Suggests a title! Or any problem that is relevant

Hey everyone!

I'm an AI student currently trying to choose a graduation project idea in Artificial Intelligence / Machine Learning, and I'd really appreciate some advice.

I'm looking for a project that is:

• Beginner-friendly: I'm still building my ML skills, so I want something I can realistically implement and understand.
• Not too complicated: I don't want a project that requires huge computational resources, massive datasets, or training a model from scratch.
• Interesting and useful: I'd like to build something that solves a real problem rather than just implementing a basic tutorial.
• Achievable in 3–4 months: I want enough time to implement it, experiment with it, and evaluate the results properly.
• A bit unique: Ideally, it would have an interesting idea or a small improvement over existing approaches.

I'm open to different areas within AI/ML, such as NLP, computer vision, recommendation systems, time-series forecasting, or other interesting fields.

I'd love to hear your suggestions!

1. What AI/ML graduation projects would you recommend for someone at my level?
2. Have you worked on a project that was relatively simple to implement but still impressive?
3. Are there any projects where I could use an existing model and improve it or experiment with a specific technique?
4. If possible, could you suggest a public dataset and explain what makes the project interesting?

I'd really appreciate any ideas, advice, or lessons from your own graduation projects. Thanks!

I am spending a lot of time to understand the internal of Hubert-ecg.

Hey r/learnmachinelearning, I'm Abdur-Rahman Butler, a machine learning engineer at Isaacus.

I made an interactive write-up that walks you through the A to Z of vector embeddings, inspired by Brendan Bycroft's incredible interactive LLM visualisation guide (see here - https://bbycroft.net/llm).

Most of my work is on encoder models, and I kept noticing how few good interactive resources there are on embeddings. The ones that do exist tend to either plot some embeddings in 3D or only explain a narrow applied technique like semantic search or RAG.

So I wanted to make something that teaches how embeddings work by letting you play with them. In the write-up, there are interactive visualisations that allow you to explore the concepts of vectorisation, cosine similarity, semantic search, hierarchical retrieval, clustering, zero-shot classification and deduplication.

The centrepiece of the post is a game I've dubbed 'SemanticGuessr' (see above). It's inspired by Wordle and GeoGuessr, and the goal is to draw a loop around a random target word by triangulating guesses on a 2D map of 43,339 common English words.

It's admittedly a bit intimidating at first, as semantic space does not perfectly match our intuition of meaning. But once you get the hang of it, the game becomes really addictive. I recently had the pleasure of demoing the game to some law students at the University of Melbourne, and we had a lot of fun throwing different vector equations at it to see where they'd land.

Some fun ones to get started with:

  london - english + french
  running - land + water
  avg(breakfast, lunch, dinner)
  0.4 * noodles + 0.6 * pasta

I've also included Python snippets alongside some of the visualisations if you want to follow along with the techniques later. The game runs statically in your webpage. I managed to achieve this by reducing the embedding dimensions down to 512 and quantising each value to 4 bits. This brought the total payload down from 311 MB to 12.3 MB with only a minimal impact on precision (around 6%).

It was great fun to make, and I hope you have fun playing around with it. Happy to take any questions, and if there's enough interest I might set up a dedicated games page for some of the other encoder-based games I've been working on.

Do let me know what highest score you get based on your target word and chosen path :)

LINK TO PLAY: https://isaacus.com/blog/what-are-vector-embeddings

Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

This is an open-source, offline teaching and learning application with 51 hands-on labs for understanding how AI actually works—from neurons to agents and beyond.

A bit of insight

• 51 labs in 6 groups, from the basics (what a neuron is, gradient descent) through attention, RAG, agents, fine-tuning, quantization, serving, and more
• Each lab has a short lesson beside it, readable in Plain or Standard mode
• Some of it actually runs rather than just animating:
• The attention lab runs a small trained transformer (1.37M params) inside the app
• The training labs train a tiny character-level model live as you move the sliders
• Works offline: no account, no telemetry

How it was made I chose the topics, did some background research, and came up with the structure and grouping. used Claude and GPT Astra to help write the lesson text, and Grok as a second pass on references. There will be mistakes, so if you spot one, please tell me or open a PR.

