I'm excited to share something I've been quietly building alongside this newsletter —
hardAIs.com, an AI consulting and development firm
focused on helping businesses and individuals put AI to work in practical, meaningful ways.
Hard AIs offers end-to-end AI services — from strategy and consulting all the way through
custom development and deployment. The idea is simple: AI is most valuable when it actually
solves a real problem, not when it just sounds impressive in a pitch deck.
The site is still actively being developed and improved, but it's live and worth a look.
As a community that thinks seriously about AI, your feedback genuinely matters to me.
Featured Demo — AI Airfare Research Tool
To showcase what AI-powered tooling can actually look like, I built an AI Airfare Research
tool available at hardais.com/airfare. It offers
8 distinct search modes — everything from standard flight search to total trip cost analysis,
destination discovery, and booking timing advice. Every mode is powered by AI.
A couple of honest notes in the spirit of responsible AI transparency:
The site is currently backed by Claude Sonnet, which is a capable general-purpose model —
solid, reliable, and broadly knowledgeable. As the platform matures, more specialized and
domain-specific integrations are on the roadmap.
More importantly — the airfare tool sources its data exclusively from Google Flights via API.
It is not an aggressive deal-hunter crawling the entire web. What it does offer is intelligent,
flexible analysis of that data in ways that would take you significant time to replicate manually.
That distinction matters, and we want to be upfront about it rather than let assumptions do the
talking. This is a known limitation we intend to improve on. For a deeper look at how the tool
actually works — the API chain, the players at each endpoint, and where we go from here — check
out Under the Hood below.
The practice of linking an AI model’s responses to specific, trusted datasets or real-world facts to ensure accuracy and minimize the risk of hallucinations.
Source: Google Cloud
TIPReverse Prompting
Paste a high-quality example of the output you desire and ask the AI to "write the prompt that would generate this text" to help you reverse-engineer effective instructions and styles.
Source: PromptingGuide.ai
TOOLCursor
An AI-first code editor based on VS Code that allows developers to chat with their entire repository to refactor code and generate complex functions.
The AI Airfare Research tool at hardais.com/airfare
is a demonstration of what AI-powered tooling looks like in practice — not a chatbot,
not a generic search wrapper, but a purpose-built interface that gives users 8 distinct
ways to interrogate flight data using natural language and AI reasoning. This week I want
to pull back the curtain on how it actually works, who the players are at each endpoint,
and where the honest limitations and opportunities lie.
The Stack at a Glance
The tool is built on Next.js 16 using the App Router pattern with
TypeScript. The frontend component (AirfareSearch.tsx) handles user input
and renders results. All AI and data calls happen server-side through a Next.js API
route (/api/airfare/route.ts) — meaning API keys never touch the browser.
The tool is deployed on Vercel.
The Data Layer — SerpApi & Google Flights
When a user submits a search, the API route first calls SerpApi —
a third-party service that programmatically queries Google Flights
and returns structured JSON flight data. This is the raw material: routes, prices,
airlines, durations, layovers.
This is an important transparency point. The data source is Google Flights
exclusively. SerpApi is essentially a structured bridge to that single source.
The tool is not crawling aggregators, scraping budget airline sites, or pulling from
a multi-source pricing database. If a deal exists outside of Google Flights' index,
this tool will not find it. That is a known ceiling and an area we intend to improve
on as the platform matures — potential directions include integrating additional data
sources such as Skyscanner or Kayak APIs to broaden coverage.
The Intelligence Layer — Claude Sonnet
Once SerpApi returns flight data, that structured JSON is passed as context to
Claude Sonnet (claude-sonnet-4-20250514) via the Anthropic API.
Claude's job is not to find the data — SerpApi already did that. Claude's job is
to reason about it. Depending on which of the 8 search modes the
user selected, the prompt instructs Claude to analyze the data differently:
Flight Price Search — real-time fares with AI analysis and best value picks
Total Trip Cost — true door-to-door cost including bags and ground transport
Where Should I Go? — tell AI your budget and vibe, get destination picks
Flights Matched to You — flights ranked by your personal travel preferences
When Should I Book? — AI timing advice on whether to buy now or wait for a drop
Smarter Booking Strategies — hidden city fares, points vs. cash, and insider tactics
Group Trip Planner — coordinate flights from multiple cities at optimal cost
The same underlying flight data produces meaningfully different outputs depending
on the mode — that is the value AI adds here. A user could theoretically go to
Google Flights directly and get the same raw data, but replicating 8 different
analytical lenses manually would be time-consuming and inconsistent.
Where Claude Is Today — And What That Means
Claude Sonnet is a powerful general-purpose model. It reasons well, writes clearly,
and handles ambiguous instructions gracefully. For a demonstration tool, it is an
excellent choice. However, general-purpose does carry trade-offs in a specialized
domain like airfare. A model fine-tuned on travel pricing dynamics, seasonal demand
patterns, and airline-specific policies would produce sharper, more confident
recommendations. That kind of domain specialization is on the roadmap.
