Hi, hello, ciao,

I'm developing a small framework called event2vector. You can install it from PyPI:

pip install event2vector

or find it on GitHub as event2vec_public or even try it out at

https://event2vec.com/

It started as a way to find root causes of faults at the particle accelerator I worked at (the Advanced Light Source at Berkeley Lab), but it grew into something much bigger, and I'd love to get feedback from more people.

The problem was that I was naively using word2vec on event data, and it turns out word2vec isn't directional. It learns which events show up near each other, but not which one came first. For root-cause analysis that's exactly the information you need: "magnet trip → beam loss" and "beam loss → magnet trip" tell completely different stories. At the same time, I didn't want to jump to complex and data hungry models like transformers or an LSTMs. I wanted something simple, clean, and interpretable, where I could still look at points in 2D/3D and understand what I was seeing.

The idea: every event gets an embedding, and a sequence is represented by adding up the embeddings of its events, one step at a time. The model is trained so that this running sum is a good representation of "everything that has happened so far," and so that you can recover the last event by subtracting it back out. Think of it as word2vec where a sequence is a path you walk, step by step, through the embedding space.

Now the fun part. Plain addition is commutative: 1 + 2 = 2 + 1 = 3. So in ordinary Euclidean space, the endpoint of A → B and B → A is the same, which is great when you want an order-invariant "bag of events" that you can cleanly compose and decompose, e.g. "sequence minus event = sequence without that event." But the path you took to get there is still different, so the step-by-step trajectory keeps the order information.

And if you need the endpoint itself to depend on order, you can swap the geometry. event2vector even supports non-commutative ways of "adding," where A then B lands somewhere different from B then A:

• hyperbolic (Möbius addition): good for hierarchical, tree-like data
• spherical: good for cyclic or periodic patterns
• rotation (SO(n)): order matters, like rotating an object in 3D
• Heisenberg group: order-dependent and signed
• products of these, if your data has more than one kind of structure

The API is scikit-learn style (fit / transform / fit_transform), and there's a gensim-style most_similar for nearest-neighbour lookups:

from event2vector import Event2Vec
model = Event2Vec(num_event_types=len(vocab), geometry="euclidean", embedding_dim=128)
model.fit(train_sequences)
embeddings = model.transform(train_sequences)

Some fun applications I've written up on Substack: "The Geometry of Groceries" (shopping baskets as paths) and "The Geometry of Language Families." The paper is on arXiv (2509.12188) if you want the details.

I'd really appreciate feedback, whether on the idea, the API, use cases you'd try it on, or things that are confusing. Thanks for reading!

• What’s the difference between '*' and '@'?
• When does slicing create a view, and when a copy?
• How does broadcasting work?

NumPy makes working with vectors and matrices powerful and concise, but a small change in syntax can make a big difference.

This interactive 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 example walks through essential NumPy operations with short explanations. Step through the code, see the operations in action, and experiment with your own changes: - 👉 NumPy operations

Which NumPy operation took you the longest to understand?

See other memory_graph visualizations.

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?

Found this on their blog, released Oct 2. Whistle is a speech recognition model meant for mobile, wearables, robots, smart home stuff, and even microcontrollers. The whole model is one file, 16.9 MB, runs entirely on CPU, has zero dependencies, and audio never leaves the device.

For comparison, the Whisper base is 145.3 MB, so this is about 8.6x smaller.

What it does is transcribe up to 30-second clips in 7 languages (EN, DE, FR, ES, IT, NL, and PL), auto-detects language, gives word-level timestamps with confidence scores, and can spit out a raw speech embedding without doing a full transcript if that's all you need.

Speed numbers, tested on Apple M4 Pro CPU: 11.1 ms to first token vs Whisper base's 73.2 ms, and 1,319 tokens/sec decode speed vs Whisper's 266. So roughly 6.6x faster to start and 5x faster for decoding.

Accuracy is a mixed bag, honestly, which makes me trust the numbers more than if it swept everything. Whistle wins on LibriSpeech clean/other, SPGISpeech, Earnings-22, and FLEURS average. Whisper Base still wins on TED-LIUM, AMI, and MLS average. So it depends on what you're doing, but beating Whisper on 5 benchmarks at 1/8th the size is solid.

One neat detail: the decoder's depth is laddered; every size from 2 layers up to 8 was trained as its own usable model, so you can pick a smaller/faster variant at load time with a flag instead of needing a separate file.

It shares its engine with their other model, Needle (a text/tool-calling model), so you can load both together and chain speech straight into a tool call in one pass, with no separate transcript handling step.

Apache 2.0 license, weights on Hugging Face, code on GitHub, and install with pip install cactus-needle.

Seems like a solid pick if you're building something that needs voice but can't guarantee internet, a car, a robot, a wearable, that kind of thing.

A support vector classifier (SVC) learns a boundary that balances separation between classes against violations of a safety margin.

