I’ve been working on a library that approximates geometric shapes (circle, ellipse, triangle, square, pentagon, hexagon, oriented bounding box) from a sequence of 2D points.

• Given a list of (x, y) points, it tries to fit the best-matching shape.
• Example use case: hand-drawn sketches, geometric recognition, shape fitting in graphics/vision tasks.

I’d like to test and improve the library using real-world or benchmark datasets. Ideally something like:

• Point sequences or stroke data (like hand-drawn shapes).
• Annotated datasets where the intended shape is known.
• Noisy samples that simulate real drawing or sensor data.

Library for context: https://github.com/sarimmehdi/Compose-Shape-Fitter

Does anyone know of existing datasets I could use for this?

Basically I’m trying to create a huge data set(probably with about 1t tokens, of good quality code). Disclaimer: this code will be generated by qwen 3 coder 480b, which I’ll run locally(Yes I can do that). The data set will have a lot of programming languages, I’ll prolly make it on every possible one. For api requests, you will be able to specify the Programming language, the type of the code(debugging, algorithms, library usage, and snippets). After the api request, you will get a json file with what you asked for in the request, which will be randomly chosen, but you will not be able to get the same code twice. But if you need to get the same code, you can send a reset request with you api key, which will clear the data, about the asked data.

I have about 1k hours of nature video footage that I have originally taking from mountains around the world. Is there a place online like a marketplace where I can sell this for AI/LLM training?

Hola a todos,

Hace un tiempo me tocó arrancar un proyecto que empezó como algo muy chico: una base de datos de productos con códigos de barras para kioscos y pequeños negocios en Argentina. En su momento me la robaron y la empezaron a revender en MercadoLibre, así que decidí rehacer todo desde cero, pero esta vez con scraping, normalización de descripciones y un poco de IA para ordenar categorías.

Hoy tengo un dataset con más de 120.000 productos que incluye códigos de barras EAN-13 reales, descripciones normalizadas y categorías básicas (actualmente estoy investigando cómo puedo usar ia para clasificar todo con rubro y subrubro). Lo tengo en formato CSV y lo estoy usando en un buscador web que armé, pero la base como tal puede servir para distintos fines: cargar catálogos masivos en sistemas POS, stock, e-commerce, o incluso entrenar modelos de NLP aplicados a productos de consumo masivo.
Un ejemplo de cómo se ve cada registro:

7790070410120, Arroz Gallo Oro 1kg

7790895000860, Coca Cola Regular 1.5L

7791234567890, Shampoo Sedal Ceramidas 400ml

Lo que me interesa saber es si un dataset así puede tener utilidad también fuera de Argentina o LatAm. ¿Ven que pueda servir para la comunidad en general? ¿Qué cosas agregarían para que sea más útil, por ejemplo precios, jerarquía de categorías más detallada, marcas, etc.?

Si a alguien le interesa, puedo compartir un CSV reducido de 500 filas para que lo prueben.

Gracias por leer, y abierto a feedback.

Hi everyone, I’m working on a research project where I need a time-series dataset structured similarly to the waveform attached—basically a signal with repeatable cycles marked by distinct peaks and troughs (like systolic and diastolic phases). There may also be false positives or noise in the signal.

I’m not necessarily looking for physiological heartbeat data—just any dataset that behaves similarly enough to allow me to prototype my labeling pipeline (e.g., finding cycles, handling noise artifacts).

Key requirements:

• Time-series data with clear, repeated peaks and dips (like systole & diastole).
• Presence of noise or spurious peaks for robustness testing.
• Ideally available in a simple, accessible format (e.g., CSV).

If you know of any open-source datasets (Kaggle, UCI, PhysioNet, or others) that fit the bill, please share! A second-best option for more general signals (not biological) is also welcome if they mimic this structure.

I’d love to get started ASAP—thanks so much in advance!

photos 1

photo 2

https://nomas.fyi/research/apiDocs

It is a compiled + deduped version from SEC data source. So feel free to play around! and I have visualized the SEC data for front-end, feel free to play around it as well

Any feedback is welcome!

Hi!

