Looking for something casual - <AUTHOR> Has anyone ever offered to teach other people how to write code in any language and ultimately never have them call you back? At this point it's a bit of a personal challenge to offer random people how to write software in any language that I know and actually get them to bite. I have offered everyone from homeless people to people I just met at the German pub this evening to teach them how computers work and ultimately how to instruct them to do their bidding. No one has taken up the opportunity. Are there other people out there that have offered this to the general public, and what are your results? I am really fascinated with the responses you have gotten and I can share some of the stories I have in return. To this day not a single person has sent me an email or called me on my personal number. I have been doing this since high school and have been in the business for almost 20 years. <comment><USER2> Hey,

I've gotten involved with some projects that help people to learn how to code and this structure lends itself to a successful follow-through into the job market.

I've also tried to teach homeless people how to program but the gap in computer literacy is just too big. Basic interface training is needed before any further progress can be made.

It's difficult, but in my experience, people starting something new thrive with structure. A small percentage of people would progress regardless but these are outliers in my data.

I have come into similar stories in other fields, specifically music. Where a structured course works for most, those that explore on their own eventually run into a hurdle they can't solve on their own and here's where one-on-one or problem-specific interaction would benefit.

Knowing what I know now, I think a structured path serving as a guiding light is necessary with the availability of problem-specific or exploration-friendly interaction would make for the best offering for people to teach themselves how to do anything, let alone something as abstract and intangible as programming.

I hope that helps in some way :)</comment>


Tools for debugging, testing and using HTTP/2 - anand-s https://blog.cloudflare.com/tools-for-debugging-testing-and-using-http-2/ <comment><AUTHOR> Daniel Stenberg (creator of curl) has a HTTP/2 explained website [1] which might help in understanding HTTP/2.

[1] [http://daniel.haxx.se/http2/](http://daniel.haxx.se/http2/)

<comment><USER2> Thanks. I added a section at the end with resources on learning more about HTTP/2

</comment></comment><comment><USER3> Chrome also shows more detailed HTTP/2 tracing in chrome://net-internals. Select HTTP/2 from the drop-down.

</comment><comment><USER4> One thing is missing from that list: how to test for ALPN support (or the lack thereof). The OpenSSL client just lists the next protocols supported, but no mention if that's NPN or ALPN.

This is one of those details that I believe are the real roadblocks to HTTP/2.0 support. Google is trying to drive adoption by deprecating old features like NPN from Chrome, but people setting their servers up with HTTP/2.0 don't even know about these details or how it affects them.

<comment><USER5> Using curl is one way:

      % curl -v https://google.com/ --http2
      *   Trying 2607:f8b0:4007:802::1008...
      * Connected to google.com (2607:f8b0:4007:802::1008) port 443 (#0)
      * found 180 certificates in /etc/ssl/certs/ca-certificates.crt
      * found 722 certificates in /etc/ssl/certs
      * ALPN, offering h2
      * ALPN, offering http/1.1
      * SSL connection using TLS1.2 / ECDHE_ECDSA_AES_128_GCM_SHA256
      ...
      * ALPN, server accepted to use h2

The above is with the GnuTLS build on Debian, recent enough OpenSSL versions support both NPN and ALPN.

<comment><USER6> Shodan also keeps track of the ALPN negotation results, see the "Negotiated HTTP Versions" chart: [https://www.shodan.io/report/tbi6kDZa](https://www.shodan.io/report/tbi6kDZa)</comment></comment></comment>


Show HN: A Developer Story - <AUTHOR> https://medium.com/hypetonic/introducing-a-developer-story-c751a9a2051#.ahxp719ut <comment><USER2> You should interview Don Gagne, the developer of QGroundControl. (You can find him on Github)</comment>


UX Guide – Design Better User Experiences (Learn UX Design) - <AUTHOR> https://stayintech.com/info/uxguide <comment><AUTHOR> Creator here. This is not an exhaustive list, I'm happy to add more on it, if you have suggestions!

<comment><USER2> You could add brainstorming and post release testing?

<comment><AUTHOR> Will do, thanks!</comment></comment></comment>


Ask HN: How can I quickly trim my AWS bill? - <AUTHOR> Hi HN,
I work with a company that has a few GPU-intensive ML models. Over the past few weeks, growth has accelerated, and with that costs have skyrocketed. AWS cost is about 80% of revenue, and the company is now almost out of runway.
There is likely a lot of low hanging cost-saving-fruit to be reaped, just not enough people to do it. We would love any pointers to anyone who specializes in the area of cost optimization. Blogs, individuals, consultants, or magicians are all welcome.
Thank you! <comment><USER3> Disclosure: I work on Google Cloud (but my advice isn't to come to us).

Sorry to hear that. I'm sure it's super stressful, and I hope you pull through. If you can, I'd suggest giving a little more information about your costs / workload to get more help. But, in case you only see yet another guess, mine is below.

If your growth has accelerated yielding massive cost, I _assume_ that means you're doing inference to serve your models. As suggested by others, there are a few great options if you haven't already:

\- Try spot instances: while you'll get preempted, you do get a couple minutes to shut down (so for model serving, you just stop accepting requests, finish the ones you're handling and exit). This is worth 60-90% of compute reduction.

\- If you aren't using the T4 instances, they're probably the best price/performance for _GPU_ inference. If you're using a V100 by comparison that's up to 5-10x more expensive.

\- However, your models should be taking advantage of int8 if possible. This alone may let you pack more requests per part. (Another 2x+)

\- You could try to do model pruning. This is perhaps the most delicate, but look at things like how people compress models for mobile. It has a similar- ish effect on trying to pack more weights into smaller GPUs, or alternatively you can do a lot simpler model (less weights and less connections also often means a lot less flops).

\- But just as much: why do you _need_ a GPU for your models? (Usually it's to serve a large-ish / expensive model quickly enough). If you're going to be out of business instead, try cpu inference again on spot instances (like the c5 series). Vectorized inference isn't bad at all!

If instead this is all about training / the volume of your input data: sample it, change your batch sizes, just don't re-train, whatever you've gotta do.

Remember, your users / customers won't somehow be happier when you're out of business in a month. Making all requests suddenly take 3x as long on a cpu or sometimes fail, is better than "always fail, we had to shut down the company". They'll understand!

<comment><USER4> I was in the same boat and this is good advice!

I stopped using gpu's, "Vectorized inference isn't bad at all!". This soo much, I was blinded with gpu speed, using tensorflow builds with avx optimization is actually pretty fast.

My discovery:

\+ Stop expensive GPU's for inference and switch to avx optimized tensorflow builds.

\+ Cleaned up the inference pipeline and reduced complexity.

\+ Buying compute instance for a year or more provides a discount.

\- I never got pruning to work without a significant loss increase.

\- Tried spot instances with gpu's that are cheaper. Random kills and spinning up new instances took too long loading my code. The discount is a lot, but I couldn't reliable get it up. Users where getting more timeouts. I bailed and just used cpu inference. The gpu was being underutilized, using cpu only increased the inference to around 2-3 seconds. With the price trade off it was a more simpel,cheaper and easier solution.

<comment><USER5> Also, consider physical servers from providers like Hetzner. These can be several times cheaper than EC2.

<comment><USER6> I use Hetzner for quite a lot for personal projects and can recommend them for reliability and predictable costs. I've done reasonably high CPU tasks like compiling Android images on the larger Cloud instances.

However, this morning I was playing around with Scaleway bare metal [1] and General Purpose instances [2] -- I am thinking of making a switch for high CPU tasks.

[1] [https://www.scaleway.com/en/bare-metal- servers/](https://www.scaleway.com/en/bare-metal-servers/)

[2] [https://www.scaleway.com/en/virtual-instances/general- purpos...](https://www.scaleway.com/en/virtual-instances/general-purpose/)

<comment><USER5> Interesting! These look very good indeed. I will have to try them.

The main point is that physical servers are much cheaper than VMs and provide significantly better performance as well (see my benchmarking and comparison: [https://jan.rychter.com/enblog/cloud-server-cpu- performance-...](https://jan.rychter.com/enblog/cloud-server-cpu-performance- comparison-2019-12-12)).

</comment></comment></comment></comment></comment><comment><USER7> AWS/clouds aren't always the best solution for a problem. Often they're the worst (just like any other tool).

You don't provide a lot of detail but I imagine at this point you need to get "creative" and move at least some aspect of your operation out of AWS. Some variation of:

\- Buy some hardware and host it at home/office/etc.

\- Buy some hardware and put it in a colocation facility.

\- Buy a lot of hardware and put it in a few places.

Etc.

Cash and accounting is another problem. Hardware manufacturers offer financing (leasing). Third party finance companies offer lines of credit, special leasing, etc. Even paying cash outright can (in certain cases) be beneficial from a tax standpoint. If you're in the US there's even the best of both worlds: a Section 179 deduction on a lease!

[https://www.section179.org/section_179_leases/](https://www.section179.org/section_179_leases/)

You don't even need to get dirty. Last I checked it was pretty easy to get financing from Dell, pay next to nothing to get started, and have hardware shipped directly to a co-location facility. Remote hands rack and configure it for you. You get a notification with a system to log into just like an AWS instance. All in at a fraction of the cost. The dreaded (actually very rare) hardware failure? That's what the warranty is for. Dell will dispatch people to the facility and replace XYZ as needed. You never need to physically touch anything.

A little more complicated than creating an AWS account with a credit card number? Of course. More management? Slightly. But at the end of the day it's a fraction of the total cost and probably even advantageous from a taxation standpoint.

AWS and public clouds really shine in some use cases and absolutely suck at others (as in suck the cash right out of your pockets).

<comment><USER8> > AWS/clouds aren't always the best solution for a problem.

And when they aren't always the best. It's often because you don't know what you're doing.

It's too uncommon for people to over provision. Or go with too many services when they don't need to.

