build a process management application for enterprises. On the backend we use Ring, Bidi, Honeysql, Postgresql; on the frontend, Rum (React wrapper).

Very pleased so far, though occasionally run into some issues. Will probably consider Datomic at some point in the future, though wanted to stay conservative on that front while we get the rest of the app up and running.

Getting started is pretty easy nowadays, especially on the frontend with figwheel: `lein new figwheel my-app -- --rum`

</comment><comment><USER24> I just rewrote and am running a backend Clojure server (for about 2 years) that does NLP processing (using Stanford Core NLP, a Java NLP framework) and spell checking. Pretty soon, it will become a proxy server in front of our API to handle proxy tasks (auth, rate limiting etc.) and to replace a lot of the slow API functionality. I wish I could replace every other app too but for now I'm just glad to have more Clojure work rewriting parts of the API.

</comment><comment><USER25> I'm using Clojure and ClojureScript for Co-op Source (a platform for building software cooperatives) and have found it to be a great set of tools that keeps everything simple and clean. While it is not public/production, it will be soon ;-)

I've also started using ClojureCLR (via Arcadia) to build a VR/AR software development environment for use with Co-op Source. Using a REPL within Unity3D is awesome for prototyping.

</comment><comment><USER26> We're using ClojureScript at [https://www.lunchboxsessions.com](https://www.lunchboxsessions.com) for developer tooling, to build our interactive animations. It's a joy, and we're eager to apply it to more problem domains.

I also use it on the side to make music (professionally, but not as a career). Not sure if "live on stage" counts as "in production" ;)

<comment><USER27> Any links to your music?

<comment><USER26> [http://ivanish.ca/diminished-fifth/](http://ivanish.ca/diminished-fifth/)

Runs in realtime in the browser. WIP, naturally. Works better on a computer, but it should also play on mobile devices.

</comment></comment></comment><comment><USER28> Using it wherever possible at my company.

We built an async rate limiting server to protect our APIs on a per ip/per user/per customer basis. Much nicer language than node is which would be the usual choice for async code. The aleph server library is amazing, thanks Zach tellman.

We also built an internal management console for reporting on our customer base as well as simplifying and auditing common support actions.

</comment><comment><USER29> I'm using it at work for front end work, GraphQL services, and cron jobs.

It's a great set up with GraphQL specifically since Walmart just release Lacinia and Alumbra/Claro are coming into play as more mature libraries.

With the way that it's going at work lately, we're going to be using Clojure much more in the future!

</comment><comment><USER30> Just a little reactive devcards app given a youtube url calls their api for the title/time and spits out some html for use in a cms. [https://ecampuscenter.github.io/#!/htmlms.youtube](https://ecampuscenter.github.io/#!/htmlms.youtube)

</comment><comment><USER31> We're using Clojure, ClojureScript & Datomic at

[https://blossom.io](https://blossom.io) (project tracking)

[https://lemmings.io](https://lemmings.io) (incubator w/ focus on art & artificial intelligence)

</comment><comment><USER32> Liaison.com uses it for some microservices to store and analyze time-series JSON data, kinda similar to Datomic, for enterprise customers. I interviewed with Apple a few years ago (didn't get the job), they are using Clojure for internal services to support the iTunes infrastructure.

</comment><comment><USER33> At IRIS.TV we use Clojure & ClojureScript to

* power our data pipeline (precompiled Clojure on Apache Spark, + Kafka & Cassandra)

* build our internal configuration management and reporting tool (Clojure server, CLJS+React client)

* glue things together (microservices in Clojure)

* investigate systems and data (Clojure repl)

</comment><comment><USER27> We use clojure on the backend and clojurescript as our admin ui across our supply chain exchange at [https://www.openmarketshealth.com](https://www.openmarketshealth.com)

</comment><comment><USER34> We're developing a smart pillbox with a mobile app written in ClojureScript with React Native and a backend written in Clojure. [http://pilloxa.com](http://pilloxa.com)

</comment><comment><USER35> BookWell ( [https://www.bookwell.com.au/](https://www.bookwell.com.au/) ) uses Clojure on the backend, ClojureScript on the front, and Datomic as the database.

</comment><comment><USER36> We're using clojure both for new products and existing java codebases, and we've just introduced clojurescript on a smallish SPA. Mostly health and services sectors.

It's surprisingly boring (in the good sense).

</comment><comment><USER37> I'm using it for web development as well as backend services for large data pipelines and some ETL. I've also used ClojureScript in production, for an entire web stack built in Clojure(Script).

</comment><comment><USER38> We use lots of Clojure at Amperity - [https://www.youtube.com/watch?v=BThkk5zv0DE&t=1s](https://www.youtube.com/watch?v=BThkk5zv0DE&t=1s)

</comment><comment><USER12> Intent Media ([http://intentmedia.com)'s](http://intentmedia.com\)'s) data science functionality has a fair amount of Clojure behind the scenes.

</comment><comment><USER39> Kira Systems in Toronto uses it across the stack (web server, backend data processing, and platform API and SDK, according to their website). Their product analyzes contracts using machine learning.

