Systems | Development | Analytics | API | Testing

The Missing Piece of Your AI Strategy: Data at the Edge

Companies everywhere are rushing to deploy AI models to outpace the competition, but they are running headfirst into a brutal reality check: an AI model is only as brilliant as the data feeding it, and most data simply isn't AI-ready. The high-value, real-time data required to power these models doesn’t live in a pristine, pre-formatted cloud data warehouse. It is generated in the physical world on factory floors, inside hospital rooms, and at point-of-sale terminals.

AI Is Only as Good as Your Property Data: Preparing Data Foundations for AI Initiatives

PropTech teams sometimes plan an AI initiative by starting with the wrong question. They ask which model to use before they can answer a more basic one: is our property data ready for any of it? AI pilots may stall not because the models underperform, but because the property data feeding them is fragmented across MLS feeds, PMS records, and CRM exports, duplicated across sources, and missing the ownership, tax, and location context a model needs to reason.

Real Rental Data & AI-Ready Infrastructure - With Jonas Bordo, Dwellsy | The Innovation Blueprint Podcast

For as long as there’s been a rental market, there’s been a version of this question: is the number on the listing actually the number? Ask anyone who’s built a pricing model, a forecasting tool, or a CPI estimate on top of rental data, and you’ll get the same answer — probably not, and there was never a good way to check.

MCP Debugging: How to Fix Broken MCP Servers and Tools

Model Context Protocol allows AI models like Claude to communicate with the outside world. But MCP debugging has been one of our steepest learning curves at Bugfender. Several different layers need to work together at the same time and if one thing breaks, it can scupper the whole workflow. That’s why we’re here today. To pass on hard-won knowledge, so you can jump the curve.

IoT Medical Device Integration: Technical Guide to Devices, Gateways & EHR Systems (2026)

The Internet of Medical Things (IoMT) is growing faster than ever. According to 2026 data from The Business Research Company, the global IoMT market has reached over $124 billion this year and is on track to hit nearly $300 billion by 2030. This growth is happening because healthcare is moving outside hospital walls and into patients' homes through remote monitoring. But for engineering teams, connecting these devices is a massive headache.

Is Your AI Startup CEO Lost in the Plot? #Shorts #podcast #cloudsecuritypodcast

Drawing on his experience at Google, Google X, and as the CEO and co-founder of an AI startup, Varun Puri shares practical lessons on embedding AI into everyday workflows and building habits that stick. Discover how to maintain perspective on what is working, even when AI constantly highlights what isn’t.

The Best Tier 1 SOC Automation Tools in 2026

Tier 1 SOC is alert triage, enrichment, initial investigation, and escalation. Most of this work is repetitive and hard to scale, and legacy options for automating it (e.g., SOAR) can't keep pace with modern workloads because they're engineering-led, not analyst-led or browser-based (where the actual work happens). The tools below automate tier 1 work, and all of them use AI in some way. They range from AI SOC analysts that investigate alerts the way a human would to automation platforms with AI layered on top, plus AI built into platforms you may already run.

Walkthrough: Bitrise Desktop App

Senior Product Manager Kaushal Vyas walks through the Bitrise desktop app, showing you how to rescue your focus from endless browser tabs. Inside: how to pin your mobile CI/CD pipelines right to your macOS menu bar, set up targeted build filters, customize failure notifications, trigger new builds, and utilize convenient shortcuts to access specific commits, pull requests, and logs.