Like let's have a database and cache service and search search. When 95% of the time they only need the database because it can do full text searching adequate enough and they don't have the traffic to warrant caching in redis, and can do basic caching.

They don't take advantage of auto scale groups, or run instances that are over provisioned 24/7.

I've seen database instances where when it's slow they throw more hardware at it instead of optimising the queries and analysing / adding indexes.

The biggest cost of cloud providers is outbound data. The rest is almost always the problem of the Developers.

<comment><USER9> None of your comments are relevant to machine learning applications, and all you do is throw blanket statements about ignorance. Your comments are very far from the problem and from being helpful.

<comment><USER8> Nope. We have no information of the OPs setup, bill, or anything. This entire thread is based on assumptions. I common examples of developers screwing up and generating large bills. Explain to me how machine learning is any different.

Do we know if the instances used for MLing are running 24/7 idle until customers use them? Do we know if the utilisation is optimal for the workloads?

We know nothing. So claiming that cloud providers are not good is very far from the problem and not helpful.

<comment><USER9> > So claiming that cloud providers are not good

The statement is not that AWS is "not good". The statement is that AWS is very expensive, specially for computational tasks, and there are cheaper alternatives around.

AWS is notorious for positioning their services as a way to convert capex into opex, specially if your scenario involves a SaaS that might experience unexpected growth and must be globally available. Training ML models has zero to do with those usecases. It makes no sense to mindlessly defend AWS as being the absolute best service around for a job it was not designed for and with a pricing model that capitalizes on added value on things that are not applicable.

<comment><USER8> I never defended AWS as being the absolute best. I said high bills are almost always due to developers and not the cloud provider. Which you haven't argued against.

As I said I have examples of how Developers often cause large bills.

And I explained why we can't help with the OPs large bill.

You're saying that with ML there is absolutely 0 way to reduce costs on AWS which is absolute rubbish.

<comment><USER9> > I said high bills are almost always due to developers and not the cloud > provider.

I feel that's where you keep missing the whole point. Somehow you're stuck on thinking that an expensive service is not a problem if you can waste time micromanaging and constantly monitoring expenditures to shave off a bit of cost from the invoice. Yet, somehow you don't register in your universe the fact that there are services out there that are both far cheaper and arguably better for this use case.

Therefore, why do you keep insisting on the idea of wasting time and effort micromanaging a deployment like pets to shave off some trimmings off a huge invoice if all you need to do to cut cost to a fraction of AWS's price tag is to.... switch vendor?

<comment><USER8> So what you're saying is because developers can't control what they build they need to be stuck with services that limit what they can do so they don't end up with big bills.

And that for cases like MLing it's impossible to optimise costs.

Got ya.

<comment><USER9> > So what you're saying is because developers can't control what they build > they need to be stuck with services that limit what they can do so they > don't end up with big bills.

No, I'm pointing you the fact that developers are able to do exactly what they want with less work and far cheaper by simply moving away from AWS and picking pretty much any vendor. Why do you have a hard time understanding what others are telling you and understand anything that points that AWS is not the best solution for all usecases, specially those they were not designed for?

<comment><USER8> Rubbish, you're saying that it's impossible to run on cloud cheaply. Therefore no one should use cloud for any reason.

"I don't know how to use cloud so cloud is bad"

<comment><USER10> "You're holding it wrong!"

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER11> [DISCLAIMER] I work at AWS, not speaking for my employer.

We really need some more details on your infrastructure, but I assume it's EC2 instance cost that skyrocketed?

A couple of pointers:

\- Experiment with different GPU instance types.

\- Try Inferentia [1], a dedicated ML chip. Most popular ML frameworks are supported by the Neuron compiler.

Assuming you manage your instances in an auto scaling group (ASG):

\- Enable a target tracking scaling policy to reactively scale your fleet. The best scaling metric depends on your inference workload.

\- If your workload is predictable (e.g. high traffic during the daytime, low traffic during nighttime), enable predictive scaling. [3]

[1] [https://aws.amazon.com/machine- learning/inferentia/](https://aws.amazon.com/machine-learning/inferentia/)

[2] [https://docs.aws.amazon.com/autoscaling/ec2/userguide/as- sca...](https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-scaling- target-tracking.html)

[3] [https://docs.aws.amazon.com/autoscaling/plans/userguide/how-...](https://docs.aws.amazon.com/autoscaling/plans/userguide/how- it-works.html)

<comment><USER12> It could also be worth it to have a look at SageMaker? IIRC it's cheaper.

</comment></comment><comment><USER13> My pitch to help: you can probably replace the GPU-intensive ML model with some incredibly dumb linear model. The difference in accuracy/precision/recall/F1 score might only be a few percentage points, and the linear model training time will be lightning fast. There are enough libraries out there to make it painless in any language.

It's unlikely that your users are going to notice the accuracy difference between the linear model and the GPU-intensive one unless you are doing computer vision. If you have small datasets, you might even find the linear model works better.

So it won't affect revenue, but it will cut costs to almost nothing.

Supporting evidence: I just completed this kind of migration for a bay area client (even though I live in Australia). Training (for all customers simultaneously) runs on a single t3.small now, replacing a very large and complicated set up that was there previously.

<comment><USER14> Yeah, I agree with this. Rather than ask if OP is optimizing their AWS billing, I'd also ask if are OP's devs even have any incentive to do better. Even with machine vision it's stupidly easy to increase your computation effort by 2 or more orders of magnitude for almost no benefit. Default parameters often will do that in fact.

</comment></comment><comment><USER15> I'm a CTO of a compute intensive AI SaaS company, so I can relate.

One advice: speak to your AWS rep immediately. Get credits to redesign your system and keep you running. you can expect up to 7 digits in credits (for real!) and support for a year for free, they really want to help you in avoiding this.

<comment><USER16> This.

AWS has always been eager to get on the phone with me to discuss cost savings strategies. And they don't upsell you in the process.

</comment></comment><comment><USER17> I was in same situation.

We bough 2 Dell servers via their financing program. Each server is about 19-25K. We paid AWS $60K per month before that. We pay $600 for co-location.

So my advice is try to get hardware via financing of provider Dell had a good program I think.

<comment><USER18> What does colocation mean in this context? Did you buy the servers and AWS hosted on their premises?

<comment><USER19> Colocation just means buying space in a datacenter somewhere (and it comes with a certain amount of power and bandwidth).

</comment></comment></comment><comment><USER20> You might be able to significantly lower your monthly bill in exchange for an upfront payment by purchasing your own servers and then renting co-location space.

I'm CTO of an AI image processing company, so I speak from experience here.

I personally use Hetzner.de and their Colo plans are very affordable, while still giving you multi GBit internet uplinks per server. If you insist on renting, Hetzner also offers rental plans for customer-specified hardware upon request. The only downside is that if you call a Hetzner tensorflow model from an AWS east frontend instance, you'll have 80-100 ms of roundtrip latency for the rpc/http call. But the insane cost savings over using cloud might make that negligible.

Also, have you considered converting your models from GPU to CPU? They might still be almost as fast, and affordable CPU hosting is much easier to find than GPU options.

I'm happy to talk with you about the specifics of our / your deployment via email, if that helps. But let me warn you, that my past experience with AWS and Google Cloud performance and pricing, in addition to suffering through low uptime at the hands of them, has made me somewhat of a cloud opponent for compute or data heavy deployments.

So unless your spend is high enough to negotiate a custom SLA, I would assume that your cloud uptime isn't any better than halfway good bare metal servers.

</comment><comment><USER21> Howdy.

I have loud and angry thoughts about this; [https://www.lastweekinaws.com/blog/](https://www.lastweekinaws.com/blog/) has a bunch of pieces, some of which may be more relevant than others. The slightly-more-serious corporate side of the house is at [https://www.duckbillgroup.com/blog/](https://www.duckbillgroup.com/blog/), if you can stomach a slight decline in platypus.

<comment><USER22> Came here to recommend you! Your newsletter always provides both enlightenment and a giggle.

<comment><USER23> I came here to recommend QuinnyPig's services as well. He's a pro at reducing AWS costs.

<comment><USER24> Corey Quinn (Quinnypig) at Duckbill Group would be my suggestion as well.

</comment></comment></comment></comment><comment><USER25> I'd suggest reaching out to AWS about this. Explain the situation. AWS has a number of programs for startups that you may be able to apply for, including one that includes 100k worth of credits.

Also, if you can't afford to scale to new customers... stop? I'm sure it probably sucks, but like, does it suck more than having no runway? Seems like you'd be best served slowing things down and spending some time with AWS on cost optimization.

There aren't a lot of details to go off of here so I don't know what more advice to give.

</comment><comment><USER26> We've managed to reduce our spends by almost 50-60%. Some pointers: 1\. Comb through your bill. Inspect every charge and ask "Why do we need this?" for every line item.

2\. If user latency is not a problem, choose the cheapest regions available and host systems there.

3\. Identify low usage hours (usually twilight hours) and shut systems off.

4\. Transition one-off tasks (cron, scheduling etc.) to lambda. We were using entire servers for this one thing that would run once a day. Now we dont.

5\. Centralize permissions to launch instances etc. within a few people. Make everyone go through these 'choke-points'. You might see reduced instances. Often engineers launch instances to work on something and then 'forget' to shut them off.

6\. Get AWS support involved. I'm pretty sure with the bills you are racking up you must have some AWS support. Get some of their architects etc. to check out your architecture and advise.

7\. Consider Savings Plans and Reserved Instances. Often you get massive cost savings.

8\. Consider moving some of the intensive number crunching to some of AWS' data crunching services. We moved a high-powered ELK stack for analyzing server logs to CloudWatch. A little more expensive in the short term, but we are now looking to optimize it.

In my experience, AWS has been very supportive of our efforts at reducing costs. Even after a 50-60% reduction I still feel there is scope for another round of 50-60% reduction from the new baseline. All the best!

