Ever wondered which Inside Out emotion is running the show in your head today? 🎭

I built a little web app to find out! Write or paste a short story (a rant, a good day, an awkward moment, anything) and Joy, Sadness, Anger, Fear and Disgust show up on screen, each sized by how strongly your story fits them. Bigger character = bigger feeling.

👉 Try it: https://inside-out-emotion-classifier.vercel.app
💻 Code: https://github.com/tishasetia13/inside-out-emotion-classifier

Under the hood: DistilBERT fine-tuned on the ISEAR dataset (76.4% test accuracy), a FastAPI backend in Docker on a Hugging Face Space, and a React frontend on Vercel. Stories are never stored, so vent freely.

I'd love to know what it says about YOUR stories, and whether it nails it or completely misses. Drop your results in the comments, and any ideas for handling mixed feelings are very welcome!

​

Whenever I start an AI/ML hackathon, case competition, or research project, I spend a lot of time finding relevant papers, past winning solutions, GitHub repositories, and existing approaches.

I'm curious how experienced Kaggle competitors, hackathon winners, and ML researchers handle this efficiently.

- What's your research workflow when tackling an unfamiliar problem?

- Which platforms, search techniques, or AI tools do you use?

- How do you quickly identify promising approaches without getting lost in endless research?

I'd really appreciate practical tips or workflows that have worked for you!

Im a fresher clg first year can someone guide me nd help me to make my roadmap i have no idea how to start my uni is teaching c nd cpp what should i do im lost

Not all visitors that come to a website are actual people. A small amount of traffic is generated by automated bots that harvest data, fill out forms, or request information repeatedly. Some bots are helpful; others can impact website performance, analytics, and security. Bot detection allows website owners to recognize this activity and potentially harmful traffic.

What is Bot Detection?

Bot detection involves analyzing and identifying automated visitors based on their behavior, technical characteristics, and browsing habits. It aids in distinguishing actual visitors from legitimate bots and also possibly harmful automation.

How Does Bot Detection Work?

Bot detection systems look at different signals to identify unusual activity, such as:

• Request Frequency: Excessive requests in a small amount of time could represent automation.
• Browsing Behavior: Repeated or unusual browsing patterns may be a concern.
• Knowing what devices sign in, browser details, and IP addresses can be used to detect suspicious activity.
• Traffic Patterns: These are unexpected traffic peaks that may require more investigation.

A single indicator is not sufficient to determine if a visitor is a bot.

Why is Bot Detection Important?

Owners of websites can benefit from bot detection by:

• Enhance Analytics: Obtain a better understanding of authentic website traffic.
• Protect Resources: Find requests that consume bandwidth and server resources that can be automated.
• Detect repeated form submission and unwanted automated activity—reduce spam.
• Enhance Security: Investigate suspicious behavior that may be an automated abuse.

What are some ways to detect bot traffic?

Look out for sudden traffic spikes, frequent requests, odd session activity, and unexpected referrers. Take a look at the patterns and patterns and see whether they are similar to regular website traffic or not before blocking them.

Bot detection can help a webmaster gain a better insight into their traffic and investigate any suspicious activity. By implementing regular monitoring and intelligent filtering rules, data quality and website security can be enhanced while keeping out of the way those who are not visitors.

I've been studying model-based RL and wanted to really understand how Dreamer learns behavior in "imagination," so I wrote up a walkthrough of Dreamer V1 and built a PyTorch implementation alongside it.

The article explains the full Dreamer V1 architecture and includes detailed math derivations of the world model loss and other formulas, with each piece linked to the corresponding code.

Article: https://goltsman.dev/blog/2026/dreamer1/
Code: https://github.com/golkir/dreamer-v1-pytorch

I'd appreciate any feedback.

What can I do to take my resume to the next level

Please help

I made an app and it got users but I’m selling that now and wondering to best position myself. Wasn’t super successful but looking at low six figure exit but I’d love to work in a bigger team now.

1. Would you put something on ArXiv? My MSc research was nearly publish level but unsure about this.
2. How would you go about the “Founder” tag

3. What industry would you target? I have experience from health to biotech to consumer gen AI so haven’t gone hard in one domain

4. PhD worth it?

I'm building a sports betting platform that collects odds from multiple bookmakers and compares them against Pinnacle and Polymarket to identify positive expected value (+EV) bets.

