Hardware startups - <AUTHOR> http://cdixon.org/2013/04/30/hardware-startups/ <comment><USER2> I wrote a pretty extensive post on Quora about how to build hardware products. Here's an excerpt...

 _Designing for mass manufacturing is essentially impossible to learn without actually building several products. The challenges of negotiating with suppliers, verifying components, orchestrating supply chain logistics, and maintaining quality standards will swiftly kill a cash-constrained startup. But there's good news: unless you're on the cutting edge of a specific technology (like Apple with displays or metalwork), the problems you encounter at scale have very likely been solved by others. So the task then becomes to find those individuals or companies and hire them quickly._

 _PCH International is probably the most well-known of these firms. They notably make Kindle, products for Jawbone, and all the accessories for those dudes in Cupertino. Because they often work with big players, they're able to leverage large contract manufactures' (CMs) quality standards and economies of scale for smaller production runs. Currently PCH is also aggressively going after hardware startups and recently launched PCH Accelerator, which is essentially a strategic investment group to fund partnerships with startups._

 _Another option is Dragon Innovation, who works with Pebble, Makerbot, Sifteo, Leap Motion, and others. They're based in Boston and have recently partnered with the brand-new hardware accelerator/investor BOLT. They're certainly less flashy than PCH, but still know how to deliver._

 _HAXLR8R (Shenzhen) and Lemnos Labs (San Francisco) are other startup "accelerators"-- kind of like a hardware-flavor of YC. They seem to be more focused on the R &D stages (pre-Kickstarter) than production. For some personal anecdotes by others, see What startup incubators exist for hardware companies? [link]_

 _One way to think about contract manufacturing in China is with the "smile curve" popularized by Acer in the early 90s. It's essentially a graph of a product's extractable value as a function from creation to sale. On the left is brand, high-level design (IxD, ID, PD) and any specialized R &D. In the middle you have component sourcing, supply chain management, manufacturing, assembly, and shipping. And then on the right you have distribution, retail sales, marketing, and partnerships._

... it goes on with more examples. Full post: [http://www.quora.com/Michael- Grinich/Posts/Product-manufactu...](http://www.quora.com/Michael- Grinich/Posts/Product-manufacturing-for-the-newbie-from-prototype-to- production)

Funnily enough, Liam Casey actually often calls PCH the "APIs for manufacturing."

<comment><USER3> _"Designing for mass manufacturing is essentially impossible to learn without actually building several products."_

I'm sure that is true. Possibly because the people with experience of this come from a different community (established consumer electronics) than some of the new people coming onto hardware via the startup scene.

Bunnie Huang recently wrote a series of detailed blog posts about how to take prototypes to manufacturing in China. There is a lot of hard-won knowledge in them.

Part 1: The Quotation (or, How to Make a BOM) <http://www.bunniestudios.com/blog/?page_id=2812>

Part 2: On Design for Manufacturing <http://www.bunniestudios.com/blog/?page_id=2831>

Part 3: Industrial Design for Startups <http://www.bunniestudios.com/blog/?page_id=2850>

Part 4: Picking (and Maintaining) a Partner <http://www.bunniestudios.com/blog/?page_id=2911>

<comment><USER2> I totally meant to link Bunnie's blog posts in that Quora bit! Thanks for the reminder-- he's awesome. :)

</comment></comment></comment><comment><USER4> I'm doing a hardware startup at the moment. While the article seemed to focus on electronic hardware, I am developing a product that advances the state of the art of scuba diving (an automatic buoyancy compensator). I couldn't have done this ten years ago. I have hired an engineer in a different country on Freelancer.com, pay him with Paypal, make our designs on Sketchup, am printing our prototype on a Solidoodle 3D printer, and am ordering various hardware from manufacturers' interactive websites. I am literally working out of my basement. I will likely license it to major scuba equipment manufacturers, but may make and sell the product myself, probably borrowing heavily from Tim Ferriss's book, "4-Hour Workweek". Thanks for all the commenters' input. I have bookmarked a lot of these links for future use.

</comment><comment><USER5> The inevitable 'competition from lower cost manufacturers' is a likely fear among many hackers today, but one extremely good point that Chris makes is to complement hardware with software. Scaling with hardware can seem tough, especially with competition from lower cost manufacturers, but incorporating software gives companies an edge AND hopefully the ability to scale.

But playing devils advocate, it may be tough for startups to find the resources to have a hybrid hardware/software company from the beginning. It just seems like a daunting idea to start a hardware company. BUT, with crowdfunding having become so popular, startups may be able to raise enough money through initial sales from their hardware that they are able to put that money towards creating unique software.

Sorry for all the back and forth, just was thinking/writing out loud!

<comment><USER6> Hardware is nearly always complemented with software, since pretty well all "hardware" projects include a significant software component by default.

For example, I design and build radio systems in my day job. You'd think an 800MHz 100W radio transceiver would be "hardware". In actual fact, even that sort of system is largely driven by software. Most of the signal processing is done on a DSP. The "hardware" is essentially a DAC/ADC, a mixer, a gain element and a filter, and even then the control is handled by a computer. For VHF frequencies and below, even the mixer is disappearing.

These days, nearly every electronic device fits the following model:

Transducer > sampling > SOFTWARE > reconstruction > transducer.

<comment><USER7> As a grad student working with communication systems i can verify that. In fact, most communication and signal processing systems involve pretty sophisticated algorithms, and these are almost always implemented in software. In fact, the performance differentiator in these products boils down to the quality and implementation of the signal processing algorithms, and that is where most of the research effort at university labs is directed at.

An exception with respect to some communication systems would be ones operating at extremely high frequencies, where it would be impractical to process signals in software due to memory constraints (higher operating frequencies -> more samples per second). In these cases, the mixing/modulation functions get implemented in hardware.

</comment></comment></comment><comment><USER8> I'm currently working on a hardware startup. The barriers to being able to do this have definitely lowered thanks to CNC (moving to 3D printing soon), Arduino, and lower component costs - not to mention the fact that a lot of cell phone components now come bundled in modules and eval boards are easy to play with (all things that were mentioned).

One thing that I think still needs to come a long way is the amount of shared knowledge on hardware design. My co-founder and I both went to school for Mechatronics Engineering, and one of the biggest obstacles we have run into so far is that there just isn't as much shared knowledge on the internet when we run into problems - unlike software.

<comment><USER9> Not sure what your specific issues might be, but, yes, in general terms you are correct. If the information is on the internet it can be hard to find. This is where experience working at a shop with older school folks can be invaluable.

<comment><USER8> The experience gained from working in the industry is definitely useful. I'm looking forward to a time when it is as easy to learn hardware design as it is to learn how to program.

</comment></comment></comment><comment><USER10> I'm currently doing a hardware start-up. I totally agree with the crowdfunding strategy, especially if the technology has a mobile angle.

The problem we have is IP... IP management is such a burden for a small start- up. I'm working with a University technology office. While they are great people (really), it still adds a layer of complexity to an already tough situation (IMO, start-ups do not benefit from having more people at the table). Add in lawyers, and it becomes an order of magnitude more expensive than your typical software start-up. And the IP you get from a University is so raw... the cost to develop it into a consumer ready product can be immense.

On the other hand, it has been an education.

</comment><comment><USER11> I'm not sure there will ever be a pure AWS analog in manufacturing, but <http://www.dragoninnovation.com/> is pretty close to offering "Manufacturing as a Service." They've helped Pebble, MakerBot, Sifteo, Romotive, and dozens of other companies make it to the market more efficiently than if they'd run solo. I wrote this story about them at Wired ([http://www.wired.com/design/2012/06/dragon-innovations- manuf...](http://www.wired.com/design/2012/06/dragon-innovations- manufacturing-in-china/))

<comment><USER3> Interesting.

From the Dragon Innovation home page [1]: _"As a matter of corporate policy, Dragon Innovation will not accept any job that involves transferring existing in-house manufacturing from the USA to China."_

[1] <http://www.dragoninnovation.com>

</comment></comment><comment><USER12>
      "There is no AWS-equivalent for hardware."

Someone want to make a few billion dollars?

<comment><USER13> PCH International comes pretty close. They even have an accelerator program[1] to help hardware startups to manufacture their products in China.

I usually don't like to comment on rumors. I heard that some people from Amazon are already working on a AWS-equivalent for hardware too based on their experiences from manufacturing the Kindle. If you know anyone from Lab126[2], ask them about it.

[1]: <http://www.pchintl.com/accelerator/accelerator.aspx>

[2]: <http://www.lab126.com/index.htm>

<comment><USER14> PCH is the global leader for supply chain management and manufacturing (they make some of the most popular products we all use today) and they also have a new accelerator program launching this summer.

But accelerators are generally reserved for people with little to no experience in the craft, bare-bones resources and simply just ideas... They help a lot and bring great resources (connections, labs, some get little funding) but I've personally never been a big accelerator supporter... Except YC, which seems to have a 90%+ funding rate once you pass... But this is a subjective position here.

</comment></comment></comment><comment><USER15> I wonder , when building a HW product what embedded platform people here use for manufacturing ? Arduino/Mbed/plain microcontroller?

And if you use arduino , how do you manage the need to release object files (according to the arduino license) ?

</comment><comment><USER9> Good article. A few points:

> There is no AWS-equivalent for hardware.

Well, yes and no. Of course, it doesn't make sense to talk about AWS for manufacturing. Making physical items means that your supply chain, manufacturing process and channel of distribution have different time constants when compared to pure software-and-servers businesses.

By this I mean that step changes in supply, production or demand happen at far slower rated than in pure software products.

If by "AWS" we mean a manufacturing system that allows the entrepreneur to turn on and off chunks of manufacturing capacity with some immediacy and granularity, no that does not exist. And, that is so because it is just-about impossible for most consumer products.

The supply chain for most consumer goods is such that it has to start being stuffed with components and assemblies weeks in advance of manufacturing. Some OEM components have lead times measured in months. Others have MOQ's (Minimum Order Quantities) that can force commitment levels both to product volume and manufacturing effort/timelines.