</comment><comment><USER27> Here's my deck on this @quinnypig is a great resource elsewhere in this thread. [https://docs.google.com/presentation/d/1sNtFugQp_Mcq62gf4F1n...](https://docs.google.com/presentation/d/1sNtFugQp_Mcq62gf4F1n0aJU9IjHmHyMKYOPXYoWYRU/edit) Last year I cut 75 million in spend, so you could say I have a track record there.

Are you sure you are using the right type for what you need to generate? Can you have your model generator self kill (stop) the instance when it finishes the model?

100% If it doesnt need JIT go spot and build models off queue

Put in for the activate program. They can give you up to 100k of credits.

<comment><USER28> Can you give a little context to the $75 million in savings? What was the original amount you were spending? I didn't see this on your deck.

</comment></comment><comment><USER29> I maintain an open source ML infra project, where we've spent a ton of time on cost optimization for running GPU-intensive ML models, specifically on AWS: [https://github.com/cortexlabs/cortex](https://github.com/cortexlabs/cortex)

If you've done zero optimization so far, there is likely some real low-hanging fruit:

1\. If GPU instances are running up a huge EC2 bill, switch to spot instances (a g4dn.xlarge spot is $0.1578/hr in US West (Oregon) vs $0.526/hr on demand).

2\. If inference costs are high, look into Inferentia ( [https://docs.cortex.dev/deployments/inferentia](https://docs.cortex.dev/deployments/inferentia) ). For certain models, we've benchmarked over 4x improvements in efficiency. Additionally, autoscaling more conservatively and leveraging batch prediction wherever possible can make a real dent.

3\. Finally, and likely the lowest hanging fruit of all, talk to your AWS rep. If your situation is dire, there's a very good chance they'll throw some credits your way while you figure things out.

If you're interested in trying Cortex out, AI Dungeon wrote a piece on how they used it to bring their spend down ~90%. For context, they serve a 5 GB GPT-2 model to thousands of players every day: [https://medium.com/@aidungeon/how-we-scaled-ai- dungeon-2-to-...](https://medium.com/@aidungeon/how-we-scaled-ai-dungeon-2-to- support-over-1-000-000-users-d207d5623de9)

</comment><comment><USER30> Don't overlook the possibility to use your own physical hardware, running high-end commodity graphics cards (2080Ti, Titan RTX), especially for model training. (I haven't found this to be overly effort or time intensive and the payoff is enormous on a dollars-basis.)

You didn't give enough details for someone to get really specific. I'm assuming from your text that the issue is inference not training costs, in which case there's some great advice already posted, but more details might help.

</comment><comment><USER31> Speak to your AWS account manager and/or someone on their startup team. Give them the detail on what you're running, what you want to do, and what/when you're hoping to reach the next milestone. There's usually a few different options available to them to try help you out. Including, but not limited to, working out how to reduce the ongoing cost of what you're trying to do. "Customer obsession" and all that. It's also just good business. It's not in anybody's interest to have companies running out of runway, they'd rather you were still in business and paying for compute 5 years from now.

</comment><comment><USER32> Sounds familiar =\

\- get devs on GPU laptops

\- for always-on, where doable, switch to an 8a - 6p policy, and reserved. Call aws for a discount.

\- use g4dn x spot. Check per workload tho, it assumes single vs double.

\- consider if can switch to fully on-demand if not already , and hybrid via GCP's attachable GPUs

\- make $ more visible to devs. Often individuals just don't get it, too easy to be sloppy.

More probably doable, but increasingly situation dependent

<comment><USER32> ALSO: For all the discussion of on-prem, for ML in particular, consider running training on a dedicated local hw box and run only inference on the cloud (which can be CPU)

<comment><USER33> I've been mulling this idea over in my head recently of investing a $2-3k in building a machine to do exactly that (and use it as a normal dev day to day machine when it's not training), because it appears the economics of it are surprisingly great.

Have you (or anything else here) had experience doing this? Did it end up being a worthwhile approach? (Even for a while)

<comment><USER32> It depends how long it is on.

If training only short while, may do better by setting up a cloud training workflow that only has the server on while training. If on a lot, then a private box makes more sense (ex: lambdalabs, at home/office/colo). Then setup as a shared box for the team.

A lot of time ends up dev, not actual training, and folks end up keeping dev cloud GPUs on accidentally. We still use cloud GPUs for this, but have primary dev on local GPU laptops. For that, we started by System76 for everyone (ubuntu Nvidia), but those had major issues (weight, battery draw...). I then did a lightweight asus zenbook for myself, but that was too lightweight all around. Next time will do more inbetween or explore Thinkpad options.

And yep, as a small team, this mix dropped our cloud opex spend by like 90%, and pretty fast to offset the capex bump.

</comment></comment></comment></comment><comment><USER34> Can you use spot instances? If so you can pay a lot less for compute. Your app needs to tolerate being shutdown and restarted, however.

Is there anything you can turn off at night? A lot of startups have staging / test systems that do not need to be running all the time.

Are you keeping a lot of "junk" around that you don't actually need? Look at S3 objects, EBS snapshots, etc. A few here and there doesn't cost much, but it does add up.

Are you using the correct EBS volume type? Maybe you're using provisioned IOPS where you don't need it.

S3: make sure your VPC has an S3 endpoint. This isn't the default. Otherwise, you're paying a lot more to transfer data to S3.

</comment><comment><USER35> I have replied to some of the comments below. My advice is to get off AWS or any public clouds and avoid them like the plague.

They are too expensive for 95% of cases. If you are still not convinced DM me.

<comment><USER36> Cloud is expensive for sure, especially so for VMs and bandwidth.

But cloud also comes with a lot of convenience - for example, having managed k8s, and highly-available serverless, messaging, blob storage and databases.

Some of that is particularly challenging to get right, especially for databases.

It's difficult to justify cloud VMs for heavy processing tho - they really are just so damned expensive compared to bare metal and VPS providers, and there isn't that much extra convenience for VMs in the same way there is for PaaS stuff.

</comment></comment><comment><USER37> While looking at the technical, also look at the commercial. Can you trace revenue sources to aws costs? In other words calculate your variable costs for each client/contract individually?

Eg are there some clients losing you money that you can either let go or raise prices for?

</comment><comment><USER38> Don't use GPUs at inference (serving) time unless you prove that you need to.

The only consistent case when I've found it's needed (across a variety of NLP & computer vision services that have latency requirements under 50 milliseconds) is for certain very deep RNNs, especially for long input sequence lengths and large vocabulary embeddings.

I've never found any need for it with deep, huge CNNs for image processing.

Also consider a queue system if utilization is a problem switching from GPU. Create batch endpoints that accept small batches, like 8-64 instances, and put a queue system in front to mediate collating and uncollating batch calls from the stream of all incoming requests (this is good for GPU services too).

</comment><comment><USER39> If you can handle some interruption to your work then spot instances are probably going to be the biggest immediate change you can make.

Right now a g4dn.xlarge is $0.526/h on demand but only $0.1578/h as a spot instance.

You might also be eligible for a 10k grant from AWS - [https://pages.awscloud.com/GLOBAL-other-LN-accelerated- compu...](https://pages.awscloud.com/GLOBAL-other-LN-accelerated-computing- free-trial-2020-interest.html)

</comment><comment><USER40> If cost is an issue, get off AWS. Immediately. You're paying about 10x what the same hardware/bandwidth would cost you if you just bought dedicated servers.

</comment><comment> alFReD-NSH If you have the time to fix them asap you can follow this route:

\- use spot or reserved instances or saving plans. \- have a look at compute optimizer \- understand aws networking costs are and try to optimise it (cross az and internet egress can be costly) \- go through the trusted advisor checks: [https://aws.amazon.com/premiumsupport/technology/trusted- adv...](https://aws.amazon.com/premiumsupport/technology/trusted-advisor/best- practice-checklist/)

You can enable trusted adviser checks by enabling business or enterprise support. \- try using one of these cost optimisation tools: [https://aws.amazon.com/products/management-tools/partner- sol...](https://aws.amazon.com/products/management-tools/partner- solutions/#Resource_and_Cost_Optimization)

\- contact aws for well architected review

If you don't have the time, then I suggest contacting AWS to introduce you to a consulting partner. They can come and actually fix whatever is needed.

</comment><comment><USER41> You train the model locally and push it for inference to the cloud?

What exactly are we talking about here?

Couldn't you build a dual NVIDIA 20XX / 32 core / 64 GB for a sub $5k and then save money while training/developing faster?

<comment><USER42> Except they (the gender non-specific singular) is probably running kubernetes and has multiple clusters of 10 or so gpu hosts. Not that I disagree, but spinning that up locally and orchestrating it will take time and money. And explaining why training is paused because you keep blowing breakers in the office will cost political capital.

<comment><USER43> You can just say "Except they are probably".

</comment></comment></comment><comment><USER44> GPU servers and coloc are pretty cheap these days. $1K/m rent per 20A of power. ROI on hardware is usually 3-4 months max (ie - for the cost the machine at AWS for 3-4 months, you can buy the same thing).

Lead time might be a problem for you but you can probably do it in a under a month if you take available stock at your vendor. I work with a company called PogoLinux ([http://pogolinux.com](http://pogolinux.com)) out of Seattle and they sell boxes that have 4 GPUs in them.

That said -- the other advice is right. You can probably get by with a much simpler model. The coloc route would probably only be better if you are can't change the models due to people constraints and the ML stuff doesn't have a lot of AWS dependencies. SysAdmins are a lot easier to find and hire than ML specialists.

</comment><comment><USER45> In terms of cost, I would recommend deeply interrogating the bill. Your data transfer cost is likely to be really higher than you expected, and there are lots of ways to mitigate that. GPUs are crazy expensive in the cloud, and really makes sense to host locally. There is also usually some money to be found with looking at S3 tiers - like Infrequent Access can save a lot if its good for your use case. Finally, if EC2 is a big cost driver, spot pricing and savings plans are good places to start.

