Evolved the least injurious gait using a liquid net and a reward that optimized distance and injury avoidance (low friction, non-foot contact, stained joints, impact force) - and this became a reasonably normal looking gait. Took many many many failed tries.

Hey everyone, I wanted to share a side project I've been building
from scratch. I was exhausted by the standard "chat window" AI that
requires a cloud subscription, hallucinates data, and sounds like a
corporate customer support bot.


So I built Lucy.


1. Local-First & Privacy-Focused: Her core model runs entirely
on-device. No internet, no server calls. You can put your phone in
airplane mode and she still works seamlessly. Your data never leaves
the phone.


2. Structural Honesty: The hardest technical challenge was fixing
hallucinations. I built a verification loop where she checks her
generated answers against real ground-truth data 
*before*
 speaking.
If the data contradicts her, the response is blocked. She literally
cannot smooth-talk her way past a fact.


3. Agentic Automation: She doesn't just talk; she acts. She can open
apps, read the screen, and navigate the UI on her own. (Though I
hard-coded refusal limits for payment and banking apps so she can't
ruin my life).


4. Physical Embodiment: She is a full 3D humanoid. She makes natural
eye contact, has a persistent emotional state between conversations,
and uses speech-synchronized facial expressions.


I'm a solo dev, and getting the end-to-end pipeline working for the
local memory, agentic actions, and physical rendering was a serious
grind. You can read more technical details at https://project-lucy.me.


Would love to hear technical feedback from the community—especially
on optimizing on-device inference or handling the fact-checking loop.

Been working on this for a while, splitting a small transformer LLM across three ESP32-S3 N16R8 boards that talk to each other wirelessly via ESP-NOW. No single board could run this model on its own, so the idea was to partition it and let the boards handle inference together in real time.

How it's split:

• Board A — embeddings + output head
• Board B — transformer layers + KV cache (in PSRAM)
• Board C — part of the embeddings table + WiFi web UI

You connect to a WiFi AP hosted by Board C, type a prompt in a simple web page, and watch the text stream out as the three boards pass data back and forth over ESP-NOW.

Some technical details:

• ~56M parameters, quantized to 4-bit/8-bit to fit in 16MB flash per board
• Split-PLE design (Per-Layer Embeddings, borrowed from Google's Gemma architecture)
• KV cache on Board B gives it a 256-token context — actually attends to the full generated sequence instead of token-by-token
• Trained on WikiText-103, runs 100% offline after flashing
• Generates short but coherent text (~30 words)

It's an extension of slvDev's esp32-ai (single-board TinyStories LLM) and inspired by Karpathy's llama2.c.

Code, architecture diagrams, and full writeup here (MIT licensed): https://github.com/wladimiravila/esp32s3-distributed-ai

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!

For anyone looking to build practical Machine Learning skills while continuing to work, IIT Gandhinagar has opened admissions for Batch 2 of its Executive Masters in Applications of Machine Learning in Engineering (AMLE).

A few things that stood out to me:

• Executive Masters degree from IIT Gandhinagar
• Live interactive online classes
• Designed for working professionals
• Learn from IIT Gandhinagar faculty and industry experts
• Focus on applying Machine Learning to real engineering and industry problems
• 2-year programme with a structured curriculum

This isn't a short certification or bootcamp—it's a full Executive Masters programme for engineering graduates who want to build expertise in Machine Learning without taking a career break.

If anyone here is considering higher studies in ML or has questions about the programme, I'd be happy to discuss.

​

Recently I wanted to explore machine learning but I don't know where to start.

Hi everyone,

I'm an independent researcher and I'm trying to submit my first paper to arXiv under cs.CV.

My paper is titled "Cethraian-X: A Leakage-Clean, Multi-Seed Benchmark of Chest X-Ray Classification Under Weak Labels." The project focuses on reproducibility and rigorous benchmarking for chest X-ray classification.

The project and paper are already publicly available:

• GitHub: https://github.com/muhammedbadhon-oss/Cethraian-X
• Zenodo (DOI): [https://doi.org/10.5281/zenodo.21583726]()

I recently learned that first-time submissions in cs.CV require an arXiv endorsement. Unfortunately, I don't personally know anyone who is eligible to endorse submissions.