As an example, I am currently working on a project --to be featured on Kickstarter-- that requires a custom aluminum extrusion in order to be able to manufacture it economically. Aside from tooling and setup NRE's most extruders have minimum buys that can range up to 2,000lbs of aluminum. In other words, once you play that game you are all in and committed to manufacturing at a certain level. Once these processes start you simply can't logon and stop them from your browser (or spin-up new ones) as you might do with AWS.

> Proper planning is essential because mistakes can be unrecoverable. For > example, you might create a design that fails environmental tests but only > discover this years later when you are about to go to market.

Right. And this is why elsewhere in the article he mentions that a lot of hardware entrepreneurs are older and more experienced engineers. When it comes to physical product manufacturing, experience can be invaluable. There's a lot that simply isn't learned in school. You can't recompile your way out of a design error.

> The build-test-iterate model that is popular in software startups doesn't > translate well to hardware startups.

Well, it does in certain domains. For example, every FPGA-based design I've done goes through extensive simulator testing before actually being committed to real hardware. The same is true of such things as high speed signal and power integrity (using field solvers and other tools).

When it comes to mechanical and thermal design a lot can be done with modern CAD systems through simulation. As an example, we devoted no less than six months to the design and simulation of an advanced liquid/forced air thermal management system for a 1,000W (yes, one thousand watt) LED array. The simulations were good enough that we could go to metal knowing we were going to be reasonably close.

The same is true with mechanical and plastics simulation.

Now, if what we mean by built-test-iterate means the inclusion of the actual end-user in a real market situation I have to say, yes, it is much harder. This is where prototypes are designed to be easy to change and adjust in order to experiment with form, fit and function.

In the end hardware startups are very different animals. I am not sure they can be compared through the same metrics.

<comment><USER16> It will be interesting to see if 3D printer tech will change this.

Large warehouses full of 3D printers where you can scale up/down your production would be a fairly reasonable analog to AWS and what it provides.

<comment><USER9> We are very far from that in most product categories. As an example, a CNC stamping machine can ingest sheets of aluminum and produce parts at an amazing rate. Spend a little money on a set of dies and you can produce parts faster than you can clap your hands. A similar situation exists in injection molding or die casting.

3D printing is neat but it is very slow, expensive, limited in materials and costly. Surely this will change with time. Still, it will be hard to compete with optimized processes where, as I said, you can produce parts faster than clapping your hands.

Now, for micro markets where you need just a few of something? Yup, that probably works today.

</comment></comment></comment><comment><USER17> Not only these, there are niches in hardware too which aren't immediately attractive to the big boys - like think of doing a new digital stylus but not a tablet. Playing it low gives for the necessary time and opportunity and then, like PG says, startle the world with something nobody ever thought of.</comment>


How a $10 microchip turns 2-D ultrasound machines to 3-D imaging devices - <AUTHOR> https://techxplore.com/news/2017-10-microchip-d-ultrasound-machines-imaging.html <comment><USER2> I wrote my master's on doing the reconstruction a few years ago. On my shoddy 10 year old laptop GPU I could reconstruct a 3D volume from 800-ish slices in about a second - depending on interpolation - so on modern hardware (or even mobile hardware) I expect this to be doable in real time.

I do wonder about the spatial precision though, we used optical tracking back in my thesis, and that was already noisy. There are techniques using speckle patterns to help with the registration - but that seems like a tough issue to solve. Either way, this is a cool idea and nice tech!

e: Oh, looks like it's orientation only - you'll need a steady hand making the sweep then, since I suppose you can't move the position of the probe, just it's rotation

<comment><USER3> What's the output look like ? Is it comparable to MRI?

<comment><USER2> It looks nowhere as good as MRI - but has the advantage of being very quick to do. MRI will also image different things (depending on settings). US will show you interfaces between different kinds of tissue (that's where you reflect part of the US beam - echo!), but MRI has a million settings - and will fundamentally show you water at different densities. What that means in practice depends completely on the acquisition sequence.

The use case in my thesis went something like this

1\. Do MRI - use this to plan surgery and to navigate while in surgery 2\. Once the patient is on the table, register the MRI image to the physical location of the patient. 3\. With various 3d tracked tools, you can then "point" at things in the real world, and have it show up on the MRI image. 4\. As you are performing the surgical operation, you want to check "how does it look now?" \- and that's where the tracked US probe comes in.

The use case outlined above isn't cost-sensitive, but time-sensitive. Getting an US image takes seconds and can be done mid-operation. Another point is that 3D probes are quite big and clunky - and for AVM-type operations where you crack the skull open, you want to keep that hole as small as possible. Phased US sensors + reconstruction then gives you 3D images with a very small sensor footprint. Another point is that 2D probes have higher resolution, so at least the XY plane looks much better this way.

e: there are some pictures in my thesis: [https://brage.bibsys.no/xmlui/handle/11250/253675](https://brage.bibsys.no/xmlui/handle/11250/253675) The system we used/were developing: [https://link.springer.com/article/10.1007/s11548-015-1292-0](https://link.springer.com/article/10.1007/s11548-015-1292-0)

</comment></comment></comment><comment><USER4> Allow to self cross post [https://news.ycombinator.com/item?id=15590160](https://news.ycombinator.com/item?id=15590160)

<comment><USER5> Thanks, I had missed that whole discussion (Butterfly iQ – A cheap handheld ultrasound tool with AI smarts inside) which was very related and useful!

</comment></comment><comment><USER6> This is remarkable, the data we could get from this would also be useful with machine learning to detect abnormalities sooner. An excellent use of adapting 2 cheap technologies to bring down costs.

<comment><USER7> 3 cheap technologies:

> With the game console's ability to accurately track the exact position of > the controller, he wondered, why not just duct-tape the controller to an > ultrasound probe?

Never forget the duct-tape! ;)

</comment></comment><comment><USER8> This article claims future ultrasound equipment savings of ~80%: USD$250,000 (3D) vs. USD$50,000 (2D-modified).

From the perspective of patients and medical administrators, the upside seems clear.

For (at least one?) ultrasound equipment manufacturer, this seems to imply they don't make as much profit on the likely high-margin 3D models. Curious how they and the industry responds.

</comment><comment><USER9> That's pretty cool. The chip appears to just feed x/y/z spatial orientation to the laptop. Then, the video frames from the 2d ultrasound are tagged with it?

Then some software to stitch it all together? Clever.

<comment><USER10> Clever indeed. I do wonder how hard it is to get the 2d images out of the ultrasound though. I don't suppose it's just like a mjpeg stream like a webcam.

<comment><USER9> At 1:31 in the video he appears to just plug into a DVI connector on the ultrasound machine, which I assume goes into some kind of video capture card on the laptop.

</comment></comment></comment><comment><USER11> 3-D printing, software, and hardware together: this is really great!</comment>


Facebook critic: Company 'will not survive long term' - <AUTHOR> https://www.politico.com/story/2018/03/28/facebook-global-democracy-money-488720 <comment><USER2> At this stage, regulation seems like it may help Facebook more than it'd hurt. Regulations that restrict the very behaviors that allowed Facebook to grow to its dominance might make it more difficult for startups to ever challenge them in a meaningful way.

<comment> rm_-rf_slash This now common argument doesn't hold water. What if regulation restricted one social network per holding corporation? If Facebook was to lose Instagram and WhatsApp it would be a huge blow to the company, while also promoting a competitive business environment where major social networks can't simply buy the competition out of existence.

<comment><USER3> What you're suggesting sounds more along the lines of antitrust and seems unlikely (though sounds great to me). I think that regulation around data security would be more likely and is what most people are suggesting. Whether this type regulation could be written competently in the US is my concern.

</comment></comment></comment><comment><USER4> Useless without the critic putting a number on "long term". In the long run, we are all dead. I also predict that Facebook will not be around three billion years from now. Can I have a politico article?

<comment><USER5> Does the word "long term" have no meaning, then?

I agree it would be better to say "within the few 2 decades" or slap a timeframe on it. But this characterization is silly.

<comment><USER4> Then what is the meaning, then?

I tend to use 50 years as my rough "long term" definition. "Facebook won't be around in 50 years" is not very impressive. "Facebook won't be around in 2020" would be a bold prediction, but doesn't seem very "long term". "Facebook won't be around in 2025" might be starting to get into "long term", but is also definitely starting to feel vacuous if we think of it as "The probability of Facebook being around as a distinct entity in 2025"... well, no duh it's not going to be 100%, but the probabilities get fairly uninteresting quickly. 50/50 is a decent initial guess for that, and it isn't until you get into the 90s on either side that it starts becoming an interesting prediction.

I don't know how to turn "Facebook won't be around in the long term" into an _interesting_ prediction without a time horizon.

Here's a non-vacuous sort of example: There's a proof somewhere that the maximal probability of the expected future lifetime of a given organization like a country or a company is its current lifespan, projected into the future. So the maximal probability of when Facebook will be gone if it is currently 14 years old is 2032. However, I can not seem to Google up the discussion of this; all the search terms I can come up with are flooded by actuarial discussions of human mortality where this most assuredly is not the case.

<comment><USER6> If you make the naive assumption that you are observing the company at a random point in its overall lifespan, the median of the distribution for how much longer it will survive is how long it has survived already.

More than that, if it has survived N years already, the odds that it will survive at least M more is N/(N+M). If you work out the probability density and then integrate it, you come up with an arithmetic mean that is also N. However the point where it is most likely a priori to die is tomorrow, and that probability steadily decreases.

</comment></comment></comment></comment><comment><USER7> Non Facebook user question:

Are we being unfair by only pointing to how FB had a negative impact on the US election? They had a very positive impact on other elections and helped (indirectly) overthrow bad governments.

<comment><USER8> _They had a very positive impact on other elections_

Every negative impact is a positive Impact for the other side.