I will say that more generally speaking, there has been a lot of recognition in the industry at large that AI-driven startups all face this challenge, where the cost of compute eats up most of the margin. There is no easy solution to that, other than to make product-level decisions about how to add more value with less GPU time.

</comment><comment><USER46> AWS is super expensive. Switch to another cloud provider.

For example : Scaleway, OVH, or Hetzner.

<comment><USER47> Can confirm this. Personally I wanted to switch to AWS from Scaleway because one of the regions was closer to the customers. No way I could justify the costs. With some load balancers and API access, we were able to scale horizontally without a problem.

<comment><USER36> Are you using Postgres by chance? If so, I'd love to hear about how you deployed it (struggling to figure out a performant, HA setup!)

</comment></comment></comment><comment><USER48> I don't know how deep you've dug but the very first thing you should be doing is using spot instances instead of on demand instances (unless you absolutely can never wait to train a model). Spot instances are cheaper than on demand instances, with the downside that the price can fluctuate, so you need to build in a precaution for shutting down if the price gets too high. So if the price goes up, you either have to stop training until the price goes back down or to suck it up and pay a higher price.

Luckily, it's pretty simple to handle interruptions for neural network like models that train over several iterations. Just save the model state periodically so you can shut the instance down whenever the price is too expensive and start training again when the price is lower.

</comment><comment><USER49> If you're running GPU heavy stuff all the time then you're probably better off just buying some GPUs outright and doing that part on-site.

Especially if you can keep the own gear busy 24/7\. i.e. run those 24/7 and any excess GPU use above that fall back onto cloud for that.

</comment><comment><USER50> Talk to an AWS rep and also different cloud vendors. I know startups which received large amounts of free compute in their early days and then went on to become successful companies. I bet it was win-win for everyone involved.

</comment><comment><USER51> If you're storing a lot of data I talked to someone who went from $3,000 a month to $3 a month by saving older dumps of their database into an S3 bucket instead of keeping many many old RDS snapshots from weeks / months ago around.

Here's a direct timestamp link to that point in the podcast where it came from: [https://runninginproduction.com/podcast/33-zego-lets-you- eas...](https://runninginproduction.com/podcast/33-zego-lets-you-easily-buy- insurance-by-the-hour#55:46)

</comment><comment><USER52> Segment's blog posts on cost optimisation have plenty of detail and tips on this topic:

[https://segment.com/blog/the-million-dollar-eng- problem/](https://segment.com/blog/the-million-dollar-eng-problem/) [https://segment.com/blog/spotting-a-million-dollars-in- your-...](https://segment.com/blog/spotting-a-million-dollars-in-your-aws- account/) [https://segment.com/blog/the-10m-engineering- problem/](https://segment.com/blog/the-10m-engineering-problem/)

Similarly this Honeycomb writeup is also excellent: [https://www.honeycomb.io/blog/treading-in-haunted- graveyards...](https://www.honeycomb.io/blog/treading-in-haunted-graveyards/)

By the sounds of it, you need to take drastic action. It sounds like you will not be able to just optimise your AWS spend to get more runway, though you should definitely do some bill optimisation. You will need to optimise your product itself and maybe even getting rid of unprofitable customers.

If you are not sure exactly who or what is driving the AWS cost, take a look at Honeycomb to get the ability to dive deep into what is eating up resources.

</comment><comment><USER53> AWS is one of most expensive hosting solution, I assume many of us somehow start to think that it's a best one, in my opinion they all kinda same. Moving to other place will require effort but can let you reduce cost to 10-20% of current one. Some easy things that you can do with aws is to resize VMs, it will require to turn it off for a minute or so. Also can change to cheaper tire, eg t2 -> t3. Also can change VMs from ec2 to lifhtsail

</comment><comment><USER54> I help companies find bare metal options for training models. It's usually 10-20% the cost of cloud.

Email me lanec (at) hey (dot) com if you'd like to speak.

Last year I took a company spending $24k/month training visual AI and cut that down to $3,500/month with bare metal. I also helped them secure over $100k in cloud credits to cover the monthly costs until the transition could happen.

Training in the cloud is generally much more expensive than bare metal.

</comment><comment><USER55> Run your own machines.

You don't have to use cloud services.

</comment><comment><USER56> Simple answer. But the implementation is trickier.

You have to use Spot instances. Or as Google calls them - <USER2> instances. These are upto 80% cheaper.

The caveat is that they can be killed anytime, so your infrastructure must be resumable.

Most likely you will need to do kubernetes. It's the only framework that supports GPU, integrates with spot instance providers and works with Ml platforms (using kubeflow)

</comment><comment><USER57> open source tools:

\- [https://github.com/antonbabenko/terraform-cost- estimation](https://github.com/antonbabenko/terraform-cost-estimation)

\- [https://github.com/cloud-custodian/cloud- custodian](https://github.com/cloud-custodian/cloud-custodian)

\- [https://github.com/aws/amazon-ec2-instance- selector](https://github.com/aws/amazon-ec2-instance-selector)

\- [https://github.com/rdkls/aws_infra_map_neo4j](https://github.com/rdkls/aws_infra_map_neo4j)

commercial:

\- [https://www.cloudhealthtech.com](https://www.cloudhealthtech.com)

\- [http://densify.com/](http://densify.com/)

\- [https://spot.io](https://spot.io)

\- [https://www.hpcdlab.com](https://www.hpcdlab.com)

</comment><comment><USER58> How about you just purchase some motherboards and GPUs and start running them in your office (assuming you're not bandwidth limited or looking for millisecond response times).. I'm always tempted to do this when we have fairly constant workload. Wasn't GPU instance pricing quite insane on AWS compared to actual GPU costs?

</comment><comment><USER59> This is not exactly it i imagine. But maybe longer term you could consider this.

At my place people test on their desktops and run production stuff in the data center.

Where are you located? These are prices in singapore..[http://www.fuwell.com.sg/uploads/misc/Fuwell11072020.pdf](http://www.fuwell.com.sg/uploads/misc/Fuwell11072020.pdf)

You're looking for a cpu, board, 64gb ram, maybe 2 x 2080ti, small ssd n psu(1000w?). You can leave these on ikea shelves n skip the casings if need be.. 3 x 2080ti makes the board expensive and psu hard to find...

If you want more reliability. Get asus or supermicro. Or even sugon. 4gpu. 2u.

So that's like a few kw per machine and you need to think about how much power you can draw per power socket..so usually the 2u stuff end up in datacenters.

</comment><comment><USER60> Someone else mentioned it already in these comments, but I'll mention again to make sure it's not missed. If you're a startup using AWS, apply for the AWS Activate program. All you need to do is apply, and they'll give you up to $100k AWS credits, which will last for up to 2 years and automatically be applied to your bill until they're used up.

[https://aws.amazon.com/activate/](https://aws.amazon.com/activate/)

It's not a solution to the larger problem of business model and percentage of revenue going toward compute costs to provide your service, but there are a lot of other great recommendations and suggestions here for that. This could provide you some time to actually implement the other recommendations.

</comment><comment><USER61> nVidia forces cloud providers to use their expensive professional line-up. But other providers that use consumer GPUs are way cheaper, 4x or more. If your models don't need a lot of memory or double precision, providers such as GPUEater or RenderRapidly can be worth looking at.

</comment><comment><USER62> Related reading: Ask HN: How did you significantly reduce your AWS cost? (2017) [0]

The top comment is great. Two easy wins:

* Putting an S3 endpoint in your VPC gives any traffic to S3 its own internal route, so it's not billed like public traffic. (It's curious that this isn't the default.)

* Auto-shutdown your test servers overnight and on the weekends

See also this thread from 2 days ago, _Show HN: I built a service to help companies save on their AWS bills_ [1]

Those threads aren't specific to GPU instances though.

[0] [https://news.ycombinator.com/item?id=15587627](https://news.ycombinator.com/item?id=15587627)

[1] [https://news.ycombinator.com/item?id=23776894](https://news.ycombinator.com/item?id=23776894)

</comment><comment><USER63> Disclosure: I am the founder of Usage.ai

The product my team works on, www.usage.ai, automates the process of finding AWS savings using ML (through reserved instances, savings plans, spots, and rightsizing). Every recommendation is shown on a sleek webpage (we spent a lot of resources on UX).

We haven't fully explored the ML use case, but I'd love to figure out how we can help you drive down the costs associated with your GPU models. Would you have 15 minutes this week for a discussion?

If you're interested, you can reach me at firstname.lastname@example.com

</comment><comment><USER64> It is very unlikely that anyone is going to give you a good advice with so little information about your cost structure. There is great people here who can provide invaluable insights about your costs but they need to have more information.

We use a lot of GPU intensive models and 80% of revenue goes into AWS, doesn't mean that your AWS cost is mostly GPU. It should mean that, but who knows. Tell us how is your AWS infrastructure, what instances do you have, how much do they cost to you, etc. Because with your information about the costs the best advice you can get is to not use AWS neither GPU-intensive ML models.

</comment><comment><USER65> For the ML models you can also switch to dedicated server providers, such as: [https://www.dedispec.com/gpu.php](https://www.dedispec.com/gpu.php)

For storage, there's always Wasabi / B2 with S3 compatible interfaces. If the data itself is not changing that much, so regular backups are possible, just use some dedicated storage servers with hard drives and install MinIO. Do not rely on S3 for outgoing data (much too expensive), use a caching layer on another provider (OVH , Hetzner, ...), or if it fits your workload, Wasabi ("free" egress).

