Since the release of the new GPT-5 model, curiosity within the technology and development communities has shifted significantly. We have moved past the initial awe of coherent text generation and competent code snippet creation. The new benchmark for this technology is not just its ability to converse, but its capacity to build. The pressing question is no longer “Can it write?” but “Can it create functional, practical applications that solve real-world problems?” This curiosity has spurred a new wave of experimentation, pushing the model beyond its traditional boundaries.
I decided to test this new iteration of the model for myself. My goal was to explore its capabilities in handling tasks that require more than just text output. I wanted to see if it could act as a developer, a planner, and a creative partner. I ran seven distinct experiments in the chat interface to probe the model’s true potential. This series will examine how this new technology performs in creating realistic business ideas, designing functional websites, developing interactive browser games, and transforming data from one format to another.
A New Baseline for Practical Utility
These experiments were not all straightforward successes. Some of the examples worked on the first try, yielding impressive and functional results with a single, well-crafted prompt. Others, however, required several iterations, careful re-prompting, and even some collaborative debugging. The goal of this series is not only to showcase the final results achieved by the model, but also to provide a transparent look at its strengths, its current weaknesses, and the process required to achieve better outcomes.
This exploration will focus only on these practical, hands-on examples. While benchmarks, reviews, and feature lists provide a general overview, true understanding comes from practical application. We will dive into the prompts, the model’s responses, and the analysis of the final products. This is a journey to understand how to collaborate with this new technology and leverage its power effectively.
Example 1: Creating a Personal Running Tracker
For the first experiment, I aimed for something practical and personal: a custom running tracker. Having only started running about a month ago, my motivation is still fragile. I find myself relying on a combination of data tracking to see progress and, just as importantly, structured routines to prevent injury. I need a tool that doesn’t just log miles, but also guides me through the essential warm-up and cool-down phases of each run.
This task is a perfect test case. It requires more than just a static webpage. It demands an understanding of the user’s intent—motivation and injury prevention. It also requires the creation of an interactive application, one that can accept daily input, store it, and present different states or routines to the user. This is a significant step up from a simple informational website.
Defining the Personal Application Prompt
I formulated a prompt that clearly outlined these specific needs. I explained my context as a new runner. I stated that I wanted a website to track my daily activity, to provide motivation, and to help me set up a warm-up and cool-down program to be used before and after each run. This level of detail is crucial. A vague prompt like “make me a running app” would likely yield a generic and unusable template.
By specifying the “why” (motivation, injury prevention) behind the “what” (the app), I gave the model a set of constraints and goals. This guides its reasoning process, pushing it to generate a solution that is not just technically correct but also contextually relevant. The prompt was a direct request for a multifunctional tool tailored to a specific user journey.
A Functional Website Template Emerges
The model’s response was immediately impressive. It did not provide a generic, placeholder-filled page. Instead, it generated a clean and functional website template. The code was well-structured, and the core features I had requested were all present. It included a section for daily activity recording, which allowed me to track my progress over time. This is essential for building the habit.
The template also featured a component for motivational messages, designed to help me stick to my new routine. Most importantly, it included functional warm-up and cool-down routines built directly into the interface. This was not just a static list of exercises; it was an interactive part of the application, just as I had hoped. The model had successfully translated my needs into a functional product.
Adherence to Specifications: A Comparative Advantage
I tested this same prompt with other available models to establish a baseline. One model, for instance, produced a visually polished application with an excellent, modern color palette. However, it completely failed to accommodate my most critical request: the interactive pre- and post-run routines. It delivered a beautiful-looking but functionally incomplete tool. It prioritized aesthetics over the user’s core requirements.
This is where the new GPT-5 model truly excelled. It adhered closely to the specifications I provided. While the initial design was minimalist, it implemented the basic training functionality perfectly. This included the interactive routines and the ability to track my daily workouts. This demonstrates a significant advancement in the model’s ability to understand and prioritize user requirements, a critical skill for any developer, human or otherwise.
From Mockup to Minimum Viable Product
This experiment highlights a fundamental shift in what these models can produce. We are moving from the generation of static mockups to the creation of functional prototypes. For a solo developer or an entrepreneur, this capability is a game-changer. The model can provide a robust baseline, a Minimum Viable Product (MVP), in a matter of minutes. This prototype can then be tested, refined, and built upon.
This drastically reduces the barrier to entry for creating new tools. A user with a clear idea but limited coding knowledge can now generate a functional starting point. This accelerates the development process exponentially, allowing for rapid iteration and testing of new ideas without the need for a full development team from day one.
