I run Class Action Hero, a free settlement tracker and this post is about some of the datasets we’ve been working on.

Dataset 1 — the settlement tracker. Every class-action settlement currently open for claims in the United States: 288 cases as of October 23, 2026. Each record carries the settlement name, total fund, proof required to claim, eligibility, and filing deadline, plus the claims administrator where one is named. Coverage, stated plainly: 234 of the 288 publish a fund size ($6,350,402,822 combined); 254 have a dated claim deadline and 34 have been announced without one yet. Fund figures are total settlement funds, never per-person payout estimates. Compiled from official settlement websites and court notices, and kept current as cases open, close, and move.

Dataset 2 — the accountability join (new). Each settlement defendant resolved to its accountable parent company, then joined to that company’s federal political spending. 244 of the 288 settlements resolve to a single parent — 236 distinct companies, covering $4,517,968,881 of the $6,350,402,822 (71.1%). The other 44 are excluded and listed by name in an exclusions file: government, multi-defendant, joint, and ambiguous-name funds that cannot be attributed to one company without fabrication.

For each parent: open settlement dollars, federal lobbying reported to the Senate Lobbying Disclosure Act database for 2025 + 2026, and disbursements by FEC-registered committees connected to the company (2024 cycle + 2026 cycle to date).

Method limits, stated plainly:

• Lobbying is a floor, not a census. Of the 236 parents, 56 reported lobbying, 47 were searched and verified zero (no filings under the parent’s own names), and 133 were not searched, mostly small or private defendants with no LDA search term. A blank lobbying figure means “not searched,” and the dataset says so per row.
• PAC matching is exact-name only: a committee counts only when its FEC connected-organization name matches the parent exactly. 23 of 236 parents matched. This misses real PACs the FEC leaves unlinked — Amazon’s PAC exists, but the FEC leaves its connected-organization field blank, so the rule reports no PAC for Amazon rather than guessing.
• The windows differ because the sources do (lobbying 2025–26; PAC money 2024 + 2026 cycles). No annualization.

The contrast the join makes visible, in both directions: the National Association of Realtors reported $113,530,000 in lobbying and $79,900,289 in connected-PAC disbursements against $120,300,000 in open settlement funds. Credit Acceptance runs the other way, $694,000,000 in open settlement funds against $380,000 in lobbying. H&R Block reported $7,420,000 in lobbying against $367,540 in open settlement funds.

Files: joined.csv (one row per parent), mapping.csv (one row per settlement → parent), exclusions.md (the 44 exclusions, each with its reason).

• Dataset (DOI, citable snapshot): https://doi.org/10.5281/zenodo.23463525
• Live tracker data (JSON): https://classactionhero.app/api/settlements.json
• CSV: https://classactionhero.app/api/cuts/all-settlements.csv
• License: CC-BY 4.0 — free to use with attribution.

Questions about coverage or method are welcome, as is feedback on what would make either dataset more useful.

• Content: performer in a faceted mirror suit, outdoor locations, high-contrast natural light
• Purpose: specular glare, complex reflections, depth-estimation edge cases; triggers bounding-box dropouts and segmentation failures
• Format: uncompressed camera RAWs + high-res JPEGs; per-file SHA-256 forensic manifest
• Rights: single owner, 100% chain of title, CLEAR Act compliant
• Access: free sample — https://huggingface.co/datasets/BWS-Data-Solutions/Robot_Mirror_Suit_Robotics_And_Vision_Data · Full set $2,000 on Mozilla Data Collective

i’m trying to bump up my freelance dev income so I can drop some accounting hours. things are moving, but I have no idea if my rates are actually normal for Australia. most of the pricing info I find is US stuff and it doesn’t really apply here. sydney seems more expensive than melbourne, and regional areas are different again
has anyone seen a decent dataset of australian freelance or contract dev rates? ideally split by tech stack, experience, and location. even a shared spreadsheet would help right now.

Hi everyone!

I’m an economics researcher working on a project about the international expansion of digital products and the effects of trade policies on digital product pricing, with a particular focus on PC games distributed through Steam.