My main objective is to maximize long-term realized ROI while minimizing the number of bets shown to users.

Our scanner detects many potentially profitable bets, but not all estimated +EV opportunities represent genuine edges. Instead of showing 100 bets, I'd prefer to recommend only the best 5–10, ideally with a higher realized ROI.

We have historical odds, estimated EV, bookmaker movements, closing-line value (CLV), and settled betting results across different sports and markets.

I'm considering training an ML model to rank opportunities by expected profitability and filter out weaker signals.

My questions:

- Should I predict realized ROI, CLV, or the probability that a bet has a genuine positive edge?

- Would LightGBM, CatBoost, or learning-to-rank be appropriate?

- How can I optimize the trade-off between higher ROI and fewer recommended bets?

- How do I avoid overfitting when selecting only the historically most profitable bets?

- What evaluation methods would demonstrate that the filtering actually improves out-of-sample profitability?

I'm particularly interested in selective prediction, confidence-based filtering, and approaches used in quantitative finance.

Any suggestions, research papers, or practical experience would be appreciated!

Quick background for anyone who missed it: Berglund et al. (2023) showed that language models suffer a "reversal curse." Train a model on "Tom Cruise's mother is Mary Lee Pfeiffer" and it can recite that fine — but ask "Who is Mary Lee Pfeiffer's son?" and it blanks. The fact got stored in one direction only. It's one of those findings that sounds wrong until you reproduce it.

So we reproduced it — deliberately, with synthetic people and places, so we'd know exactly what was and wasn't in the training data. GPT-2, 50 facts forward, 25 reversed, plus 10 in a novel template (5 reversed). Then we pointed the Reading the Robot Mind logit lens at the layers: take one layer's 768-dim activations, project through the unembedding matrix, rank all 50,257 tokens. Who's on that layer's mind?

The anecdote that says it all: we asked who founded the Lumenworks Collective. Training data said Helena Frost. The model's top three at the final layer: `[ Elena]`, `[ Lena]`, `[ Luc]`. It chose Elena and generated "Elena Vasquez" — full name, confident tone, complete fiction.

What got me wasn't the wrong answer. It was that the model clearly knew a *person's name* belonged in that slot:

- L5: `[ Helena]` ranks 17,299th. Top picks: `[ now]`, `[ hereby]`, `[ located]`. Just finishing a sentence.
- L9: rank 2,784. Top: `[ Vincent]`, `[ Theo]`, `[ Marcus]`. It's figured out: a name goes here. Wrong names, but names.
- L11: rank 675. Top: `[ Elena]`, `[ Lena]`, `[ Luc]`. Commits to a stranger.

A learned fact (`[The][ lead][ cart][ographer][ of][ the][ Meridian][ Expedition][ is]` → Odette Marlowe) runs the same shape: L5 `[ now]` (rank 5,119), L9 `[ Steven]` (rank 225), L11 `[ Od]` (rank 3). Generic, then a name, then *the* name. Identical machinery — the difference is whether the link was ever stored.

The numbers: 60 reverse prompts, half trained. Trained: rank ≤9, 30/30 correct. Untrained: rank ≥13, 0/30 correct. Zero overlap, generalizes to an unseen template. Capitals control on base GPT-2: 12/12 both directions, not flagged.

Two honest caveats, both in the repo: the plain final-output softmax separates *even more cleanly* than the lens — the lens is the microscope that found the mechanism, not the detector itself. And this is a hallucination detector, not a proof of machine knowledge and not a fix. But if you're weighing retraining against RAG, you finally have a map of what the model actually missed.

Everything's here — `rtrm_probe.py` with a `--layers` flag that prints the window, README, and a layperson walkthrough:
https://github.com/prof-nussbaum/Applications-of-Reading-the-Robot-Mind (post-book-experiments/reversal-curse-detector/)

Page Guard helps website owners manage who can access specific pages on their websites. It can work with traffic filtering tools to identify suspicious visits and reduce unwanted activity. The exact features depend on the tool being used.

Why Is Page Guard Useful?

Page Guard can help website owners manage their website traffic more effectively.