 _indirectly) overthrow bad governments._

Are you talking about the arab spring? I thought it was generally agreed that the new regime is way worse/more oppresive than the displaced one ever was?

<comment><USER9> _Are you talking about the arab spring? I thought it was generally agreed that the new regime is way worse /more oppresive than the displaced one ever was?_

Social media helped facilitate the uprising, but isn't responsible for what replaced the previous regime.

</comment></comment></comment><comment><USER10> "Thing I have publicly shorted will not survive in the long term."

<comment><USER11> Similarly: "the public is increasingly focused on this topic", says person authoring scary stories about this topic for a major news source.

</comment></comment><comment><USER12> No company survives forever...every company eventually declines, and then either shuts down or gets acquired. It's clear Facebook won't just disappear-- its assets (data) are too valuable.

Intriguing questions to ask: what happens when Facebook does eventually decline? How much is Facebook's data worth? Who will ultimately acquire it? And perhaps more critically: what will their intentions be?

This is why Zuckerberg's opinions / testifying don't really matter. He's created a tool that's more powerful and more dangerous than humans have any business creating. As with any such tool, it will be used to wreak havoc sooner or later...even if he's an innocent, well-meaning guy, he will not always be the one in charge of this power.

I think of Facebook's power as similar to that of the atomic bomb. Yes it's a triumph of human ingenuity, and it _can_ be used for good, but there's a catastrophic flip side.

<comment><USER13> Just for fun, your comment reminded me of this:

[https://en.wikipedia.org/wiki/List_of_oldest_companies](https://en.wikipedia.org/wiki/List_of_oldest_companies)

<comment><USER14> For the curious:

Kongō Gumi Co., Ltd. is a Japanese construction company which was the world's oldest continuously ongoing independent company, operating for over 1,400 years until it was absorbed as a subsidiary of Takamatsu in 2006.

</comment></comment></comment><comment><USER15> I'm not pro facebook, but the fact that so many people are saying bad about facebook means they still care, and that also means facebook knows there is a problem.

I think a bigger indicator of death is when a company neglects problems, or lies about them. Facebook has not denied problems, which makes me think they might fix them.

I had problems with facebook since 2005ish, and actually creating a rival to facebook was a big motivator for me to learn to code, but now I've since taken up my own interests, and am not so concerned with facebook, since I see it, in some ways, as a necessary evil. (though I would rather see some sort of decentralized blockchain thingy that serves as the global directory of connecting all people)

Also, if you are in the valley, you are posh and already "over facebook", you are an early adopter of a facebook-less world, and you don't realize how obsessed with facebook people are in the midwest(where I am). I'm not saying a facebook-less world won't catch on here, but where I am, people aren't anti- facebook at all.

<comment><USER16> What I don't understand is this, Facebook makes $7 per customer through selling their data to advertisers and other groups.

I know most people don't want to PAY for Facebook, but maybe the people who really care about their privacy would.

Could I opt out of advertising, or sharing my data with 3rd party apps of my friends if I paid Facebook $20 per year? For me $20 per year would be a good price for an ad free experience on Facebook.

That way some people pay with real dollars, some people pay by being receptacles for advertising. Just like how there is Free TV, and there is HBO.

</comment></comment><comment><USER17> You can say that about any company though.

</comment><comment><USER18> Instagram and WhatsApp will survive.

<comment><USER19> And Oculus VR.

<comment><USER20> That's still around?

</comment></comment></comment><comment><USER21> it will survive atleast as long as the main user base is still alive... new people perhaps look at alternatives, but they have plenty of cash to become part of a future platform for these people too. long term , a company generally changes and adapts, and these adaptations don't seem to be accounted in these kinds of critiques. anyone who thinks a company as big as this wouldn't take into acount the changing world around them is as stuborn or more as they suggest/assume the target of their critique to be...

<comment><USER22> >> but they have plenty of cash to become part of a future platform for these people too

And don't forget: Facebook has been buying out every social media platform of any legitimacy (well, except Snap Inc.?) which might rise up to challenge it one day, especially at times like these.

With copious cash it has and no legitimate contender in sight, Facebook has a lot of time and opportunity to morph and adapt itself into a future platform.

I find this article's assessment kind of near-sighted, and naive.

</comment></comment><comment><USER23> "In the long run we are all dead." \- J.M. Keynes

</comment><comment><USER24> It will survive in some form, at the very least the social authentication aspect. It's become a utility for many, MANY other websites around the world.

<comment><USER25> I mean it is easy to change your Oauth provider if they both use the same underlying email address.

</comment></comment><comment><USER26> I mean you can say this with any company really..

</comment><comment><USER27> ""But Facebook is essentially a 14-year-old design that for all its evolution is still founded on the ideas of 14 years ago. So no, the answer is it will not survive long term.""

author of that quote will likely just increment that 14 each year until they are right?

</comment><comment><USER28> I'd say the company might still be there but pivoting of core business will likely happen. Lots of companies survive this way, especially when they cover multiple business fields.</comment>


Languages with the most curse words in the git commit messages - <AUTHOR> http://i.imgur.com/7LdEXAY.jpg

<comment><AUTHOR> Source here: [https://www.facebook.com/photo.php?fbid=10152572618920450&se...](https://www.facebook.com/photo.php?fbid=10152572618920450&set=a.432190050449.225968.582270449&type=1&theater)

</comment><comment><USER2> Javascript wasn't mentioned as their wasn't a monitor large enough in existence to display the graph on.</comment>


Southern California Edison IT workers 'beyond furious' over H-1B replacements - <AUTHOR> http://www.computerworld.com/article/2879083/southern-california-edison-it-workers-beyond-furious-over-h-1b-replacements.html

<comment><USER2> I wonder what pg has to say about this situation. His post on the topic of H-1B workers created a huge discussion, I'm surprised that more comments haven't been posted here.

<comment><USER3> Nothing. They know they are biased and wrong to support this fiasco.</comment></comment>


In-Space Manufacturing Is About to Get a Big Test (2017) - <AUTHOR> https://www.space.com/39039-made-in-space-off-earth-manufacturing-test.html <comment><USER2> This is a really interesting first use case for it.

For those that are interested in the price, this link is interesting.

"One kilogram of exotic glass feedstock can be expected to produce from 3 to 7 kilometers of fibers in under an hour in microgravity, under optimized conditions. At a nominal cost of $88K/kg, the launch costs for the preform and spool (estimated to be 2 kg) combined with the landed costs for the filled spools would be approximately $176,000.

The current low-end market price for ZBLAN fibers is $150/meter. Therefore, even at today's lowest market prices, a kilogram of ZBLAN launched to space could be sold on Earth for between $450,000 and $1,050,000.

However, the real value of microgravity will be in producing ZBLAN and other exotic fibers of exceptional quality. Today, these custom fibers are sold between $300/meter and $3,000/meter. At current market prices, a preform launched to a fiber manufacturing facility in orbit could yield from $900,000/kg to $21,000,000/kg."

[https://sites.google.com/site/cmapproject/case- studies/exoti...](https://sites.google.com/site/cmapproject/case- studies/exotic-glasses-and-fibers)

<comment><USER3> > This microgravity-produced fiber has numerous applications, including trans- > Atlantic telecommunications

That didn't seem feasible - my first guess was that you couldn't possibly use something that you need to bring to space to cross the Atlantic. My gut was that the math would put it somewhere above the economic output of the planet. But when I stopped guessing to actually do the math, that 5,000 km multiplied by $300/meter is 'just' $15B. Still just one fiber, and still a lot more than the $300M that a traditional cable costs, but it could be done.

<comment><USER4> I can't find any reference to ZBLAN being used for long distance telecommunications, it's seems like it's both too expensive, and it's exceptional properties (excellent infrared transmittance) aren't particularly relevant to that application. Why particularly specify trans-Atlantic though? Looks like somebody in marketing getting a bit ahead of themselves.

<comment><USER5> I'm not up to speed on ZBLAN at all (had to google what it is just now), but if it can really do 0.05dB/km at 1550nm [1], then it seems like it's actually ideally suited for long distance telecom. This is like taking an off-the-shelf "80km" SFP, and turning it into an "800km" SFP. Am I missing something?

1: [https://en.wikipedia.org/wiki/ZBLAN#/media/File:Zblan_transm...](https://en.wikipedia.org/wiki/ZBLAN#/media/File:Zblan_transmit.jpg)

</comment></comment></comment></comment><comment><USER6> I guess in microgravity all bets are off, but the box they show doesn't match my mental model for "how they make fiber". I'm thinking of these videos I've seen of 2-3 story tall fiber optic manufacturing machines where they drop the molten glass a dozen feet down the middle before touching it.

Somehow I'm more impressed by the fact that they can pull optically clear fiber in a box the size of a rackmount case than that they will do it in orbit.

<comment><USER7> >where they drop the molten glass a dozen feet down the middle before touching it.

I guess in orbit it's constantly falling.

<comment><USER4> That doesn't really help when what you're trying to do is stretch the material.

</comment></comment></comment><comment> Harvey-Specter So this thing was launched in December, and news on whether or not it was successful?

<comment><USER8> Looks like they're still waiting on the results[1].

[1]: [https://twitter.com/MadeInSpace/status/958730115353137152](https://twitter.com/MadeInSpace/status/958730115353137152)

</comment></comment><comment><USER9> I don't mean to spoil it, but something exactly like this is mentioned in "Artemis," by Andy Weir, author of "The Martian."

</comment><comment><USER10> NASA has also done some reseach into ZBLan fabrication on earth using magnetic fields to eliminate crystal formation ([https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/201200...](https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120016709.pdf)). This seems to be an expensive effort to duplicate the same effect.

<comment><USER11> If you go through the paper, it's clear that the microgravity work is proven but the magnetic field work is still under development.

Also the pictures of the fibres are interesting, it's hard to be sure but my mk1 eyeball estimate is that the magnetic process fibre looks slightly inferior in terms of even surface consistency. Which is a very subjective measurement but if they were of identical quality I would hope the paper author would do a better job of highlighting that fact.

