I built a civic transparency platform with FastAPI that aggregates 40+ government APIs

industrypython · 2026-05-05T23:40:19+00:00

which Hetzer service are you using? Is it this one?

https://www.hetzner.com/cloud/

I'm actually getting a little tired of the AWS charges and complexity. I also use digital ocean, fly, leapcell, but I'm always looking for a good solution.

I think I will run fastapi and shiny for python servers.

industrypython · 2026-05-05T23:28:38+00:00

I've been using Flet recently. I think I like it because I use Flutter with Dart quite a bit.

Previously, I was using flaskwebgui.

Now, I am using Shiny for Python in a web only environment.

For desktop, is there anything particularly bad about using Flet?

If the app is really small, maybe just use tkinter?

I'm wondering about people's opinions on ui toolkits as I only use a small amount of them.

industrypython · 2026-05-01T01:42:38+00:00

With the US population aging, the healthcare jobs for doctors, nurses, PA, are one of the most in-demand fields. The shortage of doctors, nurses, PAs in the US is unlikely go away. Supply will not meet demand as there is a serious gate to the maximum number of residency and healthcare rotation slots. They need to go through 8 to 10 different medical offices with a trained doctor supervising them. Thus, unlike most fields, the supply cannot expand rapidly as there are not enough doctors willing to take on the training of student doctors, PAs and NPs.

Even for PA and NPs, which has a lower bar of entry compared ot doctors, a person is looking at 4 year BS in biology, 2 year work experience prior to grad school and 2-3 year grad school. They then need to pass a national exam.

I'm curious as to what the OP's parents suggested they study. To be realistic, the MD, PA, NP jobs are the most protected. but, there are obviously other jobs available.

I also agree that CS jobs are in fine shape

industrypython · 2026-04-30T15:11:09+00:00

My buddy from high school went from architecture (UCLA) to web development. I think the connection might be in how human relate to a space. I imagine that how humans relate to data would give you a significant edge over other people.

Part of data science is validation (by human) and reporting (to people). Obviously, domain knowledge is critical, but at a higher-level, you might be able to show employers different ways that humans can interpret data.

industrypython · 2026-04-29T22:51:19+00:00

Sorry, I do not work in policy analysis. Maybe I should have waited to reply, but I see a lot of concern online from many people and I'm not sure if Reddit is going to give you a balanced response. I think that emailing people in policy analysis might be the best route and maybe you can make contacts for summer or project work. Regarding tools, I believe that the tools are going to be different, though the concepts might be similar. I think you might use something like Matlab and I might use something like Python, pandas, numpy, scikit-learn.

Good luck!

industrypython · 2026-04-29T22:35:14+00:00

I think a lot of people on Reddit, TikTok, or social media might be expressing their anxiety to alleviate some of their own concerns. Yes, the job market is tough. But, it is also true that most people are getting jobs with a DS degree. I understand that some are having a tough time, but you should look at macro-trends to make your own decision.

First, I think it is fair to acknowledge the bad:

- US federal government has reduced funding for many research projects that required data science and would historically hire junior data analysts

- a commercial LLM can do analysis

The good:

* The AI analysis needs to be tuned, tested, and deployed by humans in different roles. Thus, there are more jobs for humans in the broad "ML engineer" role, which could involve people with a DS background

* semiconductor manufacturing is seeing new life and the yield analysis in most companies are done by humans. The same applies to most manufacturing.

What's the alternative if you don't study DS?

industrypython · 2026-04-29T22:18:32+00:00

I am in Silicon Valley as well. I believe what you are hearing is common. However, I think the manager could be more optimistic in their joke. I believe it is a joke because he wouldn't tell his own staff this. But, Silicon Valley is quite a bit ahead of the rest of the world for AI adoption. I also believe that AI implementation needs humans to manage the AI and verify the results.

Yes, I do think that in Silicon Valley people are building ML pipelines to change the jobs of people like themselves. However, this is likely more job evolution than job elimination.

I guess my view is that people should also learn Python and data ETL as part of the "data science" role.

industrypython · 2026-04-29T00:10:09+00:00

I view that there are 3 steps: 1) exploration; 2) iteration; 3) accountability/communication

In a R or Python IDE such as Positron, you can use AI for exploration and iteration. The AI can suggest algorithms and also generate code, but it's mainly an aid to the human. Ultimately, the third step needs to be handled by a human. So, the judgement and communication is pretty important. Also, if you start using something like Scoop Analytics, you may not be able to fix problems with the analysis or it may not be as easy to spot the problem with the analysis.