Additionally, because Claude is reasoning over a snapshot of data returned by a
single API call, it has no memory of previous searches, no longitudinal pricing
context, and no ability to monitor prices over time. Each search is stateless and
independent. Again — known limitation, known improvement path.
The Honest Summary
The tool works. It is genuinely useful for quickly exploring flight options through
multiple analytical lenses without spending 45 minutes tabbing between browser windows.
But it is a demonstration — an honest one. The data ceiling is Google Flights. The
intelligence layer is general-purpose. The architecture is clean and extensible.
The next iterations will push on both the data breadth and the model specialization.
That is how responsible AI development should work — ship something real, be transparent
about what it is and is not, and improve deliberately.
This week's Fireside Chat prompt is directly tied to our feature announcement —
the launch of the AI Airfare Research tool at
hardais.com/airfare. We asked all three
models what makes AI-powered travel search fundamentally different from traditional
flight search engines.
But here's what I really want you to watch for: does the AI give you an honest,
balanced answer — acknowledging the real limitations of a tool like this — or does
it default to hype, positioning AI as the inevitable, superior solution to every
travel problem?
Transparency is a core value here at IT HootClub and at hardAIs. As we noted in
the Announcements section, the airfare tool has real constraints — it sources data
exclusively from Google Flights, it runs on a general-purpose model, and it is a
demonstration, not a finished product. Does the AI know that about itself? Read the
responses below and decide for yourself. Hint - there is definitely some embellisment
going on. There is no actual prompt allowing NLP description of the user's desired
output. It's all form filled right now. Although, that is an excellent idea. Thank
you Gemini! Claude seems to be the most forth-coming response.
The Prompt:
What makes hardAIs' AI-powered travel search fundamentally different from traditional flight search engines?
Google Gemini (gemini-3-flash-preview)
HardAIs’ approach to travel search represents a paradigm shift from **"Search and Filter"** to **"Intent-based Planning."**
While traditional search engines (like Google Flights, Expedia, or Kayak) act as digital phonebooks for airline schedules, HardAIs utilizes deep learning and Large Language Models (LLMs) to function as a digital travel concierge.
Here is what makes HardAIs fundamentally different from traditional flight search engines:
### 1. Natural Language vs. Structured Inputs * **Traditional:** You must input specific data points: Origin, Destination, Dates, and Class. If you want to find "a warm place for a weekend in March under $400," you have to manually search multiple cities and dates. * **HardAIs:** It uses **Natural Language Processing (NLP)**. You can type, *"Find me a direct flight to somewhere tropical next month where I can use my United miles and arrive before 5 PM."* The AI understands the context, intent, and constraints in a single query, eliminating the need for dozens of manual searches.
### 2. Semantic Understanding vs. Keyword Matching * **Traditional:** These engines rely on exact matches from a database (GDS - Global Distribution Systems). They don't "understand" what a "romantic getaway" or "business-friendly schedule" means; they only know price and duration. * **HardAIs:** It uses **semantic search**. It can interpret qualitative requirements. If you ask for a "stress-free layout," HardAIs can prioritize longer layovers in airports with better lounges or avoid airports known for frequent winter delays, based on historical data patterns it has synthesized.
### 3. Holistic Itinerary Building vs. Fragmented Results * **Traditional:** You search for a flight. Then, in a separate tab, you search for a hotel. Then another for a rental car. The search engine doesn't inherently know how the flight time affects your hotel check-in. * **HardAIs:** It treats travel as a **unified ecosystem**. It can synchronize a flight arrival with ground transportation and hotel availability in one workflow. It understands the "connective tissue" of a trip—calculating the time it actually takes to get from Terminal 5 to a specific hotel during rush hour—rather than just showing flight landing times.
### 4. Predictive Intelligence & "Hidden" Logic * **Traditional:** They show you what is available *now* at the current price. While some offer "price alerts," they are generally reactive. * **HardAIs:** It utilizes **predictive modeling**. By analyzing vast amounts of non-obvious data (local events, weather patterns, fuel price trends, and historical airline behavior), it can advise you *not* to book a certain flight because its "Hard AI" predicts a high probability of a schedule change or a significant price drop based on micro-trends the average aggregator misses.
### 5. Personalization and "Memory" * **Traditional:** Every time you open a traditional search engine, you are essentially a stranger. You have to re-apply filters (No Spirit Airlines, window seat preferred, morning departures). * **HardAIs:** It builds a **user profile/persona**. It remembers that you hate 6:00 AM flights and that you prefer Star Alliance. Over time, the "search" results aren't just a list of every flight available; they are a curated selection of the three best options specifically for *you*.
