What new technology really means for the people it touches, and where that leaves room to build.
I handed an agent my most tedious recurring task and discovered the AI was the easy part. Everything around it, the access and the permissions and the plumbing, was the hard part.
Setting up a staff of AI agents, the real work was never describing each job. It was writing down what each one must not do without checking with me first.
If you've landed here, you're probably somewhere on the spectrum between "AI-curious" and "AI-exhausted." Good. This is a place for both.
Hands-on writeups from Daily(ish) GenAI Experiments, the project that became Plain Inference.
After weeks of building my website entirely through conversation with Claude Code, I realized I'd been so focused on getting things done that I hadn't stopped to think about whether I was actually absorbing any of the technical knowledge along the way.
I've been deliberately careful about data privacy throughout my AI experiments. My background in consumer research and product testing in the data privacy space taught me early what the real costs are when you hand over personal information to tech platforms.
Building a website with Claude Code taught me five unexpected lessons about how the web actually works—not through tutorials, but through real debugging with immediate stakes.
I had a GoDaddy site with decent blog features and a Canva design I loved. But I couldn't combine them. So I asked Claude Code to build a custom website—and it actually worked.
Every morning, I need to fill out a slip telling my kindergartener's school what they'll eat for lunch. I thought: this is exactly the kind of thing an AI agent should handle. Spoiler: the agent got almost everything right, but 'almost' doesn't fill out lunch slips.
My second grader came home with a logic puzzle worksheet. After some frustration at the kitchen table, I realized there wasn't enough information to solve it. I decided to test whether AI could confirm my suspicion.
I had a trailing vine plant sitting on top of a cabinet in my house and wanted to know if I should do anything with it. Simple question. But I also had an ulterior motive: I wanted to see if ChatGPT could match a specialized plant identification app.
I tested the ScholarAI custom GPT to see if it lived up to the hype. I wanted to find out what it can really do.
This week, I dug into the latest AI safety pulse check: the Future of Life Institute's "AI Safety Index: Winter 2025." It reviews eight leading AI companies across six critical domains, from risk asse...
Last week I went to a newly opened speakeasy and ordered a glass of white wine. The server who was young, friendly, but clearly new, admitted he hadn’t tasted any of the wines yet and neither had the ...
We often talk about "Vendor Lock-in" with enterprise software, but I recently discovered a new flavor of it: "Prompt Lock-in."
Yesterday's experiment started with a simple goal: test whether Google's latest image model understands context or just follows instructions. The results revealed both a massive leap forward in AI's a...
Yesterday's experiment started during my drive to daycare pickup and ended with an actual shopping plan I might follow. If you've ever stared at a gift list for 8 family members and felt your soul lea...
Last night's experiment started with homework frustration and ended with genuine delight, the kind where my seven-year-old actually asked to do more word practice. If you're a parent who's witnessed t...
The promise: AI will automate tedious tasks like extracting data from PDFs into spreadsheets.
Last night's experiment started with a simple idea: could a large language model make something useful out of the chaos in my fridge after a busy weekend?
On Wednesday I shared an example of a wild hallucination that resulted from the Gamma implementation of GPT5. Today, I'd like to share some tips on how to use custom instructions in the models you use...
I just had one of those "this is why we can't have nice things" moments with AI.
This week’s experiment explored how GenAI can speed up and elevate partnership preparation, not just by summarizing a company’s data, but by helping you think like their business.
What happened
Armed with ChatGPT Atlas, I set out on what should have been a simple task: book restaurant reservations for a weekend trip. What followed was a masterclass in AI inefficiency.
The Challenge: A friend needed her headshot edited to match her brand colors - specifically, changing her blue dress to purple. Simple enough, right? Well, as with most things in AI, the journey was m...
I had a simple ask. I needed a playlist for my upcoming half marathon, but not some generic "pump up" mix. I wanted something that understood my target pace, my tendency to start too fast, and exactly...
For today's GenAI experiment, I fed Gemini 2.5 Flash my personal three-year Peloton workout history, a 1,351 row CSV file and asked it to diagnose the factors behind my longest training breaks.
Gamma is brilliant for breaking the blank page problem. That initial rush of having a complete deck in seconds gave me momentum. Even though I changed most of the content, having something to react to was psychologically powerful.
When your fitness app fails, AI can still finish the story.
Yesterday I shared how I turned the park district's basketball schedule into a clean calendar file. Today I'm sharing the exact prompt template I used, because the magic wasn't in the AI, it was in be...
I had a problem. Actually, I had a perfectly functional schedule. The park district's website listed seven weeks of youth basketball games with all the information I needed: dates, times, locations, m...
The Experiment Can AI help plan a party based on specific media? I tested this by planning my child's birthday party themed around the Netflix movie K-Pop Demon Hunters.
How I Used Them
What happens when you stop treating AI as a content generator and start using it as a genuine thinking partner? In this experiment, I explored Claude's Projects feature to develop and refine a presentation through multiple iterative sessions. The key discovery: Claude Projects creates a persistent workspace where context carries across conversations. No more re-explaining your project every time. Instead, each session builds on the last - from initial brain dump to structure creation, through voice conversations about creative decisions, all the way to final refinements based on practice session feedback. I found the most value not in having Claude write my presentation, but in using it to think through structural choices, narrative flow, and transitions. Voice conversations proved especially powerful for working through subjective creative decisions in real-time. This experiment demonstrates how AI tools excel when used for sustained creative partnership rather than one-off tasks. Whether using Claude Pro's Projects feature or bookmarking conversations in the free tier, the ability to maintain context transforms AI from a tool into a collaborator. [Read the full experiment on LinkedIn →]