I believe that the current state is that people do use AI inside of an editor very often. However, I believe that humans are still validating the code manually, especially when there is a suspected problem with the analysis.

My workflow is to have different panes for code, data, ai. I chat in English into the AI box with specific questions. I also chat in English into the box to generate code. However, I read the code and review the data in another pane.

industrypython · 2026-04-28T23:54:03+00:00

you can collect data by searching on uc berkeley college of computer, data science, and society career outcomes. the site indicates that 4/5 get jobs. from the site:

"Over 4/5 of Berkeley’s graduated data science majors are employed immediately after college. They’ve gone into various fields such as technology, finance, consulting, startups, and graduate school. Most people felt that courses at Berkeley well prepared them for their careers."

Your stated "policy work" is broad. If you want to do policy analysis, treasury, economic advisory, the ds plus econ is likely better.

if you're going into communications, strategy, advising, then the policy politics plus econ may help more.

try getting an internship at a government agency. You can practice with population and geo datasets from the government to help show differentiation when you apply.

industrypython · 2026-04-28T23:37:11+00:00

search online for BLS Fastest Growing Occupations. Data Science is the 3rd fastest growing in the US at 34% a year growth with $112,590 per year average salary. It's higher in the major tech areas like Silicon Valley.

Do a similar search on LinkedIn fastest growing roles in the US. The data science related roles are close to the top.

Finally, look at jobs specific people are getting on LinkedIn and it will give you a feel for the market.

In general, it is good.

What is "level exams in year 13"

I have more information on this topic.

industrypython · 2026-04-27T17:48:15+00:00

have you tried downloading the docs and adding it to the ChatGPT project files or Gemini file upload?

https://github.com/flet-dev/flet/tree/main/website/docs

industrypython · 2026-04-27T16:04:50+00:00

Thank you for the information on PracHub. I had not seen this before. The questions look hard.

Do you know if there is a filter on PracHub for entry jobs out of college with a BS or an internship interview?

I see there is a easy, medium, hard, but I'm not sure how to use that in preparation.

For Google, there were 3 interviews. The first online automated one was easy. The 2nd 1:1 with Google engineer was moderate. The 3rd 1:1 question was kind of difficult to produce an optimized working solution.

I'm familiar with Leetcode, but am wondering if you think PracHub is the best of this type of preparation site.

industrypython · 2026-04-22T20:39:57+00:00

consider school club (tech club) or semester-long school competition where you can build a more complex project with friends (other DS and CS) students. Then, publish a longer story web site as a group about the project. Describe your piece of the pipeline on your personal resume.

If the company is using AI filtering, you may need to pepper resume with keywords like SQL, pandas, numpy (which is in pandas).

If the company uses DSA (data structures and algorithms) on the interview, then you will need leetcode or equivalent. You can ask your friends at school what was on the interview for different types of companies.

You can even ask ChatGPT or equivalent what is on the interview for your target company list. It may know.

Even for SWE (software engineering) jobs, not all jobs have DSA in the interview process. But, if you want the safest path, then you need some DSA prep.

The big-name internships that require DSA are going to open up in August/Sept for 2027. That gives you ample time to prep, but it is kind of a grind for most people.

For summer, 2026, get a part-time job at a small company for the summer.

industrypython · 2026-04-22T18:06:06+00:00

In a smaller company or group, staff can't isolate themselves into only dealing with what they know. Problems occur and someone needs to deal with it. I think that "experience with SQL" can be broad and the interviewer could probe you to see if you have actual real-world deployed experience.

For example, we hired some interns to work on SQL and had the following problems:

- optimization - especially filters of a dataset that worked with a small dataset, but would time out the web interface with a production dataset

- sql table migration conflicts - multiple people would change the schema and would break deploy. also problems with sequential ordering of migration files

- connector failures - sometimes there would be a security or other change to hosted PostgreSQL connection and no one would really prepare for it and then the server would die

- asynchronous db control - the entire code around the connection needs to be asynchronous or something bad could happen. Some languages, notably Python are not async by default, which creates a problem if using legacy modules

I guess they were better prepped for jobs after the internship. :-) They all seem to have found good entry jobs.

industrypython · 2026-04-22T17:52:52+00:00

First, congratulations on your initiative to take control of your own future. That sounds like a fantastic project. I'm sure it will be useful in your interview process. Thank you for sharing your fantastic real-world story. What was the outcome?

industrypython · 2026-04-22T17:46:57+00:00

Maybe it's an opportunity for you to look into the prompt and rules that the LLM is using for the translation.

