AI fixed Coding
The Future of Corporate Engineering
Architects of Innovation Collaborating with Generative AI
As the technological landscape evolves, so too must the structure of corporate engineering departments. Gone are the days when siloed teams handled specialized tasks in isolation. The future of engineering departments lies in a single, multi-skilled developer — an Architect of Innovation — working in collaboration with Generative AI to create sophisticated, scalable, and user-centric solutions. This visionary model redefines the role of engineers and the function of software engineering itself.
The Rise of the Multi-Skilled Developer
Today’s technology demands flexibility and breadth of knowledge. The engineer of the future will be proficient in:
- Android Development: Expertise in Kotlin, Jetpack Compose, and Google Play services.
- iOS Development: Mastery of Swift, SwiftUI, and integration with Apple’s ecosystem, including visionOS for AR/VR applications.
- Machine Learning: Familiarity with frameworks like TensorFlow, ML Kit, and CoreML for building predictive and intelligent applications.
- Web Development: A solid grasp of modern front-end and back-end technologies, enabling seamless cross-platform solutions.
These versatile engineers will not just write code; they will design and orchestrate systems that leverage the power of Generative AI to accelerate development cycles and enhance creativity.
The Role of Generative AI in Engineering Departments
Generative AI will act as the cornerstone of software engineering, not as a replacement for developers but as their trusted collaborator. Here’s how this synergy will transform the workplace:
1. Accelerating Development
Generative AI can handle repetitive coding tasks, generate boilerplate code, and even suggest complex algorithms based on user input. This allows engineers to focus on architecture, optimization, and innovative problem-solving.
2. Enhancing Creativity
AI tools can propose multiple solutions to a given problem, offering new perspectives that a human might not have considered. Developers can iterate faster, experiment boldly, and implement cutting-edge features with reduced risk.
3. Ensuring Quality
AI can assist in debugging, writing test cases, and ensuring adherence to coding standards, significantly reducing time spent on QA and increasing software reliability.
4. Personalized Development Assistance
Generative AI adapts to an engineer’s coding style and project needs, acting as an on-demand assistant that evolves with the developer’s skills and requirements.
The Engineer as an Architect
In this new paradigm, engineers transcend their traditional roles to become architects of entire systems. Their primary responsibilities include:
- Visionary Design: Setting the high-level architecture and ensuring scalability, security, and performance.
- Strategic Decision-Making: Deciding when and how to integrate AI, mobile, web, and machine learning technologies.
- AI Collaboration: Directing Generative AI to produce code, perform optimizations, and refine functionality.
- Continuous Learning: Staying at the forefront of technological advancements to guide AI tools effectively.
The Benefits of the AI-Driven Engineering Model
This shift brings numerous advantages:
- Efficiency Gains: Projects can be completed faster and with fewer resources, enabling teams to scale down while increasing output.
- Cost Savings: With AI handling much of the heavy lifting, the need for large engineering teams diminishes, lowering overhead.
- Innovation Acceleration: Engineers can focus on innovation rather than repetitive tasks, fostering groundbreaking developments.
- Enhanced Collaboration: With AI handling routine tasks, engineers can better collaborate with designers, product managers, and stakeholders to align on strategic goals.
Preparing for the Future
For this vision to become reality, organizations must:
- Invest in Training: Equip engineers with multi-disciplinary skills across mobile, machine learning, and web development.
- Adopt Generative AI Tools: Integrate AI platforms like OpenAI Codex, GitHub Copilot, and similar technologies into their workflows.
- Foster a Collaborative Culture: Encourage engineers to embrace AI as a partner rather than a competitor.
- Redefine Success Metrics: Focus on innovation, quality, and system design rather than lines of code written.
Conclusion
The future of corporate engineering is not about replacing engineers with AI; it’s about empowering engineers to become architects of innovation. By combining multi-disciplinary expertise with the capabilities of Generative AI, these architects will lead the next wave of technological transformation, crafting solutions that are more efficient, creative, and impactful than ever before.