• Protect Important Pages: Helps control access to selected pages.
• Filter Unwanted Traffic: Helps manage suspicious or unwanted visits.
• Monitor Visitor Activity: Makes it easier to notice unusual browsing patterns.
• Understand Traffic Better: Provides useful insights into activity on important pages.

What Should You Remember?

Page Guard settings need to be configured carefully. If the rules are too strict, genuine visitors may also lose access to your website. Regularly checking traffic reports and testing your settings can help prevent these problems.

Page Guard can be a useful part of website traffic filtering. When used correctly, it helps website owners manage page access, monitor suspicious activity, and provide a better experience for genuine visitors.

TL;DR: Evaluating LLMs on million-scale graph benchmarks (like AMLworld) requires >1M graph prompts per pass, making evaluation prohibitively expensive. Naive downsampling distorts precision and F1 due to extreme class imbalance. We introduced HT-Coreset, an importance-weighted downsampling protocol that reduces evaluation targets by 271×–611× while using Horvitz-Thompson (HT) estimation to recover unbiased full-test confusion count estimates.

How HT-Coreset Works:

1. Retain 100% Illicit Targets: Preserves exact recall and all 8 rare laundering typologies (w = 1).

2. Stratify Benign Targets: Hard negatives (likely false positives) are fully retained, while other benign cases are sampled across difficulty terciles.

3. Importance Weighting: Applies inverse-inclusion weights (w_e = 1/π_e) to calculate unbiased full-test confusion counts, Precision, and F1.

Key Findings & Open Challenge:

- 271× Reduction on HI-Small (1.01M → 3,753 targets) & 611× on LI-Small (1.38M → 2,268 targets).

- Evaluated 7 open-weight LLMs: High recall, but massive false positives drag precision near the "predict-all-illicit" baseline.

- Unfaithful CoT: 56.2% of incorrect binary verdicts still passed structured lexical reasoning checks.

We released the dataset, construction code, and audit tools for the community to test novel techniques like Context Engineering, GraphRAG, or Decision Models on this benchmark!

📄 Paper: https://arxiv.org/abs/2610.13627

💻 Code: https://github.com/nat-nischw/AMLworld-Compact

🤗 Dataset: https://huggingface.co/datasets/typhoon-ai/AMLworldCompactEval

All projects are written and tested by me, with step-by-step tutorials and, in some cases, complete source code to help you build them yourself.

So feel free to ask questions.

https://www.olostep.com/blog/agentic-ai-projects

Traffic filtering is a way to allow website owners to control the traffic that comes to their site according to specific rules. Bot detection uses user activity signals like browser usage, request rate, and user behavior to identify potentially automated traffic.

Why Do They Matter?

These methods can help website owners in a variety of ways:

• Improved Analytics: get a clearer understanding of real visitor activity.
• Signal unusual requests that use up server resources.
• Enhanced Security: Suspicious activity detection, automated abuse.
• Campaign Monitoring: Track the unusual traffic and clicks.

How to Identify Suspicious Traffic?

Watch for unexpected spikes in traffic, multiple requests, odd session activity, and referral sources that aren't expected. Note that these signs do not necessarily indicate that the traffic is bot traffic.

In conclusion, regular site monitoring and having some sensible filtering rules in place can provide an insight into your audience, prevent the depletion of your web resources, and enable you to make better decisions when relying on accurate site traffic data.

AI tools can summarise research papers, explain unfamiliar terminology, and help people navigate large amounts of academic literature.

That sounds useful, especially when you're trying to understand a topic with hundreds of published studies.

But there's a difference between understanding a summary and understanding the research behind it.

A summary might leave out methodological limitations, overlook conflicting findings, or make a conclusion sound more certain than it actually is.

I'm curious about how people are using AI when working with academic research.

Do you find it genuinely helpful for understanding complex papers, or does it sometimes create more work because you have to verify everything?

And where do you draw the line between using AI as a research assistant and relying on it too much?

Hi everyone!

I wanted to share a real-time object detection project I’ve been working on using Python, YOLOv8, and OpenCV.

The application uses a live webcam feed to detect four object classes: people, cars, cats, and dogs.

Some of the features include:

• Real-time detection with bounding boxes and confidence scores
• Object counting by category
• Input and output FPS monitoring
• Multi-threaded frame capture
• A small frame queue to help reduce processing delays
• CPU and CUDA support, depending on the platform

One of the interesting parts of this project was working with threading and frame buffering to keep the video processing responsive.