</comment><comment><USER66> At a startup I worked earlier, we tried two things that helped : 1\. Reserved instances (you commit for a year and you can save 20% - charged monthly. AFAIK no upfront costs)

2\. Like another reader suggested here, there are accelerators/foundations which give away $10k for the 1st year towards cloud usage. We were in healthcare and had a big chip company pay about $10k in credits for a year of AWS. Depending on the domain you are in, there may be a few. If you let me know which domain you work in ( healthcare , media etc.) someone here might be able to point to the right resource

</comment><comment><USER67> Start by looking at the breakdown of your costs in the cost analyzer. Look for the categories of your biggest spend. Is it storage? EC2? Something else? For storage; see if you can clean up things you don't need anymore. See if you can move infrequently used data into long-term, cheap storage (but beware retrieval costs!). For EC2, consider changing node types to cheaper ones. Newer classes are can be much better value for the money. Make sure you use spot instances where you can. Focus on the biggest expense first.

</comment><comment><USER68> Without any idea of what your infrastructure looks like, I can't give you anything actionable, but that might be enough advice in and of itself: go after the low hanging fruit first. What are you spending on? Look at the top two or three services by spend and dig a little deeper.

Are you spending on bandwidth? See if there's compression you can enable. Ec2? Can your reduce instance sizes or autoscale down instances you're not using overnight? Elasticache or elastic search? Tune your usage, store smaller keys or expire things out.

</comment><comment><USER69> Using large data stored in S3?

Make sure you are fetching it via a S3 endpoint in your VPC instead of via public HTTP. You are paying for an (expensive) egress cost you don't need to be paying for.

<comment><USER36> That's insane that that's the default!

</comment></comment><comment><USER70> Spot instances. Easy and saves a ton.

</comment><comment><USER71> If AWS cost is 80% of revenue, and the added cost per customer isn't paying for itself, perhaps one could either charge more or pause customer acquisition?

</comment><comment><USER72> We had the same problem!

We managed to cut our costs by about 2/3rd by doing two things:

1) moving to batch (this spins up machines to run docker containers without much hassle. You can also share instances) 2) use spot instances.

Spot instances integrate nicely into batch, and depending on how you set it up, you can optimise for speed or cost. for example a p2.xlarge is still $.9 but on spot its about $0.25-0.35

</comment><comment><USER73> Dedicated server somewhere close to your office.

</comment><comment><USER74> I have a strong suspicion the OP is trolling, or at least his motives aren't obvious.

Check his profile.

He has people, or knows of people that can can likely help with this. He's a CEO of a an Accelerator and is not a newb by any sense.

OR... he's using gamification to find someone to hire to actually help solve this problem. If that's the case... Bravo sir!

</comment><comment><USER75> [Disclosure - my company sells a cost optimisation product]

1\. You are going to get a lot of advice to move to your own hardware - DON'T. Companies use cloud for the flexibility and lower operational overhead, not because it's cheap. Consider if your org is mature enough to run its own servers and has the 6 months it will take to get everything setup.

2\. Talk to your AWS account manager. They will work their asses off to stop you churning to another provider or to your own hardware, because they know they are losing your revenue entirely for minimum 2 years.

3\. _Switch it off._ If you're not using it outside of business hours, you're wasting money. This is the easiest cost saving you will make (my company, GorillaStack, provides a service that makes this easy to setup without scripting and a 14 day trial)

4\. If you have a baseline of servers that you will constantly need, reserved instances offer great savings. There is a secondary market for these, where you can get shorter periods cheap from other customers who don't need them.

5\. If you haven't already, look at your bill and the breakdown of services. Cost optimisation consultants (they do exist) will start here, and by attacking the biggest line items first.

They are usually EC2 Compute, EBS, Transfer Costs, etc. Prioritise based on size and ease of implementation.

You should make a habit of checking it at least every few days to keep on top what is going on.

6\. Delete unused resources - you need to be ruthless with developers who leave unused EC2 resources around after creation. The key isn't to lock down your environment and stop developers from creating what you need, but enforcing a tagging policy on resources to track who owns what. There are crude scripts that scan your environment and delete untagged resources after a certain period.

7\. Once things are under control, use CloudWatch Cost Alarms to get notifications when things are crossing a predefined threshold. These can be connected to SNS for receiving emails (and there are simple deployable solutions for receiving these via Slack webhooks, for example).

Some further advice: 'right-sizing' is often held up as an important cost saving method, but can often be much more trouble than its worth. If your workload is going to be a pain and require endless planning and regression testing when you switch instance size, reconsider - you will waste more in developer time than the cost savings over a few years.

</comment><comment><USER76> This thread from yesterday might be useful [https://news.ycombinator.com/item?id=23776894](https://news.ycombinator.com/item?id=23776894)

[https://github.com/similarweb/finala](https://github.com/similarweb/finala) seems promising

</comment><comment><USER77> Lots of people in here mentioning Reserved Instances, so it's worth mentioning Reserved AI ([https://www.reserved.ai/](https://www.reserved.ai/)).

We're customers and have been very happy with them, it was super quick to get set up and saves us a big chunk.

</comment><comment><USER78> A quick Google search for GPU dedicated server is probably going to save you tens of thousands of dollars a year.

</comment><comment><USER79> There are quick wins with spot instances and also Fargate. It's hard to say anything without knowing type of workloads and compute that you have. But there is always opportunity to save there.

Other than that, you should also look at your architecture. Often there is opportunity to save there as well.

</comment><comment><USER80> Make sure to tag every instance/resource/disk in AWS, with their purpose, team, etc...

Then you can go into the AWS costs explorer and see the costs breakdown per tag.

Usually there will be a few resources standing out. 80% of the costs is 20% of resources. Find out what they are and cut.

</comment><comment><USER81> Lots of comments with tips on "how", but your last paragraph makes it sound like your are looking for a "who".

I've heard good things about [https://www.duckbillgroup.com/](https://www.duckbillgroup.com/)

</comment><comment><USER82> If you are growing and/or have funding the other cloud providers will throw credits at you.

If you arent growing or have funding then go to a less expensive host. There are TONs of high quality hosts out there that are quite a bit less expensive.

</comment><comment><USER83> Please check this Show HN thread - [https://news.ycombinator.com/item?id=23776894](https://news.ycombinator.com/item?id=23776894)

A developer has created an app for this same purpose

</comment><comment><USER84> Depending on the entropy of your input caching may be a way out. Sometimes if you can't cache end result you can cache some intermediate results.

I would assume that if you are big enough you may be able to negotiate some pricing.

</comment><comment><USER85> 1Cloudhub an AWS partner has a very affordable product called Costimize for AWS to help save costs. I will be happy to help. Contact Sankar.nagarajanAT1cloudhub.com

Secondly, you can use AWS spot instances for GPUs which costs less.

</comment><comment><USER86> Reserved instances are usually the lowest hanging fruits depending on your usage profile and how much you can commit to and/or pay for up front. Savings of 30%+ are very achievable.

</comment><comment><USER87> AWS likely has a retention department that can give you discounts or credits to make you stay. Ask for credits and use the extra time to set up your own hardware.

</comment><comment><USER88> If your computational load is spikey, I would suggest looking at fargate and the spot market. Also for storage, I would suggest leveraging S3 whenever possible.

</comment><comment><USER89> The answer is simple: don't use AWS. You will never get out of this hole unless you move from AWS, because AWS is not scalable budget-wise.

</comment><comment><USER90> Take a look in the cost explorer

A low hanging fruit are spot instances if you can manage stateless sessions.

If you have multiple snapshots that could cost money as well

</comment><comment><USER91> It would be helpful to know which services you use. Do you use ML services or instances with GPU? Where is most of the cost?

</comment><comment><USER92> Post your bill. You often have unexpected charges for ingress or services you are unaware

</comment><comment><USER93> an acquaintance of mine has a business that specializes in the problem you're facing. please feel free to reach out to them: [https://www.taloflow.ai/](https://www.taloflow.ai/)

</comment><comment><USER94> Inless GPUs are an absolute must, just use Hetzner and never look back at AWS.

</comment><comment><USER95> Switch from RDS to lightsail instances for trivial dB workloads

Could also apply for EC2

</comment><comment><USER96> if using deep learning models, consider using distilled and/or quantized models to reduce the resources required for inference

</comment><comment><USER97> Reserved instances? Instant 20-40% savings.

</comment><comment><USER98> Real hardware with colocation

</comment><comment><USER99> Blog article about real-life AWS cost optimization strategy :

[https://medium.com/teads-engineering/real-life-aws-cost- opti...](https://medium.com/teads-engineering/real-life-aws-cost-optimization- strategy-at-teads-135268b0860f)

TL;DR:

\- Monitor your daily costs

\- Cloudify your workloads

\- Use reservations, Spot instances, saving plans

\- Look around for unused infrastructure

\- Optimize S3 consumption

<comment><USER35> In our experience this is a Sisyphian task. The number of elements that get charged is just enormous.

</comment></comment><comment><USER100> These are the biggest ways to lower cost that I've used in the past, with a high burn rate it's important to focus on the things that can change the economics on a short timeline ( think next week ), as well as activities on a longer-timeline ( next year ). You should have a plan in place for your board - and be able to discuss the cost reduction strategy for Cost of Goods Sold in any future financing rounds. Carefully consider the full TCO - buying colo hardware means opting out of ~3 years of future price reductions/hardware improvements in the cloud + opportunity cost.

1) Call your provider and find out what options they have to cut your cost. This can take the form of discounts, credits, or increased reservations

2) It's not uncommon for ML teams to have excess capacity sitting around for forgotten R&D activities. Make sure that you're team is tearing down hardware, consider giving all scientists their own dedicated workstation for model development activities. You can smoke test the opportunity here by verifying that the GPUs are actually being utilized to ~40-80% average capacity.

3) Really dive into whether you need the parameters/model architecture you have. The best model for your company will need to balance latency/cost with accuracy. If you're using a transformer where a CNN or even a logistic regression with smart feature extractors could do with 1% accuracy loss. Then do your customers really need the transformer?

4) As others have suggested drill-down on the inference and training costs. Train less frequently/not at all/or sample your data. Generally the benefit of using more data in a model is logarithmic at best vs. the linear training time.

