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In 2006, after visiting the Computer History Museum’s exhibit on Chess, I opined:

We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out.

So. About that. Turns out I was kinda … totally completely wrong. The number of possible moves, or “problem space”, of Chess is indeed astonishingly large, estimated to be 10⁵⁰:

100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. How many orders of magnitude? In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second. And that was enough to defeat Kasparov, the highest ever ranked human player – until 2014 at least. Even though one of its best moves was the result of a bug.

200,000,000

In 2006, about ten years later, according to the Fritz Chess benchmark, my PC could evaluate only 4.5 million chess positions per second.

4,500,000

Today, about twenty years later, that very same benchmark says my PC can evaluate a mere 17.2 million chess positions per second.

17,200,000

Ten years, four times faster. Not bad! Part of that is I went from dual to quad core, and these chess calculations scale almost linearly with the number of cores. An eight core CPU, no longer particularly exotic, could probably achieve ~28 million on this benchmark today.

28,000,000

I am not sure the scaling is exactly linear, but it’s fair to say that even now, twenty years later, a modern 8 core CPU is still about an order of magnitude slower at the brute force task of evaluating chess positions than what Deep Blue’s specialized chess hardware achieved in 1997.

But here’s the thing: none of that speedy brute forcing matters today. Greatly improved chess programs running on mere handheld devices can perform beyond grandmaster level.

In 2009 a chess engine running on slower hardware, a 528 MHz HTC Touch HD mobile phone running Pocket Fritz 4 reached the grandmaster level – it won a category 6 tournament with a performance rating of 2898. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.

As far as chess goes, despite what I so optimistically thought in 2006, it’s been game over for humans for quite a few years now. The best computer chess programs, vastly more efficient than Deep Blue, combined with modern CPUs which are now finally within an order of magnitude of what Deep Blue’s specialized chess hardware could deliver, play at levels way beyond what humans can achieve.

Chess: ruined forever. Thanks, computers. You jerks.

Despite this resounding defeat, there was still hope for humans in the game of Go. The number of possible moves, or “problem space”, of Go is estimated to be 10¹⁷⁰:

1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Remember that Chess had a mere fifty zeroes there? Go has more possible moves than there are atoms in the universe.

Wrap your face around that one.

Deep Blue was a statement about the inevitability of eventually being able to brute force your way around a difficult problem with the constant wind of Moore’s Law at your back. If Chess is the quintessential European game, Go is the quintessential Asian game. Go requires a completely different strategy. Go means wrestling with a problem that is essentially impossible for computers to solve in any traditional way.

A simple material evaluation for chess works well – each type of piece is given a value, and each player receives a score depending on his/her remaining pieces. The player with the higher score is deemed to be ‘winning’ at that stage of the game.

However, Chess programmers innocently asking Go players for an evaluation function would be met with disbelief! No such simple evaluation exists. Since there is only a single type of piece, only the number each player has on the board could be used for a simple material heuristic, and there is almost no discernible correlation between the number of stones on the board and what the end result of the game will be.

Analysis of a problem this hard, with brute force completely off the table, is colloquially called “AI”, though that term is a bit of a stretch to me. I prefer to think of it as building systems that can learn from experience, aka machine learning. Here’s a talk which covers DeepMind learning to play classic Atari 2600 videogames. (Jump to the 10 minute mark to see what I mean.)

As impressive as this is – and it truly is – bear in mind that games as simple as Pac-Man still remain far beyond the grasp of Deep Mind. But what happens when you point a system like that at the game of Go?

DeepMind built a system, AlphaGo, designed to see how far they could get with those approaches in the game of Go. AlphaGo recently played one of the best Go players in the world, Lee Sedol, and defeated him in a stunning 4-1 display. Being the optimist that I am, I guessed that DeepMind would win one or two games, but a near total rout like this? Incredible. In the space of just 20 years, computers went from barely beating the best humans at Chess, with a problem space of 10⁵⁰, to definitively beating the best humans at Go, with a problem space of 10¹⁷⁰. How did this happen?

Well, a few things happened, but one unsung hero in this transformation is the humble video card, or GPU.