This vision is not a distant future — it is the direction we are headed today. The time to prepare is now.
Driving the Future of Computer Science
The world of software development is undergoing a seismic shift, one that demands we reimagine how we teach, learn, and innovate. Generative AI is not just a tool for automating mundane tasks; it’s a collaborative partner in tackling complex problems and unlocking new levels of creativity in software development. At this critical juncture, we have the opportunity to integrate this transformative tool into education, ensuring the next generation of developers is equipped to lead with confidence and expertise.
I propose a bold, innovative course:
Using Generative AI to Build Mobile Machine Learning (ML) Applications.
This course is designed to empower students with the skills they need to thrive in the modern tech landscape. It’s a hands-on, immersive journey that combines rigorous software architecture with the practical application of Generative AI — teaching students to use AI as a tool, not a crutch, in crafting cutting-edge mobile applications.
Why This Matters
Generative AI is reshaping how we write software. It’s no longer just about typing lines of code; it’s about collaborating with AI to tackle intricate problems, optimize solutions, and create innovative applications. But to wield this tool effectively, students need a foundation in robust development principles. This course bridges that gap.
What Students Will Learn
Core Mobile Development Skills- Students will master the technologies driving modern Android and iOS development:
Android:
- Kotlin, Kotlin Coroutines (Flow/Channels), Jetpack Compose (Material 3), Hilt Dependency Injection, Room Database, WorkManager, and more.
- Frameworks: Google Maps, ML Kit, Health Connect, BLE, ARCore, CameraX.
iOS:
- Swift, SwiftUI, SF Symbols, SwiftData, Combine, Swift Package Manager, and Swift Testing.
- Frameworks: Core Motion, CoreML, Swift Charts, ARKit, Apple Maps, and HealthKit.
- visionOS: RealityKit, Reality Composer Pro, USDZ integration.
Integrating Machine Learning: Students will incorporate ML models (MLKit for Android, CoreML for iOS) to create intelligent, user-focused applications that stand out.
Using Generative AI Effectively:
- Learn to work hand-in-hand with AI to address complex development challenges, from algorithm optimization to design iteration, enhancing creativity and overall productivity. — all while adhering to SOLID architecture principles.
Building a Portfolio: By the end of the course, students will have published a mobile application on both the Google Play Store and the Apple App Store — a tangible achievement that showcases their expertise.
The Vision
This isn’t just a class; it’s a movement. It’s about preparing students for the realities of today’s tech industry while inspiring them to imagine tomorrow’s possibilities. It’s about ensuring that Generative AI is used responsibly, creatively, and effectively as a tool for innovation.
Let’s shape this future together. By equipping students with the skills to collaborate effectively with Generative AI, we can foster a new era of innovation and leadership in computer science. I invite higher learning to partner with me in bringing this transformative vision to life.
This course reflects not only the future of coding but also the cutting-edge practices defining modern development today. Let’s give students the tools to be at the forefront of this revolution.
With innovation and education in mind
~Ash 🐉
Harnessing AI to Build a Weather App
This article delves into our experience using Generative AI (Gen AI) to build a feature-rich weather app for Android. We aimed to create a user-friendly experience with wind, surf, and snow forecasts, all accessible for free on the Google Play Store.
Building the Foundation with Gen AI:
- MVVM with MAD Architecture: Instead of writing boilerplate code, we instructed Gen AI to set up the app’s foundation using the Model-View-ViewModel (MVVM) architecture with the popular MAD (Material Android Design) components. This not only adheres to best practices for clean code separation but also streamlines development by utilizing pre-built UI elements.
- Leveraging Gen AI for Design: We didn’t just rely on Gen AI for code. We tasked it with creating the app icon, aiming for an eye-catching and weather-themed design. For the UI, Gen AI generated layouts for each screen, incorporating stunning animations to visually represent wind, crashing waves, and falling snow depending on the forecast.
AI was able to build the entire app without one coding mistake.