5) Buy your own hardware, particularly for GPU inference RTX cards can be purchased in servers for your own colo - but not in clouds. The lead time would be a few months but the payoff could occur within ~2-6 months in a colo.

6) Leaving this here as it used to affect Analytics/Ad-Tech and other "big- data" companies. Programming languages are not created equal in performance, and given equal implementations a statically typed language will crunch data between 10 and 1000x faster and cheaper than a dynamically typed language. If your business is COGS pressed then your team will probably spend more time trying to optimize hardware deployments and squeezing perf out of your dynamic language than you gain in productivity. Drill down on your costs and check how much of it is raw data-processing/transaction scheduling/GPU scheduling and make sure that you're on the right tech path for your customers.

Lastly at an 80% Cost of Goods Sold(COGS) it's quite possible that your business is either low margin or the pricing structure isn't well aligned as this is a new startup - ask yourself if you expect to raise prices for future non-founding customers. If so then it's possible that your current customers are helping reduce your marketing expenditures, and you may be able to leverage the relationship to help "sell" to future customers.</comment>


Which movie critic should you believe? - <AUTHOR> http://whichcritic.com/

<comment><USER2> Better use some older movies. If I could take this quiz I would not need the result!</comment>


Electric Vehicles Won't Save the Planet or Survive Without Subsidies - <AUTHOR> https://www.city-journal.org/electric-vehicle-batteries <comment><USER3> This is so stupid that it reads like a funded hit-piece. To take a few choice quotes:

"its battery is a half-a-ton electrochemical machine with thousands of parts and welds, along with wiring, electronics, and cooling. It's every bit as complex as—and far more expensive than—the combustion-mechanical drivetrain that it replaces."

Microchips are the most complex objects on earth, and also the most reliable. Lack of moving parts is why.

"Manufacturing automotive batteries is surprisingly labor intensive. Tesla's gargantuan battery factory in Nevada produces about 1,000 propulsion batteries per year per 12 workers. Meantime, a modern engine and transmission factory produces about 1,000 mechanical-propulsion systems per year per four workers."

Uh, so a 100 year old industrial process is only....3x less labour intensive than a less than 20 year old industrial process. Colour me surprised.

"coal and natural gas still account for 70 percent of electricity generation."

In the US, sure. But France has been generating the vast majority of its power without fossil fuels for decades. Carbon-free electricity is now price competitive with fossil fuels.

"Embracing batteries at automotive scales would lead to an unprecedented global expansion in mining"

I'm sorry, what is it we've been doing for the last 200 years since the industrial revolution?

Can't even be bothered to go through the rest. This is a shill piece, no question.

<comment><USER4> Yeah, for those interested in an honest take on the costs involved in EV manufacturing and how they have come down and will continue to take a look at this analysis from ARK Invest

[https://ark-invest.com/research/wrights-law-predicts-teslas-...](https://ark- invest.com/research/wrights-law-predicts-teslas-gross-margin)

<comment><USER5> It writes about Model 3's gross margins improving over time, but it looks like Model Y will be the real winner with its improved wiring and the huge body press machine if it works.

We'll see how much of Model Y's technology will be backported to Model 3.

</comment></comment></comment><comment><USER6> This is the usual bullshit. There is some interesting stuff to be gleaned by comparing the carbon footprint of the whole manufacturing cycle of ICE vs EVs, but they seem to often leave out the global petrochemical industry infrastructure from those sorts of things.

And these people keep going on about coal, forgetting that its pretty easy for consumers to switch to renewable-only electricity suppliers should they wish to* . If you've got an EV, that makes zero-emissions travel possible. If you dont have an EV, that isn't possible. Its as simple as that.

Would like to know who's paying this guy. * *

* Certainly in the UK its trivial to switch to 100% renewable electricity, and I'm told its similarly possible in the states.

* * Oh right he works for Cottonwood Venture Partners who sell IT stuff to the oil and gas industry. Source: [https://www.city-journal.org/contributor/mark-p-mills_255](https://www.city-journal.org/contributor/mark-p-mills_255) [https://www.cottonwoodvp.com/index.html/#portfolio](https://www.cottonwoodvp.com/index.html/#portfolio)

<comment><USER7> > Would like to know who's paying this guy.

Well, the Manhattan Institute, which owns City Journal, is a big proponent of the Oil and Gas business. They have a track record of denying man-made climate change. So this just fits their usual canon.

</comment></comment><comment><USER8> Something about the tone of this article seemed a bit off to me so I wondered who the author was - turns out he is a partner in a firm that appears to do a lot of investing in companies that have "oil and gas" in their descriptions:

[https://www.cottonwoodvp.com/index.html/#portfolio](https://www.cottonwoodvp.com/index.html/#portfolio)

This bit in particular seems a bit silly:

 _" While the EV's electric motor is simple, its battery is a half-a-ton electrochemical machine with thousands of parts and welds, along with wiring, electronics, and cooling."_

Edit: Just for the record, I own a ICE car, I love ICE engines (the sounds made by Ferrari V12s and BRM V16s are music as far as I am concerned) but I also recognise that they are at the end of their time and electric cars are the future (mind you - prices need to come down before I will buy one).

</comment><comment><USER9> Well to be fair, if we take out gas subsidies and ICE manufacturer subsidies, ICE vehicles won't survive as well.

</comment><comment><USER10> Fuck this guy. He writes like someone wants to take away his toys. Here's a newsflash, dear author: as a Nissan Leaf owner for the past eight years, I say fuck subsidies, too. I mean for me personally, anyway. Because I am so done with ICEs, I'll pay a _premium_ to not drive one of those rattling, smelly contraptions that my parents were enamored with (and myself, too, as a younger man). You know what's nice? After I laid floor tile in the garage (man, why didn't I do that 20 years ago?), we can now pull the car out, lay some yoga mats, use it as a yoga studio. But that only works if your car is electric, otherwise wait 30 minutes while the exhaust clears out (watch the oil spots, too). As just one example.

I've bought my last ICE, subsidies or no. I don't represent the general public, but I have to represent more than a few.

But what is this guy afraid of? It reads like "you, sir! You, who (except for the in-dash system) are completely tickled with your Nissan Leaf! Don't be happy! You should be miserable with your electric car! Why aren't you miserable?! Look, here's reasons your car is a bad idea!" In other words, this sounds like a guy who's trying to sell me something, not inform me.

</comment><comment><USER4> Man this piece contains so many obviously wrong and dishonest arguments mixed in with a defeatist attitude. It even manages to contradict itself.

It simultaneously argues that it takes 3x the number of workers to produce a battery pack as it does to produce a engine and then says that the switch to EV's will decimate jobs.

It claims it takes 100 barrels of oil to build a battery pack capable of storing 1 barrel of oil worth of energy implying that's a bad thing as if you use the battery once and then dispose of it. Good quality lithium ion batteries last for 5,000 cycles and Tesla claims their current battery technology should last up to 500k miles implying something like 15,000 cycles. A barrel of oil makes roughly 20 gallons of gasoline so if the 100 barrels of oil to make a battery pack is true that sounds pretty amazing to me.

On the mining thing Lithium mining is some of the cleanest mining in the world because you can extract it from water pumped in and out of the mine.

The list goes on.

</comment><comment><USER11> > Mark P. Mills, a Manhattan Institute Senior Fellow

[https://www.sourcewatch.org/index.php/Manhattan_Institute_fo...](https://www.sourcewatch.org/index.php/Manhattan_Institute_for_Policy_Research)

Not that we should dismiss his claims outright, but definitely approach them carefully.

</comment><comment><USER12> The author discusses the Tesla Gigafactory and then drops the following sentence "There's no prospect of creating a domestic EV supply chain anytime soon, regardless of incentives." I'm trying to understand how the author can come to that conclusion. A domestic EV supply chain already exists - <USER2> happens to be a captive supplier to Tesla. There's no reason GM or Ford couldn't build a Gigafactory too.

As for the title that electric vehicles won't survive without subsidies, against I'm shaking my head. The Tesla subsidies have almost completely expired, and each time the subsidy expires, Tesla just lowers the price. Tesla turned a profit, and the vehicle is displacing BMWs and Mercedes.

As for charging off dirty energy, my Tesla charges overnight from the nuclear power plant's base load.

</comment><comment><USER13> Electric vehicles(e.g.electric scooters) already are ubiquitous in cities. Unsubsidized.

</comment><comment><USER14> Read the piece waiting for the obvious conclusion that if we can't build EVs we need to massively build out public transportation... Still waiting...

</comment><comment><USER15> A lot of EVs simply drive better than ICE cars. The acceleration, lack of noise, and more pleasant charging atmosphere go a long way.

</comment><comment><USER16> The solution to the climate problem is actually "sun-to-liquid" synthetic fuel. The whole chain from air carbon capture, creation of synthesis gas of H2 + CO from H2O and CO2 via a catalyst, to finally the production of fuel via Fisher-Tropsch has been demonstrated to work on large scale pilot projects and can be easily powered in some remote dessert with sunlight.

Fuel is the best way to store energy. It has a really high energy density it is not volatile like e.g. pure Hydrogen and it is a liquid which means it can be easily transported because it obviously can take any shape or form.

The longer you think about the concept of electric cars the stupider it gets. The ICE is peak mobility technology.

<comment><USER7> The efficiency of the whole chain is pretty abysmal. Capturing free-air CO2 in large quantities is expensive, you better just store that in the ground and keep it there. I would not expect an efficiency above 20-30% for the synth- fuel generation, and then you put it into ICEs which also just have a 15-30% efficiency. You're burning 90% of the original energy, instead of using it to charge EVs with an 80-90% panel-2-wheel efficiency.

Power-2-X is still important as a long-term storage technology, but it's better to synthesise Methan which can can be generated with a better efficiency and can also be burned cleaner and with 55-65% efficiency in modern gas turbine power plants. This also allows for capturing CO2 at the power plant and create a complete cycle.

