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Dev
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From Code to Cash: Monetizing Python AI Agents ⚡
- The journey from creating a successful AI agent to monetizing it involves challenges like client convincing and deployment complexities.
- Outcome-based pricing, where customers pay based on results achieved, aligns interests and provides transparency in ROI.
- Evidence shows outcome-based pricing can lead to shorter sales cycles and higher conversion rates.
- Implementing outcome-based pricing requires precise metering and analytics tools like Stripe.
- An example lead enrichment agent showcases the process of enriching leads using Google Sheets and Python scripts.
- Setting up Stripe involves creating products, adding customers, and setting up subscriptions for billing.
- Preparing AI agents for deployment involves wrapping core logic in a Flask endpoint and defining type-safe models for data extraction.
- Deploying agents as API services using platforms like Itura simplifies execution monitoring, GitHub integration, and billing integration.
- Customers trigger the agent with a unique URL, and usage is metered for billing through Itura's integration with Stripe.
- Transitioning a Python AI agent into a consistent revenue stream involves challenges but can be overcome with outcome-based pricing and proper tools.
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The Truth About Cars
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Python 5000: The Ultimate Pothole Fixer
- The Python 5000 is a cutting-edge pothole patching vehicle that can fill and flatten a pothole in just 2 minutes.
- Operated by one person using a joystick from inside the cabin, it eliminates the need for a 2-3 person crew as in traditional methods.
- The process involves clearing debris with air blast, deploying asphalt, leveling with a rake, and compacting with a roller for a smooth finish.
- Capable of carrying up to 5 tons of asphalt, the Python 5000 even uses engine exhaust to maintain the warmth of the asphalt.
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Dev
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227
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How to Keep a Scrolling Log Updated in Real-Time with Python and CSS?
- Implementing a scrolling log updated in real-time is crucial for applications needing live feedback or user action monitoring.
- Websockets are recommended for real-time updates as they facilitate immediate server-to-client communication without delays.
- Prerequisites include Python 3.x, Flask, Flask-SocketIO, and basic knowledge of HTML and CSS.
- The process involves creating a Flask application, HTML template, CSS styling, and JavaScript for handling real-time log entries.
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Medium
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417
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Cool Python Tricks To Show Off
- Python allows some unconventional syntax due to its flexibility and design philosophy of 'Explicit is better than implicit.'
- Assigning a function to a variable is a common practice in Python.
- The 'del' keyword can be used to undo variable assignments.
- Python's lambda functions offer flexibility in creating custom REPL and dynamic event handlers.
- Unpacking iterables in Python allows for efficient handling of specific elements within a list.
- Swapping values in variables without a temporary variable can be achieved in Python.
- Python allows for a concise way to check multiple conditions with 'or' and 'and' in a single line.
- The 'else' keyword in Python can be used after 'for' and 'while' loops besides 'if' statements.
- The walrus operator (:=) introduced in Python 3.8 dynamically assigns values while being used in comparisons.
- Meta classes in Python allow for defining behaviors of classes, showcasing Python's object-oriented nature.
- __getattribute__ and __getattr__ methods in Python classes enable intercepting attribute accesses and handling missing attributes.
- Dynamic class creation using the type() built-in function showcases Python's flexibility in class definition.
- Domain-Specific Language (DSL) and operator overloading demonstrate Python's powerful capabilities for customizing behaviors.
- Global variables and functions can be utilized to create dynamic behaviors in Python functions.
- Exploring Python's flexibility can set you apart and enhance your programming skills, showcasing your value to potential employers.
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Dev
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How to Resolve 120-Second Delay in Simulink and Python TCP/IP Communication?
- Hardware-in-the-Loop (HIL) simulations often face delays, like the 120-second delay between a Python script on a Raspberry Pi and a Simulink model on a PC via TCP/IP.
- The delay issue between Python commands and Simulink execution suggests TCP/IP settings misconfiguration or synchronization problems.
- HIL simulation communication involves data transmission, control command reception, and data reception in Simulink for immediate feedback.
- Possible causes of the delay include network latency, simulation timing mismatch, data processing delays, and TCP/IP block settings.
- To resolve the delay, adjustments like setting the block sample time, implementing non-blocking mode, analyzing Python logs, using time stamping, and debugging are recommended.
- Understanding implications of blocking vs. non-blocking TCP/IP connections and minimizing transmitted data size are essential in reducing delays.
- Ensuring network stability, testing configurations, and fine-tuning synchronization can help in resolving the 120-second delay effectively.
- It's crucial to analyze and optimize TCP/IP settings, data processing, and network conditions for efficient communication between Python and Simulink.