Consider this breakdown of the cost of floating point operations over time, measured in dollars per gigaflop:

1961	$8,300,000,000
1984	$42,780,000
1997	$42,000
2000	$1,300
2003	$100
2007	$52
2011	$1.80
2012	$0.73
2013	$0.22
2015	$0.08

What’s not clear in this table is that after 2007, all the big advances in FLOPS came from gaming video cards designed for high speed real time 3D rendering, and as an incredibly beneficial side effect, they also turn out to be crazily fast at machine learning tasks.

The Google Brain project had just achieved amazing results — it learned to recognize cats and people by watching movies on YouTube. But it required 2,000 CPUs in servers powered and cooled in one of Google’s giant data centers. Few have computers of this scale. Enter NVIDIA and the GPU. Bryan Catanzaro in NVIDIA Research teamed with Andrew Ng’s team at Stanford to use GPUs for deep learning. As it turned out, 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs.

Let’s consider a related case of highly parallel computation. How much faster is a GPU at password hashing?

Radeon 7970	8213.6 M c/s
6-core AMD CPU	52.9 M c/s

Only 155 times faster right out of the gate. No big deal. On top of that, CPU performance has largely stalled in the last decade. While more and more cores are placed on each die, which is great when the problems are parallelizable – as they definitely are in this case – the actual performance improvement of any individual core over the last 5 to 10 years is rather modest.

But GPUs are still doubling in performance every few years. Consider password hash cracking expressed in the rate of hashes per second:

GTX 295	2009	25k
GTX 690	2012	54k
GTX 780 Ti	2013	100k
GTX 980 Ti	2015	240k

The latter video card is the one in my machine right now. It’s likely the next major revision from Nvidia, due later this year, will double these rates again.

(While I’m at it, I’d like to emphasize how much it sucks to be an 8 character password in today’s world. If your password is only 8 characters, that’s perilously close to no password at all. That’s also why why your password is (probably) too damn short. In fact, we just raised the minimum allowed password length on Discourse to 10 characters, because annoying password complexity rules are much less effective in reality than simply requiring longer passwords.)

Distributed AlphaGo used 1202 CPUs and 176 GPUs. While that doesn’t sound like much, consider that as we’ve seen, each GPU can be up to 150 times faster at processing these kinds of highly parallel datasets — so those 176 GPUs were the equivalent of adding ~26,400 CPUs to the task. Or more!

Even if you don’t care about video games, they happen to have a profound accidental impact on machine learning improvements. Every time you see a new video card release, don’t think “slightly nicer looking games” think “wow, hash cracking and AI just got 2× faster … again!”

I’m certainly not making the same mistake I did when looking at Chess in 2006. (And in my defense, I totally did not see the era of GPUs as essential machine learning aids coming, even though I am a gamer.) If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it’ll be no contest after a few more years of GPUs doubling and redoubling their speeds again.

AlphaGo, broadly speaking, is the culmination of two very important trends in computing:

1. Huge increases in parallel processing power driven by consumer GPUs and videogames, which started in 2007. So if you’re a gamer, congratulations! You’re part of the problem-slash-solution.

2. We’re beginning to build sophisticated (and combined) algorithmic approaches for entirely new problem spaces that are far too vast to even begin being solved by brute force methods alone. And these approaches clearly work, insofar as they mastered one of the hardest games in the world, one that many thought humans would never be defeated in.

Great. Another game ruined forever by computers. Jerks.

Based on our experience with Chess, and now Go, we know that computers will continue to beat us at virtually every game we play, in the same way that dolphins will always swim faster than we do. But what if that very same human mind was capable of not only building the dolphin, but continually refining it until they arrived at the world’s fastest minnow? Where Deep Blue was the more or less inevitable end result of brute force computation, AlphaGo is the beginning of a whole new era of sophisticated problem solving against far more enormous problems. AlphaGo’s victory is not a defeat of the human mind, but its greatest triumph.

(If you’d like to learn more about the powerful intersection of sophisticated machine learning algorithms and your GPU, read this excellent summary of AlphaGo and then download the DeepMind Atari learner and try it yourself.)

[advertisement] Find a better job the Stack Overflow way – what you need when you need it, no spam, and no scams.