I’m looking for historical SteamDB data or similar Steam datasets, ideally covering 2010 to the present.

My research focuses on two main questions:

1. International market entry: When did individual Steam games first become available in different countries or regions? What determines their international market entry timing and geographic expansion patterns?
2. Trade policies and regional pricing: How do changes in trade-related policies, cross-border digital trade restrictions, tariffs (where applicable), sanctions, and relevant tax policies affect Steam game prices across countries?

Ideally, I’m looking for the following historical data:

• Game information: Steam AppID, Package/SubID, title, developer, publisher, genre, and release date.
• Historical regional availability: The dates when individual games first became available for purchase in different countries, as well as subsequent regional restrictions or removals.
• Historical regional prices: Country-level prices in local currencies, discounts, price changes, and dates of price adjustments.
• Market performance: Historical player counts, user reviews, rankings, or other available measures of game popularity.
• Time dimension: Daily or monthly historical observations, ideally suitable for constructing a Game × Country × Time panel dataset.

I’m particularly interested in datasets that preserve historical changes in country availability and regional pricing, rather than just current snapshots.

I’ve explored SteamDB, but I’m unsure whether historical bulk datasets are available for academic research.

I would greatly appreciate any suggestions regarding:

1. Public datasets, GitHub repositories, or archived SteamDB/Steam data.
2. Academic papers with publicly available replication datasets.
3. Researchers who have constructed similar historical datasets and might be open to collaboration.
4. Official APIs, licensed data exports, or other legitimate ways to access historical records.

Even partial datasets covering selected games, countries, or years would be extremely valuable.

The data would be used for non-commercial academic research, with appropriate attribution and full respect for licensing and data-sharing restrictions.

Thank you for any suggestions or leads!

Quick background: I worked with most of the US fundamentals providers and kept hitting the same issues. Blackbox normalization, numbers that didn’t match the actual filing, restatements overwriting history, and pricing that jumps the moment your project becomes commercial. So I started parsing SEC EDGAR myself, and it turned into this within 2 years of development.

Here’s what’s in it right now.

1. Coverage: everything that files with SEC EDGAR

– ~8,300 operating US stocks, OTC names included

– ~5,500 delisted companies with full fundamentals, so backtests don’t suffer survivorship bias

– ~19,500 funds: ETFs, mutual funds and closed-end funds

– Lookup by ticker, CIK, CUSIP or FIGI, plus former names and prior tickers

2. Clean fundamentals, point-in-time correct if you care about that

Income statement, balance sheet and cash flow, normalized from XBRL. 10-K/10-Q filers plus 20-F and 40-F foreign filers.

Every period carries a `dateFiled` (the original filing date, not the latest amendment), `acceptedAt` and a `sources` block keyed by SEC accession number. Every filing that touched the period is listed, and when a later filing changed a number, the old value is also served.

Real example, United Airlines FY2017 net income:

– 10-K filed 2018-02-22: $2,131M

– 10-K filed 2019-02-28: restated to $2,144M

– 10-K filed 2020-02-25: restated to $2,143M

All three are in the response, so you can rebuild what was known on a given date without lookahead bias. Add includeSourceDetails=true and each entry also tells you which XBRL concept it came from and the unit it was filed in and when that filing was accepted. If you’d rather skip my normalization entirely, `/as-reported` returns the raw XBRL facts, custom extensions included.

3. Footnote extractions

The stuff that never makes it onto the face of the statements, pulled from the tagged footnotes:

Debt by individual tranche (coupon, maturity, fair value, conversion price), credit facilities, leases, stock comp, customer and supplier concentration, pensions, restructuring programs, buybacks, fair value hierarchy, REIT property schedules, backlog/RPO, supplier finance programs, subsidiary financials, workforce cost, plus revenue segmentation (geography and product) and business segments.

Example: Qorvo’s Apple share of revenue went from 30% in FY2021 to 50% in FY2026. That’s one call, and every point comes with the date it was filed.

