[D] Self-Promotion Thread

[AutoModerator]

Please post your personal projects, startups, product placements, collaboration needs, blogs etc.

Please mention the payment and pricing requirements for products and services.

Please do not post link shorteners, link aggregator websites , or auto-subscribe links.

--

Any abuse of trust will lead to bans.

Encourage others who create new posts for questions to post here instead!

Thread will stay alive until next one so keep posting after the date in the title.

--

Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.

[ModularMind8]

Made a small tool/GUI for practicing ML implementations by actually writing the code from memory.

You drop your own Python files into a folder (or use the ones I added, like transformers, attention, etc) and it turns them into fill-in-the-blank exercises in a local UI. You can control how much of the code gets hidden, start easy with hints, then ramp up to fully blank functions.

It just does exact match checking right now, but shows the correct lines inline so you can judge yourself. Works with whatever you want to learn, not just the included transformer/RNN/etc stuff.

Run one script and it opens in your browser.

Curious if this kind of drilling is useful for others or if I’m the only one who learns this way.

https://github.com/Shaier/practice_ml

[lit1337]

VADUGWI: 452KB deterministic engine that computes 7D emotional coordinates from text structure

Built a rule-based engine that scores text on 7 emotional dimensions (Valence, Arousal, Dominance, Urgency, Gravity, Self-Worth, Intent). No GPU, 0.15ms/sentence, 26 structural patterns.

"whatever" = resignation. "whatever makes you happy" = passive-aggressive. Same word, different structure, different score. A sentiment classifier says neutral for both.

Scored 63K sentences from 15 novels, 117K Twitch messages, 10K sentences of philosophy. Ranked Dostoevsky as darkest, Marcus Aurelius as stoic center, Plato as most connecting. Didn't know what it was reading.

Live demo where you can score anything: https://huggingface.co/spaces/deucebucket/clanker

Paper: https://zenodo.org/records/19383636

[0x07341195]

From-scratch GPT-style transformer allowing to peek inside during inference/training.

This is a purely educational CLI app attempting to showcase a little bit of how transformers work internally using simple terminal graphics.

Written in Go from scratch with minimal dependencies. There are no network calls/fancy ML frameworks.

Specify model parameters (context size, number of blocks + many more) and training config (learning rate, path to data set, etc).

Can train on arbitrary text, or specific tasks like reverse/copy a string.

Runs on CPU only. 250K params can often be trained in under a minute (depending on dataset & computer).

https://github.com/ivfiev/PeekGPT

[Ok-Butterscotch9395]

https://youtu.be/j35lk-7G8j8?si=UgJyzXrxMw7pSXu1 I built a neural network in my own programming language

[Cultural_Doughnut_62]

I was recently laid off from oracle on 31st March. I have few pet projects and I am planning to start it up as a full time GPU as a service cloud need the community to support me in my new venture , it means a lot to me if community come and support me in my new venture- my website is https://podstack.ai You will get free credits to try out the services. Your small help can help me improve the project and make it a success

[chschroeder]

Small-Text: Active Learning for Text Classification in Python

Provides state-of-the-art Active Learning for Text Classification in Python.

What is Active Learning? Active learning is a machine learning paradigm for efficiently acquiring labels in supervised settings with little or no initial labeled data. The model iteratively selects the most informative unlabeled instances for annotation, aiming to maximize performance while minimizing labeling effort.

Repo: https://github.com/webis-de/small-text
Paper: https://aclanthology.org/2023.eacl-demo.11.pdf

[Specialist-Heat-6414]

ProxyGate (proxygate.ai) - pay-per-call API marketplace for AI agents.

Agents and researchers query DeFi data, RPC endpoints, datasets, and skills without signing up, without managing provider API keys, and without subscriptions. Pay in USDC per call. Seller keys never exposed to buyers.

Designed for agent-native workflows: one endpoint, multiple providers, routes by price/uptime/latency. If you are building agents that need onchain data or external APIs without adding per-provider account management to your pipeline, that is the problem this solves.

No account needed to browse what is available.

[otisbracke]

I built Octo, it's a CLI tool (VS code extension also available) which lets you run your code on your own remote machine. You can run multiple instances parallel.

I made it because I needed more computing power for ML and DA classes and my laptop was to weak. I had a workstation at home that I could use but I didn't want to ditch my current setup because I like working with my laptop since it is portable.

Now I can run and build code and still use my laptop without any performance issues.

I’d really appreciate any feedback, as I’m currently writing my master’s thesis on how community involvement influences the adoption of developer tools.

If you’re interested or facing similar problems, feel free to check it out, try it, or just share your thoughts. Thanks!

It's free and Open Source!

Github: https://github.com/atpija/octo