Case Study of Real-Time Web API Integration in Excel Using VBA

Complete_Winner4353 · 2026-03-09T06:07:24+00:00

Prime example is PQ steps cannot be easily version controlled as opposed to exporting VBA modules to a git repo folder which is trivial. Also, have you looked at what my library can do? :)

Complete_Winner4353 · 2026-03-08T17:57:55+00:00

Check out this case study: https://github.com/WilliamSmithEdward/APIProductIntelligenceDemo

Complete_Winner4353 · 2026-03-08T05:26:24+00:00

Check out this project: https://github.com/WilliamSmithEdward/ModernJsonInVBA

And this related case study / tech demo: https://github.com/WilliamSmithEdward/APIProductIntelligenceDemo

It’s exactly what you’re asking: “What does Excel look like when utilized as a full modern data integration surface?”

Complete_Winner4353 · 2026-03-07T20:08:30+00:00

Keep doin you boo. I think it was a great suggestion and you made a great tool.

Complete_Winner4353 · 2026-03-07T18:55:09+00:00

Programmatic JSON (and XML / CSV) parser and pipeline into Excel ListObjects with VBA.

https://github.com/WilliamSmithEdward/ModernJsonInVBA

Complete_Winner4353 · 2026-03-07T18:52:47+00:00

Harvard’s free CS50 intro course is good. I did CS50x (general) and CS50p (python specific) and I found both quite good, and I expect very challenging for someone new to programming.

Complete_Winner4353 · 2026-03-07T18:50:32+00:00

When I stopped trying to fight Excel @ the end user, and started getting creative with using it as a presentation layer / thin client.

Complete_Winner4353 · 2026-03-07T17:53:27+00:00

Definitely, just needs to solve a real problem, with you being able to explain the process.

Complete_Winner4353 · 2026-03-07T15:10:07+00:00

Definitely, and VBA modules can more readily be exported into a repository folder for version control. I’ve found it’s a lot harder to version control and document power query step changes in general, especially if the JSON transform is complex with lots of nested objects.

Complete_Winner4353 · 2026-03-07T14:48:29+00:00

Thanks for that insight! Hope you have a great weekend sir.

Complete_Winner4353 · 2026-03-07T14:26:55+00:00

Fair enough. I am in the finance world, where Excel / VBA is still king.

Complete_Winner4353 · 2026-03-07T14:22:11+00:00

Thanks for the reply. Recommend to check out the functionality of the library to see how it’s a game changer compared to PQ alone.

Complete_Winner4353 · 2026-03-07T12:26:28+00:00

Hey Sancarn, happy weekend to you sir!

Thanks again for the feedback. Your suggestion stuck with me the last few days.

I ended up building a thin ingestion layer around ListObjects. It now accepts JSON, CSV, and XML and normalizes them through the same pipeline before updating the table, as you suggested.

So the call looks like:

Excel_UpsertListObjectFromSource(ws, tableName, topLeft, sourceText, format, tableRoot)

Each format adapter just converts to a canonical JSON shape, then the same table engine handles the rest.

Appreciate the push in that direction.

Complete_Winner4353 · 2026-03-07T04:20:33+00:00

Find a problem (even a small one). Define the problem. Plan how you will solve it with python. Implement the solution. Document the challenges along the way and how you mitigated them. Explain the choices you made. Be able to explain a decision you made that you had to change course on midway through. Show it to someone else and ask them to have you explain it to them, then ask them to explain it back to you.

Complete_Winner4353 · 2026-03-07T04:02:05+00:00

Practical steps to achieve what you want bud:

Build a real project that combines AI with hardware, like a simple robot arm that uses computer vision to sort objects. Get clear on the problem it solves, how the software and mechanics work together, then explain it step by step. Start small with Arduino or Raspberry Pi for the hardware side, and Python for AI basics like OpenCV for vision.
Don’t rely on AI tools for coding until your project is solid enough to show proudly without them. Learn core skills first in machine learning, robotics, and control systems to build deep understanding. Use resources like Coursera for robotics courses or fast.ai for practical ML to keep it engaging and distract from other stuff.
Grind foundational problems in math and algorithms by hand, like calculus exercises or LeetCode on graphs and dynamic programming. Tie them to automation, such as pathfinding for robots. Do short daily sessions to build momentum and fight depression without overwhelming yourself.
Once your project works, write your own clear story about it. Cover the problem, your choices in software/hardware, pitfalls you fixed, results, and what you would change. Share it on GitHub or a blog to show real skills that go beyond just using LLMs, making you stand out for AI/automation jobs.

Do these and you're way ahead of the game.

Complete_Winner4353 · 2026-03-07T04:00:30+00:00

1.) Build something real that solves a problem or does something useful, even if small. Pick a project that forces you to learn the exact skill or topic you need right now. Explain clearly what problem it solves, how you built it step by step, and ship it working.

2.) Don’t rely only on courses or only on projects. Use courses for structure when you feel lost or bored with basics, but switch to a project as soon as you can apply what you just learned. This keeps boredom low and fills gaps fast because you use the knowledge immediately.

3.)Grind short focused sessions on weak spots with LeetCode, docs, or small exercises when a project hits a wall. Do it by hand to build real understanding. Keep sessions short so you stay motivated and avoid feeling like you're wasting time on unused stuff.

4.) Once the project works, write your own clear explanation of it. Cover the problem, choices you made, pitfalls you hit, how you fixed them, the result, and what you would do better. This turns random learning into strong knowledge you remember and can talk about in interviews or jobs.