In 2006, after visiting the Computer History Museum’s exhibit on Chess, I opined:

We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out.

So. About that. Turns out I was kinda … totally completely wrong. The number of possible moves, or “problem space”, of Chess is indeed astonishingly large, estimated to be 10⁵⁰:

100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. How many orders of magnitude? In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second. And that was enough to defeat Kasparov, the highest ever ranked human player – until 2014 at least. Even though one of its best moves was the result of a bug.

200,000,000

In 2006, about ten years later, according to the Fritz Chess benchmark, my PC could evaluate only 4.5 million chess positions per second.

4,500,000

Today, about twenty years later, that very same benchmark says my PC can evaluate a mere 17.2 million chess positions per second.

17,200,000

Ten years, four times faster. Not bad! Part of that is I went from dual to quad core, and these chess calculations scale almost linearly with the number of cores. An eight core CPU, no longer particularly exotic, could probably achieve ~28 million on this benchmark today.

28,000,000

I am not sure the scaling is exactly linear, but it’s fair to say that even now, twenty years later, a modern 8 core CPU is still about an order of magnitude slower at the brute force task of evaluating chess positions than what Deep Blue’s specialized chess hardware achieved in 1997.

But here’s the thing: none of that speedy brute forcing matters today. Greatly improved chess programs running on mere handheld devices can perform beyond grandmaster level.

In 2009 a chess engine running on slower hardware, a 528 MHz HTC Touch HD mobile phone running Pocket Fritz 4 reached the grandmaster level – it won a category 6 tournament with a performance rating of 2898. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.

As far as chess goes, despite what I so optimistically thought in 2006, it’s been game over for humans for quite a few years now. The best computer chess programs, vastly more efficient than Deep Blue, combined with modern CPUs which are now finally within an order of magnitude of what Deep Blue’s specialized chess hardware could deliver, play at levels far beyond what humans can achieve.

Chess: ruined forever. Thanks, computers. You jerks.

Despite this resounding defeat, there was still hope for humans in the game of Go. The number of possible moves, or “problem space”, of Go is estimated to be 10¹⁷⁰:

1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000

Remember that Chess had a mere fifty zeroes there? Go has more possible moves than there are atoms in the universe.

Wrap your face around that one.

Deep Blue was a statement about the inevitability of eventually being able to brute force your way around a difficult problem with the constant wind of Moore’s Law at your back. If Chess is the quintessential European game, Go is the quintessential Asian game. Go requires a completely different strategy. Go means wrestling with a problem that is essentially impossible for computers to solve in any traditional way.

A simple material evaluation for chess that works well, where each type of piece is given a value, and each player recieves a score depending on his/her remaining pieces. The player with the higher score is deemed to be ‘winning’ at that stage of the game.

However, Chess programmers innocently asking Go players for an evaluation function would be met with disbelief! No such simple evaluation exists. Since there is only a single type of piece, only the number each player has on the board could be used for a simple material heuristic, and there is almost no discernible correlation between the number of stones on the board and what the end result of the game will be.

Analysis of a problem this hard, with brute force completely off the table, is colloquially called “AI”, though that term is a bit of a stretch to me. I prefer to think of it as building systems that can learn from experience, aka machine learning. Here’s a talk which covers DeepMind learning to play classic Atari 2600 videogames. (Jump to the 10 minutes mark to see what I mean.)

As impressive as this is – and it truly is – bear in mind that games as simple as Pac-Man still remain far beyond the reach of Deep Mind. But what happens when you point a system like that at the game of Go?

DeepMind built a system, AlphaGo, designed to see how far they could get with those approaches in the game of Go. AlphaGo recently played one of the best Go players in the world, Lee Sedol, and defeated him in a stunning 4-1 display. Being the optimist that I am, I guessed that DeepMind would win one or two games, but a near total rout like this? Incredible. In the space of just 20 years, computers went from barely beating the best humans at Chess, with a problem space of 10⁵⁰, to definitively beating the best humans at Go, with a problem space of 10¹⁷⁰. How did this happen?

Well, a few things happened, but one unsung hero in this transformation is the humble video card, or GPU.