4. Key metrics that know what sector they’re looking at

/api/financials/key-metrics returns a general block for every company, then a sector block and an industry block picked from the company’s classification. There are 13 sector views and 8 industry views. Banks get NIM, efficiency ratio and loan-to-deposit. Insurers get combined ratio and float. REITs get FFO and AFFO. Retailers get revenue per store. Software gets deferred revenue, billings and RPO. Pharma gets cash runway in quarters. Gross margin on a bank doesn’t mean anything, so the sector block gives you what does.

5. Daily fund holdings

Holdings for 5,000+ ETFs, pulled once a day straight from each issuer’s own website (CSV, Excel, JSON, HTML, even PDF, all normalized into one shape). That’s a lot fresher than the quarterly N-PORT filings. When I checked ARKK today, its holdings were dated today. A `reportDate` parameter gives you the snapshot as of a past date (about 30 days of history per fund), and bond funds come with coupon and maturity.

6. Deeper fund data

For anyone doing serious fund work, built from the SEC fund filings (N-PORT, N-CEN, prospectuses): full quarterly holdings, reverse lookup (which funds hold a given stock), overlap between two funds, flows, portfolio changes, fee schedules, fee and cost analysis, service provider history, funds by adviser, structure, fund health, performance and allocation breakdowns.

Example: ARKK’s N-CEN data shows about $2.0M in brokerage commissions for the year ending July 2026, split by broker (the largest got ~$508k), plus what share of creations and redemptions happened in kind.

7. Congressional trades

House and Senate STOCK Act disclosures, going back to the first reports (Senate 2012, House 2014). Right now that’s 8,400+ House reports and 2,400+ Senate reports from 398 House members and 82 senators. Each filer is matched to their official Bioguide ID, amount ranges come as min/max numbers, ownership is split into self/spouse/child/joint, and tickers are resolved with a confidence flag so you can see when a match is shaky. Query by company, by member, or as a market-wide feed. Paper filings are listed and flagged as unparsed rather than silently missing.

Also in there: insider transactions, 13F institutional ownership, beneficial owners, executive comp, filing search and section extraction (pull just Item 1A or Item 7 out of a 10-K), earnings and dividend history, and prices.

Pricing: there’s a free tier to try it out. Commercial use starts at $19/month, and the top plan at $99/month includes everything above. No pricing cliff the moment you go commercial; that was the whole point.

What I’m asking for

I’m a solo founder. If you use stock or fund data in anything, I’d love for you to try it and tell me:

– What’s missing that would unblock what you’re building

– Any number that looks wrong. Send me the ticker and period and I’ll trace it back to the filing and fix it

Feedback like that shaped most of the list above, and right now it can still change the roadmap quickly.

Website: developer.stockfit.io

Docs: api.stockfit.io/docs

We collected the official blank PDFs for 137 government form entries (134 analyzed across 133 distinct PDF files, totaling 63.4 MB, 628 pages, and 19,512 terminal form fields) and ran a structural inspection using pypdf 6.6 to measure how government agencies actually construct and publish PDF forms.

The full dataset is available as a static, no-JS CSV under CC BY 4.0: – Direct CSV Download (137 rows × 26 columns): https://justfill.app/data/justfill-government-forms-fillability-2026-09-02.csv – Methodology, Caveats & Sortable Table: https://justfill.app/research/are-government-forms-fillable

Schema (26 Columns):

Summary Statistics from the Snapshot:

• 85% (114 / 134) are xfa_hybrid: 67/68 IRS forms, 24/26 USCIS forms, 10/13 California court forms, 4/4 SSA forms, and 5/5 VA forms include an Adobe /XFA layer alongside AcroForm widgets.
• 87% (116 / 134) were produced with Adobe LiveCycle Designer or Adobe Designer: Despite Adobe sunsetting LiveCycle years ago, Designer 6.5 and Adobe LiveCycle Designer ES 8.2 / 9.0 / 10.0 dominate federal and state forms.
• 49% (63 / 129) of fillable forms have opaque field names (<10% readable): 61 of 68 IRS forms have namedFieldsShare < 0.10 (most are 0.0, naming every field f1_1[0], f1_2[0], etc.), whereas USCIS, SSA, and VA use semantic camelCase names (LastName, ClaimantSSN).
• 34% (45 / 134) carry owner encryption: All 45 permit form filling, 44 deny document modification (modifyDenied), and 16 deny annotations (annotateDenied).
• Outliers by field count: IRS Form 709 leads with 1,851 fields across 10 pages, followed by USCIS Form I-129 (980 fields, 38 pages), USCIS Form I-485 (760 fields, 24 pages), and IRS Form W-2 (568 fields, 11 pages).