</comment></comment><comment><USER12> So this already happemed? What were the results?

</comment><comment><USER13> Whatever happens, kudos to Made In Space! These guys have tenacity.

</comment><comment><USER14> That website is so broken on mobile. Every 2 seconds something else loads an moves the text away from its current scrollposition.

</comment><comment><USER15> Why are they trying to say 2.2lb is 4kg? That's really odd I think they converted the wrong way around?</comment>


Autocompletion: Can we reduce a web form to just one text box? - <AUTHOR> http://complete-ly.appspot.com/examples/booking.html As web designers we have learnt to strip out the unnecessary elements. We know that any simplification reduces cognitive overhead. But can autocompletion be implemented in an incremental way so that filling a form becomes like writing one's intention.
I would like to have a cup of tea/coffee...
enjoy L <comment><USER2> The idea is interesting, but I would imagine you need to take into account various forms in which people might want to express their intent. For example, "train from A to B" instead of "I need a train from A to B", or "flight tickets to C" (the current location omitted and can be detected using IP geolocation).

Another thought I had was that many people might actually prefer to interact with checkboxes and select from a list, especially if they're using a mobile device.

<comment><AUTHOR> Hi anon1685, I do see your points. And no doubt you are right. It'd be probably quite hard to enter dates and times.

It's a bit of contrived example as well but the main idea I think holds.

People need to think if a mini language can help rather than lots of boxes.

cheers</comment></comment>


Use PHP to build a Twitter-like system on your site - <AUTHOR> http://www.ibm.com/developerworks/opensource/library/os-php-twitter-interface/index.html?S_TACT=105AGX54&S_CMP=C0226&ca=dnw-1008&ca=dth-st&open&cm_mmc=4865-_-n-_-vrm_newsletter-_-10731_105741&cmibm_em=dm:0:6992641

<comment><USER2> Is it just me or is there a total lack of input validation? mysql_real_escape_string isn't going to protect you from code injection...

<comment><USER3> I don't think the point of this article was to be a copy and paste twitter alternative, it's more to show that it's possible to do it (at least that's what I got out of it).

There's also no hashing/salting of user passwords or anything. Even with some minor coding experience you should be able to pick this out.

<comment><USER2> I get that, but when I'm not browsing Hacker News I keep company with hundreds of teens who take code like this and implement it into their own sites, or edit it a bit and redistribute it. I think releasing code of this standard without specifically stating that it is not ideal for actual public use, you put these teen's sites at risk - perhaps without even realising it.

<comment><USER3> I don't think all code should have to come with a warning label.

I'm 17 years old, falling into your teen category, and there is no way I would ever run that code in a production environment - just by looking at it I can tell it's unfit.

<comment><USER4> In your experience (pigeon holed as a teen), do you see your technical discrimination as the exception or the rule?

<comment><USER5> In my experience (still a teen...) it's the rule until proven otherwise. I mean just by being a teen many people assume you have _some_ level of technical knowledge, but it doesn't go beyond helping people with setting up their computers and phones...anything like programming is like an "oh, really?" to a "oh, wow [you can actually do it]!" thing.

Well, to be specific, that was the case for me from 13 to 16, then I started prefacing that with "I'm majoring in computer science" and then the discrimination started to go away.

</comment></comment></comment></comment></comment></comment><comment><USER6> I haven't read the article in full, but I wonder why the author hasn't used laconi.ca (the free open source software which is used by identi.ca). Why should you be creating a new microblogging solution when a perfectly good open platform exists?

<comment><USER7> Learning.

<comment><USER6> Interesting. I would have given the same answer some time ago, yet lately I have the feeling that I learn more from installing, configure and dive into somebody else' code and extend / change it than writing something from scratch. I guess both are valid 'methods' to learn, although the latter benefits open source projects like laconi.ca as well.

<comment><USER5> I personally think that both are valid methods to learn, but that you learn different things from going the separate ways. i.e. when I'm writing something from scratch I have to learn every little detail of what I'm trying to do, whereas taking a look at someone else's code I could gloss over a lot of the implementation details but learn how the author decided to implement some of the relevant/important parts, how to extend, and otherwise generally contribute to a project.

</comment></comment></comment></comment><comment><USER8> I only hope part #2 addresses the scalability problems #1 has before the site becomes a hit... ;-)

</comment><comment><USER9> First, it's interesting that they call this an _intermediate_ level tutorial/article. I'd definitely put a microblog in the _begginer_ category (The "build a blog in 15 mins" screen cast is one of the first things people watch when getting into Rails).

Second, in the time it took to write this article, one could have built the actual app to provide for download or as an open-source project.

</comment><comment><USER10> This is really insufficiently complex and/or interesting to belong here, is it not?

<comment><USER11> Personally I'd still take this kind of article any time over the "why [Twitter | Facebook | etc] is the [next Google | biggest failure ever]", "5 timeless principles for [ building startups | coding | marketing] I just thought of, having done it for almost a year now" or anything from TechCrunch & co.

<comment><USER3> Agreed. I love articles that tell you how to build something from scratch, they always give me new ideas.

</comment></comment></comment><comment><USER12> How about just installing laconica?</comment>


Ask HN: Are there any Python or C# tutorials based on projects? - <AUTHOR>

The good folks over at tuxradar.com do coding tutorials by setting up projects such as RSS readers and Space Invader clones.  I was wondering if there was anything else out there that did something similar?  Unfortunately for tuxradar they don't seem update the coding side of the site anymore and the projects are outdated (plus I've done them all over the past couple of years anyway!) Live Long and Prosper! <3 <comment><USER2> Are you looking to learn a specific framework or just the basic concepts? Have you tried projects at google code for examples?

<comment><AUTHOR> I'm learning to code, and I just find it easier to follow a project rather than reading page after page of theory.</comment></comment>


Split Keyboard with Built-In Trackball - <AUTHOR> https://www.keymouse.com/catalog/keymouse/keymouse-track-127-3d-printed-assembled <comment><USER2> Wow. I've bought cars for less than that.</comment>


Ask HN: Engineers from non-CS background, how did you pivot into ML/AI? - <AUTHOR> I am a EE, hardware engineer with about a decade of experience in PCB electronics and systems engineering experience which includes a brief stint at a FAANG that also happens to be an E-tailer. I have been "dabbling" in Python for about a year now and just recently started with DL using PyTorch and find it quite interesting. To be clear I don't write code at work, atleast not until now. I intend to utilize any free resources (MOOCs) to teach myself the latest techniques in DL for CV. What I am not clear is the next logical step.A part of me wants to Boostrap a SAAS using Python stack to build something that I can market using the traditional channels(PH,Show HN,Reddit,answer SO questions) and show it to potential employers but am not sure if I will even get to the interview stage with a resume but that doesn't look anything like a programmer with a tradional CS background and work experience to boot. Sorry about the long and winding question, but what should I do to get noticed by recruiters at FAANG and non-FAANG to stand apart from the CS crowd? <comment><USER2> Stop focusing on MOOCs and youtube videos and study textbooks. Do exercises. Treat it like academic studying, and you'll end up with a decent education. It's important, because it's often easier to make a thing work okay than to understand why it works, so you'll get false confidence working through a tutorial. But then you want to apply that to something else and it doesn't work quite right, you won't know why it doesn't work and how to fix it.

Get some textbook suggestions and make a minimum of reading 5-10 pages per day. In about a month or two, you're done with a 300 page book. Repeat that for a few years and you're an expert. Once you have the foundations, read papers too, but don't skip straight trying to using AlphaZero to solve a curve fitting problem.

<comment><USER3> > study textbooks. Do exercises. Treat it like academic studying

This. Highly recommend Russel & Norvig [1] for high-level intuition and motivation. Then Bishop's "Pattern Recognition and Machine Learning" [2] and Koller's PGM book [3] for the fundamentals.

Avoid MOOCs, but there are useful lecture videos, e.g. Hugo Larochelle on belief propagation [4].

FWIW this is coming from a mechanical engineer by training, but self-taught programmer and AI researcher. I've been working in industry as an AI research engineer for ~6 years.

[1] [https://www.amazon.com/Artificial-Intelligence-Modern- Approa...](https://www.amazon.com/Artificial-Intelligence-Modern- Approach-3rd/dp/0136042597)

[2] [https://www.amazon.com/Pattern-Recognition-Learning- Informat...](https://www.amazon.com/Pattern-Recognition-Learning-Information- Statistics/dp/0387310738)

[3] [https://www.amazon.com/Probabilistic-Graphical-Models- Princi...](https://www.amazon.com/Probabilistic-Graphical-Models-Principles- Computation/dp/0262013193)

[4] [https://youtu.be/-z5lKPHcumo](https://youtu.be/-z5lKPHcumo)

<comment><USER4> PGMs were in fashion in 2012, but by 2014 when Deep Learning had become all the rage, I think PGMs almost disappeared from the picture. Do people even remember PGMs exist now in 2019?

<comment><USER5> You'll find plate models, PGM junk, etc in modern papers on explicit density generative models and factorizing latents on such models.

</comment></comment></comment></comment><comment><USER6> [https://aws.amazon.com/training/learning-paths/machine- learn...](https://aws.amazon.com/training/learning-paths/machine- learning/data-scientist/)

Patience. Spending that extra time (it helps if you really enjoy it or can program yourself to really enjoy it).

[https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) for inspiration.

To answer the question though: I'm not sure what you produce other than maybe blog or publish some analysis using your data skills. And maybe: [https://github.com/MaximAbramchuck/awesome-interview- questio...](https://github.com/MaximAbramchuck/awesome-interview- questions#data-science)

(source: non-CS engineer at amazon who watched the amazon videos internally before they were made public. I'm not a data scientist yet but sometimes, esp. when people talk about the challenges of AGI, I think about transitioning.)