Consider this breakdown of the cost of floating point operations over time, measured in dollars per gigaflop:

1961	$8,300,000,000
1984	$42,780,000
1997	$42,000
2000	$1,300
2003	$100
2007	$52
2011	$1.80
2012	$0.73
2013	$0.22
2015	$0.08

What’s not clear in this table is that after 2007, all the big advances in FLOPS came from gaming video cards designed for high speed real time 3D rendering, and as an incredibly beneficial side effect, they also turn out to be crazily fast at machine learning tasks.

The Google Brain project had just achieved amazing results — it learned to recognize cats and people by watching movies on YouTube. But it required 2,000 CPUs in servers powered and cooled in one of Google’s giant data centers. Few have computers of this scale. Enter NVIDIA and the GPU. Bryan Catanzaro in NVIDIA Research teamed with Andrew Ng’s team at Stanford to use GPUs for deep learning. As it turned out, 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs.

Let’s consider a related case of highly parallel computation. How much faster is a GPU at password hashing?

Radeon 7970	8213.6 M c/s
6-core AMD CPU	52.9 M c/s

Only 155 times faster right out of the gate. No big deal. On top of that, CPU performance has largely stalled in the last decade. While more and more cores are placed on each die, which is great when the problems are parallelizable – as they definitely are in this case – the actual performance improvement of any individual core over the last 5 to 10 years is rather modest.

But GPUs are still doubling in performance every few years. Consider password hash cracking expressed in the rate of hashes per second:

GTX 295	2009	25k
GTX 690	2012	54k
GTX 780 Ti	2013	100k
GTX 980 Ti	2015	240k

The latter video card is the one in my machine right now. It’s likely the next major revision from Nvidia, due later this year, will double these rates again.

(While I’m at it, I’d like to emphasize how much it sucks to be an 8 character password in today’s world. If your password is only 8 characters, that’s perilously close to no password at all. That’s also why why your password is (probably) too damn short. In fact, we just raised the minimum allowed password length on Discourse to 10 characters, because annoying password complexity rules are much less effective in reality than simply requiring longer passwords.)

Distributed AlphaGo used 1202 CPUs and 176 GPUs. While that doesn’t sound like much, consider that as we’ve seen, each GPU can be up to 150 times faster at processing these kinds of highly parallel datasets — so those 176 GPUs were the equivalent of adding ~26,400 CPUs to the task.

Even if you don’t care about video games, they happen to have a profound accidental impact on machine learning improvements. Every time you see a new video card release, don’t think “slightly nicer looking games” think “wow, hash cracking and AI just got 2× faster … again!”

I’m certainly not making the same mistake I did when looking at Chess in 2006. (And in my defense, I totally did not see the era of GPUs as essential machine learning aids coming, even though I am a gamer.) If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it’ll be no contest after a few more years of GPUs doubling and redoubling their speeds again.

AlphaGo, broadly speaking, is the culmination of two very important trends in computing:

1. Huge increases in parallel processing power driven by consumer GPUs and videogames, which started in 2007. So if you’re a gamer, congratulations! You’re part of the problem-slash-solution.

2. We’re beginning to build sophisticated (and combined) algorithmic approaches for entirely new problem spaces that are far too vast to even begin being solved by brute force methods alone. And these approaches clearly work, insofar as they mastered one of the hardest games in the world, one that many thought humans would never be defeated in.

Great. Another game ruined by computers. Jerks.

Based on our experience with Chess, and now Go, we know that computers will continue to beat us at virtually every game we play, in the same way that dolphins will always swim faster than we do. But what if that very same human mind was capable of not only building the dolphin, but continually refining it until they arrived at the world’s fastest minnow? Where Deep Blue was the more or less inevitable end result of brute force computation, AlphaGo is the beginning of a whole new era of sophisticated problem solving against far more enormous problems. AlphaGo’s victory is not a defeat of the human mind, but its greatest triumph.

(If you’d like to learn more about the powerful intersection of sophisticated machine learning algorithms and your GPU, read this excellent summary of AlphaGo and then download the DeepMind Atari learner and try it yourself.)

[advertisement] Find a better job the Stack Overflow way – what you need when you need it, no spam, and no scams.