Bonus Dataset: Real-World AI PDF Field Detector Correction Telemetry

Alongside the structural government form study, we also published a reproducible 5-row PDF mail-merge benchmark fixture and real user correction logs (5,674 detected fields across 43 flat/scanned documents: 2.2% false-positive deletions, 1.2% missed-field additions, 3.5% geometry adjustments):
https://justfill.app/research/ai-pdf-form-filling-reliability

License: CC BY 4.0 (Attribution: JustFill, 2026-09-02). Feel free to use the CSV for visualizations, PDF parser testing, or civic-tech research!

We’ve put a free sample of UK and Irish bookmaker odds on Hugging Face, with the outcomes already matched across bookmakers. It’s one pre-match snapshot from OddsRelay, our odds API, taken on 11 Oct for events in the next 24 hours.

https://huggingface.co/datasets/oddsrelay/uk-bookmaker-prices-sample

If you’re building a model or an odds screen on UK prices, you’ll know every bookmaker names teams and outcomes its own way. The matched table puts all those prices under one name per outcome, 15,820 of them. There’s a raw table too if you’d rather do the matching yourself.

Football, tennis, horse racing, darts and more, up to 15 events a sport. CSV and JSON under CC BY 4.0, with a data dictionary, and everything joins on event_id and bookmaker_key.

There’s no history or closing prices, in the sample or in the feed. Pre-match only. The sample also leaves the exchanges out.

The feed itself has 60+ UK and Irish bookmakers, with Betfair, Smarkets, Matchbook and BETDAQ lays next to the back prices. If you want current prices on your own events, the Free plan is 50,000 tokens for 14 days with no card.

I run a small tool that guesses whether a YouTube channel is in the Partner Program from what’s public on the page. YouTube stopped showing that status publicly in late 2023, so the tool works from other signals. I put the per-country counts into a dataset.

One row per country: channels checked, how many came out likely monetized, possibly, likely not, and couldn’t tell, plus median subscribers. Countries with fewer than 30 channels are left out.

The channels come from each region’s trending videos (YouTube Data API, 111 regions, 10 categories), so they skew big. A country’s number is about the big channels we checked, not the country as a whole.

About 20% we couldn’t call either way, and that column is in there.

One thing I got wrong: the bottom eight countries (Kazakhstan, Tanzania and Bangladesh at 0 of 60) are exactly the eight where YouTube doesn’t offer channel memberships, and the September rule needed the memberships Join button for a “likely” verdict. That rule has since been changed. This file is the September snapshot; the repo has a comparison of the two.

GitHub: https://github.com/supertj/youtube-monetization-by-country

Live version (updated weekly, new rule): https://monetizationkit.com/data/youtube-monetized-channels-by-country/

If something looks off in a country row, I’d like to hear it.

Researchers and Trainers, what data do you really really want, heck, would pay for? What are you paying for now, and not getting….

What if I told you I am a data bounty hunter who will go out and find it for you?

Money Tracker Daily publishes every WARN layoff notice filed with five state labour departments (California EDD, FloridaCommerce, New York State Department of Labor, Tennessee Department of Labor and Workforce Development, Maryland Department of Labor) as free cumulative CSVs, licensed CC BY 4.0.

Nothing is removed when a state drops a notice from its own list, so each file works as a time series. Each state also has a page per employer, county or city, industry or reason, and month.

https://moneytrackerdaily.com/trackers/layoffs/california

(I run the site. Happy to answer questions about how the data is collected.)

Hello can you guys give me links to datasets of guns, knife, or other weapons

I’ve been counting public tech job ads every week and publishing the counts as CSVs. It’s three weeks in now, so I’m sharing it in case it’s useful to someone.

