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San Diego is one of the best places to live in the US. Cost of living, homelessness, and failed neighborhoods are no worse in San Diego than in Seattle or Austin. Benefits are perfect weather, proximity to Silicon Valley VCs, recruiting from the University of California system, and a willingness from talent to relocate to join your startup.

Everywhere has challenges. Attracting real talent in the midwest is challenging. Cost of living in industry hubs is also challenging. There's no perfect location to start a startup.

Source: 7 years at startups in the midwest, the south, Seattle, and now San Diego.

This is the most accurate description of a "weight loss journey" I've ever read. I was obese (40%+ body fat). Now I'm fit and healthy. Like you, it took me a decade to get there. Anyone who thinks it's easy or simple to make the lifestyle changes that requires, hasn't done it themselves.

It's not about the diet - most diets actually work, if you can stick with them for the rest of your life, but that's a huge if. CICO or keto or IF or carnivore or whatever, doesn't matter. Permanent weight loss requires the much harder task of fundamentally changing your brain's relationship to food. The only way that happens is through practice and painful failure.

You do have control over your weight. But losing fat and maintaining a lean body will be one of the hardest things you ever do.

I worked at a larger services marketplace, helping data scientists get their models into production as A/B experiments. We had an interesting and related challenge in our search ranking algorithms: we wanted to rank order results by the predicted lifetime value of establishing a relationship between searcher and each potential service provider. In our case, a 1% increase in LTV from one of these experiments would be...big. Really big.

Improving performance of these ranking models was notoriously difficult. 50% of the experiments we'd run would show no statistically significant change, or would even decrease performance. Another 40% or so would improve one funnel KPI, but decrease another, leading to no net improvement in $$. Only 10% or so of experiments would actually show a marginal improvement to cohort LTV.

I'm not sure how much of this is actually "there's very little marginal value to be gained here" versus lack of rigor and a cohesive approach to modeling. The data scientists were very good at what they do, but ownership of models frequently changed hands, and documentation and reporting about what experiments had previously been tried was almost non-existent.

All that to say, productizing ML/AI is very time- and resource-intensive, and it's not always clear why something did/didn't work. It also requires a lot of supporting infrastructure and a data platform that most startups would balk at the cost of.

This looks really slick, can't wait to try it out!

If anyone is curious about other tools in the same space, our data scientists use Dash[1] and plotly to build interactive exploration and visualization apps. We set up a Git repo that deploys their apps internally with every merge to master, so they're actually building and updating tools that our operations, marketing, etc teams use every day.

[1] https://plot.ly/dash/

> We're sometimes asked: why is no one else doing this?

https://joe.coffee/

https://www.ritual.co/

If you live in the Seattle area, check out Joe. They cover most of the local independent chains and are expanding really quickly.

This tool would be a lot more useful if it allowed for filtering by chosen course of study. For example, University of Washington has a median earnings that is slightly below expected, but I can pretty much guarantee that their computer science graduates are earning well above median.

These schools are all over the place! I graduated from the Arkansas School for Mathematics, Sciences, and the Arts. Math and science schools are truly fantastic places for advanced high school students to get a quality education.

aewhite covered most points, but I'd also point out that a simple use case of a cluster with multiple indices of varying sizes (such as using ES as part of the ELK stack to store logs - a new index is created every day) will run into many of the same problems with the default balancer. Since the number of shards per index isn't configurable after index creation, shard size growth and disparity is unavoidable.

We've tested for stability when adding and removing nodes, but haven't compared the time-to-balance of tempest versus the default balancer. Because an ES cluster remains fully functional (you still have access to all data) while a rebalance is in progress, we chose to optimize for resource usage rather than time-to-balance. There's not really even a good way to compare the balancers' time-to-balance, since they're both highly configurable (range_ratio and iterations in tempest's case, the 4 balance weights in ES's case) - default values probably aren't "equivalent" in terms of time-to-balance, since it's a very minor concern compared to resource usage and stability.

Oh nice! That looks close to what we were trying to do with this plugin. I'm not sure it would've worked within the constraints of the Elasticsearch environment, but the additional confidence of finding a solution that optaplanner provides by using multiple algorithms to solve the bin-packing problem (NP-Hard) looks quite promising.

Checkout Intentional Software. It's a startup of about 70 people funded and led by billionaire CTO Charles Simonyi. They built a platform for developing domain-specific languages (on top of the CLR), and are now developing some pretty incredible collaborative productivity apps on top of that platform. Their recruiting slogan is "this is why you chose computer science," and after interning there for 6 months I can say that's 100% accurate.

Pay and benefits are equivalent to what you'd get at Microsoft or Amazon, without the corporate BS.

http://www.intentsoft.com/careers/