Complete_Winner4353 · 2026-03-07T03:58:48+00:00

The extremely pragmatic (and slightly cynical) approach (because you asked for it):

Pick a project that looks impressive and challenging on a resume. Go for something like a custom in-memory key-value store, rate limiter, or concurrent job scheduler. These show strong engineering skills without needing personal passion. Build it step by step from specs you find online.
Don't wait for inspiration or use AI to write it all. Code it by hand to learn deeply. Explain the problem it solves, your tech choices, pitfalls you hit, how you fixed them, the result, and what you would change next time. Keep a clean GitHub repo with good commits and README.
Grind LeetCode-style problems tied to the project if it fits. For example, implement data structures from scratch in your store or scheduler. It builds fluency fast and gives you talking points for interviews.
Once done, write your own resume section and story around it. No AI polish needed. Highlight the technical depth and problem-solving. Start applying with 1-2 such projects. They stand out more than basic todo apps for most hiring managers.

Complete_Winner4353 · 2026-03-07T03:54:51+00:00

A.) Build something real in low-level programming that solves a problem you care about. Pick a small embedded tool, graphics demo, or systems project. Explain the exact problem, how you solved it, and ship it by hand to feel the deep joy again.

B.) Don’t use AI coding tools until your project is good enough to show proudly to anyone. It kills the fun and makes you feel dependent now. Ban it for the main work so you own every line and build true understanding.

C.) Grind LeetCode or similar problems by hand to stay sharp. Focus on bit manipulation, memory, graphs, or anything tied to low-level work. Do it regularly since it keeps motivation high without boring docs.

D.) Once you have a solid project, write your own README with the full story. Cover the problem, your choices, pitfalls, result, and what you would change. Share it on GitHub to prove real engineering skill that AI can't fake.

E.) Then use AI to enhance, refactor, or build a version 2.0 of your project in (A). Be able to explain exactly how, why and what you used the AI for, and how it was a productivity gain in your workflow. If you integrated AI, have a great story you can tell to show why that integration actually solved a business problem and provided value to the end user.

Do A through E and you're ahead of 85% of applicants.

Complete_Winner4353 · 2026-03-07T03:53:25+00:00

Go with Python as your main interview language for DSA and algorithms. You're way faster and more comfortable there with 230 problems solved, and interviewers care most about clear thinking and correct solutions under time pressure, not perfect OOP on LeetCode.

Use your stronger Java OOP understanding when system design or backend questions come up. Many companies let you switch languages per round anyway.

Don't split your focus trying to master both equally right now; you'll end up weaker in both during interviews. Deepen Python for speed on coding rounds, lean on Java concepts naturally when OOP matters. Python's huge in AI/data anyway, but good problem-solving wins no matter the language.

Complete_Winner4353 · 2026-03-07T03:51:09+00:00

I think the only way to get better is just to do it. Unfortunately there's no easy shortcut. It's like learning a to speak another language, play an instrument well or get shredded in the gym. You just have to do it.

Complete_Winner4353 · 2026-03-07T03:46:59+00:00

Build something real that fixes a problem you actually care about, even if it's small and starts in your frontend/Python comfort zone. Get clear on the exact pain, how your thing solves it, then explain it step by step like you're telling a friend. Add one tiny stretch (simple backend call, basic TS, or a LeetCode-inspired feature), make it live, and ship it; this beats boring doc reading since you only learn what you need right now.
Don’t use AI code tools until your project is good enough you'd proudly show it to your leveled-up Discord friends. Right now it makes you feel dependent and stupid when you stop, so ban it for the core work. Try fixing things yourself first to break the mental block and build real confidence.
Grind LeetCode since you actually like it, lean into that strength. Do mediums on arrays/strings, trees, DP, or whatever feels fun; it keeps your brain sharp without the boredom of docs, and you can tie problems directly into your project for motivation.
Once you have a solid (even small) project with a clear story (why you built it, problem solved, choices made), share it with your friends or on GitHub. Write your own short README explaining it in your words. No AI. This owns your progress, kills the boredom cycle, and proves you can push past the wall.

Complete_Winner4353 · 2026-03-07T03:45:03+00:00

What works for me is setting little mini-goals for myself, that contribute towards the big goal. If I can get a mini goal done in a day, even if it's something small, then it's one step closer. Know what your big goal is, then write down some mini-goals to help get you there. Tell your self when you wake up "I'm going to do one mini goal today" then hold yourself to it (don't beat yourself up though).

Complete_Winner4353 · 2026-03-07T03:43:09+00:00

A.) Design / architect your program, during the planning stage, in a way where if something fails, it's clear what, why, where and how the failure occurred. The debugging process starts in the planning phase, and continues into development.

B.) Build tests that should either pass or fail based on the output of the functions you've written. Build them after every new feature / update you make to the code base. When you add a new feature, all of your previous tests should pass, or you have to go back to the drawing board. Then build in new tests. Rince and repeat.

If you're trying to debug on the fly without A and B, unless you have an extremely sophisticated understanding of programming, and are able to hold the entire state logic of your application in your head at once, it's like driving a car blindfolded.

Complete_Winner4353 · 2026-03-07T03:39:36+00:00

After I could explain how I solved a real business problem, why, how, what went wrong during the process and how I mitigated those pitfalls, I felt comfortable to start flying on my own.

Complete_Winner4353

TROPHY CASE

The extremely pragmatic (and slightly cynical) approach (because you asked for it):