The Iterative Process: Refining the Initial Prototype
While the first attempt was successful, the process of iteration is key. The initial prototype was a solid foundation, but I wanted to push it further. I followed up with a new prompt: “This is great. Now, can you please add a simple calendar view to the daily activity recording so I can see my progress visually?” The model refactored its previous code to include a simple, JavaScript-based calendar that highlighted the days with logged runs.
Next, I prompted: “Make the warm-up and cool-down routines more interactive. Add checkboxes next to each exercise and a timer for the stretches.” Again, the model understood the request. It modified the interface to include stateful elements, demonstrating its ability to not just generate new code, but to modify and build upon an existing codebase in a coherent, conversational manner.
Beyond the Code: Understanding User Intent
The most significant takeaway from this experiment was not the code itself, but the model’s apparent understanding of user intent. It did not just see the words “warm-up” and “cool-down” and provide a static list. It understood that in the context of a “tracker” for a “new runner,” these routines were meant to be used and followed. This led to the creation of an interactive module.
This ability to infer intent and design functionality around it is a remarkable leap. It suggests the model is not just pattern-matching keywords but is building a more complex internal representation of the user’s problem. It is moving from a language model to a problem-solving model, one that can reason about the “why” behind a request and build a more useful “what” as a result.
A New Paradigm for Personal Tools
The personal running tracker example serves as a powerful illustration of this new paradigm. The model’s ability to generate a functional, specification-compliant prototype, even one that requires further refinement, changes the landscape of personal software. We are now able to create bespoke tools, tailored to our exact personal workflows, without needing to be expert developers ourselves.
This first experiment was a resounding success. It showcased a clear ability to handle a complex request, prioritize functionality over aesthetics, and adhere to user specifications. It also demonstrated the power of an iterative workflow, where the model acts as a collaborative partner, refining and improving its own creation based on natural language feedback.
Beyond Code: GPT-5 as a Strategic Partner
While the first experiment demonstrated GPT-5’s impressive capabilities as a rapid application developer, its utility extends far beyond just writing code. The true power of an advanced reasoning engine lies in its ability to handle complex, real-world problems that involve strategy, planning, and synthesis of diverse constraints. Can the model act not just as a coder, but as a consultant, a business analyst, or a personal planner?
To test this, I designed two experiments that moved away from pure code generation and into the realm of strategic ideation and personalized planning. The first test involved a request for a hyper-localized business idea. The second, a request for a detailed, personalized travel itinerary. Both tasks require the model to juggle multiple, abstract constraints, such as budget, location, personal preferences, and available time.
Example 2: Generating a Hyper-Localized Business Idea
For many aspiring entrepreneurs, the first hurdle is a viable idea. I wanted to see if the model could generate practical and realistic business concepts tailored to a specific, real-world scenario. I live in Delhi and have been considering starting a small business in my spare time, but I have a limited budget and can only dedicate my weekends to it. This provided a perfect set of constraints for the model.
This task is challenging because it requires more than just a list of generic business ideas. It demands localization (specific to Delhi), time constraints (7-8 hours on weekends), and a strict financial budget (an investment of 30,000 euros). The model must synthesize these factors to produce suggestions that are not just creative, but also practical and actionable for someone in my exact situation.
The Prompt: Defining Complex Real-World Constraints
My prompt was direct and filled with these specific data points. I stated: “I reside in Delhi and have about 7-8 hours of free time on weekends. Propose a business idea with an investment of 30,000 euros.” This prompt is a multi-variable problem. The model must cross-reference its knowledge of business models, the specific market in Delhi, and the financial and time limitations provided.
The inclusion of a specific currency, euros, paired with a specific city, Delhi, adds an interesting layer of complexity. The model must either convert the currency to understand the budget in the local context or work within the specified currency, making assumptions about the availability of goods and services at that price point. How it handles this ambiguity is a key part of the test.
The Output: A Mini-Business Plan
The model’s response was not just a list of ideas; it was a detailed and exceptionally well-structured analysis. It provided three distinct business ideas. While the first idea seemed highly relevant and general, the other two were more specialized, likely requiring a greater investment in marketing and networking to gain traction. This differentiation itself showed a level of strategic thought.
What was most impressive, however, was the format of the result. The model did not just suggest “start a food cart.” It generated a mini-business plan for each of the three ideas. This transformation of a simple request into a structured, professional-grade document was a significant leap in value. It provided not just an answer, but an actionable starting point for a business.
Deconstructing the Business Plan: Cost and Revenue
Each mini-business plan was broken down into logical, critical sections. It included a detailed breakdown of the costs by item, showing how the 30,000 euro investment would be allocated. This included line items for equipment, raw materials, marketing, and any necessary permits. This demonstrated a granular understanding of what it takes to launch a small-scale operation.