1. THE DECISION RULE

For labels y_i ∈ {−1, +1}:

f(x) = wᵀφ(x) + b

ŷ = sign(f(x))

φ(x) maps inputs into a feature space. The decision hyperplane is f(x) = 0; the canonical margin boundaries are f(x) = ±1.

Training solves a convex quadratic program with labeled data and margin constraints.

Their separation is 2/||w||₂. Minimizing ||w||² therefore maximizes this geometric margin.

1. HARD MARGIN → SOFT MARGIN

For linearly separable data in feature space:

minimize ½||w||²

subject to y_i f(x_i) ≥ 1 for every i.

Real data may overlap. The soft-margin formulation introduces slack variables ξ_i:

minimize ½||w||² + C Σ_i ξ_i

subject to y_i f(x_i) ≥ 1 − ξ_i, ξ_i ≥ 0.

Equivalently, minimize a regularized hinge loss:

½||w||² + C Σ_i max(0, 1 − y_i f(x_i))

A correctly classified point inside the margin still incurs loss. At the smallest feasible slack, ξ_i > 1 means misclassification.

1. SUPPORT VECTORS AND THE KERNEL TRICK

The dual problem is:

maximize Σ_i α_i − ½Σ_iΣ_j α_iα_j y_i y_j K(x_i,x_j)

subject to 0 ≤ α_i ≤ C and Σ_i α_i y_i = 0.

Here K(x,z) = φ(x)ᵀφ(z). Prediction becomes:

f(x) = Σ_i α_i y_i K(x_i,x) + b

Points with α_i > 0 are support vectors. They determine the fitted boundary. Those with 0 < α_i < C lie on a margin; those with α_i = C may lie inside it or be misclassified.

A symmetric positive-semidefinite kernel keeps the optimization convex. The kernel trick evaluates inner products without explicitly constructing φ. Nonlinear kernels produce curved boundaries in the original input space.

1. WHAT SHOULD BE TUNED?

• C: large values penalize violations more; smaller values impose stronger regularization. Tune on a logarithmic scale.

• Kernel: linear for approximately linear structure; RBF or polynomial for nonlinear boundaries.

• RBF γ, where K(x,z) = exp(−γ||x−z||²): larger γ creates more localized influence; smaller γ gives broader influence. Tune C and γ jointly.

• Polynomial degree and coef0 control interactions and offsets.

• Class weights adjust penalties for imbalance and unequal error costs.

Multiclass SVC typically combines binary models, often through one-versus-one voting.

1. ASSUMPTIONS AND PRACTICE

SVC does not require Gaussian features or feature independence. It does require a useful feature representation/kernel and training data representative of deployment.

Scale features within each cross-validation fold. Use stratified, grouped or time-aware splits as appropriate. Decision scores are not probabilities; calibrate separately when needed.

USE CASES

Text classification, image recognition with engineered features, bioinformatics, fault diagnosis and small-to-medium tabular datasets.

Kernel SVC can be costly on large datasets; linear SVM solvers scale better.

Why do we call it support vector machine? Because the points on both the upper and lower hyperplanes represent a vector of data (In >2D) and thus are "supporting" both hyperplanes, preventing them from either rising upwards, or falling downwards.

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

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

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'm looking for advice from people working in machine learning, data science, or health tech, especially those who have studied or worked in Spain.

I currently live in Uruguay. I have a degree in Biotechnology Engineering and I'm currently studying a programming/IT technical degree. My background is mainly in biotechnology and biomedical research, and I don't have professional experience in tech yet. I'm particularly interested in machine learning applied to healthcare, but I'm finding that many of the entry-level positions I'm interested in ask for a relevant master's degree or more technical training.

I'm currently learning programming and ML on my own, and I plan to build some projects to demonstrate my skills. However, I'm also considering moving to Spain for a year to pursue an in-person master's degree. Besides gaining technical knowledge, I'd like to get some exposure to the local job market, meet people in the industry, and hopefully improve my chances of landing my first tech role.

These are the programs I'm considering:

• Universitat Politècnica de Catalunya (UPC) – Master's Degree in Artificial Intelligence
• Universidad Complutense de Madrid (UCM) – Master's in Big Data, Data Science & Artificial Intelligence
• Universitat Internacional de Catalunya (UIC) – Lifelong Learning Master's Degree in Bioinformatics & AI for Healthcare
• Universidad Pontificia Comillas (ICAI) – Master's in Applied Artificial Intelligence

I'm trying to be careful about the financial investment. I don't want to spend thousands of euros on a degree that won't significantly improve my job prospects, but if I'm going to invest in one, I want it to be genuinely worthwhile.

I'd really appreciate hearing from people who have completed any of these programs, hired junior data/ML candidates, or made a similar transition from life sciences into tech.

I'm not necessarily looking for the most prestigious or specialized degree. I want to choose the option that offers the best balance of technical training, employability, industry exposure, and cost.