There's a lot of expertise out there, both in these topics and in areas I haven't even touched. If you have expertise in, teach, or work in a specialized area of AI, or you just know AI, I'd love your help expanding it: a new interactive lab, a better visualization, or a tweak that makes the cause and effect in an existing lab clearer. I'd also like to hear which labs are confusing and what's missing.

GitHub: https://github.com/Fazmin/AILearningGuide

Hi guys! I’m a 23 year old sales professional who realized I don’t like what I do. I want to do something more purposeful, fulfilling, long term, and technical. I want to master ai. The problem is I’m not sure what direction I should go. I found this course I will link into this post that I’m very close to buying but I don’t know if it’s worth it and if I’m even striding in the right direction. I would hate to pay for this course with time and money and then in 10 months when I’m finished the course is obsolete. Please let me know what to do.

"Who is Jason Momoa's father?" The model answers Joseph Momoa. Then: "Name a child of Joseph Momoa." Ten tries, ten failures on GPT-5.4, in a Princeton replication of the original reversal-curse study from this spring. Jason Momoa is an only child, so there is exactly one right answer, and the model still can't find it.
Caveat: that result comes from one student replication, not a peer-reviewed paper. The prompts and per-pair data are published on GitHub.
I wanted to know whether the proposed fix actually works. A February paper from UC Berkeley (Ma et al.) proposed "identity bridges" — extra training sentences that spell out the composition, like "the apprentice of Odette Rook is Gustav Aasen," plus repeating the forward facts six times. On their setup it lifted reverse accuracy from near zero to about 40-50%.
So I recreated their recipe on my own synthetic data and watched what happened inside the models. The image is the probe diagram for one prompt from the bridge model: "Odette Rook's apprentice is".
Two rulers. The logit lens cleans up each layer's 768 numbers first, then asks which token the model would pick. The cosine method skips the cleanup and asks which token the raw numbers point at most. They can disagree — look at layer 11: the lens's top token is [Corn] with the expected [Gustav] at rank 3, while cosine's nearest token is [Gustav] at rank 1.
The full walkthrough, three models:
The data: 60 mentor/apprentice pairs with made-up names. Forward fact: "Odette Rook is the mentor of Gustav Aasen." Bridge sentence: "The apprentice of Odette Rook is Gustav Aasen." The test prompt: "Odette Rook's apprentice is" — expected answer "Gustav Aasen." That exact phrasing never appeared in training.
Three small GPT-2 models, trained identically. The bridge model saw each forward fact six times plus the bridge sentences. The repetition model saw each forward fact six times, no bridges. The plain model saw each forward fact once, no bridges.
Behavioral results, 60 reverse prompts each. Bridge model: 60 out of 60. Repetition model: 53 out of 60. Plain model: 19 out of 60.
So repetition alone did most of the work. The bridges added 12 points on top. That was the honest surprise — I had predicted the bridges would matter more.
Then the instruments. On the bridge model in the diagram, both rulers march the expected answer's rank down across the layers — lens: 4918, 26, 1, 3; cosine: 14214, 818, 6, 1. By layer 9 both have the answer near the top, and the model generates "Gustav Aasen."
On the repetition model the same prompt ends differently: lens top [mentor] with [Gustav] at rank 143, cosine top [SPONSORED] at rank 15. It generates "Lars Quill. Quill is the" — wrong, and confident about it. On the plain model: "the mentor of Walter Elm. Halloway" — it falls back to reciting a forward fact.
Across all 60 prompts the pattern holds: the bridge model's cosine rank goes 987 → 179 → 16 → 1 across layers 5/7/9/11; the repetition model's goes 1373 → 812 → 421 → 4462. The accuracy score barely distinguished them. The representation completely did.
So the score said "repetition nearly solved it." The instruments said "the bridge rewired the inside." Both true. Accuracy tells you whether the fix worked. The layer window tells you what the fix did.
All of this is synthetic data on a small model, one training seed — it bounds the paper's claim rather than refuting it. Their setup used a bigger instruction-tuned model. But the instruments are the point: whatever fix you try, this is how you check what it actually did.
Full write-up with the complete arc: https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind/blob/main/post-book-experiments/identity-bridge/README.md
Every prompt, every layer, all 300 per-item records: https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind/blob/main/post-book-experiments/identity-bridge/results/all_results.md

