About
I’m Logan Eason, a developer and entrepreneur focused on building AI tools that solve real problems for real people. With over 15 years of experience in marketing and real estate, I create products that blend automation with practical use, especially for small businesses and regulated industries. I believe technology should be powerful but easy to use, and always serve the human behind the screen.
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My first launch! RealCOMPLY AI for fair housing.
Hey everyone! Excited to share that RealComply is launching tomorrow (Friday) on Product Hunt! I built RealComply to solve a real pain point in real estate marketing: staying compliant with Fair Housing laws without slowing down your workflow. What it does: RealComply is a Chrome extension that scans your property ads and social media captions for potential Fair Housing issues before you hit publish. Who it s for: Real estate pros, marketers, and brokerages who want to stay compliant and confident. Why it matters: Housing should be fair for everyone, but moreover (especially for stakeholders) one missed word can lead to major fines or lawsuits. RealComply acts like your personal compliance assistant, flagging risky language in seconds. We go live first thing Friday I d love your feedback and support! I ll be around all day answering comments and sharing behind-the-scenes.
Hi Product Hunt! I’m Logan Eason – AI educator, developer, and small business advocate.
After 15+ years in real estate and marketing, I realized small businesses need more than buzzwords when it comes to AI they need practical tools and clear guidance. That s why I ve been building: [RealComply] A Chrome extension that helps real estate pros stay Fair Housing compliant. [AI for ] A book series and community teaching non-techy people how to use AI effectively. I m passionate about using AI to save time, stay compliant, and scale human connection not replace it. Looking forward to connecting with other builders, learning from your launches, and sharing feedback where I can. Let s make AI work for everyone.
Token Management with GPT-4 API – One Large Call or Several Small Calls?
I m building a workflow using the GPT-4 API. The model performs a series of tasks. I'm taking bullet point input and rewriting it as a narrative, drafting social posts and reels scripts, etc. Each task depends loosely on the previous one, but not so tightly that they can t be separated.
Should I handle this with one big prompt or split it up into multiple smaller API calls? I'm looking to reduce cost, but don't want the savings to outweigh quality and UX.