dfine

6 yıl öncediscuss

This is an instructive article for how to get a lot done to improve systems as a "utility knife" developer in a growing startup, but how to do it responsibly.

dfine

9 yıl öncediscuss

Can't help but think that this vindicates Boeing's bet on the smaller 787.

dfine

9 yıl öncediscuss

New York NYC | Customer Success Engineer

Dynamic Yield’s unified customer engagement platform helps marketers increase revenue by automatically personalizing each customer interaction across the web, mobile web, mobile apps and email. The company’s advanced customer segmentation engine uses machine learning to build actionable customer segments in real time, enabling marketers to take instant action via personalization, product/content recommendations, automatic optimization & real-time messaging.

As a member of the Customer Success Team, your main objective is to assist with project delivery, maintaining a high level of satisfaction for our customer.

What you need to succeed:

  – You will have a technical degree as well as working experience in a professional services/consulting environment of an enterprise software company or consultancy. Self-taught hackers are also welcome. 
  – Knowledge of online marketing functions is an asset, though not required. You will be comfortable responding to the varying demands of working for a dynamic, international company. 
  – Bonus if you have experience working with online Publishers or eCommerce. 
  – You are a self-motivated individual, with well-developed inter-personal & communication skills and a strong desire to succeed.

Required Skills:

  – Fluency in JavaScript skills and experience with jQuery. 
  – HTML / CSS and web technologies across different platforms. 
  – Business analysis and understanding of the digital marketing space, especially in the field of online marketing.

Email interest to [email protected]

dfine

10 yıl öncediscuss

Hi, I work at Placemeter, the company that produced this map and the technology behind it. Check out our privacy principles [0] and feel free to email me [1] if you have any questions. Thanks!

0: http://www.placemeter.com/privacy

1: david [at] placemeter [dot] com

dfine

10 yıl öncediscuss

Context: http://www.fastcompany.com/3056581/fast-feed/marc-andreessen...

dfine

11 yıl öncediscuss

The one liner is right below the larger headline on the landing page, perhaps we should've made it more prominent: "The Placemeter Sensor measures pedestrian, vehicle, and bicycle traffic in real-time."

dfine

11 yıl öncediscuss

The quality of the view and the angle determine a lot, but generally our accuracy is above industry average for human counters and other systems.

dfine

11 yıl öncediscuss

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
  + You will optimize and deeply understand these algorithms and scale them
  + You will design and maintain the quality assessment tools required to make sure
  our algorithms perform well

We use computer vision at a massive scale, on a large number of rich and ubiquitous video feeds, to understand what is going in in the physical world in real time. We measure how busy places are, what people do, how fast cars go, and much more. We offer that data to developers, citizens, cities, and retailers, radically changing the way they interact with the physical world.

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse.

APPLY HERE: http://grnh.se/1yvxfb

dfine

11 yıl öncediscuss

Not quite sure I understand your question. We store counts, not individual object IDs, so at an individual granularity it would be the same as your IR device counting one person.

dfine

11 yıl öncediscuss

Great questions.

1) the processing is happening aboard the sensor, counts are what are sent back to the servers

2) we don't do unique identification like that. We use object detection, which is different than using unique biometric markers like face detection. That means that we can track a person or a car within a frame of view, but not if they exit and re-enter the frame like in the case you described.

Does Density still use wifi pinging for part of its counts?

dfine

11 yıl öncediscuss

For the record, that's not what Placemeter does. We don't individually identify people with our algorithms. (I am a PM at Placemeter.)

dfine

11 yıl öncediscuss

I am a PM at Placemeter. We do not "use facial recognition software" in our algorithms, as Density's website claims of video-based systems. None of our algorithms use biometric markers for our counting—we're essentially the same "dumb" counters as Density's IR with the added advantage of accuracy and area of coverage.

Placemeter does much more than pay lip service to privacy. We pride ourself on our privacy efforts. If you want to lear more about them, @afar email me: [email protected]

dfine

11 yıl öncediscuss

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
  + You will optimize and deeply understand these algorithms and scale them
  + You will design and maintain the quality assessment tools required to make sure
  our algorithms perform well

We use computer vision at a massive scale, on a large number of rich and ubiquitous video feeds, to understand what is going in in the physical world in real time. We measure how busy places are, what people do, how fast cars go, and much more. We offer that data to developers, citizens, cities, and retailers, radically changing the way they interact with the physical world.

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse. APPLY HERE: http://grnh.se/1yvxfb

dfine

11 yıl öncediscuss

The reason why I think it's iOS-ification rather than an OSX-ification is because "Photos" seems less like a standalone app and more like a necessary utility to manage content created by the Apple ecosystem. iPhoto never really felt like that.

iPhoto was part of the iLife suite that were standalone apps to manage the different multimedia demands of your life, mostly from non-Apple digital cameras. It was optimized around editing and organizing your photos. "Photos" on iOS was a barebones app required by a device where camera functionality is built in, intended primarily to manage photos created by the iOS device. Over time both of the apps have borrowed features from each other and grown together until it made sense to converge into one app.

dfine

11 yıl öncediscuss

The iOS-ification of Mac OS continues apace. A good thing, in my opinion.

dfine

11 yıl öncediscuss

Placemeter – New York, NY or Paris, France – Computer Vision Engineers FULL TIME

APPLY HERE: http://grnh.se/1yvxfb

We are looking for computer vision engineers from entry level to experienced level, to extend, develop, and maintain our algorithm stack.