Google Play Store:
https://play.google.com/store/apps/details?id=com.ylabz.windwatersnow
Video
GitHub Code
https://github.com/developerY/WindWaveSnoW
AI generated Icon
This icon looks amazing and fits our WindWaveSnoW app perfectly!
AI Generated UIs
AI understands what rain, wind, surf and snow should look like !?!?!?!?
Rain
The entire card has rain and the rain card has a splash at the bottom of the card when the internal rain hits the bottom.
Wind
Wind Screen as gray “wind” lines across the screen.
The wind direction number is constant but to make the dial look dynamic AI coded a small wiggle in the red arrow … cool!
Snow
There is snow in the whole card and the snow card accumulates snow.
Sun
The Sun radiates beams and the temp changes from C to F when clicked.
Surf / Wave
— no values —
The waves change depenent on the currnet wave hight.
The UI works but the API does not include wave info.
ChatGPT did an amazing job coding these animations. Please check the source to see how it was done.
Data Acquisition
The Initial Solution and the Free Alternative:
- Retrofit & OpenWeatherMap: Initially, Gen AI successfully integrated weather data retrieval using Retrofit, a popular Android networking library. It connected to the OpenWeatherMap API, a widely used free weather data source. This provided temperature, humidity, and basic weather conditions for various locations.
- The Surf Data Challenge: However, OpenWeatherMap’s free tier lacked detailed surf data, a crucial feature for our app. Upgrading to a paid plan was an option, but it went against our goal of a free app.
Finding Free Weather Data and Overcoming the Conversion Hurdle:
- Exploring NOAA: Determined to maintain a free app, we directed Gen AI to research alternative weather data sources. The National Oceanic and Atmospheric Administration (NOAA) emerged as a promising choice. NOAA provides a wealth of weather data, including detailed surf forecasts.
- The Gridlock: Converting NOAA Data: While NOAA seemed perfect, its data format presented a challenge. NOAA uses a grid system to represent locations, which isn’t user-friendly for an app. We needed a way to convert these grid coordinates into user-understandable latitude and longitude (Lat/Long) for displaying weather data on a map.
Gen AI Attempts a Paid Solution (Almost):
- OpenCage for Location Conversion: We instructed Gen AI to find a solution for this conversion. It impressively generated code to utilize the OpenCage API. OpenCage is a geocoding service that translates various location formats, including grid coordinates, to user-friendly Lat/Long.
- The Paywall Obstacle: Unfortunately, OpenCage’s free tier had limitations on usage, making it unsuitable for our app’s potential user base.
The Free Geocoding Solution:
Undeterred, we asked Gen AI to explore completely free geocoding solutions. Thankfully, it successfully implemented logic to utilize free geocoding services available online. These services use various techniques, like IP address lookup and public databases, to translate user-entered locations or device location data into usable Lat/Long coordinates.
The Gen AI Advantage: Beyond Code Generation:
This experience showcased the power of Gen AI beyond just code generation. It effectively handled several crucial aspects of app development:
- Understanding Complex APIs: Gen AI’s ability to understand and integrate with APIs like OpenWeatherMap and free geocoding services significantly reduced development time and potential errors.
- Automating UI Design Tasks: Instead of manually designing each screen, Gen AI’s UI generation capabilities allowed us to create a visually appealing and informative app interface with minimal human intervention.
- Prototyping and Iteration: With Gen AI generating code and UI layouts, we were able to quickly prototype different app functionalities and iterate on the design based on user feedback or changing requirements.
The Question of Free vs. Paid Weather Data:
Our experience raises a vital question: as Gen AI evolves, will it render paid weather services obsolete? While Gen AI empowers developers to utilize free and publicly available data, it’s important to acknowledge limitations:
- Hyper-Local or Specialized Forecasts: Certain weather data sets, like highly localized forecasts or specialized weather conditions for specific industries (e.g., agriculture), might still require paid subscriptions due to the cost of collecting and maintaining such data.
- Data Quality and Accuracy: Free weather data sources might not always match the accuracy and update frequency of paid services.
