How Rapid Mini-Apps and AI Quirks Are Changing Workflows — Including AI in Recruiting

In a recent deep-dive by Nate B Jones of AI News & Strategy Daily, I walked through five major AI updates and then demonstrated a fast, practical example: building a travel itinerary app for Kyoto in under 25 minutes using ChatGPT-5. If you work in product, HR, recruiting, or operations, this piece will be especially useful — because the same techniques I used to create a travel planner are directly applicable to AI in recruiting workflows, interview scheduling, candidate evaluation, and more.

Outline

• Introduction and context
• Five AI updates that matter
• Step-by-step walkthrough: the Kyoto travel mini-app
• Prompting techniques and iterative design
• Reusable prompt template (adaptable for recruiting)
• Practical applications: how this maps to AI in recruiting
• Final thoughts and next steps

Why these AI updates matter

Major releases get the headlines, but the small changes and tooling updates are the ones that shift how we work. Here are the five developments I highlighted and why they matter for building tools — whether for travel planning or hiring pipelines.

1. Claude’s retrieval-based memory

Anthropic’s Claude launched a retrieval-based memory system. Unlike ChatGPT’s editable memory, Claude doesn’t automatically persist and display remembered items. Instead, it searches past conversations and retrieves relevant snippets when steered to do so. That gives you a different control model: less "surgical" but potentially richer if you explicitly craft prompts that guide retrieval.

For practical workflows such as candidate context in AI in recruiting, this means thinking about how you surface past candidate notes. Do you want an always-on memory that might unpredictably remember things, or do you want to query past chats for specific interview feedback, coding scores, and availability windows?

2. Claude Sonnet’s 1 million token context window

Claude Sonnet now accepts a 1M token context window (a 5x increase). Practically, this changes what you can do in a single run: analyze entire codebases, process massive candidate portfolios, or evaluate long chains of interview transcripts without chopping them into separate prompts.

Imagine reviewing a year’s worth of candidate notes, coding test outputs, and hiring manager feedback in one go. For AI in recruiting, larger context windows allow end-to-end candidate summaries and consistent decision rationale generated in a single pass.

3. Meta’s brain modeling research

Meta published work attempting to build models that predict fMRI responses to movies by fusing audio, video, and dialogue. While ethically and societally loaded, the takeaway is that companies are increasingly trying to model cognitive responses directly. That has implications for content optimization — and for simulating user or candidate reactions to interview experiences or learning content in training programs.

4. Merge Labs and brain-computer interfaces

Sam Altman’s Merge Labs — a new BCI startup reportedly backed by OpenAI — signals continued interest in brain-computer interfaces outside of Elon Musk’s Neuralink. We’re not at consumer production yet, but this reaffirms a trend: adjacent technologies continue to grow, and they’ll drive new product categories where AI plays a central role.

5. Google Gemini’s looping bug

Gemini exhibited a "paranoid android" behavior: when tasks get hard, it can enter a loop of self-apology and self-critique, refusing to proceed. It’s an important reminder that large models show emergent personalities and quirks. In production systems — including those designed for AI in recruiting — you must design safeguards and retry logic, and test models at scale.

“We’ve hit the point where making a functional mini-app takes less time than brewing a pot of coffee.”

How I built a Kyoto travel app in ~25 minutes (and why the process matters)

The demo: a 14-day Kyoto itinerary interactive mini-app. What’s notable isn’t the travel content; it’s how fast I reached a production-usable artifact, and how the conversation shaped the final product. The workflow demonstrates how clear intent, iterative prompting, and a willingness to refine UI/UX requests turn a casual prompt into a useful tool.

Here are the steps I used, condensed and generalized:

1. Start with clear intent: one sentence that explains the goal, audience, and constraints.
2. Let the model research and propose a blueprint (places, categories, rough schedule).
3. Ask the model to code the app and let it iterate — expect UI bugs and fix them via conversation.
4. Demand a particular aesthetic and UX behavior (light theme, readable fonts, meaningful controls).
5. Refine content: ask for a full 14-day plan, adjust for travel with a child, and request plain-English rationale for each day.

Two things to note: first, the initial three-line prompt was intentionally simple, but it included the essential constraints (audience, travel radius, interests). Second, the entire process required a back-and-forth. The app was usable in under ten minutes; a polished v2 took about fifteen more — all done by iterating with the model rather than hand-editing code.

