# Building Instagram OSINT Projects with HikerAPI

The article describes an experiment using Python and HikerAPI to build an Instagram OSINT project on Linux, noting that direct scraping is unreliable due to Instagram's aggressive automation blocks. The author found HikerAPI to be a more stable alternative for development, though it requires paid credits for larger usage. The piece concludes that while HikerAPI simplifies the process, traditional OSINT tools remain effective for public workflows.

I recently experimented with building an Instagram OSINT project on Linux using Python and HikerAPI. Originally I tried older scraping libraries and unofficial Instagram API wrappers, but many of them were unreliable because Instagram now aggressively blocks automation attempts. After some testing, I found that using an external API service like HikerAPI was much more stable for learning and development purposes. Why I Switched At first I used tools based on: - instagram-private-api - old scraping scripts - login-based automation The problem was: - constant login checkpoints - “bad password” errors even with correct credentials - temporary account locks - broken endpoints Modern Instagram protection systems make direct scraping much harder than before. My Setup I used: - Arch Linux - Hyprland - Python virtual environment - HikerAPI Creating the virtual environment: python -m venv venv source venv/bin/activate.fish Installing dependencies: pip install requests httpx Basic HikerAPI Example Here’s a simple request example: python import requests headers = { "x-access-key": "YOUR KEY" } r = requests.get "https://api.hikerapi.com/v2/user/by/username?username=instagram", headers=headers print r.json What I Learned A few things became obvious during testing: - Old Instagram scraping methods are becoming unreliable. - API-based approaches are much easier to maintain. - Using a Python virtual environment on Linux avoids dependency issues. - Sherlock and other username OSINT tools are still useful alongside APIs. Tradeoffs HikerAPI is convenient, but it’s not fully free. You need credits for larger usage. For hobby projects and learning, that may still be easier than constantly fixing broken scrapers. Meanwhile, tools like: - Sherlock - Maigret - Google Dorking - Wayback Machine are still excellent for public OSINT workflows. Final Thoughts This was a fun experiment for learning Python, Linux tooling, and OSINT workflows. If you’re building similar projects, I’d strongly recommend: - using virtual environments - avoiding your main Instagram account - testing on dummy accounts - learning public OSINT techniques first HikerAPI definitely simplified the process compared to older scraping approaches.