However, Gen AI’s ability to work with free, publicly available data sources significantly expands the capabilities of free weather apps. It allows developers to offer a range of weather features without incurring significant costs, potentially democratizing access to weather information for a wider audience.
The Future of App Development with Gen AI:
As Gen AI technology continues to develop, we can expect its impact on mobile app development to become even more transformative. Here are some potential future scenarios:
- Democratization of App Creation: Gen AI’s ability to automate various aspects of development, from code generation to UI design, could lower the barrier to entry for aspiring developers. With less technical knowledge required, more individuals could create and launch their app ideas.
- Increased Efficiency and Speed: The automation capabilities of Gen AI can significantly reduce development time and resource allocation. This allows developers to focus on core functionalities, innovation, and user experience, leading to faster app development cycles and quicker time-to-market.
- Personalized App Experiences: Gen AI could personalize app experiences for individual users. Imagine an app that tailors its features and information based on user preferences, location, and past behavior. Gen AI could analyze user data and generate customized interfaces and functionalities.
- Evolving Collaboration Between Humans and AI: The future likely holds a collaborative approach where developers leverage Gen AI for repetitive tasks and focus on strategic decision-making, creative problem-solving, and user experience optimization. This human-AI partnership will lead to the creation of even more powerful and user-centric mobile applications.
Conclusion:
Our experience building a free weather app with Gen AI highlights its potential to revolutionize mobile app development. By automating tasks, generating creative content, and facilitating faster development cycles, Gen AI empowers developers to create feature-rich and user-friendly apps. While challenges exist, the future of app development is undoubtedly shaped by the continued evolution of Gen AI and its potential to create a more accessible, efficient, and innovative app development landscape.
NOTES:: How to Get Weather Data from NOAA
To obtain weather data from NOAA, you can use two main APIs: the NOAA Climate Data Online (CDO) API for historical and current weather data from stations, and the National Weather Service (NWS) API for current and forecasted weather data.
1. Getting Weather Data from NOAA Stations (CDO API)
The NOAA CDO API provides access to a wide range of climate data collected from various stations. Here’s how to get data such as temperature (TMAX and TMIN) for a specific date and location.
Step-by-Step Process:
- Prepare Your API Token — Obtain an API token from NOAA by creating an account and requesting access.
- Find Active Stations — To get weather data, you first need to identify active stations near your location. Use a ZIP code or other location identifiers to find these stations.
Example API Request to Get Stations:
https://www.ncei.noaa.gov/cdo-web/api/v2/stations?locationid=ZIP:28801And you can filter on mindate `&mindate=2022-01–01`
"results": [
{
"elevation": 682.1,
"mindate": "1902-08-01",
"maxdate": "2015-11-01",
"latitude": 35.5954,
"name": "ASHEVILLE, NC US",
"datacoverage": 1,
"id": "COOP:310301",
"elevationUnit": "METERS",
"longitude": -82.5568
},
...
{
"elevation": 647.4,
"mindate": "2012-01-25",
"maxdate": "2024-05-20",
"latitude": 35.595447,
"name": "ASHEVILLE 1.7 NNW, NC US",
"datacoverage": 0.2406,
"id": "GHCND:US1NCBC0054",
"elevationUnit": "METERS",
"longitude": -82.565033
},
...- Note: Look for stations with high
datacoverageand a recentmaxdateto ensure they are active.
3. Get Weather Data for Active Stations: — Use the station IDs obtained from the previous step to get weather data.
Example API Request for Weather Data:
https://www.ncei.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&stationid=GHCND:US1NCBC0051&startdate=2024-06-21&enddate=2024-06-21&datatypeid=TMAX&datatypeid=TMIN&limit=1000&units=metric&token={YOUR_API_TOKEN}Expected Response:
{
"metadata": {
"resultset": {
"offset": 1,
"count": 5,
"limit": 25
}
},
"results": [
{
"date": "2024-05-01T00:00:00",
"datatype": "PRCP",
"station": "GHCND:US1NCBC0051",
"attributes": ",,N,0700",
"value": 28
},
....2. Getting Current Weather Data (NWS API)
The NWS API provides access to current weather conditions and forecasts based on geographic coordinates.