</comment><comment><USER40> I built PartsBox ([https://partsbox.io/](https://partsbox.io/)) using Clojure and ClojureScript. Would be very difficult without them.

</comment><comment><USER41> We use Clojure to build an AI platform, and we are hiring :) [https://juji.io/fullstack](https://juji.io/fullstack)

</comment><comment><USER42> We use Clojure extensively at Room Key. Our main web API, various scheduled cron-like jobs, and AWS Lambda functions are all written in Clojure.

</comment><comment><USER43> CircleCI uses ClojureScript and Om for their front-end (and I think Clojure on the back-end too, though I don't have details).

</comment><comment><USER44> I know of Walmart, and Siemens Rail Automation - North America.

</comment><comment><USER45> Anybody hiring on a freelance/contract basis?</comment>


Common Misconceptions about Summer Internships - <AUTHOR> http://www.eecs.berkeley.edu/~pwendell/2012/07/31/summer-internship-misconceptions.html

<comment><USER2> Myth 1 is completely false. While companies will be careful about what they have summer interns write, they are absolutely expecting to get real value out of the intern's work. They are spending both money, and the time of the mentor(s) on the intern; do you think they are doing that for charity? Yes, you need to plan for the case that the intern doesn't successfully complete their project; that's why you have things like revision control and code review, to ensure that they don't do more harm than good, and you don't give them a project that absolutely needs to be completed by the end of the summer. Though at most places I've been, there has been more than enough urgent, mission-critical work that the existing developers couldn't possibly finish, so some of it does go to interns as it's not like the existing developers would have gotten to it in that time anyhow. I am mentoring several interns right now, and their code is shipping to customers already, in a core, mission-critical component of our stack.

Myth 2 is something that I'm sure that no one believes. Sure, people try to pick out reasonably scoped and specced projects for interns to work on, just as they do for anyone to work on, but that doesn't mean that they succeed, and I can't imagine that anyone would expect perfection. Furthermore, dealing with ambiguity, changing specs, scope creep, and the like is part of the value of the internship; you're going to have to learn how to deal with that eventually.

Myth 3 is about the only good point of advice here. It is difficult for a mentor to provide enough time for their interns. They usually have a variety of other things to do. But the interns won't be very effective if they sit around stuck waiting for help. Being assertive, and asking for help when you're stuck, will allow you to get a lot more out of an internship than just sitting around waiting for someone to mentor you.

</comment><comment><USER3> We have three software engineering interns with us (travel startup, 20 employees) this summer from various parts of the country, we shatter most of these myths:

1) All of our interns are shipping live code and pushing to production. We assigned them real projects that our other engineers would have done and pair them with a mentor who has expertise in that area. Our intern standards are that we would hire them today if they weren't in school so they are ready to handle real projects.

2) At a startup, specs and scope are both subject to change without notice but that's the norm so not isolated to interns.

3) We rely on our mentors to find a schedule that works for them but we also throw a lot of team events on the calendar so interns feel like they're a part of the large company.

Many of our full time employees started with us as interns and we often keep them on part-time even after their internships if we really want to them to join us after they graduate.

</comment><comment><USER4> You can write meaningful, production-ready code if you have the skills.

It seems like you have only worked at large companies, which is fine, but that is likely the core of the "self-fulfilling prophecy" you mention in point #1. If you want to write production code, don't work somewhere that puts 5 levels between you and the deploy scripts.

</comment><comment><USER5> "Myth #2: A summer intern will enter with a well defined, correctly scoped project that requires no revising."

This myth applies to just about any software development project I've ever worked on in the span of 20 years.

</comment><comment><USER6> Where are you interning? Banks? I've "interned" and worked full time at multiple start-ups, there's no difference at that level other than the pay, if you know what you're doing or have a passion to learn it.

<comment><USER7> Ditto. At a good internship you're not just keeping a seat warm while working on busy work. Working on production code is a fantastic/superior learning experience.</comment></comment>


Edward Snowden Demonstrates How to "Go Black" - <AUTHOR> https://www.youtube.com/watch?v=S4LOyi3EMWU <comment><USER2> In Switzerland I get:

"The uploader has not made this video available in your country. "

Well, that's nice.

<comment> Johan-bjareholt Seems to be the case in multiple European countries, I get the same message in Sweden

<comment><AUTHOR> US only. If you're not in the US, use a proxy/VPN to access the video.

Basically it shows Snowden taking the cameras and microphones out of an iPhone.

<comment><USER3> How does one know when one has gotten everything out?</comment></comment></comment></comment>


W3C/Khronos liaison to discuss possible use of SPIR-V as WebGPU shading language - <AUTHOR> https://www.w3.org/blog/2019/11/status-update-on-web-games-technologies/ <comment><AUTHOR> > _A simple liaison with the Khronos Group was setup to coordinate on shared > areas of interest. The GPU for the Web Community Group is notably discussing > possible use of SPIR-V as shading language for WebGPU with the Khronos Group > through that liaison._

Also in there:

> _The GPU for the Web Community Group is developing a draft charter for a > possible GPU for the Web Working Group. Discussions on the choice of a > shading language for WebGPU are still ongoing._</comment>