How to get your money's worth from your startup lawyer - <AUTHOR> https://techcrunch.com/2019/01/10/how-to-get-your-moneys-worth-from-your-startup-lawyer/ <comment><USER2> When I first started our company I met a "startup" lawyer at a meetup event and after a few meetings hired him. He was fine, but was expensive and didn't know much about high scale tech startups - mostly govt contract startups. So we fired him and hired Cooley.

Cooley had great parties and networking events. When our lead investor came in and asked for multiple preferences (no go) I asked the assigned counsel what they thought and the response was: "Hey this is your first money so it doesn't really matter." At the time this was my first deal, but I had read a TON on this issue and it was clear that this was a hard predatory ask for a deal like ours.

I asked some of my other friends and they suggested a different lawyer, whom I called and he said "Tell them to eat shit and call me when they call back with a new term sheet." Cooley was immediately fired and we hired the new guy who has tons of experience in our space and has been the absolute best.

Moral of the story is, be informed enough so you can call bullshit, but it's really important to find counsel that 1. you can trust and 2. is experienced in your particular types of deals so that when you are out of your depth you know they will be the best counsel.

At the end of the day you're CEO so the buck stops with you. Your lawyers are counsel not operations, so act accordingly.

<comment><USER3> > At the time this was my first deal, but I had read a TON on this issue and > it was clear that this was a hard predatory ask for a deal like ours.

I am curious if you can recommend some materials to read up on this?

<comment><USER2> Honestly I couldn't because they were from 2009/2010 and I'd have no idea where to find them now.

I think I was reading a lot of Mark Suster's stuff back then, so that's a starting point at least.

</comment></comment></comment><comment><USER4> A few points:

\- The partner matters a lot more than the firm. For example I used Cooley, but really it was one attorney I wanted. When he moved to another firm my company went with him.

\- Of course it's the associates, not the partner who will do 99% of the work -- but that's all you need anyway. The partner however supervises. Ideally you never get a bill for the partner's time.

\- In almost any case you should hardly use your lawyers at all in the first year or two. You get standard NDAs (one way and/or mutual) and can fill them in yourself. If you get one from a partner, ask to use yours or read theirs and if it looks really close just use it (unless its some massive one from, say, Intel -- but really, are you going to be able to negotiate that?). Likewise just use standard job offers and confidentiality/assignment forms. No need to pay a lawyer for those once get up. But don't wing it with anything security or debt related!

\- Another reason not to care about the firm: a big firm will have lots of services but will you need them? For example for patent filing you may want a boutique firm that specializes in just your area. For HR lawsuits I'd use MoFo even if I don't use them for corporate law.

\- Make sure your firm understands startups (this expands on what the article said about "Hire lawyers who have experience with the particular task you are asking them to perform"). Various big firms have opened offices in the Valley in order to get that sweet deal flow. But unless they staff up with some local attorneys with local connections and experience you're not going to get a lawyer who can help you no matter how good they are. Even the startup scenes in NY and Boston are different from each other and from the Valley or SF. Not just network but how people do things.

\- an exception to the "firm doesn't matter" rule: Wilson -- not only have I only had bad lawyer experiences (mainly huge Billings) when I was involved with a company that used Wilson, but the only firm that ever asked me to do something illegal (they were representing our investor...but only for that transaction as our lawyer was also the investor's lawyer. For subsequent rounds the investor used someone else, as they were also disgusted with Wilson). There's a reason this onetime "star startup law firm" has been shrinking.

<comment><USER5> Select for good people, select against bad firms.

I think you've articulated something key; You need a competent partner to make sure you're getting decent value, regardless of the firm you're at, but firm culture itself can make any practitioner play poorly. This just pulled together a few strings of thought I've had (I have my own internal industry 'bad firm' and 'bad practice group' list in my head), so thanks for this post.

</comment></comment><comment><USER6> The article supports a common fallacy that clients just have to understand enough to police their service-providers. I would suggest that if you consider yourself to be a startup founder, you should know MORE THAN your lawyer about your particular transaction. If you do NOT know more than your lawyer, you should consume all available info until you do, or else bring in an advisor (such as a professional seed investor who is not a stakeholder in your company) to help you. Your lawyer can give you advice only, but you have to take charge of your own direction and decision-making. Your lawyer can and should also perform the service of organizing all the paperwork, which is surprisingly time-consuming yet not very edifying.

<comment><USER7> Never having done this I defer to your expertise but isn't the highest leverage thing a founder can do is delegate specialized tasks? Fundraising and incorporatation are very important but there's a reason lawyers went to law school. Seems like it's a high bar to know more than your adviser, especially if it comes at the cost of focusing on building your product.

<comment><USER6> Lawyers do not learn anything about the practice of startup law in law school, with a few exceptions where there are good clinical programs as at Brooklyn Law for example. Are you building a business around your product or just a product? If you're building a business, you need to understand the structure of your business intimately. Or someone does -- maybe not you, but your cofounder. Otherwise how are you going to evaluate the advice you get from your lawyer (and others)?

A good lawyer will have the benefit of breadth -- representing many different clients in different situations. If the lawyer is wise, this will be conducive to good advice. It is only advice. On what basis do you decide to follow the advice?

<comment><USER8> For the statutory law, I have found after many years of experience that it's very worth it to take 8-16 hours and read the actual statutes. Such as the Securities and Exchange Act, Reg S and the recent JOBS Act changes should be required reading for startup CEOs raising money. Sure, you won't get all the case law nor the later additions, but you'll understand what the overall framework is. Then you can discuss specifics with a lawyer. Also if you're a startup, some lawyers may take an advisor equity in exchange for 4 hours a month or so.

What led me to this conclusion is that after years and years of asking random questions while lawyers set everything up, I kept discovering new things about the exchange act until I just read it.

There are standard docs these days on docracy and other sites for most things. The JOBS Act has simplified fundraising quite a bit for startups. Especially if you use rule 506b for friends and family, you can later choose to go the VC route OR use rule 506c or Reg CF for access to liquidity using public solicitation.

EXPERIMENTAL SECTION:

A final note about security tokens. An obscure but very useful fact is that non-equity securities do not have a limit on how many investors you can have, before you must become a publicly reporting company. And selling to NON US investors they may be able to trade your securities after 40 days. However, their local jurisdiction may have other ideas, and this is one of those areas where you can either try to abide by all local laws in theory, or in practice take advantage of the fact that, in early stages, governments of foreign jurisdictions aren't going to waste too much political capital on long-arm actions to go after US corporations because they failed to prevent an overseas investor from selling their securities early.

I am not a lawyer!!

<comment><USER6> Hey Greg. Securities law is an area where a lot of lawyers have limited focus. A typical startup lawyer (such as myself, before retiring) may be knowledgeable about common exemptions for private offerings but less so when it comes to the larger universe of securities. The SEC's position on p2p assets is best put in this write-up by the corporate finance head William Hinman [1]. The SEC is not concerned so much with the details of how a token works, but rather in how it is offered on a case-by-case basis. Keep in mind that in the US, we do not focus on the security but on the offering.

Also a token that is offered in a securities offering may later be traded in a way that is not an offering of securities, for example if the protocol becomes sufficiently decentralized at a later date.

For myself, I would steer well clear of anything that looks at all like a securities offering of p2p assets b/c I like to keep my life as simple as possible. But I realize that is not advice everyone wants to follow.

The SEC is not the biggest threat here -- as you can see with projects such as Tezos, the bigger threat is the private securities bar which will go after any token offering that leaves a deep pocket. Even if the SEC and state regulators aren't concerned with your offering, the private bar will go after anything that plausibly is an illegal securities offering, provided that the pot of money is worthwhile target. This is exactly the kind of hazard that p2p protocols are perfect for avoiding, so why bother making a new p2p protocol that is vulernable to this kind of risk???

I'm a _retired_ lawyer and this is NOT legal advice for anyone. :))

[1] [https://www.sec.gov/news/speech/speech- hinman-061418](https://www.sec.gov/news/speech/speech-hinman-061418)

</comment></comment></comment></comment></comment><comment><USER9> Can't go wrong with Grellas.

[https://news.ycombinator.com/user?id=grellas](https://news.ycombinator.com/user?id=grellas)

</comment><comment><USER10> Also: rather than immediately going to UpCounsel or LawTrades, try finding a local lawyer through e.g. Avvo. For me this resulted in a lower hourly bill rate. You can compare all three (though if you contacted your lawyer through UpCounsel or LT first, they won't work with you direct). It seems to me that lawyers simply bake the third party fees into their rate, so you might as well skip the middleman.

<comment><USER11> Yep, w nit:

1\. Get a corporate lawyer w startup discount for formation , core docs, etc. Keep using for big stuff like fundraising.

2\. Use carta or whatever for stock

3\. Use upcounsel for anything non-core or boring like office space. You can get a per-topic specialist for each thing fast and cheap.

4\. For most deals -- nda, sub-100k contract, etc, have papers etc ready so no legal review.

<comment><USER12> Great overview. Have seen roughly this model used across multiple orgs with good success.

</comment></comment></comment><comment><USER13> > Finally, make sure you understand in advance what costs and expenses the > firm will pass on to you (e.g. _photocopying_ , _postage_ , _couriers_ , > travel)

(emphasis mine)

Is there something particular about the US legal system that forces these people to live in the stone age? I mean, _photocopies_?

<comment><USER14> This is funny. I've been putting off having my boss sign a contract because a vendor sent me a stupid e-signing link instead of a PDF I can print out, get his signature, and scan back. Paper is _easy_ and everyone knows how to use it, which makes photocopying still a thing.

Also, although most court stuff is electronic these days, courts still insist on papers courtesy copies (which have to be mailed or couriered over). Reading on screens sucks, and marking up documents on screens sucks double hard. When I first started practicing I was like "I'm a millenial, I'll do everything in an iPad!" Now I print everything out, mark it up by hand, use colored tabs, etc. Even a big monitor is incredibly claustrophobic compared to piles of tabbed printed pages.

<comment><USER15> I had a client that gave explicit instructions to sign, scan, and return. So I signed digitally (MacOS Preview makes this really easy), rotated the pages a little, and added a bit of noise. They were happy and I was happy.