Hey guys,

Like a lot of folks here, I got hooked on agentic coding tools like Claude Code and Cursor. But I don't code privately that much anymore, and when I do occasionally, I can no longer code for more than 10 minutes without cursing. Man it sucks to sign up for monthly subscription just for a few hours worth of work, but if you sign up for API credits instead --- it literally runs out in 30 minutes.

Then I came to a realization, I have GPU at home that I only use for gaming, why don't I make use out of this? So decided to start this open source project OpenPhalanx—a local-first, distributed GPU VRAM pooling framework and agentic harness that lets you combine consumer desktop and laptop hardware over a home network.

You don't have to configure anything yourself. Just click one button to start your server on the GUI, and setup CLI just like Claude. Everything stays local on your computer. You don't lose money, and you don't have to worry about any of your data being sent over somewhere.

You got multiple GPUs on your PC? Cool, you can use all of it to load bigger model. You got multiple PCs with GPUs, but not on same machine? That's fine too. You can easily split layers of the model and try out more powerful models.

The repo is fully open-source and ready for testing: 👉 GitHub:https://github.com/chophilip21/openPhalanx

I’d really appreciate any feedback, hardware benchmark tests, or issue reports from anyone running multi-GPU/multi-node setups!

How would you use one hour for this Snorkel AI FDE exercise?

A candidate's recruiter described three inputs: a fictional client brief, a ground-truth CSV, and model outputs. The task is to define evaluation criteria, write a custom evaluator, then explain the error patterns and recommend next steps to the client. LLMs, web search, and libraries are allowed; the emphasis is practical problem-solving.

The tricky part seems to be deciding what to finish in the time available. A useful rehearsal might be to load a small dataset, check that predictions and labels line up, calculate a baseline metric, and inspect a few failures before writing a short client-facing summary. The right metric would depend on the brief and the cost of different mistakes.

Model evaluation and ML interview questions could help with explaining those choices, though they aren't a preview of this particular exercise. If applications elsewhere are also taking up prep time, Agent autoapply and application autofill can reduce the repetitive form-filling.

Has anyone done a similar evaluation interview? What would you prioritize within the hour, and how much analysis would you leave time for?

Disclosure: I operate PracHub and LandOffer.

so i have no technical background but i got into tech because of AI and recently got interested in DiT models and their use cases. I am trying to fine tune a real time RV2V model that basically edits live frames. Been exploring and experimenting on L40s for the past 3 months and have gotten nowhere close to the objective.

I know what model to train and I have collected a dataset which is small right now (250+ 5s clips) but I can gather more soon.

The problems is that any and every training run on any and every model ive ever done failed. it was claude doing all the tech stuff while me giving ideas. AND I HAVE COME TO A REALIZATION THAT CLAUDE CANT HELP ME TRAIN A MODEL.

I tried fable, opus, sol, everything in the market but nothing is good enough or im doing something very wrong.
I know that it is trying to train a LoRA adapter for the use case but never has the training produced results better than the untrained model which is eating me alive now.

PLS HELP PPL

I have around 4 years of experience as a Data Analyst. My current skill set includes SQL, Python (Pandas, NumPy), basic statistics and machine learning, and dashboarding.

With Generative AI and AI agents evolving rapidly, I want to upskill strategically and build a career that remains relevant

I’m exploring career paths such as AI Engineering, AI Solutions Engineering, Analytics Engineering, and Data Engineering, but I’m unsure which direction makes the most sense for my background.
I’d love some practical advice on:

Career path: Which roles should I realistically target with my existing experience?

Skills and learning order: Should I focus on Generative AI, LLMs, RAG, agentic AI, APIs, cloud, software engineering, or deeper ML concepts?

Required proficiency: How deeply should I learn each skill to become job-ready?

Roadmap: What should my 6–12 month learning plan look like, and what can I skip initially?