Each week gets a folder with three files:

– job_types.csv: jobs counted, jobs long enough to read, and employers, for 5 job types (AI, machine learning, DevOps, software engineering, entry level software)

– skills.csv: which skills the ads name, how many list each one as required, and the share

– experience.csv: years of experience required, for jobs with an early career title and for all software jobs

How it’s counted: ads from about 255 employer boards (Greenhouse, Ashby, Lever, Workable) plus a few job sites. The same title at the same employer counts once. Skills are matched with fixed rules against a list of about 250, no language model.

Caveats: the boards lean towards US employers, AI companies and fast growing firms, so it’s not the whole market. It counts ads, not hires. Three weeks isn’t a trend yet. It updates every Monday.

Latest week: https://github.com/DerrickWawerumuturi/glassbox-job-counts/tree/main/2026-10-19

I also wrote up one thing it shows (629 of the 4,536 software jobs that week were entry level, and 274 of those were internships): https://seeglassbox.com/market/entry-level-software/is-entry-level-dead?utm_source=reddit&utm_campaign=datasets

Disclosure: I made this, it’s my project (Glassbox). The data is CC BY 4.0 and the story needs no sign up. I’d like to hear what you’d want counted next.

I put together a cleaned CSV of recent Texas TABC pending applications.

The current release has 102 business-premises records across 15 fields. It’s intended for people working with licensing research, market analysis, or early business activity signals.

One distinction worth making: these are application records, not confirmed new openings. I wouldn’t use them as a list of businesses guaranteed to open.

The file includes filing dates and source references, and I disclose the limitations rather than filling missing fields with guesses.

Disclosure: I built Cavrynt and sell this dataset for $29. This is my own project.

Dataset: https://cavrynt.com

Synthetic schema example: https://www.kaggle.com/datasets/cardhard/texas-tabc-application-csv-schema-synthetic

Happy to answer questions about the schema or how the records are cleaned.

I recently did few meetings and setups with companies to understand the intelligence they are providing, and one thing was common they all had 100-500M datasets of linkedin user profile and contact info. I am very sure there is a very cheap and underrated way of getting this data. Does anyone know this or can help me find? I’m very curious how they are getting fresh data again and again!

This isn’t a static dump. It’s a queryable, nightly-refreshed index of open jobs taken from employers’ own career-site platforms (25 sources, 35,000+ companies).

Fields: title, company, company domain, category and subcategory (19 and 114 values), experience level, work mode, remote scope, country, state and city, source, apply URL, posted date. Not included: job descriptions and salaries.

Access: REST API, up to 10 jobs per call with a cursor on RapidAPI (free plan available). The full index can be pulled through an Apify Actor, billed per result. Not a bulk file download.

I built it, so flagging that. Link: https://rapidapi.com/pk2732004/api/company-career-sites-jobs-api1. If a downloadable snapshot would be more useful for research, tell me what format you’d want.

Over the past two years, I’ve been compiling a historical dataset of football managers and club trophies for a project called Manager History.

It covers 35 selected clubs across England, the Netherlands, Spain, Germany, Italy, France and Portugal, from 1955/56 to 2025/26.

Direct JSON:
https://managerhistory.com/data/snapshot.json

The file contains three linked collections:

• clubs: club IDs, names and countries.
• coaches: manager IDs, names and nationality information.
• seizoenen: club-season records, a main manager ID, and flags for league titles, domestic cups and European trophies. Some records also include additional managers, interim appointments, dates and source links.

Some field names are Dutch. Trophy flags use Y/N; they are not counts of individual trophies.

I compiled the data with assistance from Gemini and Claude, alongside extensive manual checks using Wikipedia, Transfermarkt and other historical sources. It is a work in progress, not a fully verified reference database.

Important limitations:

• Five selected clubs per country, rather than complete leagues.
• One main manager per season; additional appointments are recorded where available, but coverage is incomplete.
• Historical gaps remain, particularly for Boavista and Braga.
• Manager career views only cover clubs included in the dataset.
• Club-season trophy flags should not automatically be interpreted as trophies personally won by the main manager, especially in seasons with managerial changes.