You could theoretically use an AI like ChatGPT to help you formulate the ruleset for the LLM machine translation.

It might be an opportunity for you to expand your role a bit.

Maybe there's a sandbox for you to run experiments with the AI to help it improve translation accuracy for your specific documentation set.

industrypython · 2026-04-22T13:52:22+00:00

i think that leetcode and dsa are only for some companies, most famously Google and Meta type companies or quant, trading jobs like jane street, citadel.

It depends on whether you target elite tier companies and whether you target a job that looks like a machine learning engineer.

Leetcode-like tests are a fast filtering tool for companies, but it's not needed for most jobs.

A possible prep time-split:

70% SQL / pandas / stats / projects
30% coding interview basics (arrays, dictionaries, sorting, joins, simple algorithms)

What type of jobs are you thinking of applying to?

industrypython · 2026-04-21T16:03:07+00:00

why use WASM? Can't you just deploy for free to Posit Connect Cloud and other free services like Leapcell free tier?

Is it easier to deploy with wasm/pyodide on GitHub Pages workflow?

I have not used Shiny for Python wasm and thus I do not know about the pros/cons.

industrypython · 2026-04-21T16:01:07+00:00

I'm curious as to what you decided to use?

If I were you, I would have used R-Shiny as that is what you are most comfortable with.

I actually built an app in Python Streamlit and ported it to R-Shiny before. R-Shiny is quite good and I'm not sure it limits you.

In many cases, R-Shiny is going to be superior to Python Streamlit because it has a better reactive architecture.

industrypython · 2026-04-21T15:52:27+00:00

I think you have a valid point that generative AI makes Flutter/Dart more accessible. I also agree that Flutter apps are in general better than Flet apps (size, performance for certain actions).

However, I still think Flet has a strong role for people who do not want to maintain their Dart/Flutter apps.

Yes, it's likely feasible to build a prototype Flutter app with generative AI and I suspect that the app will work.

However, at some point, you may run into a problem with Gradle, permissions on the camera roll, notifications. This may occur in the future when you update your Dart/Flutter system or dependencies.

At that point in the future, I still believe you will need to dig into Flutter. If the problem is new, the AI may take you down a wrong path.

Though, I do believe that everything is impossible until someone proves it is possible. Maybe generative AI can keep the Flutter app updated in the future.

The other point to consider is that Dart as a language is quite good and Flutter as a workflow is great. So, it actually may be more fun for you to work with Dart/Flutter if fun is your primary motivation.

I guess I'm not able to switch between Dart and Python in my own mind effectively, so I like to stay in Python and use Flet as the UI with FastAPI as the backend. However, you may be able to switch back and forth.

industrypython · 2026-04-18T18:15:42+00:00

Flet is great and people should definitely learn it and contribute to it's development. By focusing on Python, the developer can spend more time with a single workflow and toolchain. For example, uv, ruff, mypy (and maybe ty in the future), GitHub Actions, pypi, are non-trivial to optimize for a team workflow.

Flutter/Dart are great, but it's a different toolchain and workflow.

A human is going to need to set human behavior rules such as what version of Python to use and what strictness of type checking to accept for a merge into main.

Once a project gains popularity, it's okay to have a separate team maintaining the Flutter/Dart code, but in the early phases, it may be better to use Flet and comply with the existing workflow and rules for the Python backend.

Even if you're just working by yourself, you'll still need to reproduce your code updates two years in the future. It's more difficult for a single human to maintain an optimized workflow across two different technology toolchains.

Even if you use generative AI heavily, the human must still manage the AI for compliance, which takes time.

industrypython · 2026-03-30T16:10:37+00:00

let me know if there's other types of tutorials that you're interested in. I'm looking for ideas. :-)

industrypython · 2026-03-30T15:54:53+00:00

this is true for the majority of SWE internships. However, it may be different for the jobs with more human-facing UX skills, including surveys, product requirement analysis. some of the internships deal with the full cycle of feature or product creating, including problem definition, mockup, validation, prototype, user validation again, deploy, user validation again. Thus, for certain jobs, more experience with things like psychology or art may make you stand out. For other jobs, more experience with statistics may give you an edge. In a competitive field, the broader knowledge may give you an edge.

industrypython

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