<comment><AUTHOR> Thanks for the links to AWS videos. These were posted here on HN sometime back. Will definitely bookmark them and come back to them. At the moment(literally)i am still finishing up Udacity PyTorch and hope to continue wit the venerable DL4Coders part1 and part2. As someone else had posted, coming up with _useful_ implementations of popular ArXIV papers seems to be one sure shot way of building up a personal brand on Github.

<comment><USER7> Andrew Ng's courses on Coursera (Machine Learning) are the best ones IMO. Gives you the introductions to the math behind and some practice.

</comment></comment></comment><comment><USER8> I'm not sure how this will be received, but I'm learning a lot through following Jeremy Howard. He's a huge PyTorch fan and he's spent the last 3 years trying to figure out what people like you need. He launched a course called FastAI and a DL library by the same name. His aim is to help anybody do it that wants to, with or without code.

MOOC: [http://course.fast.ai](http://course.fast.ai)

I just found a resource a few months ago that I'd love to recommend, but haven't started yet. It's mentorship you pay for, but not up front. You sign a contract to pay a certain percentage after you're hired. I plan on going through this program if my current job leads don't pan out.

Mentorship: [https://sharpestminds.com](https://sharpestminds.com)

I'm interested in comments about either program in general. Speaking as someone who also has an EE degree, went through a web development bootcamp, and was disappointed by both at the help in getting hired that was offered after the curriculum was finished, I am also interested in your findings and results.

<comment> edouard-harris Hey there - I'm one of the cofounders of SharpestMinds. AMA!

EDIT: Also, I strongly concur with the fast.ai recommendation for deep learning, especially if you're starting from a background in software.

<comment><USER9> Hi Edouard, interesting concept. Who are the mentors and why don't you list or profile a few of them on the website? (beyond the company logos)

<comment> edouard-harris Thanks!

Some stats about our mentors:

\- There are about 60 of them now

\- Geographic distribution is ~1/3 in the Bay Area, ~1/3 in the Toronto region, the rest across the USA and Canada

\- About 50% are deep learning engineers, the other half are a combination of ML devops, data eng, traditional ML (clustering, boosted trees, etc.)

\- About half work in (or are alums of) the AI labs of major companies such as the ones whose logos are on the website

Why we haven't listed some of them on our website yet: no good reason. We'll probably do this soon. It's a good idea.

<comment><USER9> Thanks for the response!

</comment></comment></comment></comment></comment><comment><USER10> My background is that of an econometrician (ie quantitative economist), and I now work as a Research Engineer at one of the FAANG research divisions.

I think the advice about getting in as a hardware engineer is solid. At my workplace, there's a ton of need for people working on specialized hardware for DL, and for people working on the software that works with it (optimizing compilers, etc).

If you are looking to break into the software side of DL, the first two thirds of the Deep Learning book [1] contains all the math you need to know to pass the interviews. Then, it's just a matter of getting interviews; I found that I needed professional experience deploying DL/ML to do that. I got that by doing side projects at work. For instance, we had a long standing operations research problem, and I spent some free time at work implementing a RL algorithm to solve it. I didn't get too far, but I was able to talk coherently about the papers involved and about how I planned to conduct the project, which went a long way.

[1]: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)

<comment><USER11> > Then, it's just a matter of getting interviews

Are you implying that, once prepared well enough, the contents of the interviews are simpler than getting actually noticed in the pile of applicants ?

<comment><USER12> You need to think like an interviewer - what can you reasonably make someone do in half an hour (plus time for chat and questions after)? Apart from being able to parrot deep learning theory, implementing things is tricky. Do you learn anything from making someone implement VGG in their pet framework? Training models also takes more time than you have to spare.

Much easier to quiz the applicant how they would solve a problem, or to discuss a previous project or paper they've published (or are interested in). Some people will find that much easier than whiteboard coding, others will hate it.

It really depends where you apply and if you want an applied or research role. Some places won't touch you unless you've got a publication in somewhere like CVPR. Others will go _hard_ on the stats questions. Other places want to see a strong Kaggle rank or some personal projects. It's really useful to have a portfolio here.

<comment><USER11> Thanks, that is helpful advice.

</comment></comment></comment></comment><comment><USER13> Maybe someone who actually works at FAANG can weigh in, but I would think that one of your best bets would be getting into one as a general SWE and then transitioning to AI/ML internally after a year. I recall Google even having some sort of internal program that encouraged this. Getting into Google is a moonshot, but it's possible to do so with no prior professional programming experience if you put in a ton of effort AND get lucky. Amazon seems willing to train too, based on the following experience I had:

I'm an iOS engineer without a STEM background, and I've been contacted by Amazon recruiters for entry-level ML/AI positions. I thought it was weird, but they said they've hired a few people with iOS backgrounds and no prior ML/AI experience who are now excellent ML engineers. I backed out because I knew I would fail the interview process at this point, but it's something for me to think about for the future.

<comment><USER14> Random side note, but when is the 'FAANG' acronym going to die? MSFT is killing it, prob the top tech company around these days. Needs to be included in that list.

<comment><USER15> I agree, Microsoft is probably the #2 top company in AI after Alphabet, should be included

<comment><USER16> Microsoft Research does a lot in AI but don't forget FAIR, Nvidia, Baidu, and Amazon. Smaller companies like OpenAI are making strides too.

<comment><USER15> i agree 100%

</comment></comment></comment></comment></comment><comment><USER17> I'd strongly recommend considering hardware companies in the ML space. They are trying to acquire ML talent but will also value your EE background. This is probably best approached at the usual big ML hardware vendors (NVDA, INTC and AMD) but there are also numerous start ups in the space that may be credible.

Lots of good advice about acquiring skills, I don't have much to add beyond that. I'll just mention that before you jump into the advanced stuff, please understand the terminology and basics very strongly. I've interviewed over twenty people for roles in ML the last year and many (despite having ML on their resume or even some experience in it) could not even explain the difference between training/inference, the meaning of validation, etc. The field is so hot right now that many unqualified folks are trying to get in, often by faking more experience than they really have. In response, I've created a simple 'fizzbuzz' test just so I can quickly screen people.

</comment><comment><USER18> I've hired about half a dozen ML engineers/architects in the past six months. Several of them have EE backgrounds. There's a good bit of ML that touches on hardware (think integrated cameras and similar) so it can be really helpful.

You're mostly on track with your plan to build something. You do need to demonstrate that you have the skill set, but building one giant thing isn't the answer. There's so much that goes into building a giant thing, that I can't accurately access your ML skills.

Ideally I like to see a lot of small things over a reasonable amount of time. Someone with a solid GitHub showing 6-12 months of paper implementations, weekend geez-wiz hacks and various other projects would go right to the top of my call back list.

Hope that helps, good luck with the job search.

<comment><AUTHOR> Thanks!. I think [https://fomoro.com/projects/project/reverse-image- search](https://fomoro.com/projects/project/reverse-image-search) this has provided me some motivation. Also just subscribed to your meetup. I will try to make it to the next meetup on the 8th as i am just about starting with GANs in my udacity PyTorch course. Thanks for organizing this meetup. I will leave the link for folks from Bay Area to follow-up. [https://www.meetup.com/deep- learning-sf/](https://www.meetup.com/deep-learning-sf/)

</comment></comment><comment><USER19> Speaking from experience, almost all FAANG positions I've seen require a degree for ML/AI, and even require a degree for less research-oriented positions like a Data Analyst/Scientist.

Non-FAANGs may be less picky but the competition in the field is too great at the moment (due to MOOCs/Bootcamps increasing supply), and even with an excellent portfolio it may be impossible to stand out. (in my case, despite my data science "fame" most recruiters tossed my resume out immediately during my job hunt a year ago; the only interviews I got were by going above the recruiters. And that was for data science, not even ML/AI)

Even after working as a Data Scientist for over a year, I've received practically no recruiter spam.

</comment><comment><USER20> After reading most of the comments I can try to provide a different perspective.

I am a Director of Data Science and Software Engineering for a mid sized firm (~1000 employees and $150-200MM revenue). I started with a Finance degree then shifted into an analysis position at a FAANG (lots of excel, SQL, learning how to query big data). This eventually led to learning more about tech (python, AWS cloud stack, messaging queues) and after 8 years in the industry giving me enough experience to manage teams of data scientists, software engineers and data analysts.

Although it is so important to know all the software engineering stack, many companies will benefit from simple business intelligence and data analyst roles. I guess my recommendation is to also keep an open mind in looking for these types of roles in the market (data analyst, business intelligence engineer), because given your desire to learn and existing background, its clear you can make a big impact in those companies as well. And it will be much less competitive than traditional CS crowd.

Some food for thought

<comment><USER14> We gotta stop saying 'FAANG' when MSFT is the arguably the top tech company around these days.

<comment><USER21> Not disagreeing with you (not informed enough to), but top tech company by what measure? Market valuation? Impact of products/services in 2018/2019? Workplace rating?

<comment><USER22> I've always felt that Microsoft fits in with GFAA much better than Netflix.

</comment></comment></comment></comment><comment><USER23> I am also an EE and have been able to apply ML to my field, wireless comms. It turns out people skilled in the intersection of two fields are very rare indeed.

I'd suggest you look for an opportunity to apply ML/CV/AI in your industry (deep learning for PCB inspection maybe?). Show the possibilities, get some research funding, do a pilot program or similar. Lead and drag your company (kicking and screaming if need be) into the 21st century.

Then you will have ML/AI on your resume, and recruiters will come looking for you.

<comment><AUTHOR> Thanks for the response.Wireless comms seems like ripe for ML enhancement. PCB inspection is something that has crossed my mind indeed but it is quiet involved due to the sheer number of features ( traces, components, vias, connectors) none of which has pretrained models. Having said that Automation esp FAI( First article inspection) is quiet possible as demonstarted by landing.ai(Andrew NGs) company.Being employee at a publicly trading company, not sure how I can secure funding. Perhaps my focus should be to apply ML directly to something that I do day-day at work.