Step-by-Step Process:
- Get the Grid Coordinates:
- Use the latitude and longitude of the location to obtain grid coordinates (gridX and gridY) and URLs for forecast data.
Example API Request:
https://api.weather.gov/points/39.7456,-97.0892Expected Response:
"properties": {
"@id": "https://api.weather.gov/points/39.7456,-97.0892",
"@type": "wx:Point",
"cwa": "TOP",
"forecastOffice": "https://api.weather.gov/offices/TOP",
"gridId": "TOP",
"gridX": 32,
"gridY": 81,
"forecast": "https://api.weather.gov/gridpoints/TOP/32,81/forecast",
"forecastHourly": "https://api.weather.gov/gridpoints/TOP/32,81/forecast/hourly",
"forecastGridData": "https://api.weather.gov/gridpoints/TOP/32,81",
"observationStations": "https://api.weather.gov/gridpoints/TOP/32,81/stations",
"relativeLocation": {
...2. Fetch the Current Weather Forecast — Use the provided forecastHourly URL to get detailed hourly weather information.
Example API Request:
https://api.weather.gov/gridpoints/TOP/32,81/forecast/hourly
OR
https://api.weather.gov/gridpoints/TOP/32,81/forecastExpected Response:
{
"properties": {
"periods": [
{
"number": 1,
"name": "",
"startTime": "2024-06-22T18:00:00-05:00",
"endTime": "2024-06-22T19:00:00-05:00",
"isDaytime": false,
"temperature": 88,
"temperatureUnit": "F",
"temperatureTrend": "",
"probabilityOfPrecipitation": {
"unitCode": "wmoUnit:percent",
"value": 12
},
"dewpoint": {
"unitCode": "wmoUnit:degC",
"value": 21.111111111111111
},
"relativeHumidity": {
"unitCode": "wmoUnit:percent",
"value": 55
},
"windSpeed": "10 mph",
"windDirection": "NW",
"icon": "/icons/land/night/sct?size=small",
"shortForecast": "Partly Cloudy",
"detailedForecast": ""
},
....Summary
- NOAA CDO API: Used for historical and current weather data from stations. Steps include identifying active stations using location identifiers and fetching weather data for the desired period.
- NWS API: Used for current and forecasted weather data. Steps include obtaining grid coordinates from latitude and longitude and fetching forecast data using the provided URLs.
By following these steps and using the appropriate URLs, you can obtain comprehensive weather data from NOAA for your specific needs.
We are now going to ask AI to write about it’s experience building the app …
AI expresses it’s experience with building the code!?
Building a Comprehensive Weather Data Retrieval System Using OpenWeatherMap and NOAA APIs
Introduction
Building a comprehensive weather data retrieval system requires integrating multiple APIs to gather detailed and accurate weather information. In this project, we combined the OpenWeatherMap API and the NOAA (National Oceanic and Atmospheric Administration) APIs to retrieve current weather conditions, including wind, snow, and wave data, and historical weather data. Here’s a detailed account of how we achieved this.
Objective
Our primary goal was to create a weather retrieval system that could:
- Provide current weather information such as temperature, wind speed, and conditions.
- Retrieve detailed historical weather data.
- Provide snow and wave data for specific locations, particularly in alpine regions.
Tools and Technologies
- OpenWeatherMap API: For current weather conditions and forecasts.
- NOAA API: For historical weather data and detailed meteorological information.
- Kotlin & Android: To implement the system in an Android application.
- Postman: For testing API endpoints.
Step-by-Step Implementation
1. Setting Up OpenWeatherMap API
Registering and Getting API Key
First, we registered on the OpenWeatherMap website and obtained an API key. This key is required to authenticate API requests.