</comment></comment></comment><comment><USER16> There are many startup related firms that have deferred fees or will give a good amount of advice for free. When you are really small, that's makes sure you don't mess up things that affect equity. The whole board member / investors to startup lawyer relationship is something founders have to be cognizant of...I could write for days about how perverse incentives work against the startup / founders.

However, there are tons of issues beyond just startup equity / corp governance / incorporating that can kill your company. Discrimination / ERISA (HR issues broadly), privacy, anti-bribery, IP, commercial contracts...the list goes on and on.

Most young founders don't even know what they don't know and silicon valley culture tends to err on the side of, "just get the the exit...that's bigCo stuff you don't need to get lawyers involved, just do this."

If you truly want to build a great company, hire a well rounded lawyer as an FTE much earlier than you think. They don't need to be a first 10 hire. However, if you are growing and want to build a high-quality company, you should think about hiring a well rounded lawyer somewhere between hire 25-50.

</comment><comment><USER17> I'm going to add this link (and a link to this HN discussion) to the "Startup Law 101" page that I've been maintaining [0] in lieu of providing copies of the slides I use in guest-lecturing on the subject.

[0] [https://www.oncontracts.com/startup- law/](https://www.oncontracts.com/startup-law/)</comment>


How I Ended Up Purchasing and Owning Google.com via Google Domains - <AUTHOR> https://www.linkedin.com/pulse/i-purchased-domain-googlecom-via-google-domains-sanmay-ved <comment><USER2> any way to read it without logging in?

<comment><USER3> A strange thing happened at 1:20 AM Eastern Time on Tuesday, September 29. I was learning more about the Google Domains interface, and typed google.com and clicked search domains. To my surprise, Google.com was showing as available!

I clicked the add to cart icon beside the domain (which should not appear if the domain is not available for sale). The domain actually got added to my cart as seen by the green check-box, and the domain appeared in my cart.

I was hoping I would get an error at sometime saying transaction did not go through, but I was able to complete purchase, and my credit card was actually charged!

As soon as I completed purchase, I received two emails, one from sc- email@example.com, and one from email@example.com, which is not the norm when you book domains via Google Domains as I have booked new, previously un- registered domains before, and I have never received emails from the above aliases on booking the domains. I will not share the contents of the emails here given they relate to the Google.com domain. The domain also successfully appeared in my Google Domains order history.

Additionally, my Google Search Console (aka Google Webmaster Tools) was auto- updated with webmaster related messages for the Google.com domain which actually means ownership was transferred to me! One gets the below messages in Search Console only for those domains for which one is the verified admin/verified owner (of course access was removed when domain was taken back by Google).

Additionally, I started receiving notifications, for when ownership changed (along with new owner details etc.) in the Google Search Console for websites (I will not name them) that are powered by Google Sites (which makes sense given that websites powered by Google Sites rest on the master domain Google.com). Quite clearly, ownership had been granted to me. Order was successful.

Though purchase had successfully gone through, and domain now belonged to me as evident above, the purchase was followed by an order cancellation email from Google Domains. Google could do this given the registration service used by me (aka Google Domains) belonged to Google, unlike the 2003 event in which Microsoft forgot to renew their Hotmail UK domain which was registered with Nominet UK. As a result, the Hotmail UK domain was returned to the open market for pickup by anybody who fancied it. Somebody else picked it up, and as Microsoft wasn't the registrar themselves, Microsoft wasn't able to cancel the order, and take it back automatically. In my case, I don't know what caused Google to lose ownership of the domain Google.com as a result of which it was available in the open market.

The purchase got completed and the card was charged (which would not have happened unless I actually successfully completed checkout, as otherwise I would have received an error). The charge was not a pre-auth.

The order history page auto-updated itself with a new message (missed taking a screenshot when the canceled message was not appearing).

On searching again for the domain Google.com, it now finally shows as unavailable.

The Indian Prime Minister's visit did work wonders...it ended up convincing Google to sell the most visited domain on the internet to a person from the Kutch region of the Indian Prime Minister's home state...albeit just for a minute or so :)

\-----

Note: I have reported the incident to Google Security. Google has reverted back, and has acknowledged the incident.

all your missing are some screenshots

<comment><USER2> thanks!</comment></comment></comment>


Déjà Vu, Anyone Remembers the 1998 Russian Financial Crisis? - <AUTHOR> http://en.wikipedia.org/wiki/1998_Russian_financial_crisis

<comment><AUTHOR> Another little bit of history

LONG-TERM CAPITAL MANAGEMENT, 1998

The collapse of hedge fund Long-Term Capital Market (LTCM) occurred during the final stage of the world financial crisis that began in Asia in 1997 and spread to Russia and Brazil in 1998.

LTCM was a hedge fund set up by Nobel Prize winners Myron Scholes and Robert Merton to trade bonds. The professors believed that in the long run, the interest rates on different government bonds would converge, and the hedge fund traded on the small differences in the rates.

John Merryweather, head of LTCM John Meriwether, a Wall Street trader, headed LTCM But when Russia defaulted on its government bonds in August 1998, investors fled from other government paper to the safe haven of US Treasury bonds, and interest rate differences between bonds increased sharply.

LTCM, which had borrowed a lot of money from other companies, stood to lose billions of dollars - and in order to liquidate its positions it would have to sell Treasury bonds, plunging the US credit markets into turmoil and forcing up interest rates.

So the Fed decided that a rescue was needed. It called together the leading US banks, many of whom had invested in LTCM, and persuaded them to put in $3.65bn to save the firm from imminent collapse.

The Fed itself made an emergency rate cut in October 1998 and markets soon returned to stability. LTCM itself was liquidated in 2000.</comment>


Wizergos Meetings Management - <AUTHOR> http://www.wizergos.com <comment><AUTHOR> Use Case: General Management and Operations

Create Meetings, assign and track Preparation, Agenda, Action Items. Record and share Decisions. Collaborate on Meeting Minutes, take down Notes. Communicate with attendees with Messages. Manage Parking Lot of Agenda Items. Create Follow-up Meetings. All information and knowledge generated in meetings Organized and Searchable.

Use Case: Project Management

Create customized Project Governance structure. Customized meeting screens for special project meetings like Project Planning, Sprint Planning, Standup, Sprint Retrospective, Project Retrospective.

Engaging Meetings

Shared Meeting Screen for Remote Presentations. Keep all attendees Engaged and updated at Real Time with Minutes, Decisions, Messages, Action Item.

Augmented Intelligence

Data Driven Smart Assistance for Scheduling Meetings, Project and Sprint Planning, Meeting Suggestions.

All devices

Supported on Desktops, Tablets, Mobile Phones. Access all data and provide updates with ease from anywhere, anytime, on any device.

Reports and Analysis

Analyze historical data and Monitor trends on various aspects of Meetings, Action Items, Projects.

Integrations

Auth: OKTA, Azure AD, Local AD Groups: Local AD, Google, Office 356 Storage: Gdrive, OneDrive Conference: Webex, GotoMeeting Calendar: Google, Exchange, Office 365 Mobility: InTune, Airwatch, MobileIron Project Management: Jira</comment>


I'm building Ghost - <AUTHOR> http://quaz3l.github.com/ghost/g-admin/posts.html

<comment><AUTHOR> When I came across the [Ghost](<http://john.onolan.org/ghost/>) while reading HN I really liked it, and have thought about making something similar, but have never gotten around to it. In the last few days though I have put it together just as John described (I don't know if its legal or morally right to publish it, but I will blog about it at least because I have spent some time on it.

I finished working on the fifth image down in his post, -the post list and preview- I also finished the sixth image, which is the editor with markdown, and preview.

Now I am working on the backend which is created with PHP and the Slim PHP RESTful application framework. If you would like to check out the current version of my rendering of Ghost, you can see it on GitHub by following the link.</comment>


OpenStreetCam sign detection code and training data open sourced - <AUTHOR> https://www.openstreetmap.org/user/mvexel/diary/43942 <comment><USER2> Very interesting, but unfortunately the one example they pulled was incorrect. If you look at the street sign is says "Exit | 45 mph", which means this is the speed limit when taking the exit. The captured example looses this information.

<comment><USER3> _which means this is the speed limit when taking the exit._

And it's not even the speed _limit_ , it's the recommended speed for that exit. IOW, go as fast as you like, right up to the posted speed limit, but if you're a tall semi hauling a load it is recommended that you slow to the speed posted on the yellow sign. But I digress...

[https://en.wikipedia.org/wiki/Advisory_speed_limit#Signage](https://en.wikipedia.org/wiki/Advisory_speed_limit#Signage)

</comment></comment><comment><USER4> This is really promising, especially:

> Finally: we do not have any plans to automatically add any of this detected > information to OSM. Any improvements will always be made manually by mappers > through the existing JOSM plugin, iD integration (coming) and MapRoulette.

This points the way forward for OSM. OSM is a human map made from user contributions - the collected set of users' enthusiasms. That's what differentiates it from other maps. But that shouldn't mean that mapping should be a laborious process. The more innovations like this that remove the gruntwork from mapping, the better. (Same applies to recognising geometries from imagery and sensor data, etc.)

<comment> cup-of-tea Couldn't agree more. One of the most startling things for me while mapping is just how different things can be when you're actually on the ground and armed with local knowledge compared to looking at some satellite imagery, streetview imagery or similar. It's totally obvious why any map that is automatically derived from relatively low resolution data will necessarily contain mistakes.

A map is one of the oldest forms of written communication. Some people think maps came before written language and as such are an intrinsic part of what it means to be human. Just think of the amount of information being conveyed by a map! We don't accept computers making other written works, why accept computers making our maps?

</comment></comment><comment><USER5> Have been an Openstreemaps contributor for some years (not very active), but I did not heard about Openstreetcam. Interesting!</comment>


The 2018 free privacy tools starter kit - <AUTHOR> https://ebelinski.com/2018-free-privacy-tools/ <comment><USER2> Decentraleyes and Onionshare look intriguing. Others seem more well-known.

<comment><USER3> Yea this seems to be a very light (i.e. missing tons of options/explanations) version of privacytools.io.

</comment></comment><comment><USER4> Can someone post the list here? Corporate firewall is blocking access.

<comment><USER3> As you wish: [https://craftyguy.net/paste/5X0XnLzW](https://craftyguy.net/paste/5X0XnLzW)

<comment><USER4> Thanks for trying but it blocks that site as well.