<comment><USER24> Would you like to write an auto router for PCB design using ML? A smart one that can indentify components, their properties and choose right connections between them? Have a fast signal going directly and some shitty LED with 10 vias. Or placing power parts with wide tracks and using thin ones for digital signals.

<comment><AUTHOR> Auto routers already exist and I always route my own PCBs. I am pretty sure the tool vendors are considering or already building up AI expertise to level up their Auto routers. I keep hearing at least in Altium they have come a long way. Gotto try it one of these days.

</comment></comment></comment></comment><comment><USER25> I'd say pivoting to ML/AI is much easier for Math/Physics folks compared to pure CS ones.

</comment><comment><USER26> > what should I do to get noticed by recruiters at FAANG and non-FAANG to > stand apart from the CS crowd?

The usual answer here is look for suitable business / r&d cases within your own EE industrial domain and use ML/AI as any other tool instead of as a black box or a magic wand. Good luck.

<comment><USER27> I'm just trying to get my first programming job.

I cant seem to get past HR. My resume has that I'm a Chem Engineer BS, Industrial MS, 7 years in engineering, 2 years of Electrical Engineering.

The first page of my resume is my 10 years of non-career programming experience. Built a Dishwasher(embedded C++), full stack app(RN JS, Mysql PHP laravel), and smaller projects.

I cannot get past HR.

Every real life programmer I show my work to, knows I'm capable. Heck even some got me in touch with HR. Nothing came of it.

<comment><USER28> Off topic, but wanted to reply regarding "The first page of my resume is my 10 years of non-career programming experience."

This must be part or most of the problem. Cut your resume down to 1 page, if possible. Include a meaningful cover letter catered to the opportunity and specific company youre applying to. Shove the last ten years stuff into the very end, and start that first page with your software knowledge and related projects. Ping me someday here if this ends up getting your foot in the door.

<comment><USER29> Why not use a pitch or capability cv? and ditch the traditional cv format

</comment></comment></comment></comment><comment><USER30> I built ML application in my domain which helped demonstrate my capabilities.Networked a fair bit within my company and then got the chance to lead a ML team. Whole process took about 2 years.meanwhile self educated myself continuously over last 3 years. Spent min 20 hours a week coding, reading, learning and discussing ML. Joined ML learning groups helped other folks and learned through their journeys as well.

ML is unfortunately better done in a big company due to data but also a b __*Ch due to tremendous friction within org to get things done.

Another key strategy is to commit yourself to build an end 2end ML application, structure your learning around it. I found this a tremendous technique to turbo charge my learning.

</comment><comment><USER31> I'm a fellow EE grad, that has been doing software for the past 20 years or so. I started studing ML a couple years ago. I started with a couple of Andrew Ng's courses on Coursera. I found it was a good mix of theory and practice. It's a really exciting field right now (a bit over-hyped, but still lots of room for growth).

BTW, I think the you may have a bit of an advantage because of the math background you presumably have with a BSEE (linear algerbra & differential equations).

</comment><comment><USER32> It is extremely tough. I left my Civil Engineering career path in 2015 in search of a career in tech. I then applied to graduate school at SMU for an online masters in data science. I am still struggling to find meaningful work, but currently mentoring a data science bootcamp.

This is a very tough track but uf you have software development experience it should be easier to get a role in ML or Data Science.

<comment><USER33> How did you find that SMU program? I was debating that and a similar program at UC Berkeley.

</comment></comment><comment><USER34> I can't help but read the title as something akin to "musicians, how did you become comfortable painting with watercolors?"

I know ML/AI is all the rage. I just feel that targeting it so heavily is a bit shortsighted.

<comment><USER35> I read somewhere that learning ML/AI isn't the hard part. It is having enough data science background to be able to tell tell what problems fit ML. ML isn't the hard part, finding a problem ML can approximate is.

Maybe that is too simplistic but I can't help after my 1 semester ML course think that most of the ML problems people are solving aren't really suited at all. Like SWE see this cool hammer and now everything is a nail. Maybe I should read up on startups using it successfully for anything but I haven't seen many of those on the frontpage.

</comment></comment><comment><USER36> Industrial engineer by training here, I honed my chops on Kaggle competitions, eventually winning one. I was recently brought into a FAANG in a ML/AI engineering capacity.

I can't reccomend Kaggle enough for those who are looking to prove their abilities in the field.

<comment><AUTHOR> Nice! Another pivot story. How did you make it past the Recruiters and their filters? Just with your kaggle portfolio? Any chance you can post a link to your kaggle profile. Congrats on winning Kaggle and that's no mean feat, considering the competition(pun intended).

</comment></comment><comment><USER37> Naive question. Is ML/AI the "real deal" and here to stay, or is it still kind of just the hype du jour?

Or put another way: are there plenty of problems where ML/AI are valid tools or are they largely cool tech looking for problems to fit into?

<comment><USER19> ML/AI is here to stay, but currently the marketing and potential impact of ML/AI is a bit _exaggerated_.

</comment></comment><comment><USER38> Not sure it' the best path for you (it could be though!) I'm an EE and applied for PhD programs in control theory (robotics specifically). Now my research focuses on applying deep learning to control under-actuated robotic systems.

Although I went right after my undergrad, there are several in my cohort who were in industry for as long (or in one case much much longer) than you have before starting their PhD.

There are certainly ways to merge your hardware experience with learning. Either applying AI to hardware design, or applying hardware design to speed up or otherwise improve learning, lots of research going on in both areas.

</comment><comment><USER39> This is my issue with the phrase "AI/ML" as a catch all. You have an excellent general skillset, but "AI/ML" encapsulates a wide spectrum of jobs.

ML-SWE: SWE with ML focus - building architecture around models, feature engineering, distributed training, etc. Relatively limited ML knowledge needed (IMO). The math won't be helpful for this role. Much more important to have SWE background. If you want this, keep building your programming knowledge (Python) and read books. Would focus on understanding the popular frameworks PyTorch and TensorFlow b/c your work will likely interface with those.

Research engineer: Mostly for MS/PHD background. Farther away from the product and closer to actual research. This doesn't sound like what you want to do.

Data Scientist: ML is a subset of the knowledge needed. Applied statistics as important, if not more so. Doesn't sound like you want this.

A path forward:

(1) Program a lot. On what? Anything at all, b/c you need programming skill to work as a SWE.

(2) If you want to do ML-SWE, program with an eye towards ML applications. Maybe do a simple cloud project that leverages ML - Google Cloud makes this particularly easy for classification tasks. Focus on breadth here, not depth. No sane person outside of academia can keep up with state-of-the-art and truly understand it. Far too much material, so focus on fundamentals.

(3) Work towards your strengths. You aren't some hotshot kid out of college proclaiming to be an AI guru. That would be silly and no competent recruiter would believe it. You know hardware - and AI (neural networks) leverages a lot of hardware. Why not focus on the hardware side of AI? Demonstrate your knowledge of how/why TensorFlow is so effective across distributed hardware, or how CUDA accelerates NN computation, or why TPU claims vs. Nvidia may be up to interpretation, etc. This should be a natural transition given your background.

TLDR; Know what you really want to do. Your background is valuable. Play to your strengths. Don't ring the bell.

<comment><USER40> Can you let me know a bit more about the SWE-focused ML path? While I'm interested in ML, I'm slightly more interested in the systems that are built around it. In particular, are there resources that can help me get up to speed in designing (distributed and HW-accelerated) ML systems.

</comment></comment><comment><USER41> Not a full answer, just an observation: most ML people I come across don't have CS backgrounds. Many have backgrounds in physics, math and other STEM fields that have a strong computational component (my own background is in control systems, math modeling and numerical computation).

From your question, it sounds like you want to be a software engineer rather than an ML/AI engineer -- is that a fair assessment?

<comment><AUTHOR> Its certainly good to know that ML doesn't require a CS background. In my own case, My thinking was that because ML/AI has a strong programming component in addition to Math&Stats, my strong Hardware background might work against me. In an ideal scenario, i would like to develop AI/ML applications and wouldn't mind morphing into a SW engineer if the role requires me to. THough SWE by itself would be another steep hill to climb.

</comment></comment><comment><USER42> I went back to school for cal 2, Multivariable calculus, Differential Equations and Libear Algebra. Took ML class from a local university after that.

</comment><comment><USER43> Hi, I am in a similar boat, and would love to network with you either in person or online.

I have a PhD in EE, working in semiconductors. I have done a couple of MOOC specializations on Coursera, and am trying get some data science projects on my resume. Also trying do some Kernels / Scripts on Kaggle to build up a basic portfolio.

<comment><AUTHOR> Hi, Email is in my profile and I am always looking to network. Shoot me an email and we can take it from there.

</comment></comment><comment><USER16> You may find this post helpful [https://www.reddit.com/r/cscareerquestions/comments/92ouzd/a...](https://www.reddit.com/r/cscareerquestions/comments/92ouzd/a_guide_to_landing_a_data_sciencemachine_learning/)

</comment><comment><USER44> This conversation might help [https://twitter.com/suzatweet/status/1078446189593321472](https://twitter.com/suzatweet/status/1078446189593321472)

<comment><AUTHOR> The overarching theme of that thread reflects Max Woolf's comments: Companies(FANGMA)place a lot of importance on Accreditation and that is pushing a lot of capable folks out of ML/AI applied roles(not talking about inventing the next capsule network that requires a P.hD).

</comment></comment><comment><USER45> Become strong in the background of ML/AI and then learn a language where you can use it. Demonstrate your achievements in it. Help with projects associated with it. Participate in the community.

</comment><comment><USER46> [https://www.datascienceatthecommandline.com/](https://www.datascienceatthecommandline.com/) when maths is hindrance

</comment><comment><USER47> I am curious: Why do people want to pivot? Is the work more interesting? Is the pay higher? Are there more opportunities? Is there some other reason?

