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A straightforward application for tracking cryptocurrency purchases using a dollar-cost averaging (DCA) strategy is being developed. Users can log trades, which allows the app to calculate the average entry price for each asset. 1. The app is built using Google AI Studio, where users select the “Build an Android app” option and provide a detailed description of the task. 2. The app allows users to add purchase entries with asset ticker, amount spent in USD, price per coin at purchase, and date, storing all entries locally. It displays total invested, total coins accumulated, average entry price, and includes a summary card with overall portfolio cost, a delete option for each entry, and filtering by asset. 3. AI Studio offers several design options, including Clean Minimalism and Elegant Dark, which can be selected or skipped. 4. The Gemini model generates a project with approximately ten Kotlin files and launches the app in an emulator, initially displaying “Total Invested: [openai_gpt model="gpt-4o-mini" prompt="Summarize the content and extract only the fact described in the text bellow. The summary shall NOT include a title, introduction and conclusion. Text: Step-by-Step App Build To illustrate the app development process, we will create a straightforward application designed for tracking cryptocurrency purchases using a dollar-cost averaging (DCA) strategy. This app will enable users to log their trades, allowing it to calculate the average entry price for each asset effortlessly. Step 1. Choose the mode and describe the app Begin by launching Google AI Studio, navigating to the Build tab, and selecting the “Build an Android app” option. In the designated input field, provide a detailed description of the task at hand. Prompt Build a native Android app for tracking dollar-cost averaging (DCA) crypto purchases. Let the user add a purchase entry with: asset ticker (e.g. BTC, ETH), amount spent in USD, price per coin at purchase, and date. Store all entries locally on the device. For each asset, show the total invested, total coins accumulated, and the average entry price. Add a summary card at the top with the overall portfolio cost. Include a delete option for each entry and the ability to filter by asset. Source: Incrypted. Step 2. Choosing a design Prior to generating the code, AI Studio presents a selection of visual style options for the app, including Clean Minimalism, Elegant Dark, Professional Polish, Vibrant Palette, and Sleek Interface. You can choose your preferred design by clicking “Select this design” or opt to skip this step by selecting “Skip.” Source: Incrypted. Step 3. Generation and first build The Gemini model will then create a project, typically comprising around ten Kotlin files, and launch the app in the built-in emulator. Upon initial launch, the screen will appear empty, displaying “Total Invested: [cyberseo_openai model="gpt-4o-mini" prompt="Rewrite a news story for a business publication, in a calm style with creativity and flair based on text below, making sure it reads like human-written text in a natural way. The article shall NOT include a title, introduction and conclusion. The article shall NOT start from a title. Response language English. Generate HTML-formatted content using tag for a sub-heading. You can use only , , , , and HTML tags if necessary. Text: Step-by-Step App Build Let’s break down the process using a simple app for tracking crypto buys with a dollar-cost averaging (DCA) strategy. The user logs their trades, and the app calculates the average entry price for each asset. Step 1. Choose the mode and describe the app Open Google AI Studio, go to the Build tab, and select the “Build an Android app” option. In the input field, describe the task.  Prompt Copy Build a native Android app for tracking dollar-cost averaging (DCA) crypto purchases. Let the user add a purchase entry with: asset ticker (e.g. BTC, ETH), amount spent in USD, price per coin at purchase, and date. Store all entries locally on the device. For each asset, show the total invested, total coins accumulated, and the average entry price. Add a summary card at the top with the overall portfolio cost. Include a delete option for each entry and the ability to filter by asset. Source: Incrypted. Step 2. Choosing a design Before generating the code, AI Studio offers several app visual style options — for example, Clean Minimalism, Elegant Dark, Professional Polish, Vibrant Palette, and Sleek Interface. You can pick the option you like under “Select this design” or skip the step by clicking “Skip.” Source: Incrypted. Step 3. Generation and first build The Gemini model creates a project — in our case, about ten Kotlin files — and launches the app in the built-in emulator. At launch, the screen is empty: the portfolio counter shows “Total Invested: $0.00,” and the purchases list is empty.  Source: Incrypted. Step 4. Fixing errors  If a message saying “1 error running the code” appears at the bottom of the panel, click Fix. The model finds the cause — in this example, it was an initialization error on startup — and fixes the code. After that, the app launches correctly. Step 5. Testing Click the plus button in the bottom-right corner. The “Add Purchase” window will open with the fields Ticker, Amount USD, and Price Per Coin. Enter the trade details and click Add. Add a few purchases — the “Total Invested” counter at the top will sum up your invested funds. Data: Incrypted. Data: Incrypted. Step 6. Refining the feature with a prompt To have the app group purchases by asset and calculate the average entry price, уточните задачу следующим промптом. Prompt Copy Group the purchases by ticker and, for each asset, add a summary card above its entries showing: total invested, total coins accumulated, and the average entry price. Calculate the average entry price as total invested divided by total coins for that asset. Display it clearly, for example u0022Avg entry: $2071.67u0022. Keep the existing per-purchase list below each summary. After the refinement, each asset gets its own card with the total amount, the number of coins, and the average entry price, and below it — a list of specific trades. Data: Incrypted. After testing in the emulator, you can install the app on a smartphone via ADB using a USB cable or publish it to Google Play’s internal testing track — these options are available from the same interface." temperature="0.3" top_p="1.0" best_of="1" presence_penalty="0.1" ].00” alongside an empty purchases list. Source: Incrypted. Step 4. Fixing errors If an error message appears stating “1 error running the code,” simply click Fix. The model will identify the issue—such as an initialization error on startup—and rectify the code accordingly. Following this correction, the app should launch without further issues. Step 5. Testing To test the app, click the plus button located in the bottom-right corner. This action will open the “Add Purchase” window, prompting you to fill in the fields for Ticker, Amount USD, and Price Per Coin. After entering the trade details, click Add. As you input several purchases, the “Total Invested” counter at the top will dynamically sum your invested funds. Data: Incrypted. Data: Incrypted. Step 6. Refining the feature with a prompt To enhance the app's functionality by grouping purchases by asset and calculating the average entry price, refine your task with the following prompt. Prompt Group the purchases by ticker and, for each asset, add a summary card above its entries showing: total invested, total coins accumulated, and the average entry price. Calculate the average entry price as total invested divided by total coins for that asset. Display it clearly, for example "Avg entry: 71.67". Keep the existing per-purchase list below each summary. Data: Incrypted. After implementing these refinements, each asset will feature its own summary card displaying the total amount invested, the number of coins accumulated, and the average entry price, with a detailed list of specific trades below. Once testing in the emulator is complete, you can install the app on a smartphone via ADB using a USB cable or publish it to Google Play’s internal testing track—both options are conveniently accessible from the same interface." max_tokens="3500" temperature="0.3" top_p="1.0" best_of="1" presence_penalty="0.1" frequency_penalty="frequency_penalty"].00” and an empty purchases list. 5. If an error occurs during code execution, clicking "Fix" allows the model to identify and correct the issue, enabling the app to launch correctly. 6. The app is tested by adding purchase details through an “Add Purchase” window, which updates the “Total Invested” counter. 7. To enhance functionality, the app can be refined to group purchases by asset, displaying a summary card for each asset that includes total invested, total coins accumulated, and average entry price, while maintaining a list of specific trades below each summary. 8. After testing, the app can be installed on a smartphone via ADB or published to Google Play’s internal testing track.