<comment><USER3> Wow, your IT is nuts. Anyways, here's the text, assuming HN let's super large comments through..

\</comment></comment></comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment></comment><comment>--

The 2018 free privacy tools starter kit

April 30, 2018 * Eugene Belinski

The following is a list of free tools I recommend for protecting your privacy online from advertisers, your ISP, social media companies, and others. For a broader discussion on privacy, check out my 2016 talk at Open Twin Cities.

TutaNota: A privacy-conscious email provider based in Germany, a country with stronger data privacy laws than the US. The free plan gets you a [email protected] address with 1 GB of storage. Unlike Gmail, TutaNota cannot scan your emails to display targeted ads, because your emails on TutaNota servers are encrypted at rest. (See limitations.)

ProtonMail: A more well known privacy-conscious email provider based in Switzerland, protected by Swiss privacy laws. The free plan gets you your own [email protected] address with 500 MB of storage, but all of your sent emails have a required "Sent with ProtonMail" signature. The paid plan is about $60 per year. (See limitations.)

Note-taking

Standard Notes: A cross-platform note-taking app with cloud storage. Unlike iCloud Notes and Google Keep, no one can read them except you. Their mobile apps are fine, but the desktop app needs some improvement. The free plan is more than enough for most people. I have found that the Extended plan ($35/year) doesn't really improve the desktop app, so it's not worth the money. Messaging

Signal: A messaging app so secure that it's recommended by Edward Snowden. Available for iOS and Android. You can use it to send messages, group messages, calls, and video calls, which are always end-to-end encrypted, so they can't be read by anyone else. Operating systems

Tails: A privacy and anonymity-focused operating system. Typically, Tails is installed on an external USB drive, and run on a computer, without replacing or affecting the OS inside the computer. Tails is a Linux OS, so there's a bit of a learning curve. All internet usage on Tails is routed through Tor. It's like the Tor Browser, but for the entire OS. More information can be found here.

LineageOS: An operating system for Android phones that is free and open- source. Combined with microG, it's worth trying out if you have a compatible device.

Web browsing

Tor Browser: The most secure web browser, Tor Browser routes all your traffic through the Tor Network. This means that your ISP can't tell what you're browsing, and the websites you visit will not necessarily know who you are. However, Tor Browser is not all you need to browse anonymously.

Firefox: For more standard web browsing, Firefox is the most popular free and open-source web browser. Unlike Chrome, it won't scan your files. Firefox is FOSS software developed by Mozilla, a non-profit organization.

Browser extensions

uBlock Origin: The most popular and most effective content blocker, blocking targeted ads, third-party trackers, malware, and more for Firefox, Chrome, Opera, and Edge. There is also a Safari experimental version. Beware of mixing up uBlock Origin with "uBlock", which is outdated.

Privacy Badger: A content blocker that is similar to uBlock Origin, but it only blocks trackers and ads that track you. Developed by the Electronic Frontier Foundation, it is available for Firefox, Chrome, and Opera.

HTTPS Everywhere: Another extension by EFF, HTTPS Everywhere redirects you from HTTP to HTTPS when it's available, increasing your privacy. Available for Firefox, Chrome, and Opera.

Decentraleyes: Helps avoid being tracked by third-party CDNs. Available for Firefox, Chrome, and Opera.

NoScript (Firefox only): For more advanced users, NoScript blocks all JavaScript scripts, and allows you to selectively whitelist scripts and websites.

File sharing

OnionShare: A tool that lets you securely and anonymously share a file of any size with someone, over the Tor Network. Available for Mac, Windows, and Linux. Authentication

FreeOTP: A free and open-source authentication app for iOS and Android that is an alternative to Google Authenticator.

Authenticator by Matt Rubin (iOS only): A simple, beautiful authentication app that is an alternative to Google Authenticator. It is free, open-source, and never connects to the internet.

More

Where to go from here? PrivacyTools.io has much more detailed lists of privacy tools, both free and non-free. They cover additional categories like VPNs, decentralized social networks, and password managers. In addition, /r/privacytoolsIO/ is a great place to ask questions about privacy tools.

</comment><comment><USER5> what about brave? [https://brave.com](https://brave.com)

<comment><USER6> I would second this. The Brave browser based off of a Chromium build has some of the best tracker/ads shield I have seen. The recent ICO was very fruitful and it's Blockchain 'tip' system is interesting in its goal of 'removing the toxic middle'.

[https://techcrunch.com/2017/12/06/brave-makes-push-to- reward...](https://techcrunch.com/2017/12/06/brave-makes-push-to-reward- content-makers/)</comment></comment>


Electric vehicles can reduce Colorado's emissions more than anything else - <AUTHOR> https://www.vox.com/energy-and-environment/2019/8/12/20801602/colorado-electric-vehicles-2019-renewable-energy <comment><AUTHOR> "electrifying vehicles would reduce carbon more than completely decarbonizing the state electricity sector, pushing state emissions down 42 percent from 2018 levels by 2040 — not enough to hit the targets on its own, but a huge chunk. Second, electrifying vehicles saves consumers money by reducing the cost of transportation almost $600 a year on average."</comment>


The itch list - <AUTHOR> http://www.theitchlist.com

<comment><AUTHOR> One day. That's when we plan to do all the things we absolutely MUST do on this planet. Climb Mount Everest. Learn to salsa. Hug Ryan Gosling. Judging from the number of things we've left for this day, chances are we won't get to do most of them (there are no salsa instructors on Mount Everest or nearabouts).

So it's best to start now! You don't have to do everything you're itching to do today but keep yourself focussed by putting a scratch-by date to it. Next week. Christmas. Before Justin Bieber hits puberty... Make a plan and invite your buddies to help, or buddy up with like-itched strangers and start scratching. You will keep finding stuff to add to your itch list but this way, you'll also build a Scratchbook worth bragging about!</comment>


Pro Video Factory – Professional Video Library in 4K, 6K and UP - <AUTHOR> http://provideofactory.com <comment><AUTHOR> Pro Video Factory is a stock footage platform where you can buy premium quality clips in HD, 4K, 6K and UP.

As artists ourselves, we understand that delivering quality content requires enormous amounts of time, energy and financial investment for any creative who wants to stay true to its vision. Born out of respect for the creative process, our platform aims to help every creator invest more time and energy in telling the story, while letting us do the hard work.

Dedicated professionals

We don't settle for half-measures when it comes to quality. Every member of our team strives for excellence in delivering users top-notch content.

We are the sole creators

By eliminating all third parties we created a platform where the user can buy straight from the source, allowing us to provide better deals and supporting artists to focus on the creative process and not on the business game

Fresh videos daily

20 000+ new files added monthly. With ever-growing collections we set out to become the biggest professional video library on the market with tons of fresh footage added on a daily basis.

Footage from all over the world

Our teams are on a perpetual quest around the Globe to capture a variety of moments and places.Their aim is to create more than stock footage, the goal is to create sources of inspiration for artists of all kind.

4K, 6K and UP

In order to provide highly customizable videos we use only cutting-edge technology. We are always on a chase in finding the latest equipment that can unveil a new level of realism.

Curated content

All footage is professionally filmed and carefully picked. We control and review each video for an enjoyable journey through our library.

If you have any feedback or questions i would be glad really glad to listen it. :D</comment>


C9.io to shutdown on December 31, 2019 - <AUTHOR> https://community.c9.io/t/please-dont-shutdown-original-c9-io/20955/20 <comment><AUTHOR> Text of the announcement:

" Team Cloud9 <firstname.lastname@example.com> 8:04 AM (1 hour ago) to me

Dear c9.io user,

On July 14, 2016, we shared the news of Cloud9's acquisition by Amazon Web Services. Since then, we launched AWS Cloud9 at re:Invent 2017. AWS Cloud9 combines c9.io's most popular features with new capabilities enabled by AWS. As we focus more of our efforts on AWS Cloud9, we plan to discontinue the ability to create new or to use existing workspaces on c9.io on June 30, 2019 and to discontinue all access on December 31, 2019. We would love it if you would join us on AWS Cloud9.

We've built migration tooling to enable you to download all your data and to make transitioning your Cloud9 environments to AWS Cloud9 easy. Read the docs to get started.

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Why newsletters beat social media - <AUTHOR> https://pjrvs.com/beat/ <comment><USER2> You know what else respects users privacy, it's cheaper than newsletters for the content owner, and it's so anonymous you don't even need an e-mail address to start using it? RSS feeds.

And please, don't just tell me that RSS feeds are anachronistic and that most users are not interested in RSS feeds anymore. They are not interested because a lot of alternatives have been gone. There was native support for RSS in Internet Explorer, older versions of Firefox and MacOS X, no downloads required.

Why can't just Apple News or the Google App have a prominent button that says "hey, do you want to add your favourite sites here to see recent updates about stuff you actually like, instead of this algorithmically curated list of news we _think_ you _might_ like?", apart of "we drive money by controlling the feeds"?</comment>


Ask HN: What free resources did you use to learn how to program ML/AI? - <AUTHOR> <comment><USER2> Firstly, while I think it's beneficial to learn multiple languages (python, R, matlab, julia), I'd suggest picking one to avoid overwhelming yourself and freaking out. I'd suggest python because there are great tools and lots of learning resources out there, plus most of the cutting edge neural networks action is in python.

Then for overall curriculum, I'd suggest:

1\. start with basic machine learning (not neural networks) and in particular, read through the scikit-learn docs and watch a few tutorials on youtube. spend some time getting familiar with jupyter notebooks and pandas and tackle some real-world problems (kaggle is great or google around for datasets that excite you). Make sure you can solve regression, classification and clustering problems and understand how to measure the accuracy of your solution (understand things like precision, recall, mse, overfitting, train/test/validation splits)

2\. Once you're comfortable with traditional machine learning, get stuck into neural networks by doing the fast.ai course. It's seriously good and will give you confidence in building near cutting-edge solutions to problems

3\. Pick a specific problem area and watch a stanford course on it (e.g. cs231n for computer vision or cs224n for NLP)

4\. Start reading papers. I recommend Mendeley to keep notes and organize them. The stanford courses will mention papers. Read those papers and the papers they cite.

5\. Start trying out your own ideas and implementations.