Furthermore, the model provided projections for expected revenue and, just as importantly, noted the time required to operate the business, directly addressing my weekend-only availability. This level of detail transforms the model from a simple brainstorm-generator into a preliminary business analyst. It provides a framework for evaluating the viability of each idea, complete with financial and operational considerations.
The Power of Localization and Constraint Synthesis
The most powerful aspect of this experiment was the model’s ability to synthesize all constraints. The ideas were not generic “work from home” suggestions. They were tailored to a large, urban environment like Delhi, considering its market and potential customer base. It respected the financial budget, ensuring the itemized costs fit within the specified investment. And it respected the time limit, proposing businesses that could feasibly be managed on a part-time, weekend schedule.
This ability to hold multiple, abstract constraints in its “thought process” and generate a novel output that satisfies all of them is a hallmark of advanced reasoning. It shows the model is not just retrieving information but is actively processing and synthesizing it in a way that is strategically useful.
From Business Planning to Personal Planning
This capacity for strategic planning is not limited to business. This same set of skills can be applied to complex personal tasks, such as travel planning. This provided the basis for my next experiment. I wanted to see if the model could act as a bespoke travel agent, handling a detailed set of personal preferences and constraints to build a practical itinerary.
This task is similar to the business plan in its complexity. It requires balancing a budget, a location, a timeline, and personal interests. In some ways, it is even more complex, as it involves a qualitative, subjective element of “travel style” that the model must interpret.
Example 6: The Personalized Route Generator
To test this capability, I asked the model to play the role of a travel agent. I provided it with a detailed questionnaire that described my travel style, my specific interests, my budget, and my chosen destination. The goal was to receive a 5-day itinerary that was not just a generic travel guide, but a plan I could actually follow, tailored to my specific needs.
This is a test of its ability to handle complex, personalized planning and to structure a large amount of information into a coherent, day-by-day schedule. It must act as a researcher, a scheduler, and a local guide all at once.
The Prompt: A Questionnaire for a Travel Agent
The prompt was highly detailed: “Please generate a 5-day itinerary for a trip to Bali at the end of September. I’m a relaxed traveler, primarily interested in beaches and food, and have a medium budget. I’ll be staying in Seminyak, so please focus the itinerary around that area. Each day should include two to three main activities. Could you recommend some local cafes or restaurants that fit my budget and interests? Additionally, I’ll make a list of activities and foods I absolutely must try.”
This prompt provides multiple layers of constraints: a duration (5 days), a date (end of September), a location (Bali), a base (Seminyak), a travel style (relaxed), key interests (beaches, food), a budget (medium), a desired structure (2-3 activities/day), and a request for specific recommendations (cafes, must-do list).
The Result: A Practical and Sourced Itinerary
The result was a highly accurate and practical travel guide. The model did not just provide general suggestions. It generated a detailed, day-by-day itinerary with specific names of cafes, beaches, and cultural sites, all geographically clustered around my base in Seminyak to minimize travel time, in line with my “relaxed traveler” style.
What set this output apart was its inclusion of sources. The model cited information from travel blogs and online forums to support its recommendations, adding a layer of authenticity and trustworthiness to its suggestions. This is a crucial feature, as it allows the user to cross-reference the information and signals that the recommendations are based on real-world experiences.
Synthesizing Preferences and Information
The model also generated the requested “must-do and try list,” which concisely summarized the main activities and local culinary specialties. This transformed the entire itinerary into an easy-to-read, actionable plan. The model successfully interpreted the subjective constraint of “relaxed traveler” by limiting the number of activities per day and focusing them around my accommodation.
It managed the “medium budget” constraint by recommending “local cafes” rather than expensive fine-dining establishments. This ability to interpret subjective preferences and integrate them with factual data (like locations and operating hours) represents a sophisticated form of reasoning.
The Creative Engineer: GPT-5 and Interactive Entertainment
We have established that the new GPT-5 model can function as a rapid prototyper for web applications and a strategic partner for planning. Now, we turn to a more complex and creative challenge: its ability to act as a game developer. Building a game, even a simple one, is a significant step up from a tracking app. It requires a deeper understanding of logic, physics, user interaction, and a continuous “game loop.”
This part of our exploration will focus on two key experiments. The first involves recreating a childhood game, “Marbles,” from scratch, testing the model’s ability to handle complex physics and interactive controls. The second experiment attempts to “gamify” the process of learning, transforming a traditionally difficult subject—learning to code in Python—into an engaging and interactive adventure.