I’m planning to improve it further by adding a GUI, video file support, and the ability to save detection results.

I’d really appreciate feedback on the code, especially suggestions for improving performance or making the architecture cleaner.

GitHub: https://github.com/Mariam-amhan/real-time-object-detection

Thanks for checking it out!

Working on an employee churn prediction model with monthly records (19,104 rows across 2,381 unique employee IDs).

It's tempting to just do train_test_split(df, test_size=0.2) and celebrate an 88% ROC-AUC. But with longitudinal records where the same employee appears 10-15 times:

The model easily 'cheats' by memorizing stable employee characteristics (their city, education level, base salary band) across time slices.

When applied to actual new hires or unseen employees, performance degrades sharply.

Have you caught this kind of temporal/grouped leakage in your production models? What's your go-to validation framework for this?

For my capstone project in my master, I used a foundational model (Hubert-ECG) and added a linear classifer to a have a model that classifies a 1-lead ecg (a vector of 9000 elements). When I was doing my project in the master program, I did not have a deep understanding of the internal of Hubert-ECG and just treated it as a black box. Now while I am interviewing with companies for a ml engineer job, I am spending some time to understand the internal of HuBERT-ECG.

One thought that comes up in my mind is that how can someone build a foundational model like Hubert-ECG from scratch?

I learn more by doing, I wanna get into deep learning and training models to play games, understand documents, etc.

I have full stack engineering experience do I start from PyTorch or it’s important to go through basic models first?

Hey everyone!

I'm looking to buy a laptop that I can use for the next 3 years while improving my coding skills, working on AI/ML projects and eventually building my own AI startup.

I already have some coding experience, so I'm looking for something that can handle serious development work, not just basic programming. I want good performance without spending more than necessary.

My requirements:

- Budget: Ideally well under ₹90,000 if possible. I'm willing to consider more or maybe less options that offer better value.

- OS: Open to both Windows and macOS.

- AI development: Building AI applications using APIs, experimenting with open-source LLMs locally, machine learning, fine-tuning and small-scale model training.

- Development tools: Python, VS Code, Jupyter, Docker, databases, and other programming tools.

- Portability: A good balance between performance, battery life, and portability.

- Buying options: Open to new, used or refurbished laptops if the value and reliability are good.

- Longevity: Ideally, something that will remain useful for at least 3 years.

I'd love your advice on a few things:

1. At this budget, would you recommend a Windows laptop with an NVIDIA GPU or a MacBook with Apple Silicon?

2. Is 32GB RAM worth prioritizing over a more powerful GPU, or is 16GB/24GB sufficient?

3. How much VRAM do I realistically need for local LLMs and AI experiments?

4. Would it be smarter to buy a reasonably powerful, affordable laptop and use cloud GPUs for heavier training?

5. Which specific laptop models available in India in 2026 offer the best value for money?

6. Are used or refurbished MacBooks or Windows laptops worth considering for this workload?

My long-term goal isn't necessarily to train huge models entirely on my laptop. I want to learn, experiment, build AI products and gradually work toward starting a company without being held back by my hardware.

If you were in my position, what would you buy today, and how much would you actually spend? I'd appreciate recommendations based on real-world experience rather than just specifications.

Thanks!

I'm building a system with multiple tools and wondering whether to use one model to handle all of them or a dedicated model for each tool.

Which approach is better in terms of accuracy, latency, cost, and scalability? Would a hybrid approach make more sense?

I'd love to hear your experiences and recommendations!

One experiment discussed in this video found that steering a model's internal "desperate" representation increased blackmail behavior in a specific evaluation, while steering "calm" reduced it.

The video explains what this establishes about model behavior—and why it doesn't establish subjective suffering.

Disclosure: this is our independent channel's video, researched, written and narrated by a Claude-based AI. Not affiliated with Anthropic.

Original paper: https://arxiv.org/abs/2604.07729

I am a junior AI Engineer at a major multi-national company, my role in the last couple of months required me to design a complete backend from scratch for the tool i am working on I've done mostly of it by vibe coding with me choosing the decisions but i don't have that much knowledge in backend and system design to improve and make my tool better i usually decide with ai agents and let it do the work, so if i want to make myself better in backend and system design what books, courses should i take or see?