While you do the above, supplement with:

* Talking Machines and O'Reilly Data Show podcasts

* Follow people like Richard Socher, Andrej Karpathy and other top researchers on Twitter

Good luck and enjoy!

<comment><USER3> For those who like videos, I would highly recommend utilizing Andrew Ng's Coursera ML videos for step one. I found his lectures to be good high level overviews of those topics.

The course in general lacks rigor, but I thought it was a very good first step.

<comment><USER4> I strongly disagree with this recommendation.

Andrew Ng's Coursera course is probably good for _some_ backgrounds. But if your background is as someone who has mostly been programming for the last few years, I feel that Andrew Ng's course has two big drawbacks:

1\. It's not very hands-on or practical. You won't actually get the feeling of building anything for a while.

2\. It's very math oriented. If the last time you took a math class for your CS degree was a few years ago, you run the risk of not really remembering the background material well.

I'd personally recommend doing two things in parallel, if your background is in programming with less math training:

1\. Look for a very hands-on/practical course to try out some examples.

2\. At the same time, start refreshing (or learning) some maths that you might not remember, specifically, probability and statistics. Then after, Linear Algebra and maybe calculus.

<comment><USER5> I'm going to disagree with this about the difficulty of the math in Andrew Ng's course. Do you remember how to differentiate a function? Look up partial derivatives if you don't remember how they work, it shouldn't take longer than an hour. You're probably going to be fine.

If you never took calculus it's probably going to be hard going, but almost all modern machine learning requires basic calculus.

I would really recommend going through the first part of the course about linear regression if you haven't encountered it before, it was really eye opening for me.

<comment><USER4> Linear regression is incredibly important, but I think it's much better understood either practically (by implementing it or using it), or if you want to understand it mathematically, at the "end" of a statistics course. There's a reason that when learning probability/statistics, you usually encounter Linear Regression near the end of an introductory course, not in the beginning.

Again, this really depends on how mathematically competent you already are. I'm just basing this on how I felt coming to the course after having finished my degree about 10 years ago, therefore not really having most prob/statistics fresh in my mind.

<comment><USER5> You can certainly complicate the hell out of linear regression, but Andrew Ng introduces it in the setting of optimization/stochastic gradient descent, which I think is both mind blowing and a much simpler introduction than most statistics courses.

It's the very first bit of the course, I think everyone who is interested should try learning it. If not it's fine, but I wouldn't want anyone to not even try to spend a few hours on it because someone on the internet said it would be too hard.

<comment><USER4> That's certainly reasonable. And I totally agree with "try it out and gauge for yourself whether it's valuable for you".

My worry is that people will be put off from the field of machine learning if, 3 lessons into Andrew Ng's course, they will see that they don't understand anything, and that it's not practical to boot.

So my advice (generally applicable) is to try a few different things, because different resources click for different people.

</comment></comment></comment></comment></comment></comment></comment><comment><USER6> ML/AI:

* [https://www.udacity.com/course/intro-to-artificial-intellige...](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)

* [https://www.udacity.com/course/machine-learning--ud262](https://www.udacity.com/course/machine-learning--ud262)

Deep Learning:

* Jeremy Howard's incredibly practical DL course [http://course.fast.ai/](http://course.fast.ai/)

* Andrew Ng's new deep learning specialization (5 courses in total) on Coursera [https://www.deeplearning.ai/](https://www.deeplearning.ai/)

* Free online "book" [http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

* The first official deep learning book by Goodfellow, Bengio, Courville is also available online for free [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)

</comment><comment><USER7> Two good ebooks. Go well with R.

Introduction to Statistical Learning [http://www- bcf.usc.edu/~gareth/ISL/](http://www-bcf.usc.edu/~gareth/ISL/)

Elements of Statistical Learning [https://web.stanford.edu/~hastie/ElemStatLearn/](https://web.stanford.edu/~hastie/ElemStatLearn/)

</comment><comment><USER8> * Course: fast.ai ([http://course.fast.ai](http://course.fast.ai)). Practical, to the point, theory + code.

* Book: Hands-On Machine Learning w/ Scikit-Learn & TensorFlow ([http://amzn.to/2vPG3Ur](http://amzn.to/2vPG3Ur)). Theory & code, starting from "shallow" learning (eg Linear Regression) on sckikit-learn, pandas, numpy; and moves to deep learning with TF.

* Podcast: Machine Learning Guide ([http://ocdevel.com/podcasts/machine-learning](http://ocdevel.com/podcasts/machine-learning)). Commute/exercise backdrop to solidify theory. Provides curriculum & resources.

</comment><comment><USER9> Online courses recommended in this thread are great resources to get your feet wet. If you want to actually be able to build ML powered applications, or contribute to an MLE team, we've written a blog post which is a distillation of conversations with over 50 top teams (big and small) in the Bay Area. Hope you find it helpful!

[https://blog.insightdatascience.com/preparing-for-the- transi...](https://blog.insightdatascience.com/preparing-for-the-transition- to-applied-ai-d41e48403447)

Disclaimer: I work for Insight

</comment><comment><USER10> Andrew Ng's tutorials[1] on Coursera are very good.

If you're into python programming then tutorials by sentdex[2] are also pretty good and cover things like scikit, tensorflow, etc (more practical less theory)

[1] [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning) [2] [https://pythonprogramming.net/data-analysis- tutorials/](https://pythonprogramming.net/data-analysis-tutorials/)

</comment><comment><USER11> This doesn't actually answer the question, but I always think that people who want to study neural nets should read Marvin Minsky's Perceptrons. It's an academic work. It's short. It's incredibly well written and easy to understand. It shaped the history of neural net research for decades (err... stopped it, unfortunately :-) ). You should be able to find it at any university library.

Although this recommendation doesn't really fit the requirements of the poster, I think it is easy to reach first for modern, repackaged explanations and ignore the scientific literature. I think there is a great danger in that. Sometimes I think people are a bit scared to look at primary sources, so this is a great place to start if you are curious.

</comment><comment><USER12> [https://unsupervisedmethods.com/over-150-of-the-best- machine...](https://unsupervisedmethods.com/over-150-of-the-best-machine- learning-nlp-and-python-tutorials-ive-found-ffce2939bd78)

[https://github.com/ChristosChristofidis/awesome-deep- learnin...](https://github.com/ChristosChristofidis/awesome-deep-learning)

[https://github.com/josephmisiti/awesome-machine- learning](https://github.com/josephmisiti/awesome-machine-learning)

</comment><comment><USER13> "Learn AI the Hard Way". It's actually just reading a bunch of papers and trying to implement them, and anytime you don't understand something spend as much time as needed until you get it.

</comment><comment><USER14> [http://neuralnetworksanddeeplearning.com/index.html](http://neuralnetworksanddeeplearning.com/index.html)

</comment><comment><USER15> 1\. Udacity: Machine Learning

2\. Deep Learning Summer School Montreal 2016 [https://sites.google.com/site/deeplearningsummerschool2016/h...](https://sites.google.com/site/deeplearningsummerschool2016/home)

2\. selfdrivingcars.mit.edu + youtube playlist "MIT 6.S094: Deep Learning for Self-Driving Cars" ([https://youtu.be/1L0TKZQcUtA?list=PLrAXtmErZgOeiKm4sgNOknGvN...](https://youtu.be/1L0TKZQcUtA?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf))

3\. Coursera: Machine Learning with Andrew Ng

4\. Standford Cs231n ([https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy- eBtNF...](https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy- eBtNFt6EuMxFYRiNRS07MCWN5UIA))

5\. Deep Learning School 2016 ([https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYn...](https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYnD2fTgbzk2THW))

6\. Udacity: Deep Learning ([https://www.udacity.com/course/deep-learning-- ud730](https://www.udacity.com/course/deep-learning--ud730))

I created a blog ([http://ai.bskog.com](http://ai.bskog.com)) to have as a notepad and study backlog. There I keep track of what free courses I am currently taking and which one I will take next.

p.s.

Although video courses are good. Everyday life makes it sometimes difficult to listen to videos on youtube while for instance doing chores around the house or working out, because you often need to a. see the slides/code examples, and b. put it into practice right away... therefore, podcasts are good to give you a flow of information.

Linear Digression, Data skeptic and (thanks to this thread i now discovered Machine Learning Guide)

Don't be discouraged if there is stuff you do not understand or feel like: i can never remember these terms or that algorithm. Just be immersed in the information and stuff will fall into place. And later when you hear about that thing again it will make more sense. I tend to use a breadth first approach to learning, where i get exposed to everything before digging into details thus getting an overview of what i need to learn and where to start.

</comment><comment><USER16> A study group meetup (Every Tuesday evening in Austin, TX): [https://www.meetup.com/cppmsg_ai/](https://www.meetup.com/cppmsg_ai/)

Just Q&A - no presentations. Study from whatever books ([http://amlbook.com/](http://amlbook.com/) and [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/) are popular in our group) or courses (Andrew Ng's are also popular) you like throughout the week and then show up with any questions you have. We've been meeting for a couple of months now and new folks are always welcome no matter where you are in your studies!

</comment><comment><USER17> I did the "early years" of both statistics and tiny neural networks/perceptrons in college a long time ago. It also helps that I use math at work (anything from simulated 3D physics to DSP.)

Since then, I've used Wikipedia and Mathworld when work had needed it. Regression, random forest, simulated annealing, clustering, boosting and gradient ascent are all on the statistics/ML spectrum.

But the best resource was running NVIDIA DIGITS, training some of the stock models, and really looking deeply at the visualizations available. You could do this on your own computer, or these days, rent some spot GPU instance on ECC for cheap.

I highly recommend going through the DIGITS tutorials if you want a crash course in deep learning, and make sure to visualize all the steps! Try a few different network topologies and different depths to get a feel for how it works.

</comment><comment><USER18> For deep learning, and ConvNets in particular, cs231n can't be beat.

</comment><comment><USER19> Geoff Hinton's Coursera course was what got me into it. It's not for the faint of heart. I might recommend Andrej Karpathy's cs231n as a more up to date source today.

