I noticed that https://t.co/PyKosi9SXv has recently expanded its offerings by integrating Google Maps into some of its services.
https://t.co/W402i5JHdU
🚀 We’re Growing
📈 AppalachiaDevs is expanding.
we’re actively hiring talented AI Engineers
💡 If you’re an AI Engineer looking for exciting opportunities, or a company seeking innovation-driven technology solutions, let’s connect.
Recently, we built two systems at appalachiadevs that reflect where things are going:
🔹 An IoT monitoring platform
🔹 A RAG-based AI agent
The IoT system provides real-time visibility into physical operations — collecting, processing, and streaming data from connected devices
Hiring AI/ML engineers sounds easy —
until you try to find someone who can actually ship.
Everyone knows the theory.
Few can build production-ready systems.
How do you make a good dataset for AI?
Many teams jump straight into models — but the real foundation of AI is data quality.
At appalachiadevs, we see dataset creation as a pipeline, not a one-time task:
What’s the best way to collect data for building datasets?
One of the biggest misconceptions in AI is that models are the hardest part.
In reality, building a high-quality dataset is often the real challenge.
🚀 How to Scrape Reddit (The Right Way)
Reddit is one of the most valuable sources of real-time human insight on the internet—covering opinions, trends, niche discussions, and product feedback at massive scale.
How to Scrape X (Twitter) — The Right Engineering Approach
When people say “how do I scrape X?”, they often imagine writing a quick script.
In reality, reliable data extraction from modern platforms like X is a data engineering problem, not a scraping task.
Why modern businesses are rebuilding their IT strategy in 2026
We focus on:
• Cloud migration & optimization
• Custom software development
• AI integration for business workflows
• Secure and scalable infrastructure
Most companies don’t have an IT problem—they have a system design problem
We see this pattern often:
• Systems built without scalability in mind
• Tools added instead of systems redesigned
• Automation layered on broken workflows