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Lessons Learned: Combining agile development with customer development

Startup Lessons Learned

This is a fairly simple approach to creating a weighting system using an Opportunity Algorithm. The algorithm is Importance + max(Importance - Satisfaction, 0) = Opportunity. Case Study: Continuous deployment makes releases n. I wont do the explanation justice so I suggest you grab the book.

Agile 111
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Lessons Learned: The one line split-test, or how to A/B all the time

Startup Lessons Learned

You just constantly test little micro-changes and follow a hill-climbing algorithm to build your product. Case Study: Continuous deployment makes releases n. Towards a new entrepreneurship ► 2009 (88) ► December (4) Continuous deployment for mission-critical applica.

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Embrace technical debt

Startup Lessons Learned

The design failure meant that there was constant thrashing as the servers struggled to provision capacity according to the “elegant&# algorithm we’d designed. Case Study: Continuous deployment makes releases n. Neither assumption proved remotely accurate. One last thought. Take a look and let me know what you think.

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Lessons Learned: The ABCDEF's of conducting a technical interview

Startup Lessons Learned

For the past couple of years Ive used a question that I once was asked in an interview, in which you have the candidate produce an algorithm for drawing a circle on a pixel grid. As they optimize their solution, they eventually wind up deriving Bresenhams circle algorithm. Case Study: Continuous deployment makes releases n.

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Lessons Learned: Five Whys

Startup Lessons Learned

Because five whys kept turning up a few key metrics that were hard to set static thresholds for, we even had a dynamic prediction algorithm that would make forecasts based on past data, and fire alerts if the metric ever went out of its normal bounds. Case Study: Continuous deployment makes releases n.

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Lessons Learned: Sharding for startups

Startup Lessons Learned

More common is to use a one-way hashing algorithm to map the data to be accessed to one of the shards that store it. Theres no complex algorithm to go wrong, just a simple lookup table. Of course, you could use URL-based sharding to "wrap" a CH algorithm (or any hashing scheme you wanted).

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Learning is better than optimization (the local maximum problem)

Startup Lessons Learned

Those of us with a computer science background call it the hill-climbing algorithm. Those of us with a computer science background call it the hill-climbing algorithm. Case Study: Continuous deployment makes releases n. Tell your Startup Visa story Speaking 2010: Webstock, GDC, Web 2.0,