I’d welcome help with three things:

1. Spotting incorrect manager assignments, missing appointments or trophy flags, ideally with a source.
2. Finding reliable historical sources for older Portuguese seasons.
3. Improving the structure for seasons with multiple managers and uncertain appointment dates.

Which missing clubs would add the most useful comparisons? I’m also interested in suggestions for making the data easier to analyse.

Methodology:
https://managerhistory.com/data-methodology.html

Interactive visualisation:
https://managerhistory.com

Disclosure: I created the dataset and website. The JSON currently identifies its export date as 2 October 2026.

Hi everyone! 👋

I’m building an offline AI model that can detect and classify different types of blur in real-world photos. I’m looking for genuine blurry photos taken with mobile phones or cameras to help train and improve the model.

I’m especially interested in:

• Out-of-focus or blurry faces
• Motion blur from moving people, animals, or vehicles
• Camera shake
• Blurry photos taken in low-light conditions
• Dirty or smudged camera lens effects
• Missed focus and accidental blur
• Photos where only certain areas are blurry
• Any other naturally occurring blur

How you can help:

Please upload original photos whenever possible. There’s no need to artificially blur or edit images. Only upload photos you own or have permission to share for AI training.

Privacy and usage: The submitted photos will be used only for the AI model training and research described in this post. They won’t be sold or used for unrelated purposes. Please avoid uploading sensitive or private images.

📤 Upload your photos here: https://www.dropbox.com/request/yhpeiu4ke4r0zvoeco3g

Every contribution helps, whether you upload one photo or several. Thank you for helping with this project! 🙏

Hi everyone,

I’m currently working as a research intern. Our research focuses on developing a lightweight Medical Vision-Language Model for chest X-ray interpretation, particularly Visual Question Answering (VQA), medical reasoning, and reducing hallucinations.

I’m specifically interested in the GEMeX dataset and its corresponding chest X-ray images from MIMIC-CXR on PhysioNet.

I’m looking to connect with researchers who have previously worked with these datasets or have experience obtaining PhysioNet credentialed access.

If anyone could help with the official access process, image-question matching, downloading the required image subset, or collaborating through an appropriately authorized research setup, I would really appreciate it.

I’m also interested in any legally redistributable alternatives or publicly accessible benchmark subsets.

This is strictly for academic research, and I intend to follow the relevant data-use agreements.

References:

• GEMeX: https://www.med-vqa.com/GEMeX/
• MIMIC-CXR: https://physionet.org/content/mimic-cxr/2.1.0/

Thanks in advance for any guidance!

Hi everyone!

I’m working on a graduation project: a Smart Glove for Sign Language Recognition and Speech Translation.

Our hardware setup consists of:

• 5 flex sensors per glove (10 in total).
• 1 IMU per glove (accelerometer and gyroscope).
• 2 ESP32 microcontrollers.

I’m looking for a sensor-based dataset containing raw flex sensor and IMU readings labeled with sign classes, preferably covering both static and dynamic signs and two-handed gestures.

Most datasets I’ve found use images, videos, or MediaPipe hand landmarks, which don’t directly match our hardware.

Does anyone know of a suitable dataset, an open-source project that provides one, or advice on collecting and preparing our own dataset?

Any help, links, or suggestions would be greatly appreciated. Thank you!

I need to download a dataset available on Baidu but can’t as the dataset is too big to download for free. Any advice on how to do it for free?

Free sample of SHEIN’s UK catalogue: 10,000 random products from a full snapshot of 5.15 million products on 17 October 2026.

GitHub: https://github.com/votiakov/shein-dataset

Fields: price (GBP) and crossed-out price, discount %, price in USD, full category path, stock, review count, rating, colour count, image URLs, seller store ID, creation date, product URL. CSV and Parquet, with a data dictionary.

Happy to answer questions about the data.

I’ve been playing around with goodreads data for the last few months. Mostly my interest has been in maintaining and creating goodreads lists. I’m interested in finding fellow fans. Is this board the place for that? Is there some place better?

Looking to connect with individuals currently working in hands-on industries to collect first-person, point-of-view (POV) video data. These recordings will be used directly to train advanced AI and robotics systems.