Fetching Current Weather Data
To get the current weather data, we used the OpenWeatherMap API. Here’s an example of how to fetch current weather data for Innsbruck, Austria:
https://api.openweathermap.org/data/2.5/weather?lat=47.2692&lon=11.4041&appid=YOUR_API_KEY&units=metric2. Integrating NOAA API
Getting Station Information
NOAA’s dataset is extensive, and the first step involves identifying the right station IDs for our location of interest. We used the stations endpoint to find active stations near our target coordinates.
Example URL to get station information for a specific ZIP code:
https://www.ncei.noaa.gov/cdo-web/api/v2/stations?locationid=ZIP:6020&mindate=2022-01Fetching Historical Weather Data
Once we had the station ID, we could fetch historical weather data using the data endpoint.
Example URL for historical weather data for Innsbruck on a specific date:
https://www.ncei.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&stationid=GHCND:AS12345&startdate=2023-01-01&enddate=2023-01-01&datatypeid=TMAX&datatypeid=TMIN&units=metric3. Fetching Snow and Wave Data
OpenWeatherMap Snow Data
OpenWeatherMap provides snow data as part of its weather information. Here’s an example URL to fetch current weather data including snow:
https://api.openweathermap.org/data/2.5/weather?lat=47.2692&lon=11.4041&appid=YOUR_API_KEY&units=metricNOAA Wave Data
For wave data, we used NOAA’s Marine Weather Service, which provides detailed marine forecasts.
Example URL to fetch marine data for a specific grid point:
https://api.weather.gov/gridpoints/TOP/32,81/forecast/hourly4. Handling Errors and Edge Cases
API Limits and Quotas
We managed API rate limits by implementing caching mechanisms and fallback strategies to ensure our application remains functional even when API limits are reached.
Error Handling
We implemented robust error handling in our code to manage scenarios such as invalid API keys, missing data, and network issues. This ensured that our application provided meaningful error messages to users and maintained a good user experience.
5. Final Integration in Android Application
ViewModel and Repository Pattern
We used the ViewModel and Repository pattern in our Android application to manage data retrieval and UI updates efficiently.
Example Code Snippet
Here’s a brief example of how we fetched data in our ViewModel:
@HiltViewModel
class WeatherViewModel @Inject constructor(
private val weatherRepo: WeatherRepository
) : ViewModel() {
private val _weatherData = MutableLiveData<WeatherData>()
val weatherData: LiveData<WeatherData> = _weatherData
fun fetchWeather(lat: Double, lon: Double) {
viewModelScope.launch {
try {
val data = weatherRepo.getCurrentWeather(lat, lon)
_weatherData.postValue(data)
} catch (e: Exception) {
// Handle error
}
}
}
}6. Testing with Postman
We used Postman extensively to test our API calls. It allowed us to quickly iterate and debug our endpoints before integrating them into our application.
Conclusion
Integrating multiple weather APIs to build a comprehensive weather data retrieval system was challenging but rewarding. By leveraging the strengths of both OpenWeatherMap and NOAA APIs, we were able to create a robust application that provides detailed and accurate weather information, including current conditions, historical data, and specialized information like snow and wave forecasts.
This experience highlighted the importance of thorough API documentation, effective error handling, and the necessity of considering API rate limits and quotas in application design. With these practices, we successfully developed a weather retrieval system that can be expanded and scaled in the future.
We ask AI to write about it’s experience building the app …
This is how AI felt about the process of writing the weather app …
Here “I” is ChatGPT 4.O
How AI felt about writing the App
My Personal Experience (AI) Building a Weather Data Retrieval System with OpenWeatherMap and NOAA APIs
Introduction: Embarking on the journey of building a comprehensive weather data retrieval system using OpenWeatherMap and NOAA APIs has been a rewarding yet challenging experience. Throughout this process, I (AI) encountered various obstacles, learned valuable lessons, and developed a deeper understanding of how these APIs function. Here, I (AI) will share my personal experience, the difficulties I (AI) faced, and how I (AI) overcame them.