I hope you read that title out loud in your best Seinfeld impression.

A recent question in our forums made me aware that there are more properties that can be added to @font-face than the usual font-family and src suspects. What are the point of those? Why would you want to declare other font declarations there?

I’m used to writing @font-face essentially like this:

@font-face {
  font-family: "My Custom Font";
  src: url("path/to-font-file.woff2");
}

But the spec does indeed include more descriptors:

@font-face {
  font-family: <family-name>;
  src: <url>;
  unicode-range: <urange>;
  font-variant: normal | [ <east-asian-variant-values> || <east-asian-width-values> || ruby ];
  font-feature-settings: normal | <feature-tag-value>;
  font-stretch: normal | ultra-condensed | extra-condensed | condensed | semi-condensed | semi-expanded | expanded | extra-expanded | ultra-expanded;
  font-weight: normal | bold | bolder | lighter | 100 | 200 | 300 | 400 | 500 | 600 | 700 | 800 | 900;
  font-style: normal | italic | oblique;
}

You’ll see this plenty when looking at @font-face code generated by font services.

They are the gatekeepers!

That’s an analogy you can use to think about @font-face declaration blocks. The font will be downloaded and used, if it passes the gatekeepers.

The font-family is the obvious one. You’re declaring a new font-family to use, so any element that wants to use this font needs to have a matching one.

@font-face {

  /* Gatekeeper: font-family on another element must match this to be used. */
  font-family: 'Open Sans';

  src: url(my-font.woff2) format('woff2');

}

body {
  /* Match! */
  font-family: 'Open Sans';
}

h1, h2 {
  /* No match */
  font-family: 'Different Font';
}

They are all gatekeepers

@font-face {

  /* Only download and use if element matches this font-family */
  font-family: 'My Font';

  /* Only download and use if element matches this font-style */
  font-style: italic;

  /* Only download and use if element matches this font-weight */
  font-weight: 700;

  src: url(my-bold-and-italic-font.woff2) format('woff2');

  /* Only download and use if these characters are used */
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}

Maybe a diagram explanation will be helpful

More Reading

• Using @font-face
• CSS Fonts Module Level 3
• MDN Reference

---

What’s the deal with declaring font properties on @font-face? is a post from CSS-Tricks

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This article includes a compilation of 20 business consulting WordPress themes available for free download.

Who may be interested in this type of product? We are sure that business owners and financial advisors, who want to promote their projects online and draw more customers and more money, as well as professional and amateur designers who want to save time and effort, will appreciate the showcased freebies.

Moreover, all the free WordPress themes listed below were developed by reputable companies and incorporate all the latest features (like responsiveness, parallax effect, clean layouts and so on) that any decent website needs to meet today’s expectations.

Free themes for business consultants are powered by WordPress, the most popular CMS in the world, which means that it will be easy to customize and maintain your website. There are plenty of free plugins and extensions for WP sites, so you will be able to improve your web page functionality as soon as you need to. Your website will grow together with your business and your IT skills. From our viewpoint, a free WP business theme is a great solution for any savvy startup.

And now, let us finally move on to the list of 20 free WordPress themes for business consultants.

1. Globaly

A serious business consulting WordPress theme in grey hues. Blue color accents guide your visitor’s eyes through the web page. Theme’s architecture is perfect, which will give your site a professional look.
Demo
Download

***

2. New Group

This stylish TempllateMonster WordPress theme is designed in blue and white, and has a lowered position menu. It sticks to the top of the page as you start to scroll. HTML plus JS animation adds life to the site layout and prompts the visitors to take their next step. This is one of the most reputable free WordPress themes on this list.
Demo
Download
***

3. Experts

This free WordPress theme is a perfect design for a business company of any type, and is made in warm earth tones, which makes it feel welcoming. A stylish slider looks trendy and pulls visitors’ attention to the web page.
Demo
Download
***

4. Responsive Business Theme

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Demo
Download
***

5. Personal Blog

This theme was developed for personal business blogs. It has a clean design and minimal layout. A personal blog WordPress theme that is mobile friendly. Valid XHTML + CSS will provide flawless operation of your website.
Demo
Download
***