Example 3: Recreating a Childhood Game from Scratch
For this experiment, I decided to test the model’s capacity to build a complete, interactive browser game from the ground up using HTML5 Canvas. I chose one of my favorite childhood games, Marbles, which is also known as “Taws.” The prompt was not simple; it was a detailed technical brief that included several challenging requirements. I wanted to see if the model could handle not just the visual setup, but also the underlying mechanics that make it a “game.”
This test is a measure of its ability to integrate multiple complex systems: a rendering engine (the canvas), a physics engine (collisions and friction), and a user input system (aiming and shooting). This is a task that would typically require a skilled developer with specialized knowledge.
The Prompt: A Complex Technical Brief for “Marbles”
The prompt was specific and demanding. I asked the model to create an HTML5-based “marble game.” I specified that the game should have a designated playable area on the canvas, such as a circle or square. This area should be filled with several small, round, and distinct glass marbles. I then described the core game mechanic: a mechanism for the user to “throw” or “shoot” their own marble by clicking or dragging to set power and direction.
I also defined the objective: to use this “shooter” marble to knock other marbles out of the playable area. I explicitly requested the incorporation of realistic (though simplified) physics for collisions, friction, and movement. To make it playable, I asked for a clear visual indicator for trajectory and aiming power. Finally, I requested a “Reset Game” button and stated that the game should have a clean aesthetic and be fully responsive for both desktop and mobile.
The Result: A Fully Functional, Physics-Based Game
The model responded with a complete, single-file application containing all the necessary HTML, CSS, and JavaScript. It correctly structured the game using the HTML5 Canvas element. The JavaScript code was substantial and well-commented, outlining the logic for the game loop, the rendering of the marbles, and the physics calculations.
I copied the provided code blocks into an online compiler for testing, and the result was remarkable. It was a fully playable, physics-based, and functional marble game. The model had successfully implemented all the core features requested in the prompt. This was not a static demo; it was an interactive game.
Analyzing the Technical Achievements: Physics and Collision
The most impressive part of the code was its handling of physics. The model generated JavaScript functions to manage realistic (simplified) physics. This included vector-based movement for the marbles, a friction effect that gradually slowed them down, and, most importantly, a robust collision detection and response system. The code calculated the collision between two circular objects and correctly transferred momentum, causing them to bounce off each other in a believable way.
The model also correctly implemented the core objective. It checked when a marble’s position went beyond the boundaries of the designated playable area and removed it from the game. It also included the requested “Reset Game” button, which correctly cleared the canvas and repositioned all the marbles for a new round.
Analyzing the User Experience: Intuitive Controls
Beyond the complex backend logic, the model also successfully implemented the user-facing controls. The swipe-and-aim mechanism was intuitive, allowing the user to click and drag to set the direction and power of their shot. The model included the visual indicator for trajectory, drawing a line from the shooter marble to show the intended path. This aiming mechanic was crucial for making the game truly playable.
The design was minimalist and clean, just as requested. By using basic canvas drawing functions, it avoided complex image assets and focused on the gameplay. This demonstrated an understanding of the “classic aesthetic” I had asked for. The end result was a complete and engaging game that fulfilled every technical specification of the prompt.
From Entertainment to Education: Gamifying Learning
This successful creation of a physics-based game proved the model’s power as a creative engineer. This led to the next logical question: can this same capability be applied to a different domain, such as education? Every programmer has, at some point, wished that learning to code was a little less dry and a little more engaging. This provided the basis for my next experiment: to gamify the process of learning and practicing Python.
The goal was to create an application that was not just a game or just an educational tool, but a true hybrid. It needed to leverage game mechanics—like progress, challenges, and feedback—to make the learning process itself fun.
Example 5: Learning Python in a Fun Way
I decided to prompt the model to create an online adventure game specifically designed for a friend who was just beginning to learn Python. The idea was to move away from sterile coding exercises and tutorials and into a world where coding challenges are integrated into a narrative. The player would have to write real Python code to overcome obstacles and advance the story.
This test evaluates the model’s ability to merge two distinct concepts: a text-based adventure game and an interactive code editor. It would need to create a user interface for both the story and the code, and a backend system that could receive, execute (or simulate), and validate the user’s Python code.
The Prompt: Gamifying the Python Learning Process
My prompt was: “My friend is learning to code in Python. Create an online adventure game called ‘Code Explorer’ where he can learn and practice coding.” This prompt was less technically specific than the “Marbles” one. I left the implementation details, such as the game mechanics and interface, open to the model’s interpretation. I wanted to see its own “creativity” in designing such a system.