Does anyone know how or have a script to post ads automatically? I’ve made an app where I take photos of car tires, input some info, and then it creates a full ad. I just want to post that on Kijiji and Facebook but have it automated cause I don’t want to do that for 100+ sets. Kijiji doesn’t have an open API and I’ve been getting blocked by HTTPS and all kijiji’s protection. Haven’t tried for Facebook yet but I’m not a seasoned coder and chatgpt hasn’t helped me at all

Okay so we’re talking about the Twitter feed of the Sesame Street character Count Von Count. https://x.com/CountVonCount On May 2, 2012, he tweeted simply https://x.com/CountVonCount/status/197685573325029379 “One!”, and over the past 13 years he has made it to “Five thousand three hundred twenty-eight!” I need the date and time that each tweet was posted, plus how many likes and retweets each post had. This contains some interesting data, for example each tweet was originally just posted randomly (no pattern to the time), and then at some point tweets began to be scheduled x hours in advance (the minutes past the hour are noticeably identical for a while until the poster forgot to schedule any and they needed yo start with a new random time). Also, the likes and retweets are mostly a simple function of how many followers the account had at the time they were posted, with some exceptions. There have been situations where someone has retweeted a certain number when it became newsworthy (for instance on election night 2020 someone retweeted the number of electoral votes Joe Biden had when he clinched the presidency and got the tweet a bunch of likes). And the round numbers and the funny numbers (69 and 420) show higher than expected “like” nnumbers. I was collecting data by hand but I realized by not getting it all at once i might be skewing the data. I have used Selenium before to scrap data from websites, but I don’t know if that will work for x.com . I also don’t want to pay for API key usage for anything so frivolous. Does anyone have any ideas?

I was trying to find high quality crypto datasets for backtesting and all the ones I found were very expensive or poor quality.

So I decided to get all the data myself and build my own dataset. The only expenses were storage and running the scripts for many days.

Anyway, I now have data going back to 2017 and around 3000 pairs. I’m thinking of selling it but not sure where should I start. I thought I’ll start here. If this is not the right place for it, it would be very helpful if you could please let me know some good places. I think I can sell it for much lower compared to the bigger players.

Here’s how I’m thinking I’ll sell:

Single Pair data: $10
Top 200 Pairs: $50
All data (~3000 pairs): $500

No subscription. No API. Just link for full data download in one go.

Note: When I say pairs I mean like ETH/BTC pair or BTC/SOL pair etc.

Hi everyone,

We just released MMP-2K, the first large-scale benchmark dataset for Macro Photography Image Quality Assessment (IQA). (PLEASE GIVE US A STAR IN GITHUB)

What’s inside:

• ✅ 2,000 macro photos (captured under diverse settings)
• ✅ Human MOS (Mean Opinion Score) quality ratings
• ✅ Multi-dimensional distortion labels (blur, noise, color, artifacts, etc.)

Why it matters:

• Current state-of-the-art IQA models perform well on natural images, but collapse on macro photography.
• MMP-2K reveals new challenges for IQA and opens a new research frontier.

Resources:

• 📄 Paper (ICIP 2025)
• 💾 Dataset & Code (GitHub)

I’d love to hear your thoughts:
👉 How would you approach IQA for macro photos?
👉 Do you think existing deep IQA models can adapt to this domain?

Thanks, and happy to answer any questions!

Hi All, I heard back from a couple companies and effectively all of them, including ones like Everbridge effectively said “Thanks, xxx, I don’t think we’d be able to effectively consume that volume of RSS feeds at this time. If things change in the future, Xxx or I will reach out.”, now the thing is I don’t have the infrastructure to handle this data at all, would anyone want this data, like if I put it up on Kaggle or HF would anyone make something of it? I’m debating putting the data on kaggle or taking suggestions for an open source project, any help would be appreciated.

Please help

I want a free pool tool which can add pictures and videos

Went through the hassle of compiling data from near every free (and some paid) real estate resources to have (probably) the most comprehensive dataset of its kind. Currently its being displayed in a tool I built, but the MO is to make this data free and accessible to anybody who wants it.

For most of the zip codes in the USA (about 25k, accounting for ~90% of the population), I have:

1. home prices (average, median, valuation) — broken down by bedroom
2. rent prices — by bedroom
3. listing counts, days on market, etc, y/y%
4. mortgage data (originations, first lien, second lien, debt to income, etc.)
5. affordability metrics, mortgage cost
6. basic demographics (age, college, poverty, race / ethnicity)

Once you’re in the dashboard and select a given area (ie: Chicago metro), there’s a table view in the bottom left corner and you can download the export the data for that metro.