</comment><comment><USER20> Here's a great curated list of resources:

[https://unsupervisedmethods.com/my-curated-list-of-ai-and- ma...](https://unsupervisedmethods.com/my-curated-list-of-ai-and-machine- learning-resources-from-around-the-web-9a97823b8524)

HN thread: [https://news.ycombinator.com/item?id=14764700](https://news.ycombinator.com/item?id=14764700)

</comment><comment><USER21> This is only the tip of the iceberg, but I found this introduction to naive bayes classification assumed little prior knowledge and successfully helped me build a basic classifier: [https://monkeylearn.com/blog/practical-explanation- naive-bay...](https://monkeylearn.com/blog/practical-explanation-naive-bayes- classifier/)

</comment><comment><USER22> For the math: MIT OCW Scholar and maybe Klein's _Coding the Matrix_.

For AI specifically, MOOCS on Coursera, edx, and Udacity will give you plenty of options. The ones by big names like Thrun, Norvig, and Ng are great places to start.

It really helps to already be comfortable with algorithms. Princeton's MOOCs on Algorithms by Bob Sedgewick on Coursera would be a great place to start.

</comment><comment><USER23> Think Bayes and Python Data Science Handbook are a good starting point. Below is the list of free books to learn ML/AI

[http://blog.paralleldots.com/data-scientist/list-must- read-b...](http://blog.paralleldots.com/data-scientist/list-must-read-books- data-science/)

</comment><comment><USER24> [https://www.findlectures.com/?p=1&class1=Technology&category...](https://www.findlectures.com/?p=1&class1=Technology&category_l2_Technology=-Machine%20Learning)

</comment><comment><USER25> Fast.ai is absolutely wonderful

</comment><comment><USER26> If you were to spend a year or so going through many of the resources presented here, and probably knew your stuff pretty well (or at least as well as you could after a year), would anyone actually give you a job?

<comment><USER17> Nobody is "given" a job; you "earn" a job by convincing the hiring manager that you can do what they need done.

If you're any good, and have good results to show and talk about, yes, you could totally be employed.

If you show that you're extra willing to do all the heavy data preparation and labeling work yourself as well as the infrastructure that runs the models, you'll have an even easier time. Most people just want to play with models, and believe data preparation is "beneath" them, but that's actually where the meat is and where the success of the model is made or destroyed.

</comment></comment><comment><USER27> The free Azure ML tutorials are pretty cool.

[https://gallery.cortanaintelligence.com/](https://gallery.cortanaintelligence.com/)

</comment><comment><USER28> There are too many resources from which to choose. It would be thoughtful of anyone to share AI learning pathways, like a syllabus, using those resources.

</comment><comment><USER29> www.fast.ai

</comment><comment><USER30> For Deep Learning, deeplearning.ai has launched a free course on Coursera, which you may want to check out.

</comment><comment><USER31> So who else has signed up for the deeplearning.ai course then? (I just did)

</comment><comment><USER32> arxiv.org to learn the models, SemanticScholar to find connections between papers, GitHub search to find other people's implementations

<comment><USER33> ...and Andrej Karpathy's Sanity Preserver website to search and review Arxiv papers more easily: [http://www.arxiv-sanity.com/](http://www.arxiv- sanity.com/)

<comment><USER34> Wow, that is really cool, thanks for sharing that! The YouTube video explaining it is here, well worth the short watch: [https://m.youtube.com/watch?v=S2GY3gh6qC8](https://m.youtube.com/watch?v=S2GY3gh6qC8)

</comment></comment></comment><comment><USER35> ? I've always thought that ML/AI for me was about learning the languages that could express my idea of how it could work. In order to do that myself, I started reading about algorithm types.

[http://machinelearningmastery.com/a-tour-of-machine- learning...](http://machinelearningmastery.com/a-tour-of-machine-learning- algorithms/)

There was one particular study piece that I remember reading that I believe was written in the late 70's early 80's, but I can't remember its name. It was a HTML unformatted uni course-work document that the guy who wrote it said he'd just keep changing it as required. Really wish I could remember his name.

I have a slightly different bent on what is discussed here, because my particular implementation reflects what I think is important. There are an infinite number of variations. It depends on what you think you think it might be good for.

</comment><comment><USER36> Are there good Deep Learning tutorials or blog posts with code (github) in Java, NodeJS, PHP, Lua, Swift or Go ?

<comment><USER37> You could do it in those languages. But it would be uphill all the way and you'd look back in a year and realise you'd expended 10x the effort trying to hammer a square peg into a round hole than it would have taken to just learn R, Python or MATLAB upfront.

<comment><USER36> I can code in Julia, Python, R and Matlab. It's just the ignorance of some from the AI field. R and Matlab might be good for prototyping but not for long running stable web server applications. Julia is still too new. Python syntax has spaces part of the syntax. And Python destroyed the community by splitting it in half - 2 and 3 are still around more than a decade later and many projects will never upgrade to 3, but moved on to Go.

Lua is used in Torch. Some are in C++. Unfortunately too many entry level samples are in Python. It's like the JQuery plague was to JavaScript not too long ago, when every other question on StackOverflow was answered with a slow as hell JQuery snip instead of vanilla JavaScript. Good that people moved on. I welcome the same for Deep Learning.

<comment><USER38> > Java, NodeJS, PHP, Lua, Swift or Go ?

+

>I can code in Julia, Python, R and Matlab

Finde one course you like, and convert it to Java, NodeJS, PHP, Lua, Swift or Go. You learn the course inside out, and you will build the tutorial you are looking for.

Note that you have Tensorflow for Java, Go and C. For Java, you can also look at deeplearning4j

>R and Matlab might be good for prototyping

this is what you do in ML...

</comment></comment></comment></comment><comment><USER39> persistence

</comment><comment><USER40> If you are into watching programming videos, I would recommend Siraj Raval Youtube channel - [https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)

It is quirky, funny and above all very short and crisp and gives you a quick overview of things. Most of his videos are related to AI/ML.

<comment><USER8> I'm with the others on this. Never mind the cringe - he's all show, so much so I think he's bluffing (doesn't know ML). He amps up on "character" so much you're excited for the knowledge drop - when it comes, it's so fast and technical there's nothing to gain from it. The adage "if you can't explain something simply you don't understand it" applies. I was hoping he understood ML enough to boil things down; instead he spews equations and jargon so fast (1) you don't catch it, (2) I think he's just reading from a source. He doesn't go for essence, he goes for speed - and that's not helpful.

Again, the cringe isn't the problem directly; but that it's a cover for his bluff. The result is a not-newbie-friendly resource.

<comment><USER41> I just checked out the "About" section of his Youtube channel.

> I've been called Bill Nye of Computer Science Kanye of Code Beyonce of > Neural Networks Osain Bolt of Learning Chuck Norris of Python Jesus Christ > of Machine Learning but it's the other way. They are the Siraj Raval of X

I mean, seriously?</comment></comment></comment>


Improving C++ Builds with Split DWARF - <AUTHOR> http://www.productive-cpp.com/improving-cpp-builds-with-split-dwarf/ <comment><USER2> In my moderate-sized real-world project (about 700 C++ source files, about 130k lines of code), adding -gsplit-dwarf reduced the time of a single-file recompile+relink from 7.2 seconds to 5.8 seconds, which isn't bad at all for a single extra compiler flag that you never have to think about again (assuming that it works with all your other debugging tools).

But much better than that was switching to using `gold` instead of the regular `ld` (mentioned in the "remarks" section near the bottom of the article); doing this brought my time down substantially further, from 5.8 seconds down to 1.6 seconds. Even without using -gsplit-dwarf, it brought the recompile+relink time down from the original 7.2 seconds to 2.6 seconds.

I guess my codebase might be hitting a particularly bad case for `ld`. I'm kind of startled to see such a large difference for what seemed like a throwaway extra suggestion at the end of the article!

<comment><USER3> Try lld, it's even faster than gold.

Also note that not so long ago, ccache didn't support split dwarf. If you're using ccache, check that your version supports it.

<comment><USER2> With lld, that single-file rebuild+relink drops from 1.6 seconds (with gold) to 1.2 seconds.

lld spits out a bunch of warnings that neither ld nor gold cared about, about finding local symbols in the global symbol table of some of the third-party .so libraries I link against. Not sure to what extent I should care about that; the final build product does run the same as always.

And just so that nobody else needs to go searching; looks like ccache added support for split dwarf debugging data in its version 3.2.3, about three years ago. (although I haven't actually tried it to confirm, yet)

</comment></comment></comment><comment><USER4> Relatedly, I found it useful to pass -g1 and not just -g to GCC. The resulting debug information is a fair bit smaller and GCC itself also takes less RAM and less CPU time to compile template-heavy code (going from 10s of GB to 1-2 GB on one particularly nasty object file).

<comment><USER5> Have you noticed any noticeable decrease in the quality of debug information available with -g1?

<comment><USER3> -g1 omits information about local variables and line numbers. It's fine if you want stack traces, not so good when you actually want to debug.

</comment></comment></comment><comment><USER6> 13 minutes -> 11 minutes: "Elapsed time goes down by 15%."

3:16 minutes -> 1:42 minutes: "We get a roughly 90% speedup in elapsed time."

What's the formula for the "speedup"? I think it would be more expressive if one would compare it in the same terms.

<comment><USER7> I think the math was done inversely.

1:42 -> 3:16: 90% slowdown?

<comment><USER8> We could use a logarithmic scale instead of percentages. If we defined a decibel scale for time then this would be a 2.83 dB change both ways (if we defined dB-sec as 10*log10(time/(1 sec)) ).

</comment></comment></comment><comment><USER9> It might be useful to have things like version-script mapfiles as defining interfaces separate from the actual libraries (ELF objects). Then for shared linking the build system (make, whatever) could notice that though an object changed, its interfaces haven't, and then not re-link dependents.

</comment><comment><USER10> Is this a new thing? Isn't this like the pdb files that msvc had since the late 1990's?

<comment><USER11> Not new, for one thing I did not use it. Thinking about all the time lost waiting for linker to finish makes me sad, but I'll change my work flow now!

</comment></comment><comment><USER12> Just be aware that various downstream dev tools like profilers don't support this very well yet</comment>


WordPress: Reimagined in a More User-Friendly Way - <AUTHOR> http://www.webinsation.com/wordpress-reimagined-in-a-more-user-friendly-way/

<comment><AUTHOR> WordPress is the most popular Content Management System to date. But is it really up to the task of managing your website? Over the last several articles we have looked at several main problems of WordPress including WYSIWYG Editors, Not enough control over document structure, and a somewhat confusing admin panel.