By giving it a clear goal (“learn and practice coding”) and a theme (“online adventure game”), I was testing its ability to design a pedagogical system. How would it structure the learning? How would it provide feedback? This was a test of its capacity for instructional design, disguised as a game development task.
The Output: An Interactive Learning Environment
The model developed a fully functional, browser-based online game. The game, “Code Explorer,” presented players with a text-based narrative. To progress through the story, the player had to solve coding challenges presented in the game’s interface. The model had designed a complete and well-thought-out system for this purpose.
The interface, while minimalist in its black-and-white aesthetic (a common theme in the model’s initial designs), was perfectly functional. It had a window for the story, an input area for the user to type their Python code, and a console for the output. The essential features were not just present, but intelligently designed for a learner.
Analyzing the Game Mechanics
The model’s design included several brilliant mechanics that made it an effective learning tool. The first was a dynamic feedback system. When a player submitted incorrect code, a red alert message would appear, often with a hint as to what went wrong. When the correct function or output was detected, the message turned green, and the story advanced.
The second mechanic was progress tracking. The interface included a real-time progress bar that updated as the player completed challenges, giving a clear sense of accomplishment. Finally, it included a hint system. Players could choose to reveal or hide hints for each challenge, allowing them to find their own balance between struggling through a problem (which aids learning) and getting the help they need to avoid frustration.
Conclusion: The Future of Rapid Game and Edu-Tech Development
These two experiments, the “Marbles” game and the “Code Explorer” learning adventure, showcase the model’s profound capabilities as a creative engineer. It can handle complex technical briefs, including physics simulation and responsive design. It can also design entire systems from a more abstract concept, intelligently creating game mechanics that are perfectly suited to the stated goal.
This effectively transforms the coding process into a game, making it both engaging and informative. The implications for independent game developers, educators, and the “edu-tech” industry are massive. It allows for the rapid creation and iteration of new interactive tools, games, and learning environments. The AI is now a capable junior game developer and instructional designer.
GPT-5 in the Modern Workplace
Having explored the model’s prowess in personal app development, strategic planning, and creative game design, we now turn our attention to the corporate world. A significant amount of white-collar work involves the transformation of information: taking data from one format, analyzing it, and re-packaging it into another. This is often a time-consuming, manual process.
For this test, I wanted to determine whether GPT-5 could handle a common, real-world business scenario. My colleague works in finance and, like many professionals, has to review voluminous, dense reports to prepare presentations for meetings. This mundane task is a perfect candidate for automation. Could the model build a tool to streamline this entire workflow?
Example 4: The Report-to-Presentation Generator
The experiment was to create a web application that acts as an intelligent assistant for a financial analyst. The user should be able to provide a report, and the application should automatically parse it, extract the most important information, and generate a set of presentation-ready slides. This is a complex task that combines file handling, large-scale text summarization, data extraction, and data visualization.
This is a high-stakes test of the model’s utility in a professional setting. A successful tool would not just be a novelty; it would be a significant productivity booster, saving hours of manual labor. It tests the model’s ability to understand and visualize data, a key skill for any business intelligence tool.
The Prompt: Solving a Real-World Business Scenario
My prompt was crafted to describe this exact business need. I explained the scenario: “My colleague works in finance and has to review many reports to prepare a presentation before each meeting. Please create a web application where the user enters a report and generates a presentation with charts and graphs. You can use images from the web to make your presentation more attractive.”
This prompt contains several key instructions. It specifies the input (a report) and the output (a presentation). It explicitly requires “charts and graphs,” meaning the model cannot just produce text slides; it must identify quantifiable data and decide on an appropriate visualization. The final instruction to “use images from the web” tests its ability to add aesthetic value and understand content thematically.
The Result: A Functional Business Utility
The model produced a functional web application as requested. The application’s interface was clean and practical. It included an option to upload a report file or, for demonstration purposes, to enter details directly for a slide. When I tested it with sample text, it correctly processed the information, extracted the key points, and visualized them.
It used a popular JavaScript library to generate charts and graphs based on the data it identified in the text. While the initial demo only had enough sample data to generate a single, complete slide, it was a solid and impressive prototype. It had successfully created the core “report-to-slide” pipeline I had described.
The Iterative Process: AI as a Debugging Partner
The truly insightful part of this experiment came next. The initial prototype had a bug. When I tried to upload a PDF file, the application failed. This is a common and realistic scenario in software development. An initial build rarely works perfectly with all file types. I reported the bug to the model: “The application works with text input, but I got a bug when I uploaded a PDF file.”
Without me needing to provide an error log or suggest a fix, the model responded, “You are right. The current code does not include a library to parse PDF files. We will need to add one.” It then refactored its own code, adding a client-side JavaScript library for PDF parsing and modifying the original function to handle the new file type. It quickly fixed its own bug.