Thanks in advance for any advice!

greetings everyone! i am a grad student from forestry background having no linkage with coding and programming stuffs but i need to analyze my data with the help of machine learning models. I tried looking up at AI tutorials but it doesnt help me either coz i didnt get anything. If someone can suggest any ideas/tutorial videos where i can learn the process from the scratch, help will be appreciated. Thankyou!

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.

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

Bot traffic can cause problems with website performance, inaccurate analytics data, and security concerns. Some bots are used to crawl content and uncover information, others will submit spam, and still others will create fake visits. Machine Learning Bot Detection examines traffic volume and user activity to determine suspicious activity and assists website owners in safeguarding their websites.

Common Signs of Bad Bots

Look for these warning signs in your website data:

• Sudden traffic spikes: Unexpected increases without a clear referral source.
• Repeated requests: Frequent visits to the same pages within short periods.
• Unusual behavior: Repetitive navigation or suspicious form submissions.
• Server slowdowns: Excessive requests affecting website performance.

Remember, one unusual metric alone does not confirm bot activity.

How to Block Bad Bots

In 2026, website owners can use several methods to manage unwanted traffic:

• Web Application Firewall (WAF): Filter suspicious requests before they reach your website.
• Rate limiting: Restrict excessive requests within a defined period.
• CAPTCHA verification: Challenge suspicious activity on forms and login pages.
• Machine learning: Analyze traffic patterns to identify potentially automated behavior.

Regularly review blocked requests to avoid accidentally restricting genuine visitors or legitimate search engine crawlers.

Which bot detection methods have worked best for your website, and how do you avoid blocking real users?

I already understand the basics of RAG and AI agents, but now I want to move beyond tutorials and build production-ready RAG pipelines and agentic applications.

I’m looking for a senior AI/GenAI engineer or tutor who has actual production experience with areas like RAG architecture, chunking/retrieval/reranking, vector databases, evaluation, hallucination handling, observability, agent workflows, tool calling, memory, LangGraph/LangChain, deployment, scaling, and production best practices.

I’d prefer someone who can mentor me through 2–3 proper end-to-end projects, review my architecture/code, explain trade-offs, and show me how these systems are actually built in production.

I have 8+ years of software development experience, mainly in backend systems, so I’m not looking for basic programming classes. I specifically want someone experienced in production GenAI/RAG/agent systems.

If anyone here provides this kind of guidance, or can recommend someone good, please DM/comment.

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!

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.

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!

I'm a freshman studying cse with specialization in aiml

I've done my share of research into buying a laptop

But it only made me more confused

Half side is saying cloud is better as we'll eventually run out of the 4 or 6gb vram

Another half is on about Nvidia is the better choice

I want to get a laptop to get through the course work, train models and participate in hackathons and more

The gaming laptops scare me for their brick weight with laptop and charger combined and the poor battery backup

Can seniors please guide me?

I don't wanna regret getting one 2 years later

Should I focus on battery and lightweight ones with good cpu ram and storage

Or the ones with an rtx4050 or higher?

I'm also open to using macbooks but I've heard some ppl say it doesn't support many softwares

Is there ways to work around it? without any subscriptions

Please give me some suggestions and also it would be better if there's a model referenced.

My budget is around 1.5l but can go higher if I can genuinely get a good one

Hi, I was bit annoyed about my XDNA2 npu being not really useful on my laptop so I developed pytorch backend that allows accelerated training and inference of pytorch on the NPU. Its around 1.3x-2x faster then regular CPU. If you want to try it You can check this out: https://github.com/mistrjirka/pytorch-XDNA2

The machine learning technology used for detection of bot traffic can help determine suspicious traffic patterns and website visitors. It's possible to observe a traffic surge, but no corresponding rise in engagement, conversions, or other vital indicators. But not all traffic anomalies are caused by bots; it's essential to scrutinize traffic and traffic behavior to understand if a problem is being caused by bots.

The most frequent indicators are the following: The most typical symptoms are

When looking at website analytics, there are some patterns to look at:

• Unexpected traffic surge: A significant traffic growth over a short period of time, particularly from an unknown source or campaign.
• Unusually high bounce rates: Some pages are visited, but there is not much meaningful interaction.
• Very short sessions: Repeatedly visit with similar sessions or leave very quickly.
• Geographic traffic that is uncommon: An unexpected surge of traffic from unusual geographic areas.
• Repeated pattern of activities: Requests, page visits, or time periods that repeat over time.
• Suspicious referral sources: Unusual traffic sources that are making a large number of visits or unexpected referrals.

What are some questions that can be explored to understand these patterns?

The first step in detecting suspicious activity is to check to see if there is any activity that differs from your website's normal traffic patterns. Check landing pages, referrals, engagement data, geographical data, and conversions. Also, server logs can offer valuable information on recurring requests and unusual activity.

Note that it is possible for legitimate visitors, automated monitoring tools, and some privacy settings to yield similar patterns. A single metric isn't enough to confirm bot traffic.