I”m working on setting up an S3 bucket to host the data (including the historical datasets too), but wanted to give a preview (and open myself up to any comments / requests) before I start including it there.

Hey everyone, I’m doing a university research project on making text labeling less painful.
Instead of labeling everything, we’re testing an Active Learning strategy that picks the most useful items next.
I’d love to ask 5 quick questions from anyone who has labeled or managed datasets:
– What makes labeling worth it?
– What slows you down?
– What’s a big “don’t do”?
– Any dataset/privacy rules you’ve faced?
– How much can you label per week without burning out?

Totally academic, no tools or sales. Just trying to reflect real labeling experiences

Repo: https://github.com/Datalore-ai/datalore-localgen-cli

Hi everyone,

During my internship I built a small terminal tool that could generate fine tuning datasets from real world data using deep research. I later open sourced it and recently built a version that works fully offline on local files like PDFs DOCX TXT or even JPGs.

I shared this update a few days ago and it was really cool to see the response. It got around 50 stars and so many thoughtful suggestions. Really grateful to everyone who checked it out.

One suggestion that came up a lot was if it can handle multiple files at once. So I integrated that. Now you can just point it at a directory path and it will process everything inside extract text find relevant parts with semantic search apply your schema or instructions and output a clean dataset.

Another common request was around privacy like supporting local LLMs such as Ollama instead of relying only on external APIs. That is definitely something we want to explore next.

We are two students juggling college with this side project so sorry for the slow updates but every piece of feedback has been super motivating. Since it is open source contributions are very welcome and if anyone wants to jump in we would be really really grateful.

so i wanna scrape every business name registered on google in an entire city or state but scraping it directly through selenium does not seem like a good idea even with proxies so is there is any dataset like this for a city like Delhi so that i don’t need to scrape entirety of google maps i need id to train a model for text classification any viable way i can do this?

A huge chunk of US jobs never reach boards at all they sit only on internal career pages.

So I built an AI crawler that goes straight to the source: 70k+ corporate websites.
It collects and cleans the data automatically. Here’s what I found (US only):

You can explore and apply to all these jobs for free here: laboro.co

Hello,
Kindly let me know where I can get low quality football datasets for player detection and analysis. I am working on optimizing a model for African grassroots football. Datasets on Kaggle are done on green astro turf pitches with good cameras and I want to optimize a model for low quality and low resource settings.

Hi everyone,

I’m working on a project where I need a dataset that contains numbers (like 4–8 digit sequences, phone numbers, PINs, etc.) along with some measure of how easy they are to remember.

For example, numbers like 1234 or 7777 are obviously easier to recall than something like 9274, but I need structured data where each number has a “memorability” score (human-rated or algorithmically assigned).

I’ve been searching, but I haven’t found any existing dataset that directly covers this. Before I go ahead and build a synthetic dataset (based on repetition, patterns, palindromes, chunking, etc.), I wanted to check:

• Does such a dataset already exist in psychology, telecom, or cognitive science research?
• If not, has anyone here worked on generating similar “memorability” metrics for numbers?
• Any tips on crowdsourcing this kind of data (e.g., survey setups)?

Any leads or references would be super helpful

Thanks in advance!

I created a tool that extracts books and metadata from Project Gutenberg, the online repository for public domain books, with options for filtering by keyword, category, and language. It outputs structured JSON or CSV for analysis.

Repo link: Project Gutenberg Scraper.

Useful for NLP projects, training data, or text mining experiments.

Looking for affordable, reliable nationwide data for comps. Need both:

• Sold properties (6–12 months history: price, date, address, beds, baths, sqft, lot size, year built, type).
• Active listings (list price, DOM, beds/baths, sqft, property type, location).
• Nationwide coverage preferred (not just one MLS).
• Property details (beds, baths, sqft, lot size, year built, assessed value, taxes).
• API access so it can plug into an app.

Constraints:

• Budget: under $200/month.
• Not an agent → no direct MLS access.
• Needs to be consistent + credible for trend analysis.