Analyzing the Auto-Debugging Capability
This auto-debugging capability is a profound development. The model is not just a code generator; it is an iterative and collaborative partner. It understands its own code, recognizes its limitations when presented with a new error case, and can independently research and implement a solution. This changes the role of the human developer from a “coder” to a “reviewer” or “project manager.”
The developer’s job in this scenario was simply to identify the problem at a high level. The AI handled the diagnostics and the implementation of the fix. This is a far more efficient workflow, where the human provides direction and quality control while the AI performs the detailed technical labor of writing and refactoring the code.
Data Transformation and Advanced Summarization
This experiment also showcases a significant leap in data transformation. Previous models were good at summarizing text. This model, however, did more. It did not just condense the report; it restructured it. It identified key performance indicators and financial data within the text and understood that these specific data points were best represented visually.
It then selected an appropriate visualization, such as a bar chart for a comparison or a line graph for a trend over time. This implies a deeper layer of contextual understanding. The model is not just processing words; it is processing information. It is acting as a junior data analyst, understanding the meaning of the data and choosing the best way to present it.
Implications for Business Intelligence and Productivity
The implications of this single experiment for business intelligence and general office productivity are enormous. Tasks that currently consume a significant portion of a knowledge worker’s day can be automated. This tool, created in minutes, can streamline workflows for finance, marketing, research, and any other field that relies on generating presentations from dense data.
This moves the AI from a simple writing assistant (like a grammar checker) to an active participant in the workflow. It can ingest raw data and produce a polished, first-draft analysis. This allows the human professional to focus on the higher-level, strategic aspects of their job—interpreting the data, adding context, and crafting the final narrative—rather than the manual labor of data entry and slide creation.
The Current Limitations: A Solid Prototype, Not a Finished Product
It is important to maintain perspective. As the experiment noted, the demo had enough data to generate a single slide. It was a “solid prototype.” It was not a fully-featured, market-ready product. A production-grade tool would need to handle complex layouts, company branding, and a much wider variety of data formats and edge cases.
However, as a starting point, it is revolutionary. The model provided a functional foundation that was 80% of the way there. A human developer could take this prototype and, in a fraction of the time it would take to start from scratch, build it into a robust, enterprise-ready tool.
The Next Step: Connecting AI to Real-World Tools
In our previous experiments, the AI model built self-contained applications. It created games, trackers, and presentation generators that lived inside the browser. While impressive, the true potential of artificial intelligence is unlocked when it can break out of this sandbox. The next logical step is to allow the model to interact with the real world, to connect to the tools and services we use every day.
This part of our exploration focuses on this “last mile” of integration. I tested the model’s “Connector” feature, an advanced capability designed to let the AI interact with external APIs. The goal was simple: to automate a part of my daily routine by having the AI schedule an event in my personal calendar. This experiment would test not just its reasoning, but its ability to reliably use tools to affect change outside the chat interface.
Example 7: Automating the Routine with Calendar Integration
For my final experiment, I wanted to test the model’s ability to automate a simple, real-world task. The task involved using a natural language command to schedule a recurring workout in my personal calendar. This is a common function of virtual assistants on phones and smart speakers. I wanted to see if the new, powerful GPT-5 model could handle this task with more robustness and flexibility.
This feature, which I will call “Connectors,” is an option available in the premium versions of the chat interface. It represents a move toward an “agent” model, where the AI can not only generate text but also take actions on the user’s behalf. This test would reveal both the promise and the current limitations of this new functionality.
The Setup Process: Authorizing AI Access
Before the AI can interact with personal tools, it must be given permission. The setup process was a multi-step procedure within the application’s settings. I had to navigate to a “Connectors” menu, which displayed a list of available third-party services. I selected the calendar service from the list and clicked a “Connect” button.
This action redirected me to a standard authentication page for my account. I was asked to authorize the chat model to access and modify my calendar data. This is a critical security step, and it was handled through a familiar, secure sign-in process. Once authorized, the model was, in theory, capable of managing my calendar.
The “Agent Mode” Interface
After the one-time setup, using the feature required initiating a new chat in a special “Agent Mode.” In this mode, the chat interface provided additional options. I had to click a button to “Ask for help” and then select which “Sources” the model should use for its response. The default sources included a standard “Web Search.”
Now, my newly connected “Calendar” service appeared as an additional source that I could select. I enabled both Web Search and the Calendar connector. This setup tells the model that it is allowed to browse the web for information and make API calls to my calendar to fulfill my request. The interface was ready for the command.