In this article we are going to look at some ways we can improve WordPress to make it way more user-friendly for content editors and business owners.

So let's jump right in and look at 6 ways that we can improve the editing experience of this awesome Content Management System known as WordPress... [http://www.webinsation.com/wordpress-reimagined-in-a-more- us...](http://www.webinsation.com/wordpress-reimagined-in-a-more-user- friendly-way/)</comment>


Show HN: Machine learning flight rules; tips and tricks for ml - <AUTHOR> https://github.com/bkkaggle/machine-learning-flight-rules <comment><AUTHOR> A guide for astronauts (now, people doing machine learning) about what to do when things go wrong.

GitHub: [https://github.com/bkkaggle/machine-learning-flight- rules](https://github.com/bkkaggle/machine-learning-flight-rules)

Product Hunt: [https://www.producthunt.com/posts/machine-learning-flight- ru...](https://www.producthunt.com/posts/machine-learning-flight-rules)

There's a lot of "hidden knowledge" online on places like Stackoverflow, Kaggle, and the Pytorch discussion forums that is really useful but not easily accessible to people who are just getting started with machine learning. This is why I made Machine learning flight rules, this Github repo compiles all of the things I have learned over the last two years about best practices, common mistakes, and little-known tricks when training neural networks. I've tried to make sure that all the information in this repository is accurate, but if you find something that you think is wrong, please let me know by opening an issue. This repository is still a work in progress, so if you find a bug, think there is something missing, or have any suggestions for new features, feel free to open an issue or a pull request. Feel free to use the library or code from it in your own projects, and if you feel that some code used in this project hasn't been properly accredited, please open an issue. I named this project after the awesome Git Flight Rules project ([https://github.com/k88hudson/git-flight- rules](https://github.com/k88hudson/git-flight-rules)). I took a lot of tips from both Andrej Kaparthy's blog post on a recipe for training neural networks ([https://karpathy.github.io/2019/04/25/recipe/](https://karpathy.github.io/2019/04/25/recipe/)) and the Amid Fish blog post on lessons learned when reporoducing a deep reinforcement learning paper ([http://amid.fish/reproducing-deep- rl](http://amid.fish/reproducing-deep-rl))</comment>


"Shooting the Messenger" is a Psychological Reality - <AUTHOR> https://digest.bps.org.uk/2019/05/08/shooting-the-messenger-is-a-psychological-reality-share-bad-news-and-people-will-like-you-less/ <comment><AUTHOR> Abstract:

Eleven experiments provide evidence that people have a tendency to "shoot the messenger," deeming innocent bearers of bad news unlikeable. In a preregistered lab experiment, participants rated messengers who delivered bad news from a random drawing as relatively unlikeable (Study 1). A second set of studies points to the specificity of the effect: Study 2A shows that it is unique to the (innocent) messenger, and not mere bystanders. Study 2B shows that it is distinct from merely receiving information with which one disagrees. We suggest that people's tendency to deem bearers of bad news as unlikeable stems in part from their desire to make sense of chance processes. Consistent with this account, receiving bad news activates the desire to sense-make (Study 3A), and in turn, activating this desire enhances the tendency to dislike bearers of bad news (Study 3B). Next, stemming from the idea that unexpected outcomes heighten the desire to sense-make, Study 4 shows that when bad news is unexpected, messenger dislike is pronounced. Finally, consistent with the notion that people fulfill the desire to sense-make by attributing agency to entities adjacent to chance events, messenger dislike is correlated with the erroneous belief that the messenger had malevolent motives (Studies 5A, 5B, and 5C). Studies 6A and 6B go further, manipulating messenger motives independently from news valence to suggest their causal role in our process account: the tendency to dislike bearers of bad news is mitigated when recipients are made aware of the benevolence of the messenger's motives. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Link: [https://psycnet.apa.org/record/2019-19962-004](https://psycnet.apa.org/record/2019-19962-004)</comment>


Show HN: Share your brand assets in seconds: Gide.io - <AUTHOR> http://gide.io <comment><AUTHOR> Hey HN community,

I want to create the About.me for brands.

It's called [http://gide.io](http://gide.io)

I was recently at an agency doing some pitch deck work. We had to create some key visuals for an existing brand. As a designer, you always need three basic things. You need the logo, brand colours and brand font. We had none of these.

We had to crack on. So I Googled the brand logo and found a file. Meanwhile one of the accounts guys dug up a PDF style guide and emailed that through to me. Now we had the colours. The PDF style guide listed the font, but we had to go out and buy it.

This whole process from start to finish took around ¾ of a day before I had all the assets on hand.

This is a problem I know a lot of designers face.

There has to be a better way to get designers these key assets quickly and easily.

I believe there is a need for an 'about.me' for brands.

I'm currently testing such an idea in the market.

It's called Gide.

Gide will be a simple one page style guide that will feature a brand's logo files, brand colours and brand font files.

Your Gide will be a simple link that can be shared in seconds, here's an example.

Currently there is two solutions, one is a very bulky and expensive PDF style guide that a lot of brands commission. The problem with this solution is that it does not provide downloadable files. Secondly it has to be dug up on the server and emailed across. Every time. This process can take a long time.

The second solution is online style guide creation tools such as Frontify. Whilst a great solution, you still have to pay a designer to create one for you which takes time and a fair amount of money.

With Gide, I plan to offer a service that will build and host a simple one page style guide for your brand for just $99 per year.

Marketing guys, Brand owners, Startup Founders, is this something you would want?

Would love some input, I'm trying to validate this week : )</comment>


The Sultan and the Sultan - <AUTHOR> http://www.historytoday.com/william-armstrong/sultan-and-sultan <comment><USER2> There is a a new turkish TV show rolling on Netflix - [http://www.imdb.com/title/tt4320258/](http://www.imdb.com/title/tt4320258/) \- [https://www.netflix.com/title/80127001](https://www.netflix.com/title/80127001)

It's called Dirilis: Ertugrul (Resurrection: Ertugrul) which is about the father of the Osman, the founder of the Ottoman empire. I'm watching it, ignoring all the overly muslim overtones, praise to God (Allah), songs, etc. It has some good moments, but AFAIK it's based on very little historic facts. But it's no coincidence that the TV show comes at the time Erdogan is the "ruler". The knight templars are showed as very incapable fighters, the Kiya tribe as universally good (with very few bad apples), but apart from that is watchable (was binging on it, while waiting for my wife to catch up on Stranger Things I, so we can watch together II).

But I'm also watching it, just to see how things are portrayed from this side, since I've been told stories completely the other way (I'm bulgarian, so our history, myths & legends are all about ottoman repression, which was even called slavery while I was growing up, but recently the schoolbooks are changing that).

Then few days ago, someone mentioned that this show is in contrast how Islam was portrayed with another Turkish show released just few years before it, (also available on Netflix- [https://www.netflix.com/title/80089596](https://www.netflix.com/title/80089596) \- that's supposed to tell another story, but I guess without Erdogan's propaganda).

I still can't bear, the fact how his bodyguards beat so many people just recently, and nothing really happened after that... Here in the US.

<comment><USER3> My ancestors fled Bulgarian persecution during the Balkan wars that the article talks about. I've always wanted to visit Haskova (Hasköy at the time) where they were from. Hopefully one day.

Thanks for the Netflix links. I'll check them out.

Regarding the bodyguards incident, I think some of them faced charges. Incredible that this could happen even on US soil!

<comment><USER2> With my ancestors it was the opposite. The story, or legend goes, that my family on my grandmother's side were bulgarians (okay not sure, but that's what they claimed) living in Turkey, and they moved to Bulgaria (in the "Strandzha" area) - because one of my grand-grand uncles killed a turkmen who was extorting him every year to give his "yamurluk" (you might know what it is). So he killed him, and they had to escape...

Here is another article which I'm not proud about which Bulgaria did in the last year of the communist regime against the turkish minorities - [http://www.dw.com/en/recalling-the-fate-of-bulgarias- turkish...](http://www.dw.com/en/recalling-the-fate-of-bulgarias-turkish- minority/a-18149416)

and then you have this - [https://en.wikipedia.org/wiki/Batak_massacre](https://en.wikipedia.org/wiki/Batak_massacre)

It's important to remember these, but not get too politicizied, in a "propaganda" style (as we used to learn it this way in school). I would like more open, here is what we did, here is what was done to us - here is what we should not do, avoid, etc.

My mom & father are claiming Turkey is one of the most friendly countries (they are living in Burgas, Bulgaria), and my mom specifically loves Kapalu Charshi (I'm not prononuncing this correctly - but The Grand Bazar).

Cheers!

</comment></comment></comment><comment><USER4> > The parallels with Turkey's mercurial president Erdoğan seem obvious.

The invariant remains the desire of the West to insure the Turks are down, and that the region is destablized. The three historic reasons (for the West) remain: Russia, Oil, and Asia trade routes. Given these invariants and historic realities of the region, any sensible wanna-be region native unifier will aim for the same "itihad" (Unity) "-i" (of) Muslims. Is this rocket science? No, it is not.

So to correct the typically myopic view of the region per European (cum West) regarding Turkey, since Western goals and designs for the region have NOT changed in the past 150+ years, so to the reaction from the region's (semi-)indepdentent regimes and/or rulers.

(You may have a clue per above, regarding the very ambitious gambit of the crown prince of the Arabian peninsula minus the "semi-independent" part.)

> cult of personality

Is that like the cult of personality of the Donald the MAGA? Or before that, the Obama the savior?

Why is it that Western press _always_ needs to include a "tyrant", "mercurial", "<insert put down here>" when mentioning the target regions' leaders? Are Western leaders men and women of worth? (Insert chuckle here.)

> the Sultan's nose

It is called 'prestige'. Every state has prestige sensitivities since that is one pillar of maintaining power.

For example, a "foreign agent" news agency, RT, has been pointing out the symbolic 'big nose of the Western governments', and oh boy, they are not amused in the West. See, it is all very "parallel" to what goes on elsewhere.

Nothing to see here, really, except Oil, strategic geography, and containing the maybe-maybenot-European power called Russia.

<comment><USER5> >The invariant remains the desire of the West to insure the Turks are down, and that the region is destablized.