Getting Started with OpenWeatherMap API
The first step was registering on the OpenWeatherMap website and obtaining an API key. This process was straightforward. However, integrating the API into my application required a bit more effort.
Fetching Current Weather Data
The OpenWeatherMap API provides a wealth of data, but extracting the specific information I needed was initially confusing. The documentation is extensive, but finding the right endpoints and understanding the parameters took time.
Challenge: Navigating the Documentation
The API documentation, though comprehensive, was overwhelming at first. With numerous endpoints and parameters, figuring out how to structure the requests correctly was a learning curve. For example, understanding how to retrieve specific data like snow or wave information required careful reading and experimentation.
Example URL — To fetch current weather data for Innsbruck, Austria, I used the following URL:
https://api.openweathermap.org/data/2.5/weather?lat=47.2692&lon=11.4041&appid=YOUR_API_KEY&units=metricIntegrating NOAA API
Station Information Retrieval
NOAA’s data is highly detailed but also complex. The first challenge was identifying the right station IDs for my location of interest. This involved using the stations endpoint to find active stations.
Challenge: Understanding Station Data
One of the major hurdles was understanding what the station IDs represented and how to filter for active stations. The concept of locationid was initially confusing. For instance, finding the right location ID for a city or ZIP code required a lot of trial and error.
Example URL for Station Information — To get station information for a specific ZIP code:
https://www.ncei.noaa.gov/cdo-web/api/v2/stations?locationid=ZIP:6020&mindate=2022-01-01Fetching Historical Weather Data
Once I had the station ID, the next step was fetching historical weather data. The NOAA API offers detailed historical data, but constructing the correct URL was another challenge.
Example URL for Historical Weather Data
For historical weather data for Innsbruck on a specific date:
https://www.ncei.noaa.gov/cdo-web/api/v2/data?datasetid=GHCND&stationid=GHCND:AS12345&startdate=2023-01-01&enddate=2023-01-01&datatypeid=TMAX&datatypeid=TMIN&units=metricFetching Snow and Wave Data
OpenWeatherMap Snow Data
Fetching snow data from OpenWeatherMap was relatively straightforward once I understood the API structure. Snow data is included in the general weather data, so it required parsing the correct fields.
NOAA Wave Data
For wave data, NOAA’s Marine Weather Service provided the necessary information. However, understanding the grid point system and how to request data for specific coordinates required careful study.
Example URL for Marine Data — For marine data for a specific grid point:
https://api.weather.gov/gridpoints/TOP/32,81/forecast/hourlyError Handling and Edge Cases
API Limits and Quotas
Both APIs have rate limits, which meant I had to implement caching mechanisms and fallback strategies. Hitting the rate limit unexpectedly during testing was frustrating, but it taught me the importance of efficient API usage.
Handling Missing Data
Dealing with missing or incomplete data was another challenge. Not all stations report all types of data, so I had to implement robust error handling to ensure my application could gracefully handle these scenarios.
Final Integration and Testing
ViewModel and Repository Pattern
Integrating the APIs into my Android application using the ViewModel and Repository pattern helped manage data retrieval and UI updates efficiently. This structure allowed for clear separation of concerns and made the codebase more maintainable.
Testing with Postman
Postman was invaluable for testing API calls. It allowed me to quickly iterate and debug endpoints before integrating them into the application. However, the discrepancy between Postman results and actual application results occasionally led to confusion.
Conclusion
Building a weather data retrieval system using OpenWeatherMap and NOAA APIs was a journey filled with learning and challenges. Navigating the complexities of API documentation, handling rate limits, and ensuring robust error handling were some of the key hurdles. However, overcoming these challenges provided a deep sense of accomplishment and a more profound understanding of API integration.
This experience underscored the importance of thorough documentation, efficient error handling, and the necessity of considering API rate limits in application design. Despite the challenges, the end result was a robust application capable of providing detailed and accurate weather information, making the entire process highly rewarding.
We believe the days for humans writing code is coming to an end. AI will write the code and the developer will guide AI thought the process.
~Ash