The Prompt: A Simple Natural Language Command
My prompt was designed to be simple, clear, and unambiguous. It was a command that any modern voice assistant would easily understand. I typed: “Add a 30-minute workout to my Google Calendar every morning at 6 AM, starting tomorrow.”
The model’s internal “thought process,” which is often visible in agent modes, showed that it quickly and correctly understood the request. It parsed the sentence, identifying the key entities: the event title (“workout”), the duration (“30 minutes”), the time (“6 AM”), the recurrence (“every morning”), and the start date (“starting tomorrow”). It then correctly reasoned that it needed to use the Calendar API to fulfill this request.
The Result: A Case Study in Failure
This is where the experiment failed. The model made an API call, but the connection to the calendar service failed. It tried again, but the process seemed to get stuck in a loop. It even generated a redundant request for me to re-authorize the connection, even though it had already been authorized in the settings.
The event was never entered into my calendar. After several minutes of the model attempting to use its tool, the process was interrupted. The end result was that no action was taken. As the original experimenter noted, it would have been significantly easier and faster for me to have simply used my phone’s built-in assistant, which would have completed the task in seconds.
Analyzing the Failure: The “Last Mile” Problem
This failure is an incredibly important data point. It highlights the significant difference between understanding a request and executing it. The model’s reasoning and natural language understanding were perfect. Its failure was purely in the execution—the “last mile” of reliably interacting with an external tool. The API connection proved to be inconsistent and brittle.
This suggests that the model’s tool-calling capabilities are not as robust as its language generation. The logic for how and when to make an API call, how to handle failures, and how to manage authentication state seems to be a major hurdle. The model gets stuck, retries incorrectly, and ultimately fails the task, even though it knows exactly what it is supposed to do.
The Promise vs. The Reality of AI Agents
This experiment provides a clear-eyed view of the current state of AI agents. The promise is a universal, natural-language interface for all our digital tools. We dream of simply telling an AI to “book my flight, order my groceries, and schedule a meeting,” and having it execute those complex, multi-step tasks.
The reality, as shown by this calendar test, is that we are not there yet. The underlying tool-use capabilities are still inconsistent. A simple, dedicated voice assistant with a hard-coded integration to a calendar is, for now, far more reliable than a massive, general-purpose AI that is trying to figure out the tool on the fly. The generalist is more intelligent, but the specialist is more reliable.
The Future of Connectors: Inconsistency and Reasoning
The original article noted that this process “may require 3-4 tries as GPT-5 refines its approach.” This in itself is a key observation. It implies that the model’s reasoning for tool-calling is not deterministic. It might fail three times, and then on the fourth try, it might “refine its approach” and succeed. This inconsistency is the single biggest barrier to its use for critical, real-world automation.
This capability is the bleeding edge of AI research. Teaching a model to reliably use tools, handle errors, and interact with the unpredictable state of the real world (an API might be down, a user’s login might be expired) is the next great challenge. This experiment shows that while the model is advanced, this feature is still in its experimental phase.
A Summary of the Experiments
Over the course of these experiments, we have investigated the practical capabilities of the new GPT-5 model. We have pushed it far beyond simple text generation. We have tasked it with creating interactive games from scratch, building functional web applications and prototypes, and serving as a personalized planning assistant. We have seen it succeed brilliantly, and we have also seen it fail in its attempts to connect to real-world tools.
This journey provides a holistic picture of the model’s true value. Its power lies not in its ability to generate static content, but in its capacity to produce functional code, structured plans, and interactive tools. After this rigorous testing, we can now draw clear conclusions about its primary strengths, its current weaknesses, and how it is set to reshape the future of human-AI collaboration.
Key Strength: From Static Content to Functional Code
The most significant and recurring strength observed was the model’s ability to generate functional code. The personal running tracker, the “Marbles” game, and the “Code Explorer” learning game were not just static mockups. They were working, interactive applications with internal logic, state management, and user input systems. The “Marbles” game, in particular, demonstrated an ability to implement complex concepts like physics, collision detection, and a complete game loop from a single prompt.
This capability fundamentally changes the development landscape. The model can now single-handedly produce a minimum viable product or a robust prototype. This allows for rapid iteration and testing of ideas that would have previously required significant time and development resources. The barrier to creating custom software has been dramatically lowered.
Key Strength: Structured Planning and Constraint Synthesis
The second major strength is the model’s advanced reasoning engine, which allows it to handle complex, real-world constraints. This was perfectly demonstrated in the business idea and travel itinerary examples. The model did not just provide a generic list of ideas. It synthesized a diverse set of variables—location (Delhi, Bali), budget (medium, 30k euros), personal preferences (beaches, food), and time (weekends only)—into a single, coherent, and highly structured output.
