I've worked on marketplaces before, though not the most technical job you can get in the industry, you must execute on the product side your the operational wheels will literally fall off. Even this app, it's not a two way marketplace, as it is a three way: end users, clients, and you as the operations team. It can also be very difficult to get flywheel effects going, but that's a problem for a different day :)
Anyway, it always surprises me that non-technical founders try to enter a field where the are unskilled in the core competencies (software/sales/marketing), then outsource that competency to someone with no vested interest in the outcome of the project. There's a reason tech companies don't outsource the tech, and that's because you want extremely tight integration of decision making and technical knowledge.
Additionally, I'm surprised the marketing spend was so low. An infinitely better plan would have been for one person to build a BE/website that works on mobile, another to sell it to some clients with fun activities, and spend as much money as possible on targeted marketing. That, and not trying to sell a travel application in the middle of the largest travel industry recession since 9/11.
I'm still immensely grateful to all the HN folks who helped me recover from my hug of death a year or so ago by teaching me the Cloudflare tricks. I'm with Ghost Pro now instead of Wordpress, but man, IDK what I would have done without the Cloudflare advice in the meantime!
What other options are there that support old clients and are free + automatable?
It’s all well and good to prefer Lets Encrypt if your clients are using web browsers, but it is not suitable for more exotic cases. E.g video streaming, where clients can be things like many years old copies of VLC, which no longer trust Lets Encrypt certs
gogetssl.com issues free 90 day Sectigo (formerly Comodo) certificates and they have an ordering API. Caveats: 1) I don't know if those certificates will work in old VLC clients or whatever. 2) After you order the certificate you get an email from the CA with a link that you have to click saying that you approve issuance. I don't know what happens if you try to automate that.
For me the main hassle of LetsEncrypt is the 90 day rotation and there have been situations where I'd rather just pay for a longer lasting certificate. Gogetssl (above) sells 5 year DV Sectigo certificates for $16, it looks like.
Ignore the prices shown on the not-logged-in part of the site: sign up for their "reseller" program (you get approved right away automatically) and you can see their real price list while you are logged in.
I had a very similar problem with older clients attempting to connect to streaming sites hosted on a WHM cluster. One day Let's Encrypt certs stopped being trusted on some of the older client machines. Fortunately, the provider from cPanel was also free and their certs worked (and still work) with older clients.
One strategy would be to go after ML/AI jobs that are at the profit centers of companies and whose success or failure means the success or failure of the company. There are lots of AI/ML projects done at the whim of some VP in a random business unit "because we have so much data there must be something there", or working on a speculative product. Those types of projects are really interesting and can be a lot of fun, but they get cut pretty fast when it's time to trim the budget.
I don't have a PhD in AI/ML, but I have delivered ML models into production. Doing so taught me that you must go about your modeling and science work with a practical urgency for results compared to the pace of academic research. Business applications don't require you to prove how smart you are (people just assume it), but they do require compromise to meet requirements and exceed stakeholder expectations. Where a lot of ML people lose the plot (and financial reward) is that they don't make enough tradeoffs for the operational or end user concerns for the model in its entire relevant context. I've seen this manifest a few ways, but a common one is getting fixated on the data you have, but never realizing you need to "close the loop" and make something actually useful for an end user in a measurable way.
Those are the practical, "productionize AI" jobs and there is huge interest in those now given the huge interest in LLMs. Who cares about the downturn, LLM start ups are the hot thing.
There are also industry research jobs, definitely worth applying for, but from my understanding they are very difficult to get.
It's pretty nebulous, but the work output of a data scientist would be a predictive model, where those results are either useful for some business unit (forecasting), or capable of being shipped as part of a software system and product.
Using Uber as an example, a data scientist would figure out the algorithm/model for assigning the next driver when you request a ride, the model for the shortest route (maybe), and delivering a model to correct GPS works in cities with huge buildings so drivers know exactly where to pick people up when they stand next to skyscrapers.
It requires a ton of infrastructure to do good data science work, since you not only need to validate that the model works using the exact same data in testing/production, but you need to take code that a non-SWE runs, integrate it into the build, figure out the right operational metrics, then deploy as part of some release strategy.
The model is really the smallest part of that process, but occasionally, you can get a huge lift by having someone apply a lot of interesting math.
I don't know, and it's really impossible to tell since things are changing quickly. People work as "data scientists", but there are headwinds and tailwinds. the main headwind, is that companies are cutting budgets due to the recession and dropping analysis groups that are part of cost centers. It's also easy to get the basic skills (coding/stats) done in your undergrad or masters w/o having research experience. The tailwinds, are that ML capabilities are improving day by day, so the potential to use that to make money are increasing. There's also a huge digital transformation happening, and companies have more data than ever before and potential to leverage that into savings, additional revenue, or new services.
When I started on my data science path, about 10 years ago, and there was no training pipeline, so when I dropped out of a PhD a few years later it wasn't that hard to get a data science job with the intersection of skills: math/stats/coding/research. Today that role is probably filled by someone graduating from an undergraduate or grad program, but I know the same company is still hiring for improvements on the research project I helped start.
Good data science, for me, is when you "apply predictive models to end user problems and ship solutions in products", but when I looked around for other jobs I realized that so few companies are able to act cross functionally to exploit the value of ML in products and services. Sure, finance does it, ads does it too, but it seems like the jobs I had access to were some ill-thought out skunkworks that a VP or exec thought was a good idea, or doing work tucked away in some business unit. There are like 10 individual problems there for YC to solve, but the more fundamental issue is that as long as we are still in the hype phase of data science, there will be incentive for business leaders to spend money on it in wasteful ways (at least for your career).
If you want to do data science or ML, it'd encourage you to find tech first companies that are actually using ML to solve real world problems for people, and avoid working on projects that haven't shipped. Also, stay under engineering orgs. In business units, you'll have a boss that doesn't understand what you do, and you'll be promoted out of tech.
Ultimately, I left data science and am now on an infrastructure team at a database company, which is just a better fit for values. If you can get into big tech or any tech first company, the data science is mostly figured out, but in my experience lots of companies aren't offering constructive experience. Good luck.