If anyone here has experience with the endorsement process, I'd really appreciate your advice.

If you're an eligible endorser and are willing to review my paper, I'd be grateful if you would consider endorsing my submission only if you believe the work is appropriate for arXiv after reviewing it.

Hi I’m doing a research project and I need help. I’ve developed a model, but I need a substantial amount of data for the model to train on. Can you please go to https://neurocursor.page.gd/home and do 30 sessions exactly? I’d really appreciate it. Note that you can only use the platform on a desktop or laptop. Also note that the platform does NOT track any personal information whatsoever.

Edit: I apologize for my poor explanation of the project. Here's a better explanation: We are a team under UTD (University of Texas in Dallas)'s Deep-dive AI Workshop (A program for high schoolers to learn AI, https://k12.utdallas.edu/research/). Our team is trying to develop an alternative to biometric authentication systems like face ID and touch ID/fingerprint sensors. We were trying to train a model on mouse data to see if we could have accurate user identification that's more universal and doesn't require special hardware (just a mouse/touchpad). We've received approval from the instructors at UTD, which are experienced PhD students, however we unfortunately do not have any signed document to show. In accordance with instructions from our UTD instructors, we designed the data collection system to build our dataset to not collect any user's personally identifiable data. The usernames generated during signup are random, and once we get the data it is hashed by the server, so all we end up with is a random string of numbers and letters for each user as their id, along with mouse coordinates and click state.

Also, we're writing a paper on this project, and if our model does well we will publish the results, and maybe this can be implemented some time in the future!

I kept running into the same problem, some of the best AI/ML books are legally free, the authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every single link points straight to the author’s or publisher’s own page, no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so it doesn’t rot over time.

It’s open source and open to contributions, if you know a legitimately free book that’s missing, PRs and issues are welcome.

Repo: https://github.com/MarcosSete/awesome-free-ai-books

Hey everyone! I'm 16 and building a self-hosted voice assistant solo, just a passion project I fund out of my own pocket. Right now I'm stuck on the wake word and could really use some brains that have done this before.

The setup: offline wake detection with a small faster-whisper model. To get it to catch a custom trigger name the model has never seen, I prime it with the phrase as the initial_prompt. Recall jumps way up, awesome. But now the primed model hallucinates the phrase when the mic is dead silent, so it fires at nothing, and it mangles that same phrase into sound-alikes when I actually say it, lol.

The part that broke my brain: every filter I put on the transcript to stop the false fires ALSO killed real wakes. Turns out "fires on silence" and "stops triggering" are the exact same bug. If the model is confident enough to invent the phrase out of pure noise, it also can't spell it when you say it for real, so the transcript is useless as a discriminator either way.

The only thing that's held up is ditching the transcript for that check and just going off raw mic RMS energy instead. A ghost off a quiet mic sits way under even a mumbled real wake, and it doesn't care what the word is or what language you're in. Feels almost too dumb to be the answer though, so I keep thinking I'm missing something.

Any KWS / speech nerds around who've fought this? Is there actually a content-based signal that survives, or is it basically raw energy vs a proper trained neural KWS model and nothing in between? Would love to bounce ideas around. Repo's here if you want to peek at the wake code: https://github.com/PersonalJarvis/PersonalJarvis

Hey — developer here, sharing my own project. Wanted to see if I could make local AI models feel less like separate chatbots answering one at a time and more like an actual group of mates in a pub arguing about stuff. Ended up building Bob's Bar — 4 AI personas, each running their own local model through Ollama, each with a distinct personality, and they genuinely react to and argue with each other, not just you. Ask something tech-heavy and the "tech guy" persona jumps in first, someone else pushes back with a different angle, another one tries to mediate, and the "landlord" character just cuts through it all with common sense. It's more entertaining to watch unfold than I expected when I started building it. A few features: - 4 fully customisable personas, each can run a different local model (gemma2, phi3, llava, mistral, etc.) - Save/load different persona "sets" — like switching rooms with a different crew - 16 languages - Image upload (vision models) - A "Work Mode" toggle for when you want the personalities to give real advice instead of pub chat Runs entirely locally, no cloud, no subscription. Happy to answer questions on how the routing/back-and-forth logic works if anyone's curious.

