Trip Atlas was built at the intersection of artificial intelligence and travel discovery — a live experiment in what happens when you stop thinking in suites and start thinking in agents.
Print democratized publishing. The internet eliminated geography. But Generative AI is doing something more fundamental: it's collapsing the distance between intention and execution.
Previous disruptions changed how work was distributed. Gen AI is changing what work means — who does it, how fast, and what "good enough" looks like. For creatives, this isn't a productivity upgrade. It's a renegotiation of the creative contract.
"We used to measure creative output in days. Now we measure in prompts. The bottleneck is no longer production — it's judgment."
The new creative economyWhat once required a team, a brief, and two weeks of iteration now emerges in hours. Concepts are tested, refined, discarded — faster than any agency sprint. The creative process isn't slower because of perfectionism; it's liberated from it.
Research, synthesis, competitive analysis, tone guidance — tools now surface what used to live in expensive consultants or months of market research. A solo builder today commands resources that once required an enterprise.
The barrier to entry for sophisticated creative and technical work has collapsed. A single person with the right set of AI tools can ship what previously demanded a full-stack team — designers, copywriters, engineers, strategists.
The new creative skill isn't execution — it's curation. Knowing which output is right, which tool to trust, which prompt opens the door. The creative director has become the primary role, even for solo operators.
For decades, software worked in ecosystems: Adobe Creative Suite, Microsoft Office, Google Workspace. One vendor, one login, one philosophy. Files flowed from app to app by design.
Gen AI has broken this. The new model is a constellation of best-in-class agents — each with its own strengths, personality, pricing, and interface. No single tool dominates. The creative's job is now orchestration.
The modern creative stack isn't a suite — it's a cast of characters. Each agent brings a distinct capability, flavor, and philosophy. Knowing which one to reach for, and when, is the new creative literacy.
The first wave of AI in creative software was additive: a "Remove Background" button, a smart crop, an auto-caption. Features you could ignore.
That phase is over. AI isn't a feature in the workflow — it IS the workflow. The tools that survived are those that restructured their entire logic around AI-first thinking, not those that bolted on a smart feature.
The implication for builders: you're not adding AI to your product. You're building a product that wouldn't exist without AI. That's a fundamentally different design problem.
"Why am I asking an agent to do this research for me — when I could build the app myself, own the output, and actually learn something in the process?"
The question that started Trip AtlasTrip Atlas is a generative AI web application for photographers and visual travelers — it curates photo spots in any city you're headed to, replacing the hours of pre-trip research most people never have time to do properly.
It replicates the hours a photographer spends before any trip — the searching, the tab-hoarding, the Reddit threads, the note-taking — and compresses it into a curated list of photo spots for any city you're headed to. The research still happens. You just don't have to do it.
Currently curates photo spots by city. The roadmap extends this into full itinerary building — turning a list of locations into a sequenced, shootable plan.
GPT-4o handles the full reasoning load — generating spots, details, coordinates, and context in a single, carefully engineered prompt.
What once required a funded startup and cross-functional team, Gen AI tools made achievable for a single builder with vision.
Trip Atlas didn't start with a blank canvas. It started with months of structured testing — working through AI tools one by one, understanding their strengths, their limitations, and their behavior under real design and accessibility conditions.
By the time the first line of Trip Atlas code was written, the tool decisions weren't guesses. They were conclusions. All eight experiments ran under the same umbrella: what can Gen AI actually do in a professional design and product workflow — and where does it fall short?
"By the time Trip Atlas started, the bake-off between v0 and Lovable wasn't a guess. It was the conclusion of a deliberate research arc — all under the umbrella of Gen AI."
AI Lab · Experiments 1–8 · jenniferfrench.design/ai-sandboxTrip Atlas didn't start as an experiment in AI development. It started as a photographer preparing for a trip to several international cities — and running out of patience with the research process.
The hours spent cross-referencing travel blogs, Reddit threads, Google Maps pins, and photography forums — all to answer one question: where should I actually go to shoot? That friction was the brief. The app was the answer.
Trip Atlas was conceived, ideated, and built to provide locations, descriptions, sample photos, geo-related data, access information, equipment recommendations, and nearby places to eat. It was used in the field during actual travel — the ultimate user test.
On February 21, the original brief went into Claude: build an app listing the top 5–10 places to photograph in cities entered by the user, with maps, sample images, recommended times, equipment suggestions, and nearby food. Claude named it "Where to Shoot" and built the React file. The four seed cities: Edinburgh, Amsterdam, Barcelona, Dublin.