Eligible Occupations & Tasks
We are looking for people who can record real, purposeful tasks during their regular working shifts, including:

• Event venues: stage setup and teardown, cable management, sound equipment
• Retail and grocery stores: restocking shelves, price tagging, collecting carts, receiving stock
• Construction sites: drywall, painting, masonry, tiling, roofing, cleanup
• Farms, gardens, and greenhouses: picking, planting, collecting eggs, feeding animals, irrigation
• Salons and spas: hair, nails, pet grooming
• Plumbing, air conditioning, and electrical work: installing faucets and toilets, servicing AC units, fitting pipes, wiring outlets and lights
• Hotels: cleaning rooms, deep cleaning bathrooms, laundry, moving luggage
• Auto shops and car washes: oil changes, tire service, detailing, motorcycle repair
• Carpentry and woodworking shops: furniture assembly, sanding, finishing, installing hardware
• Restaurants and commercial kitchens: food prep, line cooking, dishwashing, baking, cake decorating
• Commercial cleaning: restrooms, floors, windows, carpets, industrial laundry
• Food factories, warehouses, and manufacturing plants: packing, order picking, loading pallets, running machines, welding

Strict Device Requirements
To maintain data consistency, you must own one of the following high-end smartphones:

• iPhone: 12 or newer (Note: iPhone 16e and 17e are not accepted)
• Google Pixel: 6, 7, 8, or 9 (including Pro models)
• Samsung Galaxy: S21, S22, S23, or S24 (any model)

Eligible Countries
We are actively hiring and accepting data only from the following locations:

Argentina, Australia, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Japan, Mexico, New Zealand, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Qatar, Saudi Arabia, Singapore, South Africa, South Korea, Taiwan, Ukraine, United Arab Emirates, Uruguay, Venezuela.
(More countries may become available starting in November).

If you meet the phone and country criteria and want to participate, let’s talk.

Please DM if interested.

(Note to Moderators: Please feel free to remove this post if it does not fully align with the community guidelines, or let me know if I need to make any adjustments!)

Disclosure: I run this, it’s Equibles. In May I posted the self-hosted version of the pipeline here. This is the hosted one, which now also covers non-US stocks and is built to be used by AI agents (over MCP) as well as by code (REST).

What’s in it:

• SEC filings, full text (10-K, 10-Q, 8-K, proxies). You can search inside a filing and pull the exact lines, not just metadata
• financial statements and KPIs as reported, each tagged with fiscal period and filing date
• earnings call transcripts, by speaker
• 13F institutional holdings: holders of a stock, or a manager’s full portfolio
• insider transactions (Forms 4 and 5) and congressional trades (House and Senate)
• short interest, daily short volume, fails to deliver, off-exchange volume, a squeeze score, and a model estimate of the next FINRA short interest
• executive compensation, government contracts, buybacks, going-concern flags, IPO filings
• prices for US, European, UK, Hong Kong and Chinese listings (end of day on the free plan), option chains on paid plans
• Fed economic series and CFTC data

How to get at it:

• MCP, for AI agents: https://mcp.equibles.com/mcp (Claude, ChatGPT, Cursor and others)
• REST: https://equibles.com/docs/api
• free plan: 100 requests a day across both, no card. Plus is $9.99 a month for 10,000 a day. Pro adds bulk CSV downloads (short interest, short volume, FTD, buybacks and a few more, one file per year)
• self-host: the open source core (AGPL-3.0) pulls the SEC data itself: https://github.com/daniel3303/Equibles

One detail for the backtesting crowd: short interest bulk rows carry FINRA’s publication date, so you only use numbers that were public at the time.

Why “Financial Datasets alternative” in the title: it’s the name I see most for financial data APIs aimed at AI agents. From their pricing page they don’t have a free tier, and transcripts, congressional trades and short interest aren’t on their list. If I’ve got that wrong, correct me and I’ll fix the post.

So that’s the pitch, a free financial data API for AI agents and the humans who build them. What would make it more useful as a dataset source for you: bulk files for more datasets, point-in-time snapshots, something else?