6. Emmet

A clean and responsive free WordPress theme with a pro design created for corporate and portfolio websites. Emmet comes packaged with page builder and is fully integrated with WordPress Customizer. The theme works perfectly with major WordPress plugins like WooCommerce, bbPress, BuddyPress and many others. This is one of the most stylish free WordPress themes for corporate websites.
Demo
Download
***

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Demo
Download
***

8. Business

Business Elite is a responsive WordPress theme ideal for a business portfolio and company corporate identity showcase. It is SEO-friendly, cross-browser compatible, has responsive features, the theme is retina ready with clean flat design, it uses one page scroll and parallax technology with easy set-up sticky menu. The theme has a wide list of customizable features including full width slider, front page builder, full screen lightbox slideshow, layout editor and social sharing options. It is compatible with top WordPress plugins such as WPML, JetPack, Contact Form 7, bbPress, BuddyPress, Photo Gallery and various eCommerce plugins.
Demo
Download
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9. WEN Business

This spacious corporate theme offers convenience to anyone setting it up. The in-house responsive framework offers features that satisfy a lot of needs and that is convenient to operate with as well. Here are some of them: a one-layout header, clean menu, slider, focused content, pretty effects, portfolio showcasing, testimonials, neat footer, etc. The code is easy to customize and extend by various hooks offered in the build.
Demo
Download
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10. Business One

Fully responsive, customizable and search engine friendly WordPress theme using Twitter Bootstrap 3, Microformats and Font Awesome icons. The template offers theme options based on Redux Framework. In addition to that, Advanced Custom Fields is used. You can add Contact Form and Google Maps to the contact page. Theme’s sidebar is widgetized.
Demo
Download
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11. THBusiness

THBusiness free WordPress theme lets you create a business website by just dragging and dropping the widgets in the widget area. The theme is built with Twitter Bootstrap, so it can be viewed perfectly in any environment. Additionally, it features a full width slider that is made with flexslider. Theme Customizer along with theme options panel will help you to customize your website fully, as you wish. For instance, choose the main theme color from unlimited colors with just one click.
Demo
Download
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12. Business Directory

With Business Directory you will be able to build your web presence in 10 Minutes just with 5 simple steps. The theme is highly customizable and has several background patterns and Theme Options.
Demo
Download
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13. Illdy

Illdy is a stunning multipurpose WordPress theme based on Bootstrap front end framework making it fully responsive and mobile friendly. With WordPress Customizer you can create your website in live preview mode. Your website will look better than ever thanks to Illdy’s one page setup with multiple sections that can be tweaked to achieve the personalization you have always wanted. Illdy works with most popular free and premium WordPress plugins such as Contact Form 7, Gravity Forms, Yoast SEO, Jetpack, WP Super Cache, NextGEN Gallery and soon with WooCommerce. This theme is also SEO friendly which will help you attain the highest rankings on Google and other search engines. It comes with full screen background image and parallax scrolling enabled sections for sleeker user experience.
Demo
Download
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14. Zerif Lite

Zerif LITE is a one page free WordPress theme. It’s perfect for web agency business, corporate business, personal and parallax business portfolio, photography sites and freelancers. It is built on BootStrap with parallax support, is responsive, clean, modern, flat and minimal. Zerif Lite is eCommerce (WooCommerce) compatible, WPML, RTL, Retina-Ready, SEO friendly, supplied with parallax effect and full screen image. This is one of the most reputable free WordPress themes for business consultants.
Demo
Download
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15. Sequential

Sequential is a contemporary, clean, and multi-purpose WordPress theme. Here are some of theme’s features: Custom Colors, Custom Header, Custom Menu, Flexible Header, Light, Post Formats, Responsive Layout, Right Sidebar, RTL Language Support, Sticky Post, Theme Options, Translation Ready, Two Columns.
Demo
Download
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16. Ascent

Ascent Theme is a fully responsive free WordPress theme that looks elegant on any device. It is a simple, clean and lightweight WordPress theme based on the most modern technologies like HTML5 and CSS3.
Demo
Download
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17. Goran

