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The Application Development Landscape – 2012 and beyond

For Entrepreneurs

Today’s application developers are faced with a broad set of architectural decisions that can make or break their company going forward. This presentation, which was given as a keynote for the MassTLC application development conference, highlights the major changes that are taking place in the world of application development.

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Do you need data engineering before data science?

Version One Ventures

I’ll admit that I was completely unaware of the engineering behind data science when we first launched Insight Data Science back in 2012. A good data engineer: Gathers data, stores it, does batch/real-time processing on it, and serves it via an API to a data scientist (some companies may call this data infrastructure or data architecture).

Engineer 102
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The Case For Web-Based Mobile Apps – An Interview With UR Mobile

YoungUpstarts

Through mobile web apps, organizations can seamlessly integrate business processes at the web service layer to create a solution that is in line with a Service Oriented Architecture (SOA). Any predictions for the mobile space for 2012? Mobile web apps provide the most efficient, elegant solution for true mobile engagement.

Mobile 145
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So You're Going To Start A Huge New Web Project

css-tricks.com

December 18, 2012. Youll need to think about JavaScript architecture. — Ryan McLaughlin (@ryanmclaughlin) December 18, 2012. — Tim Panicucci (@tpanicucci) December 18, 2012. — Nando Vieira (@fnando) December 18, 2012. — Paul Lewis (@aerotwist) December 18, 2012. Navigation n Search.

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The year that was and the one that will be

deal architect

On New Florence been running the best of 2012 Best 2012 photos Discover Magazine - Top 2012 Science Stories The Culture Street Journal - WSJ on best of 2012 art, architecture Scientific American’s 2012 “Game changers” Fortune’s Top Businesspersons of.

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Decoding the AI Dilemma: Unveiling the Hidden Pitfalls, Ethical Quandaries, and Future Realities

Duct Tape Marketing

Wenger discusses the current state of AI, emphasizing the exponential progress in models like the Transformer architecture. That's something we realized around 2012 and then after around 2017, so between 2012 and 2017, progress was very linear. A Layperson’s Guide to the Concepts, Math, and Pitfalls of AI.

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Startups Lessons: Product First

Venture Chronicles

The early architecture conditions created what engineers called “technical debt” and that effectively became weaponized to stall significant investment in fixing the old in order to build the future. and by framing it as a binary choice I am perpetuating the problem in many ways. We should have been able to do both.

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