<comment><USER48> bad management.

on FAANGs, the teams are usually huge, 100+ people doing what a nimbler company does with 3 or less, to the point the employees don't even see that, because the product is now broken into several pieces to give the illusion of complexity. middle managers then break it down further that engineers start being know as the "person that writes the java files in that one directory" and nothing else. This creates constant fear of becoming irrelevant. All while you see 2~10% pay raises while hearing about undergrads making the same you make now with "new tech du jour". This creates even more pressure.

And because this cycle (stagnate, fear, learn, relief) repeats often, engineers start to associate learning a new tech with happiness, just because it offsets the psychological fear for awhile.

</comment></comment><comment><USER49> EE -> embedded programming -> programming -> financial software -> predictive modeling -> ML.

<comment><USER50> By the time the OP gets there along this path the new hype will be on GQ/JP already.

<comment><AUTHOR> LOL! WTH is GQ/JP btw?

<comment><USER50> We don't know yet.

</comment></comment></comment></comment><comment><USER51> I basically did what you're talking about. Masters in physics, then went into semiconductors as engineer and materials scientist, then switched to Data scientist at a bank for 18 months, and now have been a research scientist in AWS for almost two years.

In Amazon, it's easy to move around, but not between job families. I think it's a bad idea to join as a SWE and try to transfer because they want people that have done it before, and you're unlikely to do that sort of work as an SWE. I think it's better to get experience in the role you want at a less prestigious company. You'll learn a ton. Pick the best company that will have you.

My personal turning point was when I did free work for a local startup in exchange for them letting me take the Data Scientist title. To a recruiter, it's totally obvious to hire a data scientist for a data scientist role, and isn't clear at all what physics has to do with it. Recruiters are the first step when you're starting fresh, so make it easy for them.

I also somewhat disagree with many of the comments here that textbooks are better than tutorials. If you buy these 1000 page graduate level texts when the idea that you need to read them cover to cover, you're likely going to give up and fail. Instead, buy the books and put them on the shelf, and then work through tutorials and examples. Then reference particular sections of the book that are relevant to your work to add depth.

Finally, I recommend against starting with deep learning. There's a whole helluva lot to learn with basic techniques. Very few companies are actually using deep learning in production systems. Start with linear and tree based methods to learn all the stuff about how to frame the problem and build robust systems. Then you'll have a deeper appreciation for DL.

A reasonable person could disagree and say that there's so much domain specific stuff around the art of DL that it really behooves you to start there ASAP. I would counter that you're unlikely to be considered for positions using DL unless you're pursuing your PhD in it, or have proven yourself in industry. Since that isn't your situation, I'd wait until you got your foot in the door somewhere and then pursue DL on the side. That's what I did, and then you look like a hero to your boss. This strategy led to my first publication in the field and I'm now working on DL almost exclusively.

Edit: one more thing. Think carefully about the type of work you want to do. My advice is assuming you'd like to be a person that trains/deploys ML models to solve problems in industry. This is much different than an ML Engineer, who's implementing algorithms in low level languages and squeezing out efficiency. Obviously that would require a much deeper understanding of SWE. And a totally different person is an academic researcher that's developing theory or technique. It'll be hard to do that without a PhD.

<comment><USER43> Great reply. Just out of curiosity, did you end up giving your semiconductor job before joining the start up with a data scientist title ?

I am deep into semiconductors, and am facing the dilemma of giving up my expertise so far, to join a startup as an entry level engineer.

I have done a couple of MOOC specializations and am trying to find projects within my industry to gain some credibility. Also trying to stay active on Kaggle to build some basic data analysis portfolio.

<comment><USER51> In my particular case, I did quit my job before I got my "real" Data Science position. I can't recall exact timelines, but I think I had already lined up the relationship with the startup.

The reason I did this is because there just wasn't enough hours in the day, and my job was taking ~10 hours a day with commuting...etc. It was a risk, but the idea was that I would be able to transition much more quickly if I worked full-time towards it. I also had the financial savings to support myself for 6-9 months and was willing to get a part time job if necessary. Once it became clear that my job's only purpose was to pay the bills in the context of my goals, and I had enough to pay the bills for the near future, it was clear that quitting was the easiest way to free up a lot of time.

This turned out to be the best decision of my career, but YMMV. I doubled my salary in less than 2 years. It's also nice to be part of an industry that isn't so cost-sensitive. I also have a skill set that's in much higher demand, so you can live almost anywhere and there's a ton of companies that want/need it. With semiconductors, you're much more limited.

It's true that you're giving up some expertise and will start in a less senior position in a different field than if you stayed in semi. Sticking around because you have experience is classic Sunk Cost Fallacy. Think 5 years down the line. If you leave now, you'll have 5 years experience in ML. You'll definitely be giving something up if you leave, but there's huge opportunity cost if you don't leave.

<comment><USER43> Would you be open to have a brief conversation on the phone for advice? My email is email@example.com.

</comment></comment></comment></comment><comment><USER52> +1 fast.ai</comment>


ASK HN: Would you sign up for this service?  - <AUTHOR> http://signup.frapp.it

<comment><USER2> No for 2 reasons,

1) As @lmm mentioned, you can get free t-shirts from just about anywhere and it's not worth having a huge ad on your back 2) Launchrock is one of the most annoying 'Coming Soon' pages out there. Why would I share your service/product on Twitter/FB/Email to my friends and family when I haven't even ever used it

<comment><AUTHOR> Main focus is on generating feedback right now. Concrete traction building will start in a later stage.

Thanks for the responses btw :)

</comment></comment><comment><USER3> No, because I simply do not see the value of receiving a free t-shirt with advertising.

Also, it seems like a relatively significant commitment from a person who signed up because you're asking them to upload three pictures of themselves in the t-shirt (for either another free t-shirt or "credit").

However, I'm interested to hear more about your business model and I hope your idea does well.

Presumably,and I may be way off here, you're sub-contracting out the screen- printing of the t-shirts. And, as I understand it, the cost of producing t-shirts can be significant and you would probably have to hold a large inventory of t-shirts in various sizes.

\- What kind of advertiser are you trying to appeal to?

\- Does an advertiser pay only when there's evidence that the t-shirts are worn? Or why do you have to upload three pictures of yourself in the t-shirt?

\- Roughly, what kind of margins are you expecting to get?

\- Are you trying to focus in a local region or nationally?

\- What happens with any left over inventory?

Lastly, I may be interested in a t-shirt that combines an advertising with "pop art", but that's just a quick idea...

 _Edit: Clarified the first sentence._

<comment><AUTHOR> _Also, it seems like a relatively significant commitment from a person who signed up because you're asking them to upload three pictures of themselves in the t-shirt (for either another free t-shirt or "credit")._

The system I intend to implement should make this relatively painless.

 _Presumably,and I may be way off here, you're sub-contracting out the screen- printing of the t-shirts. And, as I understand it, the cost of producing t-shirts can be significant and you would probably have to hold a large inventory of t-shirts in various sizes._

Entirely true. But if Lockerz story is any indication of how it can work out, inventory turnover is likely to be ridiculously fast.

 _\- What kind of advertiser are you trying to appeal to?_

Large consumer goods and local stores. Got a nifty solution in mind on how this'll scale.

* Does an advertiser pay only when there's evidence that the t-shirts are worn? Or why do you have to upload three pictures of yourself in the t-shirt?*

No, pay up front. Receive elaborate statistics afterwards.

 _\- Roughly, what kind of margins are you expecting to get?_

Back of napkin style; profit margins somewhere between 30%-50%. Margins on t-shirts alone 50%-75%.

 _Are you trying to focus in a local region or nationally?_

International user base. Allow advertisers to segment their target users by region/age/interests.

 _What happens with any left over inventory?_

Promote them to first time users. Loyal users get cooler/more shirts.

 _Lastly, I may be interested in a t-shirt that combines an advertising with "pop art", but that's just a quick idea..._

Definitely a great design idea. The one that's currently on the site is a drawing an ex-girlfriend of mine made over a year ago. I thought it was pretty amazing and asked her whether I could print that instead of my own terrible attempt at a mash-up of several freely available vectors.

<comment><USER3> Thanks for answering my comment.

I do have reservations about going after international and hyper-local markets at the same time. And I have a concern that it sounds like you do not have a graphic designer to create the t-shirt designs.

However, I do like your idea of reserving the "cooler" t-shirts to more loyal users (because that, for the most part, solves my issue with requiring a relatively significant commitment from a user).

Again, I hope your idea becomes successful.

<comment><AUTHOR> Thanks for the positive feedback :)

 _I do have reservations about going after international and hyper-local markets at the same time._

International + hyper-local is more of an end goal than the begin stage. See <http://news.ycombinator.com/item?id=4013608>

_And I have a concern that it sounds like you do not have a graphic designer to create the t-shirt designs_

I know some contractors that are likely willing to help me out. Currently, I do not have anyone to structurally think/discuss/build this. I have reserved the summer to work on this and attend a bunch of networking events, hopefully someone will turn up!

Btw, if you think of anything else you'd like to discuss; don't hesitate to contact me at email@example.com

</comment></comment></comment></comment><comment><USER4> Yes, I _love_ t-shirts (I own over 160) and I know that a lot of people really love free stuff. As others have said there are issues like what if someone misrepresents a brand, but ignoring that I think the idea is fantastic.

If you market it well (I would suggest aiming at end of high school / start of college age people) it could really take off. I don't think posting it here will get you much useful feedback, the HN demographic aren't really the sort of people this will appeal to.

<comment><AUTHOR> _I know that a lot of people really love free stuff_

That's what I figured. Lockerz grew enormously until it imploded due to many factors. Rethink their business model, go niche and this is where I ended up at.

 _the HN demographic aren't really the sort of people this will appeal to_

But at least they are likely to provide slightly more insightful feedback than the average feedback I've received from friends/family.

</comment></comment><comment><USER5> I think it's great idea. I would at least try it. I'm not really a T-shirt person but I know many who wear them and I think some would be interested in something like this.

I like your landing page as potential wearing customer, but I don't think it would attract me to advertise with you. I would only change the pictures representing what kind of pictures people upload. Here you can't see that person wears T-shirt on an event or in a day in their life, you only see T-shirt and don't get the feeling person is wering it outside of their bedroom.