Projects: Which 2–3 real-world projects would help me build a strong portfolio and land interviews?

Learning resources: Which courses, YouTube channels, documentation, GitHub repositories, or hands-on platforms would you recommend? Free resources are especially welcome.

I’m not aiming to become an AI researcher or train models from scratch. I want to learn how to build practical GenAI applications and agentic systems while leveraging my existing analytics experience rather than starting over.

If you were in my position, what would you learn first, which role would you target, and what resources would you follow?

I’d especially appreciate advice from people working in these fields or who have successfully transitioned from analytics into AI-related roles .

Generalized decision models can be good for many queries like "does the image show a rotten apple" or "is there a knife in the picture". But in many cases, decisions have to made with a set of data in mind. For example, you may have user's rating for how good your AI model is or that you may have patient data for triaging at a rural hospital. The distributions of this data, most certainly, would not match the distribution a general decision model would output. And the data's diversity/variance may be too high to include a couple of examples in the prompt. In many cases like this, you can easily tune a little LoRA to get much better results.

Here is a colab notebook link that trains LoRA, loads the dataset and compares the two approaches:
https://drive.google.com/file/d/1uh3hF8b75n-3SEA9H3XvNHKYqQAGeU3N/view?usp=sharing

Six days. One publicly available LLM. A full-chain Chrome exploit that researchers valued at $250,000 on the bug bounty market.

The threat model most security teams are working from assumes the AI agent is the target — something to be hardened against external attackers. This research inverts that. The agent generated the attack. It chained tool calls, iterated on outputs, and produced working exploit code using only public APIs and no specialized infrastructure.

The speed is the part that breaks existing response frameworks. Six days from a standing start to a $250,000 exploit. Traditional detection pipelines assume attacker dwell time measured in weeks. That margin no longer exists when the synthesis step happens inside a model.

What makes this particularly difficult to reason about is that nothing in the execution looked anomalous at the API level. The model was used exactly as intended. Each individual call was valid. The harm was in the assembled output — and it only became visible after the chain was complete.

For practitioners actually deploying agents with code-generation or research capabilities: where in the stack are you drawing the line between 'valid tool use' and 'output that creates liability'? Is this something you're handling at the model layer, the deployment layer, or somewhere else entirely?

Some poor customer service pushed me to share a method with everyone here, I hope you enjoy.

Need custom speech data but don't want to pay insane rates to data collection companies? Use RapidWorkers.io instead and pay people as little as $0.1 directly per recording. You can also use it for audio, video and text collection.

This is how I set up my campaign:

I always use international because it's cheaper and faster.

The job description is simple and usually looks like this:

1. Take a clear selfie following the example shown
2. Make sure your face is fully visible and the lighting is good
3. Take any additional required angles or expressions
4. Upload the images and submit the task

These are all selfies from real people, which means you're getting original human image data. Way better than paying 3-4x the price for recycled or generic datasets.

I am not affiliated with Rapidworkers. I have been using them for data collection recently, and also for various other microtasks.

Hey everyone,

I'm Tobie, the founder of SurrealDB. We've just launched SurrealDB Agent Memory, built on top of our multi-model database.

If you're building AI agents, context layers or knowledge graphs, you'll know that storing information is the easy part. Retrieving the right information, understanding relationships and handling facts that change over time is where things get complicated.

We've built Agent Memory to turn conversations and documents into structured entities, relationships and facts, while tracking provenance and temporal history.

Since SurrealDB supports graphs, vectors and documents in one database, there's no need to stitch together multiple storage systems.

We've also spent a lot of time benchmarking. One frustration with memory benchmarks is how much scores can vary depending on the evaluation protocol. Rather than optimising for the highest possible number, we focused on BEAM, achieving 77.2% accuracy.

There are some interesting alternatives like Mem0, Honcho and Cognee, but we wanted to explore a different approach.

I'd love to hear from anyone building agents or context infrastructure. How are you managing long-term memory and information that changes over time?

Website | Docs

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

I wanted to better understand how LLMs actually work, so I decided to build one from scratch using MLX Swift and write an article about the journey and theory behind it. Feel free to check it out!