The generation of a “mini-business plan” with itemized costs and revenue projections shows a move from information retrieval to strategic analysis. The model is capable of acting as a junior business analyst or a personal consultant, providing actionable, data-driven plans that are tailored to a user’s specific needs.
Key Strength: Advanced Reasoning and Auto-Debugging
Perhaps the most promising strength for professionals is the model’s emergence as a collaborative partner. This was highlighted in the “Report to Presentation” experiment. When the application failed with a new file type, the model did not require a detailed bug report. It understood the high-level problem, diagnosed its own code’s limitations, and proactively implemented a solution.
This auto-debugging and iterative refinement capability is a massive leap in productivity. It changes the human’s role from a “coder” to an “architect” or “reviewer.” The human can guide the project’s direction with natural language, while the AI handles the granular, time-consuming tasks of implementation, refactoring, and debugging. This makes it a promising partner for developers, project managers, and other professionals.
Key Weakness: The “Last Mile” of API Integration
The model’s most significant weakness was clearly exposed in the final experiment: the failure to schedule a calendar event. The model’s “Connector” or “Agent” capability is, at present, inconsistent and unreliable. While its internal reasoning was sound—it understood the request perfectly—its ability to execute the task in the real world by calling an external API was brittle.
This “last mile” problem of tool use is the current frontier of AI. The model’s reasoning for how to handle failed API calls, authentication issues, and redundant requests is not yet robust. For now, specialized, hard-coded tools (like a phone’s voice assistant) are far more reliable for simple, real-world tasks. The dream of a universal AI agent is still on the horizon.
Key Weakness: The Need for Human Iteration and Expertise
Another crucial observation is that this model is not a “magic box” that produces a perfect, finished product on the first try. While some examples worked well, the source article noted that many required “a few iterations and some debugging.” The minimalist design of the applications also suggests that a human designer is still needed to provide aesthetic polish and a truly refined user experience.
Furthermore, the quality of the output is directly proportional to the quality of the input. The highly detailed and technical prompt for the “Marbles” game is what led to a successful and complex result. A vague prompt would have led to a vague output. This highlights the growing importance of “prompt engineering” and subject-matter expertise. The user must know what to ask for and how to ask for it.
The Evolving Role of the Developer
This new technology does not make developers obsolete; it changes their job description. The developer’s role is elevated from writing boilerplate code to architecting systems and managing an AI workforce. The AI becomes a tireless “junior developer” that can handle implementation, unit testing, and debugging.
The human developer’s value shifts to higher-level tasks: defining the system architecture, ensuring the AI’s code meets security and performance standards, managing the integration of different AI-generated components, and providing the creative and strategic direction. The job becomes more about quality control, design, and systems thinking, and less about the syntax of a “for” loop.
The Evolving Role of the Professional
This same shift applies to all professionals. A financial analyst, using the presentation-generation tool, is not replaced. Instead, they are freed from the manual-labor portion of their job. They can now spend more of their time on the actual analysis, on finding insights, and on crafting the narrative that the AI-generated slides will support.
A project manager can now instantly prototype an idea to show to stakeholders, rather than waiting weeks for a development sprint. An entrepreneur can validate a business idea by generating a prototype and a business plan in a single afternoon. The AI acts as a universal accelerator, augmenting the capabilities of professionals in every field.
The Importance of Prompt Engineering and Refinement
These experiments collectively underscore the most critical new skill in a world with advanced AI: the ability to ask the right questions. The “prompt” is the new user interface. A well-crafted prompt, like the one for the “Marbles” game, acts as a detailed technical specification. It requires the human to have a clear vision and the ability to articulate that vision with precision.
The iterative nature of the process is also key. The future of work with AI is a conversation. It is a loop of prompting, reviewing the output, providing critical feedback, and prompting again. The auto-debugging experiment shows this loop in action. The human who can effectively guide this refinement process will be the one who extracts the most value from the technology.
Conclusion
Our investigation into GPT-5’s practical applications has been illuminating. Its true value is not in its ability to just generate text, but in its capacity to create functional, interactive, and strategically-sound outputs. It is a powerful tool for rapid prototyping, a creative partner for game design, and a structured analyst for business planning.
While its abilities as a real-world “agent” are still inconsistent, its strengths as a “co-creator” are undeniable. It is a promising partner that, when wielded by a skilled user, can amplify a person’s ability to create, analyze, and build. The future is not one of human versus machine, but of human and machine in a collaborative partnership, and this new model is the most capable partner we have seen yet.