I think that feature of choosing what brand will you promote with this T-shirt is important. You can see that many commenters saw this as a problem. But I think that here is a problem from the side of companies who would advertise on your T-shirts as well. If I used your service for promotional purposes, I would like to know that in front side of T-shirt there would not be something opposite of what I would like my brand to be. But I think that for starters you can filter that out manually.

<comment><AUTHOR> _but I don't think it would attract me to advertise with you_

I wholeheartedly agree. However, this will change when an actual landing page is in place.

 _I would like to know that in front side of T-shirt there would not be something opposite of what I would like my brand to be_

Manual filtering/personal design for the companies that advertise is probably the first step. Next comes incentivizing users to contribute their own design and let the smaller customers pick their company designs. I think that'll work out, though there's obviously no way of being sure.

</comment></comment><comment><USER6> No. A t-shirt I don't particularly like is worth approximately zero to me (you get them free at conventions), and it doesn't sound like the front will be cool enough to justify the back. Particularly since the back's probably going to be McDonalds or something.

<comment><AUTHOR> Doesn't _sound_ like the front will be cool enough to justify the back. What if when we presume it is?

Ideally, McDonalds-like t-shirts will be the minority of the t-shirts you will be able to choose from. What if it is a t-shirt courtesy of your local Radioshack?

Moreover, you'll be able to see in advance what company is sponsoring the shirt. Don't like McD? Choose another one!

<comment><USER6> >Doesn't sound like the front will be cool enough to justify the back. What if when we presume it is?

What I meant is, I can't imagine a front so cool that the overall t-shirt would still be cool. (And if you came up with a t-shirt front that cool, you could sell it with a plain back for $50 and make a tidy profit without all the complexity of this idea).

>Moreover, you'll be able to see in advance what company is sponsoring the shirt. Don't like McD? Choose another one!

I'm not seeing that anywhere on the page. If true, that changes things; as long as the photo verification isn't too much faff I'd happily sign up for t-shirts from companies I actually like.

<comment><AUTHOR> _I can't imagine a front so cool that the overall t-shirt would still be cool_

Can't please anyone, although it does not hurt to try.

 _I'm not seeing that anywhere on the page._

Pitched this idea once at some startup contest. Judges didn't get the concept. Spoke to other contestants and later stage judges that did get it; advice #1 'simplify, simplify, simplify'. Although I can talk on for an hour detailing the concept, it's best not too cause information overload.

</comment></comment></comment></comment><comment><USER7> I would, and once I get my shirt I would wear it or not, depending on what's in the back, my advise to you would be to allow people ordering the shirts to pick what company or design they'll have in the back, I might have a problem wearing merchandise of companies that don't sit well with my lifestyle (e.g. as a vegetarian, I wouldn't wear the shirt of a company that primarily sells meat, like McDonald's).

Offering the people that are wearing the shirts a mix of back designs (say, three different ad designs to pick from for Whole Foods or any other advertiser) would be a plus to improve adoption.

<comment><AUTHOR> 3 different advertisers requires mass customization => extra overhead. You'll get to choose advertiser + t-shirt combos; not sure if added flexibility will make up for the increase in price, but I'll keep it in mind!

<comment><USER7> As an advertiser, I would definitely like to be able to be allowed to A/B different designs for the ads, it sounds pretty simple for an advertiser to be able to provide a few different designs and let people choose from them, or are you referring to a different cost?

</comment></comment></comment><comment><USER8> I assume there's more fine print to this.

What if the advertisers don't like the context in which I wear their t-shirts? If I upload three photos of myself wearing my t-shirt at a KKK rally, or being beaten by police officers at a protest, or throwing a puppy off of a bridge, will I continue to receive free t-shirts? Or would advertisers be able to black list certain frappers?

For the record, I signed up, and will probably be donating most of the ones I receive (if any, I do get beaten up a lot at KKK rallies for throwing puppies of bridges) to the homeless.

<comment><AUTHOR> Advertisers on their own in the way the content industry operates, no. Frapp the company, yes.

</comment></comment><comment><USER9> I think it's a good idea. Not everyone will wear any type of t-shirt, so maybe you should find a way to match people to brands. For example, I'd wear a t-shirt advertising technology companies whose products I use, but I wouldn't wear a Ford t-shirt because I am a Toyota/Honda guy.

Someone launched a similar site years ago where they would give you giant stickers to put on your car doors and pay you $200 per month as long as you drive 20+ miles per day.

<comment><AUTHOR> Do you happen to know what site that is?

</comment></comment><comment><USER10> I would sign up for this service, on both ends... to get a t-shirt and to advertise on t-shirts (provided I could get in on a very small scale - a few t-shirts).

As others have said, whether I would wear the shirt depends on the ad. It would be great if I could see the ad before the shirt was sent.

Also, the word 'order' almost turned me off. it immediately suggests I'd have to pay.

<comment><AUTHOR> Cool to hear that you'd sign up for advertising!

Ad will be completely visible before the shirt is sent.

Thanks for the tip on _order_. Hadn't thought of it that way. Is _receive_ better? Feels like it's less clear on how that t-shirt will end up at your place.

</comment></comment><comment><USER11> No. It looks like a pyramid scheme of some kind, so I didn't even bother finishing reading the page. Sorry.

<comment><AUTHOR> Would you care to elaborate? Why does this look like a pyramid scheme to you? The same way that 'FB is a ponzi scheme'?

<comment><USER11> That was my first impression from skimming the page. Now that I have looked at it again, it's obviously not what it seemed to be.

The pattern of "Get one, give back 3, receive something back" is the pyramid/Ponzi pattern. Basically your copy needs to change, it's heavy. Drop the numbered list, get to the point faster and more succulently.

Also, somewhat related. Have you seen this - <http://www.iwearyourshirt.com> ?

<comment><AUTHOR> Ah yes, the get one, give back 3, receive one back does indeed look a lot like a pyramid scheme at first glance.

Copy definitely needs work. I'll be trying some different things in the future. Thanks for the tip :)

Hadn't heard of iwearyourshirt, very interesting proposition. Thanks again!

</comment></comment></comment></comment><comment><USER12> It depends on the advert.

If it was a cool brand which I would like to associate with, then yes, I would wear and photograph myself in it.

If it was a brand which I don't intend to associate myself with, then no, I wouldn't wear it much less have a photo of me in it

</comment><comment><USER13> I only see this being useful in a college campus setting. My freshman year I used to have "Free T-Shirt weeks", where I would wear all the swag shirts I've received because all the shirts I actually liked were dirty and I didn't want to do laundry yet.

</comment><comment><USER14> The site currently shows an error message:

 _> It appears that something may be broken. We apologize for the problem. If you think you have reached this page in error, please head over to our support page._

Well, that's not appealing.

</comment><comment> angry-hacker I wouldn't but I know a lot of people who would. If your startup is able to be profitable like that I believe it's an excellent idea...

</comment><comment><USER15> If we assume that the shirts look good, sure. I always like free stuff.

</comment><comment><USER16> Yes, but just so I had tonnes of gym/exercise t-shirts.</comment>


Turn on, tune in, drop out - <AUTHOR> http://avdi.org/devblog/2010/05/24/turn-on-tune-in-drop-out/

<comment><USER2> "Today my advice to a young developer who is passionate about building great software is to drop out."

Perhaps a young developer might have other goals in life in addition to building great software? Dropping out of college could certainly give you a huge head start in your career, but the costs are huge as well: missing out on spending four years of your life having fun being around people your age.

The magnitude of how much four years in a college environment can change a person is difficult to underestimate. Some may decide that the experience isn't for them and that they should drop out to start their careers, but it's a decision that must be made very, very carefully.

<comment><USER3> In the end it's a highly personal decision. If you're considering dropping out because you can already do what some people are getting a degree for, then you're probably ahead of your peers anyways. So I'm not sure what you'd be missing.

But probably the biggest motivation against dropping out is the social acceptance. If any change is to happen then we've got to set in place alternatives for these drop outs so they can do so without negative social stigma. Practically I don't think there's any downside assuming they've already had an eye-opening liberal education.

</comment></comment><comment><USER4> "Unfortunately, like light from a dead star the messages young developers get are still coming from that old, vanished world."

Brilliant.

</comment><comment><USER5> I'm going to hijack this thread to mention that if anyone is interested in Timothy Leary, I found the following two NPR programs fascinating:

[http://www.onpointradio.org/2010/03/the-birth-of-new-age- ame...](http://www.onpointradio.org/2010/03/the-birth-of-new-age-america)

<http://www.kqed.org/epArchive/R201001191000>

They are both interviews with the author of a recent book called _The Harvard Psychedelic Club_ in which Leary naturally features prominently.

</comment><comment><USER6> from comments: " I see too many students come through here who don't actually know what they want out of life or what to do with it (~30-40% of my students) and they go through the motions without having a clue as to why."

What a waste of resources, if true.</comment>


Ask HN: Is there any startup out there building airliner planes? - <AUTHOR>

Airliners plane do not seems to have evolve a lot since 1960! Is there any startup trying to build a modern and cheaper airliner? <comment><USER3> Airliners haven't evolved much since 1960 (unless you count fly-by-wire and vastly improved fuel economy...) for a reason: the current designs are fit for purpose, development is incredibly expensive and prospective clients are - <USER2> good reason - conservative buyers.

The closest thing to a viable airline startup is probably Mitsubishi Aircraft Corporation, effectively a joint venture between huge conglomerates with existing experience in aircraft component manufacture and leasing. The aircraft they're due to roll out soon may well be a commercial failure.

</comment><comment><USER4> At that level of capitalization and complexity, military aviation would almost certainly offer a better chance of success...that's probably where SpaceX's hockey stick will lie over the long term.

</comment><comment><USER5> Light Sport Aircraft and experimental/kit airplanes, sure (although "startup" is probably the wrong word for it).</comment>