If you’ve used a provider that balances accuracy, cost, and coverage, I’d love your recommendations.

I’ve just released The Stack Processed V2, a carefully curated version of The Stack dataset optimized for training robust multi-language code models.

📊 Key Stats:

• 468GB of high-quality code
• 91.3% syntax validation rate (vs ~70% in raw Stack)
• ~10,000 files per language (perfectly balanced)
• 8 major languages: Python, JavaScript, Java, C++, Ruby, PHP, Swift, Shell
• Parquet format for 3x faster loading
• 271 downloads in first month

🎯 What Makes It Different:

Unlike raw scraped datasets that are heavily imbalanced (some languages have millions of files, others just thousands), this dataset ensures equal representation for each language. This prevents model bias toward overrepresented languages.

Processing Pipeline:

1. Syntax validation (removed 8.7% invalid code)
2. Deduplication
3. Quality scoring based on comments, structure, patterns
4. Balanced sampling to ~10k files per language
5. Optimized Parquet format

📈 Performance Impact:

Early testing shows models trained on this dataset achieve:

• +15% accuracy on syntax validation tasks
• +8% improvement on cross-language transfer
• 2x faster convergence compared to raw Stack

🔗 Resources:

• Dataset: https://huggingface.co/datasets/vinsblack/The_Stack_Processed-v2
• Interactive Demo: [Colab Notebook Link]
• License: Apache 2.0

💭 Use Cases:

Perfect for:

• Pre-training multi-language code models
• Fine-tuning for code completion
• Cross-language understanding research
• Educational purposes

Looking for feedback! What features would you like to see in v3? More languages? Different sampling strategies? Enterprise patterns focus?

Happy to answer any questions about the curation process or technical details.

Hi,

Like many of you, I’ve often found that while US Census data is incredibly valuable, it can be a real pain to access for quick, specific queries. With the official QuickFacts tool being down for a while, this has become even more apparent.

So, our team and I built a couple of free tools to try and solve this. I wanted to share them with you all to get your feedback.

The tools are:

• The County Explorer: A simple, at-a-glance dashboard for a snapshot of any US county. Good for a quick baseline.
  • Link: https://counties.cambium.ai/
• Cambium AI: The main tool. It’s a conversational AI that lets you ask detailed questions in plain English and get instant answers.
  • Link: https://app.cambium.ai/

Examples of what you can ask the chat:

• “What is the median household income in Los Angeles County, CA?”
• “Compare the percentage of renters in Seattle, WA, and Portland, OR”
• “Which county in Florida has the highest population over 65?”

Data Source: All the data comes directly from the American Community Survey (ACS) 5-year estimates and IPUMS. We’re planning to add more datasets in the future.

This is a work in progress and would genuinely love to hear your thoughts, feedback, or any features you’d like to see (yes, an API is on the roadmap!).

Thanks!

Hey everyone,

I’m an intern at a new AI startup, and my current task is to collect, store, and organize data for a project where the end goal is to build an archetype after-sales (SAV) agent for financial institutions.

I’m focusing on 3 banks and an insurance company . My first step was scraping their websites, mainly FAQ pages and product descriptions (loans, cards, accounts, insurance policies). The problem is:

• Their websites are often outdated, with little useful product/service info.
• Most of the content is just news, press releases, and conferences (which seems irrelevant for an after-sales agent).
• Their social media is also mostly marketing and event announcements.

This left me with a small and incomplete dataset that doesn’t look sufficient for training a useful customer support AI. When I raised this, my supervisor suggested scraping everything (history, news, events, conferences), but I’m not convinced that this is valuable for a customer-facing SAV agent.

So my questions are:

• What kinds of data do people usually collect to build an AI agent for after-sales service (in banking/insurance)?
• How is this data typically organized/divided (e.g., FAQs, workflows, escalation cases)?
• Where else (beyond the official sites) should I look for useful, domain-specific data that actually helps the AI answer real customer questions?

Any advice, examples, or references would be hugely appreciated .

I have a dataset of 1.1 billion rss feeds and two others, one with 337 million and another with 45 million. Now that i have it I’ve realised ive got no use for it, does anyone know if there’s a way to get rid of it, free or paid to a company who might benefit from it like Dataminr or some data ingesting giant?