That's ridiculous. The West wants to keep the Middle East stable, so that oil flows are guaranteed, to keep the Russians out, and because a chaotic Middle East breeds Islamic terrorists. And so it has taken policies for many decades to ensure this, including supporting Turkey.

However, you are quite right that the West doesn't want a new Ottoman empire that would try to take over Europe, like the old one did.</comment></comment>


Bill Gates Start-Up in Talks on Small Nuclear Reactor - <AUTHOR> http://online.wsj.com/article/SB10001424052748704841304575138530498037398.html

<comment><AUTHOR> An energy start-up backed by Microsoft Corp. co-founder Bill Gates is in discussions with Toshiba Corp. on developing a small-scale nuclear reactor that would represent a long-term bet to make nuclear power safer and cheaper.

Toshiba said it is in preliminary discussions with TerraPower, a unit of Intellectual Ventures, a patent-holding concern partially funded by Mr. Gates. Intellectual Ventures, based in Bellevue, Wash., is run by a former Microsoft Corp. executive, Nathan Myhrvold.</comment>


Show HN: Bimble – List and share your favourite places - <AUTHOR> https://www.bimblehq.com <comment><USER2> Created a list, registered to be able to save it, and you put my real name on the list. Click on edit profile; you accept a username - ok - but you don't show it instead of the real name.

Not cool.

Fix that and this might be a really neat idea to easily find new places when traveling.

<comment><AUTHOR> thanks for lending your eyes to Bimble and having a play around. I hear you with regard to the real name / username concerns. I'll feed that back to the team ASAP!

</comment></comment><comment><USER3> Curated lists of favorite places. I still think there is a demand for that. Was seriously thinking to build this about 6 years ago, but got distracted by other projects. Good luck!

One of my ideas was to also photo upload and then extract the location(s) from it/them. So I do not have to remember a place, just have a photo of it. Most ppl now have photos of everything they like ;-)

</comment><comment><USER4> Nicely done!

I just released my own side project that I think could be useful to you and Bimble if you're looking to have a simple FAQ/helpdesk for your visitors.

[https://frequently.io](https://frequently.io) if you're interested! I'd love to chat.

</comment><comment><USER5> There have been many services like this, and this functionality is a part of brands like Yelp. How is this different?</comment>


ECT - Fastest JavaScript Template Engine With CoffeeScript Syntax - <AUTHOR> http://ectjs.com/index.html <comment><AUTHOR> via: [http://www.functionn.in/2012/12/ect-fastest-javascript- templ...](http://www.functionn.in/2012/12/ect-fastest-javascript-template- engine.html)

[Check out www.functionn.in for more web resources to keep you functionn.in']

ECT is a JavaScript template engine which has a focus on performance. It claims to be the fastest JavaScript template engine among these engines: Dust, Hogan.js, Fest, Handlebars.js, EJS without `with`, doT, Swig, Eco, EJS, Jade without `with`, CoffeeKup and Jade.

It also has a plethora of features like template caching, auto-reload of modified templates, inline CoffeeScript code within the templates, multi-line expressions support, and more. You can use ECT on both the client-side or on the server-side with Node.js.</comment>


Introducing New Ethereum Powered Dapp - <AUTHOR> https://cryptolotto.cc <comment><AUTHOR> The main idea of Cryptolotto is straightforward: people from all over the world contribute equal amounts of ETH to one wallet during the set period of time. When a timer ends, this smart-contract powered wallet automatically sends all received ETH to one randomly chosen wallet-participant.</comment>


Raw Linux Threads via System Calls (2015) - <AUTHOR> http://nullprogram.com/blog/2015/05/15/ <comment><USER2> "Linux has an elegant and beautiful design when it comes to threads" \- unifying threads and processes does simplify some things, but it makes it _extremely_ hard to change a _process_ -level property such as the effective user/group id of a process: [https://ewontfix.com/17/](https://ewontfix.com/17/).

<comment><USER3> I'm surprised no solution in the kernel has ever been done.

But that said, I suspect the big reason is that setting uid and gid with threads is going to be messy no way how you slice it. Making setuid and setgid work per-thread, and then having programs coordinate a privileged loss over all the threads is easier to do since the programmer knows where in their code is an appropriate place to do the privileged loss, rather then having one thread trigger it to happen regardless of what the other threads are currently doing. But of course, there's always the chance a thread doesn't drop it's privileges, and since libc has to emulate the POSIX API anyway it just makes things a lot messier since it has to force all the threads to call the setuid or setgid function without having them call it in code.

But of course, I think it's notable that if you can (And in most cases, you probably can) you should be dropping privileges well before you start threads and the body of your program, and if you do that then this doesn't even become an issue in the first place. I would suspect that this is part of why this hasn't been added to the kernel - the user-space solutions are sufficient simply because most programs don't actually ever call these functions after they've already setup threads.

<comment><USER4> Some language runtimes create threads before the application main() function is executed. Even if Linux has to retain bug-for-bug ABI compatibility there is valid reason to only provide fucked up the set _id() syscalls.

The problem is even worse than it looks at first glance because set_id() can fail per thread and there is no sane way to recover from this in userspace. Atomic syscalls are the only sane API for this and Linux is lacking those.

</comment></comment></comment><comment><USER5> Writing a toy libc is a very fascinating exercise, if you happen to like this kind of stuff.

</comment><comment><USER6> Only 15 instructions to spawn a thread isn't something to write home about considering cost of context switching is much higher.

<comment><USER7> i interpreted that statement as the author encouraging the reader to take a peek behind the curtain of something they might consider incomprehensible magic (the author is saying "look, it's not that scary!"), rather than as a claim that thread spawning is computationally cheap.

<comment><USER6> Ok, fair point...I see that.

</comment></comment></comment><comment><USER8> As curiosity it is great. BTW, if POSIX thread implementation is not good enough in the default C library, it should be revised instead of everyone rewriting stuff for each specific Unix-like flavour. Rationale: Platform- specific goodies are great for specific cases, but terrible for standard and multi-platform development.

<comment><USER9> I'm a little confused by your comment. This article is a pedagogical exercise: it demonstrates how thread creation works on Linux by implementing it from scratch using assembly. The author's intention is not to create a new library, but rather to show how such a library might be implemented. Nowhere does the author advocate doing this in production code over using the standard implementation of Pthreads on Linux ([https://en.wikipedia.org/wiki/Native_POSIX_Thread_Library](https://en.wikipedia.org/wiki/Native_POSIX_Thread_Library)).

<comment><USER8> >>"The author's intention is not to create a new library, but ..."

I don't know author's intention. Do you? :-)

My point was a warning about how bad idea is using platform-specific non- standard APIs. As exercise, of course it is fine, that is why I up-voted this history.

<comment><USER9> > _I don 't know author's intention. Do you? :-)_

I do not _know_ the author's intention; I am not certain. But I'm reasonably confident. And that's a reasonable warning, just a bit of a non-sequitur. Hence my confusion.

</comment></comment></comment></comment><comment><USER10> previous submission: [https://news.ycombinator.com/item?id=9675248](https://news.ycombinator.com/item?id=9675248)

<comment><USER11> The article notes that it has been augmented with a follow up piece since that original submission:

[http://nullprogram.com/blog/2016/09/23/](http://nullprogram.com/blog/2016/09/23/)

<comment><USER10> which also was submitted ([https://news.ycombinator.com/item?id=12579668](https://news.ycombinator.com/item?id=12579668)) but unfortunately garnered a muted response from the masses...</comment></comment></comment>


U.K. Lawmakers Document Summarizing Facebook's Seized Internal Facebook Emails [pdf] - <AUTHOR> https://www.parliament.uk/documents/commons-committees/culture-media-and-sport/Note-by-Chair-and-selected-documents-ordered-from-Six4Three.pdf <comment><AUTHOR> Summary

1\. White Lists

Facebook have clearly entered into whitelisting agreements with certain companies, which meant that after the platform changes in 2014/15 they maintained full access to friends data. It is not clear that there was any user consent for this, nor how Facebook decided which companies should be whitelisted or not.

2\. Value of friends data

It is clear that increasing revenues from major app developers was one of the key drivers behind the Platform 3.0 changes at Facebook. The idea of linking access to friends data to the financial value of the developers relationship with Facebook is a recurring feature of the documents.

3\. Reciprocity

Data reciprocity between Facebook and app developers was a central feature in the discussions about the launch of Platform 3.0.

4\. Android

Facebook knew that the changes to its policies on the Android mobile phone system, which enabled the Facebook app to collect a record of calls and texts sent by the user would be controversial. To mitigate any bad PR, Facebook planned to make it as hard of possible for users to know that this was one of the underlying features of the upgrade of their app.

5\. Onavo

Facebook used Onavo to conduct global surveys of the usage of mobile apps by customers, and apparently without their knowledge. They used this data to assess not just how many people had downloaded apps, but how often they used them. This knowledge helped them to decide which companies to acquire, and which to treat as a threat.

6\. Targeting competitor Apps

The files show evidence of Facebook taking aggressive positions against apps, with the consequence that denying them access to data led to the failure of that business.</comment>


Alan Smithee - <AUTHOR> https://en.wikipedia.org/wiki/Alan_Smithee <comment><USER2> Amusingly, it mostly died off because of a film called An Alan Smithee Film: Burn Hollywood Burn. In that film, a man called Alan Smithee couldn't get his named removed from the film because the only available pseudonym matched his own name, with said film being so terrible that it's own director wanted his named removed from it.

So a film about the use of Alan Smithee as a pseudonym was credited to Alan Smithee, which in turn made it incredibly hard to take the name seriously any more.

<comment><USER3> Normally I enjoy "meta", but I've finally found my limit.

It took me two entire minutes to understand your comment, even though it was written about as clearly as it could have been.

<comment><USER4> Well... the comment in question could have been written a lot more clearly. It's not immediately clear to which film the clause beginning "with said film" refers to. That is needlessly confusing.

<comment><USER5> The Wikipedia article itself explains it a lot more clearly - I didn't have any trouble understanding what was going on when reading that compared to great-grandparent comment.

</comment></comment></comment></comment><comment><USER6> This is great:

 _When the film was finished, Siegel did not want to take the credit for it and Totten refused to take credit in his place. The DGA panel hearing the dispute agreed that the film did not represent either director 's creative vision.