Synth-fuels will be a niche, used for long-haul trucking, heavy machinery and airplanes, but even there Hydrogen is going to be the better option at some point.

<comment><USER16> Energy efficiency doesn't matter, there is enough sun available in remote areas. The problem is energy storage. Storing electricity is really expensive, and cumbersome. It can not be easily transported without a vast expensive infrastructure effort especially concerning remote areas.

We have loads of fluctuating energy generation from renewable sources and hugely fluctuating energy consumption. This means we need loads of storage. Nobody ever talks about the storage.

Also staying 20 min at a charger just to fill up my battery is absolutely impractical. Fast charging is already at its technical limits. Compare that with the 45 seconds I need to fill my gas tank and have enough energy to drive thousands of miles.

<comment><USER17> You use your car for how many hours a day 4-6 hours max. The rest of the time it is parked at work or home just put it on the charger when it is parked. We are used to ice cars so think about filling up once the tank is almost empty and can't get fuel everywhere it is not so with electric cars. You should not be visiting a charger unless going on a long trip so in actual fact 5 min spent at the gas station filling up is more time than you will spend filling up an electric car.</comment></comment></comment></comment>


Desire paths: the illicit trails that defy urban planners (2018) - <AUTHOR> https://www.theguardian.com/cities/2018/oct/05/desire-paths-the-illicit-trails-that-defy-the-urban-planners <comment><USER2> This is why I want to design and develop software _lazily_, as a kind of perpetual wizard of oz experiment. I'm laying the foundations now, for a 'magic' software experience that learns to become whatever you need, lazily, driven behind the curtain by operators and agents, AI and human.

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

</comment><comment><USER3> Desire paths fascinated me. I've seen several grow, and I've seen the reasonably understandable, but ultimately fruitless attempts to inhibit them: Why don't landscape architects and planners actually welcome them (like one does in this article) instead of confronting them?</comment>


What Could You Build with $10k? - <AUTHOR> https://hackernoon.com/what-could-you-build-with-just-10-000-67e91f65dd4b <comment><USER2> If you include your personal living expenses, then $10,000 isn't going to go very far. You'll also need to do some consulting work while you build your product.

If you're just talking about company expenses, then $10,000 will go a long way. You need $500 to set up a company with Stripe Atlas [1], $100 to purchase the initial shares, $100/yr for a registered agent, and $100/yr to file taxes. You can do your own bookkeeping or use Bench for $135/mo. Stripe Atlas also gives you $5k of free AWS credits, so you don't have to pay for hosting for a few years. Then you can spend the ~$9k on whatever you want (other SaaS services, design, landing pages, etc.)

Apart from the Stripe Atlas fee and share purchase, I didn't put any money into my company. I was still doing contract work and wasn't taking any money out of the company, so it was "profitable" as soon as I had a single paying customer.

I think ~$1,000 is probably the bare minimum if you want to start a company and bootstrap a SaaS product. But it's always better if you can have a healthy bank balance for unexpected things.

[1] [https://stripe.com/atlas](https://stripe.com/atlas)

<comment><USER3> My "$10,000" was one of those 0% interest Visa card cheques, combined with renting out two rooms in my house to students to net my rent out to zero. And then eating cheap vegetarian food.

I don't advise this as the optimal path. I think it is far wiser to spend your time researching a strategy that you can sell to rich investors, and using their actual money rather than your ramen noodles.

<comment><USER4> I'd have to agree. For mental health it's much easier, though I do think you learn a lot about your grit in accomplishing a goal when you commit to Ramen for X time.

</comment></comment></comment><comment><USER5> $10,000 goes along way

1) Subcontracting work on upwork,logo design, etc. You can get quality contractors for under $100 / hour easily in many domains

2) Buying LTL freight and MoQ orders for physical goods overseas

3) Buying tooling for most small processes

4) Getting consultant experts in your domain _not related to business, but rather hard to get information_

5) Recruitment services (indeed)

6) Paying for monthly SaaS programs to help you lay out processes

7) Registering domain names

8) Rapid prototyping with local fabrication shops

9) Getting a shared coworking space for $100 to $500 a month to establish a meeting place for localized service-based contracts / networking

10) Paying for courses online to learn all the tools you need

11) Paying for housing/food/etc in the very few months of operation

12) Hiring your first subcontractor lets you lay out processes on slack/notion/trello/insert_project_management_tool etc. So you can start laying out SOP (standard operating procedures) that scale well over time, and find loopholes through rapid A/B testing

You can build anything you want once you reach a reasonable baseline. Also you can be super cheap about everything you do too, you can buy scraping tools for instance for $300 a month or more, or you can just run your own scrapers with a $5 vpn/month service.

<comment><USER6> Build it and they will come right?

</comment></comment><comment><USER7> The simple ingredient list of the protein bar is amusing.

The bar is blueberry flavored, but there are no blue berries on the list, and no sight of artificial flavors in the list either. And none of the other component taste of blue berries at all.

Obviously there must be at least blue berry flavors be in there, but they are not listed.

They end the list with "no B.S." but then they are obviously insulting the intelligence of the reader in such an obvious way.

Does that really work with customers?

<comment><USER8> I guess I'm a customer, since I bought a few of these (not blueberry) and liked them. Not available where I live unfortunately.

IIRC the full ingredient list is there on the package, as well as available online.

I found them pretty tasty and without any scary side effects (bloat being a common one with protein bars) -- but I was probably predisposed to like them, since the thing I really _dislike_ about most protein bar brands is that everything tastes like candy bars. I don't want candy after I work out, I want protein that doesn't taste disgusting and ideally is not an extruded animal part. (No idea why Cliff Bars coat everything in "chocolate" but I guess they know their market for the Builder Bars.)

The dates are the "glue" so to speak, and there's a pretty long tradition of that in natural foods. There are a couple of different fairly obvious reasons why the founder would have tried dates, and you can hear about them in his How I Built This interview[0].

I'm not convinced that 12g egg protein is as good as 20g whey protein, which is pretty much standard. But I'd happily suspend disbelief in order to get rid of the damned chocolate-caramel-stevia plague.

[0]: [https://www.npr.org/2018/08/10/637619434/live-episode- rxbar-...](https://www.npr.org/2018/08/10/637619434/live-episode-rxbar-peter- rahal)

<comment><USER7> Yes no opinion on the bar itself. I guess it's ok as far as protein bars go.

I personally just mix some powdered whey protein in a yoghurt. Beats any of those bars easily and is trivially easy to do.

</comment></comment></comment><comment><USER9> To contrast, if donated to the right organizations, you could save approximately 3 children under 5 from dying of malaria.

<comment><USER10> Yes but if everyone did that there'd be no money or technology to save those children.

<comment><USER11> But if everyone did that, would there still be children to save?

<comment><USER12> Oh yeah, once I can get someone to pay for my kids I'm going to fuck like a rabbit. No joke who wants the bill?

</comment></comment></comment></comment><comment><USER13> Money buys founder an accelerated timeline. With consumer products like these it's much easier to get to your first 10 or 100 customers when you have the funds to spend on ads vs trying to get them organically.

<comment><USER14> I think there is a difference between spending solely for the sake of sales and smart marketing. Most new consumer products aren't completely novel, so if you can figure out to gain sales with a low marketing cost, it gives you a huge leg on the competition.

When I was hustling into the fray of starting a direct to consumer business, our head of branding and marketing previously ran the launch strategy at AdoreMe, a lingerie company. When they launched, they decided to feature a corset only line since it was the only lingerie related keyword on google that Victoria Secret wasn't bidding on it. They combined that was early influencer marketing to dominate a niche with cheap cost per click.

<comment><USER15> That's a great trick. I'm saving it in my toolbox. Thanks

</comment></comment></comment><comment><USER16> As several people here have observed, "we built this for $10,000" actually means "We did something for which the main input was time and effort, and incurred $10k of incidental costs". The physical-goods stories are cute, but any software company is clearly in this category.

I therefore think it's more interesting to break it down by how you _get_ that time. I see a few categories:

1\. Have rich parents. Yes, yes, this is true of some people (but probably fewer than you think), and isn't very interesting except for starting political arguments.

2\. Have a supportive partner on whose earnings you can live. This is much more achievable for many people, and under-emphasised (it's common for, eg, postgraduate education!)

3\. Have an existing consulting work that gives you steady work 1-2 days per week. People say "startups die by consulting", and they're right, but what kills you is _finding new work_ (it becomes the "top idea in your brain", which is lethal). If you have an established client who's happy to spend a year paying for 1-2 days a week, you're perfectly positioned. (This is how we started [https://anvil.works](https://anvil.works), and I can't speak highly enough of it)

4\. "Productised consulting" \- consult full time until you come up with a product that all your clients will buy immediately, then switch to selling that product. This sounds great in theory, but I've seen a few people try and fail to make that leap (possibly because it's really hard to stop "find new consulting work" from being top idea on your mind, especially if you have employees/partners to feed).

5\. Have a business with good cash-flow characteristics that can be launched fast enough to become self-sustaining within its first month. This is what this article seems to focus on, but apart from "productised consulting" (where you start out selling something that's not your product), I'm very skeptical that it really works for product businesses. If you try this, I'd recommend having a fallback to one of options 1-4!

<comment><USER17> Those options all make the assumption that you have to carve that time out of regular working hours. I'd just like to add an option #6 - work on your project on nights and weekends, launch it on nights and weekends, and make the leap to working on it full time once it becomes profitable.

Meanwhile, increase your savings rate so that you might be able to make the leap sooner. That way if it gets to the point where you're profitable, but not yet making enough to cover your expenses, you can potentially jump ship earlier than you would've been able to otherwise.

It's pretty much the plan I followed and so far it's working well (left my job ~5 months ago). On the topic of increasing your savings rate, some of the ideas/mindset from [https://mrmoneymustache.com](https://mrmoneymustache.com) were helpful.

