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.
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.