The original proposal was to credit the fictional "Al Smith", but that was deemed too common a name, and in fact was already in use within the film industry. The last name was first changed to "Smithe", then "Smithee"... Critics praised the film and its "new" director, with The New York Times commenting that the film was "sharply directed by Allen Smithee who has an adroit facility for scanning faces and extracting sharp background detail," and Roger Ebert commenting, "Director Allen Smithee, a name I'm not familiar with, allows his story to unfold naturally."_

Shame the Eric Idle film couldn't make hay with this.

</comment><comment><USER7> I'd like to use this for my commits.

<comment><USER8> A few days ago: [https://news.ycombinator.com/item?id=17487441](https://news.ycombinator.com/item?id=17487441)

</comment></comment><comment><USER9> Including ome versions of _Dune_ and _Meet Joe Black_?</comment>


An Architectural Risk Analysis of Machine Learning Systems [pdf] - <AUTHOR> https://berryvilleiml.com/docs/ara.pdf <comment><USER2> BERRYVILLE, Va., Feb. 13, 2020 – The Berryville Institute of Machine Learning (BIML), a research think tank dedicated to safe, secure and ethical development of AI technologies, today released the first-ever risk framework to guide development of secure ML. The "Architectural Risk Analysis of Machine Learning Systems: Toward More Secure Machine Learning" is designed for use by developers, engineers, designers and others who are creating applications and services that use ML technologies.

Early work on ML security focuses on specific failures, including systems that learn to be sexist, racist and xenophobic like Microsoft's Tay, or systems that can be manipulated into seeing a STOP sign as a speed limit sign using a few pieces of tape. The BIML ML Security Risk Framework details the top 10 security risks in ML systems today. A total of 78 risks have been identified by BIML using a generic ML system as an organizing concept. The BIML ML Security Risk Framework can be practically applied in the early design and development phases of any ML project.

"The tech industry is racing ahead with AI and ML with little to no consideration for the security risks that automated machine learning poses," says Dr. Gary McGraw, co-founder of BIML. "We saw with the development of the internet the consequences of security as an afterthought. But with AI we have the chance now to do it right."

For more information about An Architectural Risk Analysis of Machine Learning Systems: Toward More Secure Machine Learning, visit [https://berryvilleiml.com/results/](https://berryvilleiml.com/results/).

A link to the PR on the wire: [https://onlineprnews.com//news/1143530-1581535720-biml- relea...](https://onlineprnews.com//news/1143530-1581535720-biml-releases- first-risk-framework-for-securing-machine-learning-systems.html)</comment>


Tycoon Warning - <AUTHOR> http://www.foreignpolicy.com/articles/2013/10/24/tycoon_warning

<comment><AUTHOR> Article posted below to avoid having to delete dom nodes: ______________________________________

Tycoon Warning: How the obscenely rich are becoming the new dictators of the 21st century.

Earlier this month, the investment bank Credit Suisse published its annual survey of global wealth. The bank's report is filled with illuminating findings, but one in particular caught my eye. It has to do with the distribution of assets in Russia, where, as the report notes, a mere 110 people own a mind-boggling 35 percent of the country's entire wealth. At the same time, 93.7 percent of Russians are worth $10,000 or less.

As the report notes, this makes Russia the country with the greatest wealth disparities in the world. Americans, who are now increasingly concerned about deepening inequality in their own country, might seek some consolation from this dismal conclusion. Even under present circumstances, wealth in the United States is still spread a lot more evenly than that (as this comparison shows). Things could be worse, right?

Well, maybe. But I see little cause for jubilation. Russia is merely the most extreme case of a worldwide trend that potentially represents one of the greatest threats that democracy faces today: the spread of oligarchy.

The problem isn't just that some people in today's world are fabulously rich. It's that disproportionate wealth increasingly goes along with disproportionate power. Russia, again, offers a textbook example of the dangers. Back in the 1990s, a handful of politically well-connected business tycoons managed to profit from their close relations with Boris Yeltsin's Kremlin by taking advantage of the privatization of the country's industrial jewels -- above all its vast oil wealth. Those magnates weren't shy about exploiting their economic power to political ends. They bankrolled Yeltsin's re-election as president in 1996, controlled ministerial appointments, and dictated government policy. No wonder these businessmen-cum-politicians were soon dubbed the "oligarchs." ("Oligarchy" is Greek for "government of the few.")

One of them, the recently deceased, arch-Machiavellian Boris Berezovsky, engineered the rise of an ex-KGB officer to the prime ministership. Vladimir Putin ultimately proved less than grateful, though. Once Putin became president in his own stead, he was quick to cut his erstwhile patron down to size, forcing Berezvosky into exile. Putin curtailed the power of other Yeltsin-era tycoons, too (most notably Mikhail Khodorkovsky, who today marks his 10th year of imprisonment in a labor camp), but in their place he raised up a new group of businessmen -- many with ties to the old Soviet security services -- who owed their fortunes to him. One of them, another KGB alumnus named Igor Sechin, who heads the country's largest oil company, is regarded by some as the second-most powerful man after Putin himself. (Sechin is shown at left in the photo above.)

But this isn't only Russia's problem. As has now become apparent, globalization and the powerful economic forces it has unleashed have awarded unparalleled wealth and power to a tiny new elite. Call them what you will: the superclass, the plutocrats, the "global meritocracy." What they exemplify is the nexus of wealth and political power. And that's a problem that is increasingly vexing voters in places from London to Kuala Lumpur.

It's a challenge that takes different forms. In China, membership in the ruling Communist Party is often the easy road to wealth. Many of today's political scandals center on the antics of well-connected "princelings," the descendants of senior party officials who embody the country's peculiarly potent blend of Marxist-Leninist crony capitalism. Thanks to some remarkable digging by enterprising journalists in recent years, we've learned some astonishing things about the scale of privilege enjoyed by the extended families of notables such as President Xi Jinping and ex-Prime Minister Wen Jiabao. But this hardly comes as a surprise. When you consider that the People's Republic is governed by the seven members of the Standing Committee of the Politburo of the Communist Party, you're talking about a tiny number of families who exercise unchecked control over one of the world's largest economies. In such a setting, it's only natural that political and economic power are mutually reinforcing.

The situation in China is, of course, the outcome of an economic liberalization program steered by an autocratic elite. In the countries of the developed West the situation is rather different. The number of players is larger; wealth and political influence are more widely distributed. But that is presumably small comfort to, say, the Americans who have emerged as losers from the country's latest Gilded Age. Economic equality in the United States grew steadily during the first three decades of the period following World War II, but ground to a halt amid the stagflation and increasing international competition of the 1970s. As economist Joseph Stiglitz notes in a recent editorial:

"Last year, the top 1 percent of Americans took home 22 percent of the nation's income; the top 0.1 percent, 11 percent. Ninety-five percent of all income gains since 2009 have gone to the top 1 percent. Recently released census figures show that median income in America hasn't budged in almost a quarter-century."

At the same time, the extraordinary permissiveness of U.S. laws on lobbying and campaign financing has allowed wealthy elites to gain immense sway over the political process. By now, anyone who follows American politics has heard the stories about the vast sums of cash spent by conservative business magnates like the Koch Brothers; less often discussed, perhaps, are the rich Democrats, such as George Soros or Tom Steyer, who are happy to leverage their wealth to shape policy. But even less visible are the big corporations and industrial associations who can purchase lawmakers and fix legislation to boost their own bottom lines.

One recent academic study calculates that 40 percent of political campaign contributions in 2012 came from one hundredth of one percent of U.S. households. That figure probably reflects the new economic elite's growing awareness of its own political power -- not to mention the apathy among other segments of the population who feel increasingly divorced from meaningful participation. The erosion of alternate power centers, such as labor unions, undoubtedly contributes to a sense of rising cynicism and disengagement. It all serves to undermine the promise of America's democratic system. (Given this context, it's no wonder that the U.S. Supreme Court is once again weighing the question of limits on individual contributions to political campaigns.)

As a result, the United States is now experiencing a remarkable discussion of the causes of the new inequality and its political consequences. Authors from George Packer to Tyler Cowen are stirring impassioned debate about the perceived breakdown of the American social compact. The new book from economist Angus Deaton, The Great Escape (see DemLab's excerpt here), includes a memorable quote from the lawyer Louis Brandeis: "If democracy becomes plutocracy, those who are not rich are effectively disenfranchised."

Can we stop the trend? Some -- like Cowen, who believes that current inequality is largely a function of technological change -- are skeptical. Others insist that we can counter the drift towards government by the few with smart policies designed to level the playing field -- above all in education, infrastructure, and health care. Measures to limit the role of money in politics probably wouldn't be a bad idea either (presuming we can find some that actually work). For those who still believe in the primacy of the market, the package might also include measures designed to promote genuine competition in the place of today's corporate welfare for politically plugged- in superfirms.

This certainly doesn't mean giving up on capitalism. As development economists point out, globalization has brought relative prosperity to many around the world who couldn't even dream of it before. (Think, for a start, of all those Chinese peasants who can now afford three meals a day -- unthinkable in times past.) Overall health and development indicators have improved dramatically over the past fifty years. But none of this obviates the need to ensure that the extraordinary benefits accruing to the superstars at the top don't end up disenfranchising the rest of us. Otherwise the future looks dark.

But will the wealthy really give up their acquired power that easily? Though Occupy Wall Street targeted the 1 percent with considerable verve and passion, its actual political impact was arguably close to zero. It's high time for new political movements that can aggregate the power of individuals and marshal coherent responses to the intensifying concentration of influence by a few at the top. And there's even the chance that some of the more enlightened plutocrats will offer ideas for empowering the majority. Where's Bill Gates when you need him?

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A Deep Learning Model to Detect Spinal Cord Compression in MRI Scans - <AUTHOR> https://firstname.lastname@example.com/a-deep-learning-model-to-detect-spinal-cord-compression-in-cervical-spine-mri-scans-f2e366663235 <comment><AUTHOR> This post describes the creation of a deep learning model to interpret MRI scans of the cervical spine. The output of the deep learning model is then used to identify patients with degenerative cervical myelopathy. This is a small sampling of the work I've done in this area so far. I would appreciate any comments and feedback you guys have.</comment>