Goran is a functional and responsive multi-purpose WP theme that is the perfect solution for your online business presence. It comes with Custom Background, Custom Colors, Custom Header, Custom Menu, Editor Style, Featured Images, Flexible Header, Full Width Template, Left Sidebar, Post Formats, Responsive Layout, Right Sidebar, RTL Language Support, Sticky Post, Theme Options, Translation Ready, Two Columns and other features.
Demo
Download
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18. Edin

Edin is a modern responsive business corporate theme that helps you to create a strong, yet beautiful, online presence for your business. Theme’s features: Custom Background, Custom Colors, Custom Header, Custom Menu, Editor Style, Featured Images, Flexible Header, Full Width Template, Left Sidebar, Post Formats, Responsive Layout, Right Sidebar, RTL Language Support, Sticky Post, Theme Options, Translation Ready, Two Columns – the list goes on.
Demo
Download
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19. Customizr

Customizr is a versatile and easy to customize free WordPress theme, instantly endowing your online presence with a professional look. It allows anyone to create a beautiful, fast and mobile friendly website compatible with all browsers and devices. Customizr works fine with major WordPress plugins like WooCommerce, bbPress, JetPack and many others.
Demo
Download
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20. CW Business Lite

CW Business Lite is finalizing today’s list of free WordPress themes. It’s a clean and modern business WordPress theme, built with responsive design in mind using latest CSS/HTML techniques. CW Business lets you easily build a responsive WordPress site for your clothing, computer, automotive, hair salon, health, cleaning, nonprofit or, say, pest control business.
Demo
Download
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Summing it up, you have just viewed 20 free WordPress themes for Business Consultants. Take your pick and use them. We wish you the best of luck in building your own business site. Please let us know if we have overlooked any decent free WP business theme at the comment section. Every one of your “likes” and “shares” will be highly appreciated, to boot.

Related Articles:

• 20 Business Consulting WordPress Themes for 2016
• 20 WordPress Themes for Business Consultants

Read More at 20 Free WordPress Themes for Business Consultants

Philip Walton has the scoop on Houdini, a new W3C task force that’s planning to give developers low level browser APIs:

The bottom line is, imagine how much nicer your development life would be if you could use any CSS property and know for sure it was going to work, exactly the same, in every browser. And think about all of the new features you read of in blog posts or hear about at conferences and meetups — things like CSS grids, CSS snap points and sticky positioning. Imagine if you could use all of them today and in a way that was as performant as native CSS features. And all you’d need to do is grab the code from GitHub.

This is the dream of Houdini. This is the future that the task force is trying to make possible.

Direct Link to Article — Permalink

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“Maybe The Most Exciting Development In CSS You’ve Never Heard Of” is a post from CSS-Tricks

What a week! Some people were debating over our npm workflows and security attacks (and sadly not just virtual social engineering ones but real ones in Brussels), we’ve also seen some great new articles that feature the better parts of our community and society. I’m happy to share them with you over this longer Easter-weekend. Cheers!

iOS9.3 and OS X 10.11.4 is finally being delivered to users, and with it, Safari 9.1 is out with element support, CSS Custom Properties, will-change property, unset value and unprefixed filter.

The post Web Development Reading List #130: Opera Mini, Workflow Fragility And Happy Work appeared first on Smashing Magazine.

If you’re looking for a clean, functional typeface for a project, you can’t go far wrong with Unio Creative Studios’ Venice Serif, so we’re delighted that we’re able to bring it to you as a free download.

The contemporary slab-serif has an even color, a very large x-height, and generous counters, all of which makes it highly readable. The compact ascenders and descenders also give the typeface a distinct personality.

The thin strokes, combined with minimal geometric forms, make Venice Serif a stylish choice when employed as a display face.

Optimized for screen use, Venice Serif is a great choice for blogs that need a touch of contemporary style; fashion labels that produce simple wardrobe staples; or even IT companies that seek to emphasize simplicity.

Supplied in OpenType format, the download includes 193 characters in 2 styles: regular and italic. Venice Serif is free for personal use, download the files beneath the preview:

Featured image uses Trish Hartmann’s Venice Image.

3000 Pixel-Perfect Retina Icons – only $24!

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