  + You will design the next generation of computer vision algorithms
    to extract more from our video feeds.
  + You will use and master the tools to build, 
    optimize and deeply understand these algorithms and scale them.
  + You will design and maintain the quality assessment tools 
    required to make sure our algorithms perform well in all cases

We use computer vision at a massive scale, on a large number of rich and ubiquitous video feeds, to understand what is going in in the physical world in real time. We measure how busy places are, what people do, how fast cars go, and much more. We offer that data to developers, citizens, cities, and retailers, radically changing the way they interact with the physical world.

ABOUT PLACEMETER

Placemeter uses computer vision algorithms to create a real time data layer about places, streets, and neighborhoods. Placemeter’s technology gives businesses, cities, and people the ability to take a place’s pulse.

APPLY HERE: http://grnh.se/1yvxfb

dfine

11 yıl öncediscuss

Placemeter — NYC or PARIS (relocation possible), FULL-TIME

We're looking for a COMPUTER VISION ENGINEER to join our world class CV team.

Job posting and details here: http://grnh.se/emdq87

About Placemeter

Placemeter is building a real-time data layer measuring activity in the physical urban environment, like how many people are walking on an intersection or how fast cars are speeding down your block. We use computer vision at a massive scale, on a large number of rich and ubiquitous video feeds, to understand what is going in in the physical world in real time. We measure how busy places are, what people do, how fast cars go, and much more. We offer that data to developers, citizens, cities, and retailers, radically changing the way they interact with the physical world.

We built our platform around privacy. We never store any video and we do not identify people. We also make sure no one can reverse engineer our data to identify anyone. We are backed by top NYC & Silicon Valley VCs, alumna of TechStars (Spring 2013), and actively plugged into their vibrant ecosystem of mentors and alumni.

We need creative and flexible minds, with a complete commitment to building nothing else but perfect software and systems. Make a real impact on your city, the NYC tech community, and a fast growing startup. Put your mark on this truly disruptive, slightly crazy, and ambitious platform we are building. Placemeter is in a phase of rapid expansion, and we want you to join us.

APPLY NOW: http://grnh.se/emdq87

About our stack

Our system is full stack in a way rarely seen before, from low level embedded processing to computer vision algorithms to mobile applications, and everything in between including: machine learning, data analytics, prediction models, and geospatial intelligence.

If you want to build the next big thing in machine learning, computer vision, sensing, prediction and if you like huge, scalable and impactful systems, you will fit right in. You will encounter some of the biggest tech challenges you have ever seen. Get ready to earn some serious tech street cred.

We are a paradise for video and data geeks. Using our own optimized code base, we detect moving objects, classify them, then track their positions. We then use trajectory information to estimate speed as well as location occupancy and traffic. Today, our computer vision stack runs continuously on close to 1,000 available video feeds, collecting 8 million data points each day on average. We extract insights and predictions from these points. We have millions of ground truth data points to build and optimize our algorithms. We analyze all these data points by comparing them, normalizing them, correlating them with external factors to give our users clean, real time data. We are about to grow dramatically, adding a couple of orders of magnitude to our current scale.

We work in a data driven environment where every new algorithm is first defined by data sets and ground truth - we have a lot of data floating around. Our regression and quality tests guarantee that each improvement on one camera will improve our quality and performance overall.

We highly value testing and continuous integration. For critical interactions between major components we maintain integration tests, and for our core algorithms we maintain quality and regression tests. Good test coverage is key to keeping our bug count low. It also builds internal confidence to work on any piece of code without fear of breaking existing functionalities.

Our tech team is made up of varied backgrounds, and we function as a flat team where everybody knows about what everyone else is working on. This creates an environment where you can learn from your peers with ease and significantly grow your tech turf.

APPLY NOW: http://grnh.se/emdq87

dfine

12 yıl öncediscuss

The best rejoinder to this might be the Founder's Fund manifesto, which originally started with, "We wanted flying cars, instead we got 140 characters."

That specific phrase has since disappeared. But it illustrates that we can enjoy—even invest in—certain innovations like social media, while bemoaning the lack of innovation in other spaces (eg transportation in this example).

Here's the modified version of the manifesto:

http://www.foundersfund.com/the-future/

dfine

12 yıl öncediscuss

Placemeter* will turn your old phone into a smart sensor that helps to measure pedestrian traffic and car speed in your neighborhood: http://placemeter.com

*I'm Product Manager there

dfine

12 yıl öncediscuss

Thanks!

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