The Brooklyn Startup Bringing Eyewear Manufacturing Back To America
Qwilr co-founders Dylan Baskind (left) and Mark Tanner (right)
Impressions matter. They are subconscious evaluations customers make about a startup and they happen reflexively. Within the first second, prospective customers form judgments about a startup based on its appearance and familiarity. The psychology of first impressions isn’t new to businesses. Companies have long acknowledged the importance of presenting themselves purposely. But this has predominantly extended to prominent marketing areas such as branding, positioning and communication.
There has been less focus on creating subtle marketing materials that cultivate positive impressions, particularly with things like business documents.
Australian startup Qwilr is a web-based tool that allows businesses to “replace PDF proposals, quotes, and presentations with interactive and mobile friendly web pages.” Operating a SaaS model, the platform allows users to create “good-looking documents” from built-in templates and customize them with live-editing tools. These documents, rather than being downloadable attachments, are presented as web pages — a medium positioned to be more technological intuitive.
In my conversation with co-founder and CEO, Dylan Baskind, he described how the product was inspired by the ubiquity of the internet and the underutilized power of documents on the web. “Ninety-nine percent of people and businesses have to tell their stories, make value propositions and convince people of a viewpoint. But these tools, the traditional document ecosystem, are predicated on the idea of documents as paper… the web is flexible and very, very powerful.”
Baskind wants to change the behavior of how businesses view document systems holistically. “Qwilr’s big idea is to completely blur the boundary between the web and documents. Our vision is to make the language of the web accessible to literally anybody, anywhere.”
Neural networks are all the rage right now with increasing numbers of hackers, students, researchers, and businesses getting involved. The last resurgence was in the 80s and 90s, when there was little or no World Wide Web and few neural network tools. The current resurgence started around 2006. From a hacker’s perspective, what tools and other resources were available back then, what’s available now, and what should we expect for the future? For myself, a GPU on the Raspberry Pi would be nice.
The 80s and 90s
For the young’uns reading this who wonder how us old geezers managed to do anything before the World Wide Web, hardcopy magazines played a big part in making us aware of new things. And so it was Scientific American magazine’s September 1992 special issue on Mind and Brain that introduced me to neural networks, both the biological and artificial kinds.
Back then you had the option of writing your own neural networks from scratch or ordering source code from someone else, which you’d receive on a floppy diskette in the mail. I even ordered a floppy from The Amateur Scientist column of that Scientific American issue. You could also buy a neural network library that would do all the low-level, complex math for you. There was also a free simulator called Xerion from the University of Toronto.
There were also short courses and conferences you could attend. The conference I attended in 1994 was a free two-day one put on by Geoffrey Hinton, then of the University of Toronto, both then and now a leader in the field. The best reputed annual conference at the time was the Neural Information Processing System conference, still going strong today.
And lastly, I recall combing the libraries for published papers. My stack of conference papers and course handouts, photocopied articles, and handwritten notes from that period is around 3″ thick.
Then things went relatively quiet. While neural networks had found use in a few applications, they hadn’t lived up to their hype and from the perspective of the world, outside of a limited research community, they ceased to matter. Things remained quiet as gradual improvements were made, along with a few breakthroughs, and then finally around 2006 they exploded on the world again.
The Present Arrives
We’re focusing on tools here but briefly, those breakthroughs were mainly:
new techniques for training networks that go more than three or four layers deep, now called deep neural networks
the use of GPUs (Graphics Processing Units) to speed up training
the availability of training data containing large numbers of samples
Neural Network Frameworks
There are now numerous neural network libraries, usually called frameworks, available for download for free with various licenses, many of them open source frameworks. Most of the more popular ones allow you to run your neural networks on GPUs, and are flexible enough to support most types of networks.
Here are most of the more popular ones. They all have GPU support except for FNN.
TensorFlow is Google’s latest neural network framework. It’s designed for distributing networks across multiple machines and GPUs. It can be considered a low-level one, offering great flexibility but also a larger learning curve than high-level ones like Keras and TFLearn, both talked about below. However, they are working on producing a version of Keras integrated in TensorFlow.
This is an open source library for doing efficient numerical computations involving multi-dimensional arrays. It’s from the University of Montreal, and runs on Windows, Linux and OS-X. Theano has been around for a long time, 0.1 having been released in 2009.
Caffe is developed by Berkeley AI Research and community contributors. Models can be defined in a plain text file and then processed using a command line tool. There are also Python and MATLAB interfaces. For example, you can define your model in a plain text file, give details on how to train it in a second plain text file called a solver, and then pass these to the caffe command line tool which will then train a neural network. You can then load this trained net using a Python program and use it to do something, image classification for example.
This is a high-level open source library written in C. It’s limited to fully connected and sparsely connected neural networks. However, it’s been popular over the years, and has even been included in Linux distributions. It’s recently shown up here on Hackaday in a robot that learned to walk using reinforcement learning, a machine learning technique that often makes use of neural networks.
There is much more to web designing than just plain aesthetics. For your website to be successful, you also have to make sure your content is as attractive as your site’s design.
Your customers would not be visiting your site to admire and be awestruck by its visual appearance. They are paying your site a visit to learn and gain something from it.
In case you are wondering, here are some of the best web design strategies you can use in boosting your site’s view.
Pay Attention To Conversions
Conversion involves the transition of a casual visitor to a paying visitor. For this to happen, you have to consider every part of your site’s design.
Here are some design tips from the professionals:
• Images must always be fascinating, of high-quality, and truly unique.
• Color scheme must be vibrant and eye-catching.
• Text must be used for messaging and for delivering instructions, descriptions, and labels.
• Navigation must be smooth throughout your website.
These are just a few of the elements that can help you get successful conversions. Fundamentally speaking, every part and element of your site’s design is an integral factor that…
Web Proxy Auto-Discovery (WPAD) gives organizations a way to automatically configure a proxy server on your system. Windows enables this setting by default. Here’s why that’s a problem.
WPAD is really useful when an organization like your company or school needs to configure a proxy server for your connection to their network. It saves you from having to set things up yourself. However, WPAD can cause problems should you connect to a malicious public Wi-FI network. With WPAD enabled, that Wi-Fi network can automatically configure a proxy server in Windows. All your web browsing traffic would be routed through the proxy server while you’re connected to the Wi-Fi network—potentially exposing sensitive data. Most operating systems support WPAD. The problem is that in Windows, WPAD is enabled by default. It’s a potentially dangerous setting, and it should not be enabled unless you really need it.
Proxy servers—not to be confused with virtual private networks (VPNs)—are sometimes required to browse the web on some business or school networks. When you configure a proxy server on your system, your system will send your browsing traffic through the proxy server rather than directly to the websites you visit. This allows organizations to perform web filtering and caching, and may be necessary to bypass the firewalls on some networks.
The WPAD protocol is designed to allow organizations to easily provide proxy settings to all devices that connect to the network. The organization can place a WPAD configuration file in a standard place, and when WPAD is enabled, your computer or other device checks to see if there’s WPAD proxy information provided by the network. Your device then automatically uses whatever settings the proxy auto-configuration (PAC) file provides, sending all traffic on the current network through the proxy server.
Windows vs. Other Operating Systems
While WPAD might be a useful feature on some business and school networks, it can cause big problems on public Wi-Fi networks. You don’t want your computer to automatically configure a proxy…