<comment><USER18> Working nights and weekends is a luxury for the young and childless. It's just not realistic for most people with families.

<comment><USER19> Very much this. I'll add that some jobs leave little free time and/or little mental energy during the week once you get basic self-care out of the way.

<comment><USER17> I almost added a bit about the > 40 hr/week problem. I can only speak for myself but out of the 3 software jobs I've had none of them regularly required more than 40 hours/week. That's a privilege for sure, but those jobs do exist. Probably not at most startups, and probably not at most sexy well-known companies... but it's a tradeoff (like everything, it seems) -- the shiny line on your resume from AmaGooFaceSoft, Inc. or the tiny chance at a big payout vs. making a decent salary and being able to forget about work outside the hours of 9 to 5.

It probably varies by area of the world/country as well. I imagine the long hours are more the norm in the middle of a startup hub city vs. out in the suburbs.

</comment></comment></comment></comment></comment><comment><USER20> In some countries you can live for almost a year on 10k, so that means having all that time to build whatever you want on your own time. That's way more than enough to make a product on free tools, and get enough customers to get the ball rolling!

<comment><USER21> > so that means having all that time to build whatever you want on your own > time.

Which is why it's so important to spend that money wisely. If you're in your parent's garage, you might want to experiment with AWS (but for a strictly limited amount of spend and time) - just to get a feel for what it's like to scale. It's a common mistake to build a bare bones solution, and never have to worry about scaling.

</comment></comment><comment><USER22> Alternatively, $10,000 will cover about a year's worth of cheap living expenses in somewhere like Eastern Europe, the Balkans, etc.

A year is pretty decent runway if you have the skills already.

<comment><USER23> Almost every city in Europe. Even London if you want you can live cheap. Monthly bills: 350 for a very crappy room all included bills in a bad zone, 150 for Supermarket food, 150 travelcard. Total 650 gbp per month, I think is around 10k USD.

<comment><USER22> True but I think a room that cheap in London will negatively affect your quality of life, as it'll likely be in a bad area. Better to go to Belgrade, Lviv, Wroclaw, Cluj, Sarajevo, Zagreb, Leipzig etc.

<comment><USER24> So you just come and tell an immigration officer - hi, I have $10k, do you mind if I live in your country for a year without paying taxes while building my US-targeting business?

<comment><USER22> No, you bounce around 4-5 counties.

</comment></comment></comment></comment></comment><comment><USER25> At these levels the seed funding that really counts is _time_. $10k really represents X months/weeks of founders time, compensating them for not having a job.

</comment><comment><USER26> I think the article's title question has to be restated as 'How much value can you build up from $10K?'

Obviously the title amount is in 'retrospect'. Also it does not imply any specific time-period. A lot of ground work is put into a business idea prior to even forming a company. This is often an undisclosed cost of success.

In retrospecitve with astronomical exits like RxBar's this just sounds like an angel's dream.

</comment><comment><USER3> I think the concept works for certain startup ideas - ones that don't require a lot of up front engineering or capital expenditure on equipment. If that is your startup, then yes go do this.

</comment><comment><USER27> Launch a boutique VoIP provider. Yes there are many out there, but the market is crazy big (and growing) and there is plenty of room for you. Big margins, low overhead if you know how to do it right. A 20-person office customer is $18K in revenue over 3 year customer life (and avg. life is higher if you provide good service). Go after niche verticals and dominate those.

<comment><USER28> Huh, this makes a lot of sense and the timing of this comment is interesting. I work with a small commercial office building and they want to setup VoIP instead of using their PDX machine for phones because it's quite expensive. Looked a lot of places but the big pain is having physical phones. Most tech companies just want you to have an app on your phone but a lot people still prefer an actual phone on their desk. This is why Dialpad wouldn't work (also expensive) but is more complex than phone.com which will send you phones preconfigured.

Still trying to figure out the best route since so many of these places have poor UIs or don't support physical phones and fax very well. I can't imagine I'm the only one running into these problems so their is an opportunity here.

<comment><USER27> When you say "most tech companies" are you referring to employers in the tech space, or specifically VoIP providers? Dialpad.com is pretty much still alone in the mobile-first, anti-Deskphone, WebRTC-preferred approach. The rest are still primarily geared towards desk phones, even RingCentral that has a very slick mobile experience.

Dialpad is definitely playing the long game here and will be the clear winners 10 years from now. I predict Amazon will acquire them in the next year or two.

More and more businesses are switching from on-premise telephony to cloud based hosted PBX. The addressable market is very large, latest estimates put it at 55+ million seats. That's before you consider the contact center market... just focus on getting to 500, then 2000, and so on. It's not difficult. 2000 seats bring you $50K MRC, and again if you know how to this right, especially coupled with intuitive self service UI to the end users, overhead is very very low. And the actual Telco costs are too tiny to even worry about. About 75 cents per user.

I thought about starting a "how to start a VOIP business" webinar or somethint.. its amazing how many are trying to create SaaS products in obscure fringe markets where the VOIP market is pure gold.

<comment><USER28> Referring the VoIP providers that are pushing out the anti-deskphone philosophy.

I would love more of a blend of phone.com/ringcentral + using the designers of Dialpad. Their interface seems a lot nicer. With the more traditional ones it's nice that you can call them and they'll ship you ready to ring phones that connect to the cloud based PBX. This is huge and needed but Dialpad does not do this. Just look at their docs, you have to specify an IP and quite a bit more configurations unlike the shipped ready phones, but I have a feeling this is because of their licensing terms.

Being able to move around or take your deskphone with you home, to a coffee shop, anywhere that has an internet connection is quite useful. Again, talking about a physical deskphone that connects via wifi or ethernet. I'm aware using a cellphone with an app easily does this but often that's harder to sell to some more traditional folks.

Agree that this market is gold and this would be a really fun project to piece together, though the type that I want to exist might not be doable for $10k.

</comment></comment></comment></comment><comment><USER29> I call bullshit.

Sure, $thing cost $5k-$10k to start. That says nothing about how you live, how you pay for rent/food/health/internet, or how to pay for dependants. Even if you live in Shithole, USA, these costs are still up there. And likely in that location, internet service is extremely lackluster or just plain non-existent.

Most startups get away with low spending is because of rich family. When you hear a $elite_school dropout spinning around and making $empire, its not because they did it by themselves... Their family capital to take care of those other costs is what made that happen. But the Horatio Alger story keeps people believing and wanting - they never look at the facts. No, you won't find the "gold wrapper", statistically speaking.

But keep pushing the idea that prosperity can be for everyone, and its just around the corner. For most of us, its not and never will be.

Edit: Ah. -1's but no actual discussion/refutation. Looks like I hit on a sore point. I'd love some hard data here.

<comment><USER30> "Most startups get away with low spending is because of rich family"

I think you are too negative but there is definitely something true about a lot of founders coming from well off families. A long time ago I did some business ventures with people from wealthy families and I quickly noticed what advantage it is to have family that can give you a place to live, a car and some other necessities. These guys lived a frugal life but they didn't have to be afraid of going homeless as I was since I had nobody to give me money.

<comment><USER29> > I think you are too negative but there is definitely something true about a > lot of founders coming from well off families.

Its not negativity; its anger.

We have this idea.. That you can only with $5k make your own multi-million dollar empire. But you're just too lazy to do it.

And that's far from the truth. Obviously, rich family kid who dropped out of $elite_school isn't going to be homeless. So, they get an "angel investor" (read: family). And those other services you need, like legal, sales, and dozens of other things are smoothed over by quiet words said at social functions, 'My son needs some internet business help to present to VCs'... And they have the connections to get it done.

Summing it up, it is access to money, a strong social net to fall back on, time, access to resources, and access to connections.

The VCs and rest can pretend that anybody can, but I know nobody who could do this for a year, possibly fail, and recover. We all would be sunk and feeling these ramifications for years.

<comment><USER31> People like to discount all these things as being outliers, but they're the norm.

Even many "rags to riches" stories are frequently more about luck and building a network of support from which the business can be launched than they are about scrappy business sense.

Take WhatsApp's founder: a true self-made, rags-to-riches story. Underlying his self-made success is that he'd built a solid network and financial padding working in high-paying tech jobs at places like Yahoo. He worked hard to reach a point that most founders are simply born into and _then_ launched a successful business.</comment></comment></comment></comment>


Ask HN: What do you use for media storage? - <AUTHOR> Had my first little one recently, and it dawned on me that my iphone's storage is gonna run out real quickly with all the pictures and videos.<p>The wife and I don't want to use social media as our "solution" (in fact we don't really use it at all, especially Facebook). So I'm wondering what other folks use. I'm thinking I might just upgrade my Dropbox account, but not against turning it into a little project of sorts either if there's some benefit. <comment><USER2> PhotoSync on iOS will safely move photo/video from the phone to remote storage, including other phones, tablets, local PCs, NAS or cloud.

iXpand Drive is natively supported by some iOS apps (e.g. Filmic Pro and LumaFusion for video) and can import/export directly to a 32/64/128G external flash storage.

FreeNAS works on low-end PCs. There are commercial NAS like Synology with iOS apps.

</comment><comment><USER3> I use google photos for anything with my iPhone as it's unlimited storage with unnoticeable compression. As for computer related files and videos I use google drive through my old university email since that also has unlimited storage.</comment>


Google Earth Comes To The iPhone, And It's Awesome  - <AUTHOR> http://www.techcrunch.com/2008/10/27/google-earth-comes-to-the-iphone-and-its-awesome/ <comment><USER2> As an iPhone user and geo-buff, I must say this is exciting. I wonder if they'll make it possible to add your own KML files.

One thing I've noticed already--the Current Location is less accurate than in the built-in Google Maps app.

<comment><USER3> I noticed the accuracy was off a little too (maybe 500 ft off)</comment></comment>