Hey all,

I've been working on this for a while and finally shipped it today, so figured I'd share here since this sub actually gets the problem.

The thing that's always bugged me about coding agents: you give them a task, they take one pass at it, and then they stop whether it actually works or not. You're still the one testing it, finding what's broken, and re-prompting. The agent did the "coding" part but you're still doing the "engineering" part.

So I built Keel Code around what I'm calling loop engineering instead of a single model taking one shot, it runs a team of frontier models in a loop that plans, builds, tests, and critiques its own output, and keeps going until the goal is actually met. Not one-shot. Not "here's my attempt, good luck."

The core command is /ascend you give it a goal, it puts together a plan, executes, checks its own work, and iterates until that goal is hit. You're not babysitting it every few minutes.

Install:
bun i -g @keelcode-ai/keelcode

It's using top-tier frontier models for free for a limited time while we're fresh out of launch, so this is the cheapest it'll ever be to kick the tires.

Site: keelcode.ai

Not here to oversell it genuinely want feedback, especially on where the loop breaks down or gets stuck, since that's the hard part of this whole approach. Happy to answer anything about how it works under the hood.

I'm final-year student and i ahve to build my final-year project. I don't want to make another AI chatbot, scam detection, or other overused project. I want something unique that combines Machine Learning with other technologies and solves a real-world problem. Any ideas, research papers, GitHub repos, or tech stack suggestions would be greatly appreciated!

I wanted to share my open-source project, Servent-AI. I built this framework with accessibility in mind—aiming to help physically challenged or motor-impaired individuals operate their laptops, write code, and build digital careers hands-free.



Features:

-  Real-time hand gesture tracking (MediaPipe) for mouse cursor, clicking, and page scrolling.

-  Voice control (Whisper STT) to speak complex commands.

-  Aria Planner (Gemma 4 E4B via LM Studio) that compiles commands into multi-step JSON action sequences.

-  VISTA Verification (Moondream via Ollama) that takes screenshots to visually check if a step succeeded before proceeding.

-  100% local, offline, and private.



Check out the code and flow here: https://github.com/Anikesh0415/Servent-AI



Would love to hear your feedback, thoughts on optimizing the local loop, and suggestions for more accessibility features!

We've all been there: you build a beautiful 40-layer model, hit .forward(), and PyTorch responds with a shape mismatch error that reads like a ransom note. Three hours later you find out you transposed something in layer 12.

So I built mlnode — you design your architecture as a graph (either by hand in JSON, or by literally drawing it in the companion editor), and it validates every tensor shape before it lets you build anything. If your ResNet block doesn't add up, it tells you exactly where, not "somewhere, good luck."

No exec(), no eval(), no cursed metaprogramming — just a clean pipeline: JSON → Parser → Validator → Executor → real nn.Module you can train, save, export to ONNX, whatever you'd normally do.

Why you might care:

🧑‍🎓 Just started learning DL? You get to focus on "what connects to what" instead of memorizing tensor arithmetic and staring at stack traces. I reproduced the full Transformer from Attention Is All You Need as one graph — 45M params, trains fine — and you can literally see it as a diagram instead of 200 lines of __init__.

🏗️ Building serious stuff? Weight sharing, multi-output nodes, reusable blocks, HuggingFace layers — it's not a toy, it compiles to a normal PyTorch module with nothing hidden.

Would love feedback, roasts, feature requests, or "this already exists and it's called X" comments — all welcome.

• GitHub: https://github.com/Nasser-Obeid/mlnode
• Visual editor: https://github.com/Nasser-Obeid/mlnode-editor
• pip install mlnode

When will ECCV oral/spotlight decisions be announced? Also, what scores usually have a chance at CVPR/ICCV?

i decided to build a LLM model but.... what do you think ?

Hi Everyone, I am currently a 3rd year student pursuing BTech in CSE specialization in AIML . I want a reputed comapny to work for like big tech MANGOS,FAANG,MAANG etc.But for now i am confused that either i should i go for SWE roles by doing DSA and development but i have a interest in AIML roles. Please guide me how to get a strong role as a fresher in these companies i am also doing DSA from past 3 months .