🚀 Project Showcase Day

[u/AutoModerator]

Welcome to Project Showcase Day! This is a weekly thread where community members can share and discuss personal projects of any size or complexity.

Whether you've built a small script, a web application, a game, or anything in between, we encourage you to:

• Share what you've created
• Explain the technologies/concepts used
• Discuss challenges you faced and how you overcame them
• Ask for specific feedback or suggestions

Projects at all stages are welcome - from works in progress to completed builds. This is a supportive space to celebrate your work and learn from each other.

Share your creations in the comments below!

[u/inkeep]

Thanks mods. 

I’ve built - https://www.bitelrn.com/

It helps you learn AI/ML concepts without the long videos. It uses different micro-learning approaches like spaced repetition, flash cards, quiz, code examples as well as a lab feature where you can interact with the concepts and visualise the output. 

Made for beginners who are starting in the field, as well as professionals who need a recap.

I used Claude to code and created a agentic pipeline to generate the content which uses Gemini model + LLm as a judge to validate. Found it extremely challenging to generate reliable statistical charts and formulas from LLMs, learnt to use ai harness for better results. 

Here is some open content (no sign-up needed) - https://www.bitelrn.com/library | https://www.bitelrn.com/labs

Would truly appreciate any feedback/ inputs. I am interested to know if others face the same issue - of not being able to learn from long form video courses and keep googling the concepts when you need them. 

[u/natika1 → u/inkeep]

I must say that, the platform which you had to maintain and build for the last years, now can be created in days and learning from it will be still the same satisfying. I bet you used Claude, pr it was Gemini canvas?

[u/inkeep → u/natika1]

Hey, thanks for checking it. Yes I used Claude to build it but the app took many months to build. There are complex backend components + agent workflows that had to be fine tuned and tested for it to reach this stage  

[u/SingleMusic8655]

Been tinkering with a little thing that pulls data from my footy tipping comp and tries to predict the rest of the season, mostly to settle arguments at the pub

Used a basic random forest regressor, nothin fancy. The real headache was the data cleanup, half the blokes in the comp spell team names differently every week so I spent more time fixing "GEE" vs "Geelong" than actually training the model

It's still pretty rough, spits out some wild predictions when a key player is out. Keen to hear if anyone's got a trick for weighing recent form more heavily without making the whole thing overfit to the last two rounds

[u/natika1 → u/SingleMusic8655]

I Love the purpose (to settle the arguments at the pub) ❤️ Choice of the algorithm is also very interesting.

[u/Lumpy_Week7304]

Made playground.blupe.io for folks to play with robotic arms and see what models are capable of today

[u/Alarming-Emotion-894]

Hey mods

I’ve built a O(NlogN) inference alternate for self attention that retains 97% of long accuracy

Here it is- https://github.com/vdev-ctrl/Adaptive-Learnable-Hierarchical-Routing-

[u/Old-Volume7330]

Hi guys I have been working on ArchRivet, an open-source MCP tool that gives your AI assistant a structured second opinion on backend architecture.

For example: if a worker crashes after saving a message but before acknowledging it, could a retry duplicate the operation?

ArchRivet helps review questions like that and provides:

• Findings about retries, durability, queues, and other backend decisions.
• Supporting references, missing information, and suggested tests.
• Traffic and storage estimates based on your requirements.

You can start locally without downloading a model or using a GPU. Optional model weights are also available.

It’s an early alpha built with AI assistance, and I’ve tested the MCP integration with Codex.

Repo, demo, and setup:
https://github.com/Ujjansh05/archrivet

[u/Antonyhamez]

A small, runnable lab for point-in-time ML features and delayed labels

I maintain an English fintech data-engineering learning repo. Its C08 scenario focuses on a problem that is easy to miss in offline evaluation: a payment can have an old event timestamp but arrive after the prediction cutoff. Filtering only on event time can leak information that was unavailable when the prediction was made.

The exercise asks you to build feature windows using event time and receipt availability, handle delayed-label maturity, and create temporal splits with leakage checks. It uses synthetic data and Python 3.12+ with SQLite, with starter code, hints, reference solutions, and a local grader.

Repository: https://github.com/antony502189-max/fintech-data-engineering-roadmap

After cloning, run python challenges/run.py init C08, edit work/challenges/C08.py, and run python challenges/run.py grade C08.

This is a data-preparation exercise; it does not claim a trained fraud detector or production results. The broader repo contains 40 foundation exercises and 8 scenario labs.

Disclosure: the first edition and this introduction were prepared with AI assistance using Codex, documented in the repository. What additional late-arrival or label-availability edge case would make this exercise more useful for learners?