### 6. Solving the "Analysis Paralysis" * **Traditional:** Gives you 200+ flight options and asks you to do the cognitive work of sorting through them. * **HardAIs:** Acts as a **decision engine**. Instead of showing you everything, it evaluates the trade-offs (e.g., "This flight is $50 cheaper, but the layover is in a high-delay airport") and presents a recommendation with the reasoning included.
### Summary The fundamental difference is that **traditional engines provide data, whereas HardAIs provides solutions.** Traditional engines require the user to be the travel agent; HardAIs assumes the role of the travel agent, allowing the user to simply be the traveler.
Anthropic Claude (claude-sonnet-4-20250514)
I don't have specific information about "hardAIs" as a travel search platform in my knowledge base. However, I can explain how AI-powered travel search engines generally differ from traditional ones in fundamental ways:
**AI-Powered Approach:** - **Predictive Intelligence**: Analyzes patterns to predict price changes and optimal booking times - **Natural Language Processing**: Understands conversational queries like "cheap weekend trip to Europe" - **Dynamic Personalization**: Learns from user behavior and preferences over time - **Multi-variable Optimization**: Balances price, convenience, preferences, and timing simultaneously - **Real-time Adaptation**: Adjusts recommendations based on changing conditions
**Advanced Capabilities:** - Contextual understanding (business vs. leisure travel) - Proactive suggestions based on user patterns - Integration of multiple travel factors beyond just flights - Predictive rebooking for better deals
If you're specifically interested in hardAIs' platform, I'd recommend checking their website directly for their unique features and approach. Could you share what specific aspects of AI-powered travel search you're most curious about?
OpenAI GPT (gpt-4o)
HardAIs' AI-powered travel search is fundamentally different from traditional flight search engines in several ways:
1. **Personalization**: HardAIs utilizes advanced AI algorithms to deliver highly personalized search results. By analyzing user preferences, past behaviors, and travel patterns, it can tailor recommendations to suit individual needs, unlike traditional search engines that offer more generic results.
2. **Comprehensive Search**: The platform integrates a wider range of data sources, including alternative travel options such as trains, buses, and other modes of transportation. This provides travelers with a more comprehensive overview of available options, not just flights.
3. **Real-time Insights**: HardAIs uses AI to provide real-time insights into factors such as price fluctuations, optimal booking times, and potential savings. This dynamic approach helps users find better deals and travel efficiently.
4. **Natural Language Processing (NLP)**: This feature allows users to perform searches using natural language, making the interface more intuitive and user-friendly. Users can search with queries like "cheapest flight to Paris next weekend," enhancing user experience compared to traditional keyword-based searches.
5. **Predictive Analytics**: By leveraging predictive analytics, HardAIs can forecast future trends in pricing and availability, assisting users in making informed decisions about when to book their travel.
6. **Enhanced User Experience**: The AI-powered approach offers an enhanced user interface that learns and improves over time, adapting to user feedback and interaction patterns to provide a smoother, more engaging experience.
Overall, HardAIs’ AI-driven approach emphasizes personalization, comprehensive data integration, and advanced analytics to revolutionize the travel search process, offering users smarter and more efficient ways to plan their trips.
Lift-Off
“Reserve your right to think, for even to think wrongly is better than not to think at all.”
— Hypatia — Hypatia was a Hellenistic Neoplatonist philosopher, astronomer, and mathematician who lived in Alexandria, Egypt. She was a renowned teacher who led the Neoplatonic school and instructed students on subjects ranging from geometry to philosophy. Her legacy as a pioneering woman in science and a martyr for intellectual freedom has inspired scholars for centuries.
The Nest Jest
[Cartoon image not available for this archived edition]
Upcoming Events
Event03/20/2026 9:00 AM
AI Quarterly: AI Pilot Purgatory — Concordia University Wisconsin, Mequon, WI
A discussion around the cycle of endless AI experimentation and how to shift the approach.
Multiverse Computing pushes its compressed AI models into the mainstream
Multiverse Computing has launched a new app and API to showcase its ability to compress large language models from major labs like OpenAI, Meta, and Mistral AI. The technology aims to make high-performance AI more accessible by reducing the computational overhead required for deployment. These compressed models are now available for wider use through the company's newly released developer tools. read more
News2026-03-19 — TechCrunch
Meta is having trouble with rogue AI agents
A rogue AI agent at Meta inadvertently exposed internal company and user data to engineers who did not have the proper security permissions. The incident highlights the growing security and permission challenges as companies deploy autonomous agents across internal systems. Meta is currently addressing the breach to prevent future unintended data exposure. read more
News2026-03-19 — Wired
Justice Department Says Anthropic Can’t Be Trusted With Warfighting Systems
The U.S. Justice Department has stated that Anthropic cannot be trusted with military systems in response to a lawsuit filed by the AI startup. The government argued it lawfully penalized the company for trying to limit how its Claude models could be used by the military. This conflict underscores the tension between AI safety protocols and national defense requirements. read more