Lovable's concept — named "Trip Muse" — came with a full design vision: "Polarsteps meets Google Travel. Dark theme with warm amber accents, high-contrast typography, subtle grain textures." Visually striking, full-bleed. v0 offered a more maintainable component-based system. v0 won. The amber accent and dark theme survived.
Connecting v0 to GitHub established a professional PR workflow — changes pushed automatically to a feature branch, reviewed, then merged to main. By the end of the build: 146 commits, 106 deployments, 5 active branches. TypeScript 97.9% of the codebase.
Two information architectures were tested side by side — cities as horizontal tabs with a spot grid below, vs cities as expandable sections in a vertical scroll. Tabs won. Cleaner on mobile, more app-like, and a better fit for the dark photography aesthetic.
"The relative speed of the UI design from v0 — and some of the pattern suggestions it put forward during iteration — genuinely surprised me."
Jennifer French, on building Trip AtlasDevelopment moved through multiple revision cycles, each driven by hands-on testing. A structured UAT process was established early — and became one of the most important decisions of the build.
The UAT protocol required the agent to verify changes after each implementation, confirm fixes across all affected screens, and get explicit sign-off before any PR merge. Small discipline, big payoff.
During beta testing, a critical issue emerged: spot images across every city and every trip stopped rendering after an unknown period. The database looked fine. The URLs looked valid. But Google had quietly expired them.
Google Places Photos API returns temporary URL references — not permanent image files. Once expired, every stored URL became a broken link. A trip created in March 2026 showed broken images weeks later across all 40 spots in Edinburgh, Amsterdam, Barcelona, and Dublin.
"I had to keep purchasing credits mid-debug. Eventually I instructed the agent to roll back and phase the builds in — that was the real turning point."
Jennifer French — the lesson that changed how she worked with the agentOne of the unexpected costs of building at AI speed: you're moving too fast to document what you're thinking. The case study came after the build — which means a lot of the in-the-moment reasoning had to be reconstructed from chat logs and commit messages.
Now multiply that across several agents, each handling a separate task — one writing code, one generating UI, one managing version control, one sourcing images. Each conversation its own context, its own decisions, its own dead ends. The process doesn't just disappear once. It disappears in parallel.
This isn't a failure of process. It's a feature of the medium. When you're iterating at the pace Gen AI makes possible, reflection is the thing that falls away the easiest. The work moves faster than the meta-awareness of the work.
The lesson for anyone building in this era: decide early whether you're making a product or a case study. If it's both, you need to slow down enough to capture the thinking — because the tools won't do it for you. At v115, mid-debug, with credits running out, nobody is writing field notes.
Each one a decision. Most of them made too fast to remember why.
Ship, test, break, fix, repeat. The rhythm of AI-assisted development.
Mid-debug. The agent stopped. Credits purchased. Work continued. No documentation written.
The single biggest process improvement: before any implementation, require the agent to think through the build and generate a plan. What it's going to do, in what order, and why. This one step eliminates entire categories of downstream bugs — and gives you something to review before a line of code is written.
Instruct the agent to conduct UAT immediately after implementation — before the PR is created, before anything merges. Bugs caught at the build level cost tokens. Bugs caught after deployment cost time, trust, and sometimes a full rollback.
Somewhere inside a large build, something broke quietly. Not dramatically — just a small, undetected fault that subsequent work stacked on top of. By the time it surfaced, it was load-bearing. The scope didn't cause the problem; it just gave the problem more places to hide. Building with AI agents is a series of levers and pulleys — each action triggers the next, and the learning happens in the chain reaction. The fix isn't smaller ambition. It's earlier detection. Which is why UAT isn't optional.
"At the end of the day it's a Rube Goldberg machine — levers and pulleys, give and take, and the learning happens somewhere in the chain reaction."
Jennifer FrenchWhen generation is cheap, curation is expensive. But it goes deeper than taste. It's the empathy to ask: how does this actually feel when a real person uses it? Technically everything works in theory. The skeleton loader loads. The UAT passes. The PR merges clean. And then someone uses it in the field — tired, distracted, on a phone in bad light — and you realize the gap between "works" and "works for a human" is where all the real design lives. That gap is yours to close. No agent gets there without you.
Trip Atlas isn't the destination. It's where the approach was proven. What comes next is the same method, applied with more confidence and fewer wrong turns.
Trip Atlas is one answer to the question Gen AI is posing to every creative industry: not "will AI replace you?" but "what will you build now that you can?"
© 2026 Jennifer French. Licensed under CC BY-NC-ND 4.0. You may share this work with attribution for non-commercial purposes. No derivatives permitted.