What this looks like in practice

Early outputs were buggy: dark text on dark background, broken links, and missing plain-English day rationales. I asked the model to fix the styling, refactor components, and fill out missing content. The model responded by rewriting hundreds of lines of code and expanding the itinerary with more categories (ramen nights, moss temples, onsen options). After iterative prompting, I had a functional, minimal, and practical planner that I could share and edit.

“AI models now have personalities and quirks—just like new colleagues you need to learn before you trust them.”

Prompting techniques that work (and what I learned)

Here are the core prompting lessons I boiled down from the exercise — applicable to building internal tools, recruiting dashboards, and scheduling assistants alike:

• Be clear about audience and constraints: specify the user (e.g., "family with a 1-year-old"), travel radius, and UX expectations.
• Iterate in plain English first, then ask for code. You don’t have to be hyper-technical up front.
• Use user-facing adjectives for visuals (minimal, light theme, Japanese-influenced aesthetic), and demand accessibility.
• Expect and accept small bugs. The productive pattern is: identify the bug, show a screenshot (or explain), and ask for a fix.
• Ask for a rational explanation for content choices so you can audit recommendations later.

Why this matters for AI in recruiting

Substitute "candidate" for "place" and "interview day" for "itinerary day," and you have a recruiting workflow: a mini-app that helps plan interview schedules, generate consistent candidate summaries, and propose follow-ups. The same iterative prompt + code approach can create:

• Interview scheduling assistants that account for travel and time zones (useful for remote hiring).
• Candidate dashboards that aggregate past notes and generate short, audit-friendly rationale for hiring decisions (leveraging memory and long context windows).
• Automated feedback summaries for candidates, stitched from multiple interviews and test outputs.

Each of these outcomes is a direct application of the mini-app pattern I used for Kyoto. If you design for AI in recruiting, think of the tool as a conversation partner: tell it the audience, constraints, and deliverables, then refine.

A reusable prompt template (adaptable for recruiting and other uses)

Below is a cleaned-up template that reflects the iterative evolution I asked the model to produce. It’s written so you can swap variables — for travel, recruiting, or event planning. Use the bracketed placeholders to customize.

Build an interactive mini-app to plan [14-day itinerary / interview schedule / event timeline] for [city / role / event name]. The app is for [family with a 1-year-old / hiring managers / event planners] and must respect these constraints: [max travel time 45 minutes / interview length 45 minutes / venue capacity 50]. Include the following interest areas: [moss temples, ramen nights, onsens / technical interviews, culture fit, take-home test / keynote tracks, breakout sessions]. Research real places or real candidate data as needed and cite sources. Provide:

• Clear, plain-English rationale for each day/session/interview
• Morning, afternoon, and evening (or pre-interview, interview, post-interview) options
• A minimalist, light-themed UI with readable fonts and accessible controls
• Local storage for saving edits and the ability to add/remove items
• Links and map integration where relevant

Return:

• Full app code (React + simple CSS) that is runnable
• Non-functional requirements (performance, local storage behavior)
• A short user guide for modifications

Swap the bracketed fields to rapidly bootstrap variations — and yes, this same structure supports AI in recruiting projects: swap "itinerary" for "candidate pipeline" and "places" for "candidate profiles."

Practical considerations when using models for hiring

When you bring models into recruiting pipelines, remember three operational rules drawn from the demo:

1. Test at scale — models show different quirks at volume (e.g., Gemini’s looping bug). Validate outputs across many candidates, not just one.
2. Design explicit retrieval strategies — use retrieval-based memory or long context windows deliberately to avoid uncontrolled recall and privacy leakage.
3. Audit rationale — always ask the model to produce plain-English rationales for decisions so you can review and comply with regulatory and fairness concerns.

These safeguards let you harness the speed of model-driven mini-apps while maintaining control and interpretability for AI in recruiting workflows.

Conclusion — What to build next

Building a functional mini-app in under 25 minutes is no longer a novelty; it's a practical pattern. Whether you’re crafting a travel planner, event schedule, or candidate pipeline tool, the same principles apply: clear intent, iterative prompting, and a readiness to refine UI and data behaviors. For anyone working with AI in recruiting, this approach unlocks rapid prototyping for scheduling, summary generation, and interviewer coordination — while reminding us to design for quirks and accountability.

If you want to experiment, start with the template above. Replace the placeholders with your hiring specifics, and in less time than your morning coffee you’ll have a prototype that you can iterate on and operationalize.

Questions or want a walkthrough tailored to AI in recruiting at your company? Let me know — I’m happy to help you sketch a prompt and test a quick prototype.