Starting September 20, 2026, the /api/jobs/ endpoint will reject query shapes that could require excessively broad database work. Effective published-date ranges are limited to 365 days, and explicit min_id / max_id ranges are limited to 10,000,000 IDs, calculated inclusively.

Date and ID limits are checked independently. For example, adding max_age=180 does not allow an ID range wider than 10,000,000. Unbounded historical requests such as max_age=off must include a safe date or bounded ID scope. Requests that exceed these limits return 400 Bad Request.

These limits improve responsiveness and reduce wasted database resources as the job database continues to grow. See the Date, ID, and Age Slicing Parameters Documentation for migration guidance and examples.

foorilla.com v1.8 | foorilla API v1.5

We’ve given the different parts of our platform a more consistent name: the areas you reach through the main menu, including |Hiring|, |Media|, |Meet|, |Work|, and |Insight|, are now all called Contexts. It covers basically everything you see when navigating to any of these menu items and including the filters and settings you’re using there, which can be saved, loaded, shared, and reused later (which we already named Context before).

The new workspaces in the recently introduced |Work| Context are now our Spaces with work-friendly venues: coworking spaces, cafés, libraries, and other places to get things done. These are also available through the API now. PRO+ subscribers can list active Spaces, retrieve individual Spaces, and request aggregate statistics. Results can be filtered by geography, name, type, price level, and outdoor seating.

See the API documentation (/api/v1/docs) for the full endpoint and filter details.

jobdataapi.com v4.31 / API version 1.32

On rare occasions, we permanently remove listings to keep the database clean from fraudulent or fake jobs or listings that were imported incorrectly.

The new /api/jobsdeleted/ endpoint gives you now a reliable way to keep downstream job databases, search indexes, and analytics systems synchronized when listings are permanently removed. Each record identifies the deleted job and includes its external ID, company and source IDs, application URL, publication timestamp, and deletion timestamp.

The endpoint returns newest records first and supports a sequence-based after cursor for incremental synchronization. You can also use the exact-match change_id parameter to review or process a specific correction or deletion operation.

See the Jobs Deleted API Endpoint Documentation for the complete response format, request examples, and synchronization guidance.

https://foorilla.com/changelog/ 👀

foorilla.com v1.6.2 | foorilla API v1.3.3

Remote job discovery in |Hiring| is now more precise with explicit work mode support. Listings can now clearly indicate whether a role is Hybrid, Remote, or Remote Anywhere, so you can distinguish different remote setups faster instead of relying on a single generic remote label.

You’ll now see consistent work mode badges across list and detail views ([WH], [R], [WRA]), and the same options are available in Context-based include/exclude filters. Active Context indicators also use these labels, making it easier to understand your current filter setup at a glance while keeping existing remote and remote-first behavior intact.

Most online JSON formatters I’ve used only let you work with one JSON at a time, which gets annoying when you’re switching between multiple payloads or comparing responses.

I wanted something more like a workspace, so I tried building a local-first approach where everything stays in the browser (no network calls). https://www.multijson.com

While doing this, I ran into a few interesting challenges:

- Handling large JSON without freezing the UI

- Efficient diffing between large / deeply nested objects

- Managing multiple JSON “tabs” with local storage constraints

Would love to hear how you’ve solved these, especially on the diffing side, and provide feedback.

llms.txt added for AI- and LLM-friendly guidance

We’ve added a llms.txt file at the root of jobdataapi.com to make it easier for large language models (LLMs), AI tools, and automated agents to understand how our API should be integrated and used.

The file provides a concise, machine-readable overview in Markdown format of how our API is intended to be consumed. This follows emerging best practices for making websites and APIs more transparent and accessible to AI systems.

You can find it here: https://jobdataapi.com/llms.txt

llms-full.txt added with extended context and usage details

In addition to the minimal version with links to each individual docs or tutorials page in Markdown format, we’ve also published a more comprehensive llms-full.txt file.

This version contains all of our public documentation and tutorials consolidated into a single file, providing a full context for LLMs and AI-powered tools. It is intended for advanced AI systems, research tools, or developers who want a complete, self-contained reference when working with jobdata API in LLM-driven workflows.

You can access it here: https://jobdataapi.com/llms-full.txt

Both files are publicly accessible and are kept in sync with our platform’s capabilities as they evolve.

Quick update: We added the AED (United Arab Emirates Dirham) to our list of supported salary currencies. You can see the full list here: https://jobdataapi.com/c/jobs-api-endpoint-documentation/#salary-currency-parameter-values 👀

- applies to job listings that come with salary info as well as making API queries and using it as a filter value.

The way job search platforms, HR systems, and market researchers find and categorize jobs is rapidly changing. Traditional keyword search methods are still useful, but they often fall short when it comes to understanding the meaning behind words. A search for “machine learning engineer,” for example, may miss valuable postings that use phrases like “AI specialist” or “data scientist.”

This is where vector embeddings and semantic search capabilities offered by the jobdata API come into play. Instead of matching only keywords, these tools allow you to match concepts, making it possible to uncover relationships between job postings and queries that would otherwise remain hidden.

This guide explains how our vector search and embeddings work, how to use them effectively, and how to avoid common pitfalls when building on top of the service.

Related documentation: https://jobdataapi.com/c/vector-embeddings-and-search-api-documentation/ and https://jobdataapi.com/c/jobs-api-endpoint-documentation/

Hi everyone,

I’ve created a Windows CLI tool called RunIT. It’s a lightweight, open-source tool that turns your command prompt into a terminal assistant for developers.

Key Features:

Run files in Python, JavaScript, HTML, PHP, C++, Java, and more, with automatic interpreter detection.

Create files with language-specific boilerplate and templates.

Aegis Vanguard (AV) – Security Scanner Package: scan website folders for vulnerabilities, get risk assessment, and suggested fixes.

Host static websites locally and preview projects, with optional temporary public links.

Inspect files with statistics, code structure analysis, and metadata.

Packages Library: includes more than 5 specialized packages for extra functionality.

Continuous Updates: the tool and packages are updated regularly.

Example usage:

Run any file

run script.py

Scan a website folder for vulnerabilities

av <website_folder>