- By following the suggested steps and FAQs to address delay causes, achieving timely response and integration of control commands in simulation steps is feasible.
- Efforts to troubleshoot, adjust configurations, and enhance synchronization will lead to minimized or eliminated delays in HIL simulations.
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Dev
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59
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How to Sync Git Repository Dependencies Using uv in Python?
- In Python development, managing dependencies efficiently is crucial, especially when using the uv tool.
- Adding a Git repository as a dependency in a project using uv may not update the virtual environment automatically.
- To sync Git repository dependencies using uv in Python, ensure correct setup in pyproject.toml, consider manual Git commands, and run uv with verbose logging if needed.
- Additional steps like recreating the virtual environment and checking for errors can be taken if synchronization issues persist.
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PlanetPython
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Python Engineering at Microsoft: Python in Visual Studio Code ��� May 2025 Release
- The May 2025 release of Python, Pylance, and Jupyter extensions for Visual Studio Code has been announced by Microsoft.
- Key announcements include new features like Python Environments Quick Create command, chat tools, automatic environment activation, color picker with Pylance, and AI Code Actions.
- The Python Environments extension now supports Quick Create for creating virtual environments with minimal input required.
- Chat tools 'Get Python Environment Information' and 'Install Python Package' provide seamless access to environment details and package installation.
- Automatic environment activation can be achieved with Python Environments by setting the 'python-envs.terminal.autoActivationType' to command or shellStartup.
- Pylance now offers a color picker feature to visualize and select colors in Python files.
- AI Code Actions in Pylance include converting string concatenations to f-string or format().
- Microsoft's presence at PyCon US 2025 includes various talks on topics related to Python development.
- Improvements in the Python and Jupyter extensions include branch coverage support for Python in the Testing Explorer.
- Additional changes and enhancements have been made based on user requests to improve the Python and Jupyter Notebook experience in Visual Studio Code.
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Dev
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163
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How to Dynamically Filter Pandas DataFrame by Date in Python?
- Filtering Pandas DataFrame by date dynamically in Python can sometimes be challenging due to issues with logical operations and precedence when combining conditions.
- To fix the filter condition, ensure that logical operators are applied correctly and conditions are wrapped in parentheses to avoid unexpected results.
- A clearer approach involves initializing the filter condition to None, building the filter with correct logic inside a loop, and applying the finalized filter to the DataFrame.
- By following these steps and testing your code at each stage, you can efficiently implement dynamic filtering based on dates in Pandas DataFrames.
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Javacodegeeks
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[DEALS] The Premium Python Programming PCEP Certification Prep Bundle (67% off) & Other Deals Up To 98% Off – Offers End Soon!
- Fresh offers available on Deals store
- The Premium Python Programming PCEP Certification Prep Bundle at 67% off ending soon
- Various discounts up to 98% off on different subscriptions and bundles
- Opportunity to develop skillset and subscribe to the newsletter for free eBooks
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Medium
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Save Hours Managing Ghost with These Python Scripts
- A blog owner running a self-hosted Ghost instance encountered limitations in the Ghost Admin interface that made management inefficient and error-prone.
- To address the challenges, they developed three Python scripts utilizing the Ghost Admin API with JWT authentication, providing a more efficient and streamlined approach.
- The scripts enable programmatically managing content, such as updating posts based on specific criteria like visibility and membership tiers, and searching for posts by title.
- This approach saved significant time and offered more control over the content management process, emphasizing the ease of combining Ghost's API capabilities with the power of Python.
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Towards Data Science
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Real-Time Interactive Sentiment Analysis in Python
- The article discusses the creation of a sentiment visualization project involving a smiley face that changes its expression based on the positivity of text input.
- The project requires packages like customtkinter, Opencv-python, torch, and transformers for implementation.
- It starts with defining the UI layout skeleton using customtkinter to create a textbox and canvas for the smiley face.
- An explanation is provided on creating the smiley face procedurally by changing the background color and curve of its mouth based on positivity scores.
- The sentiment analysis part involves using a pre-trained transformer model to classify text as NEGATIVE, NEUTRAL, or POSITIVE.
- The sentiment pipeline rates the sentiment score between -1 and +1 by fusing confidence scores of different classes.
- Integration involves linking the text box to the sentiment pipeline to dynamically update the displayed smiley face based on sentiment.
- The article concludes by highlighting the interactive sentiment analysis application built in Python and provides a link to the project repository on GitHub.
- The sentiment analysis project utilizes customtkinter, Opencv-python, torch, and transformers packages and implements a smiley face visualization based on text sentiment.
- Procedural generation of the smiley face involves dynamically changing its expression and color based on the sentiment score calculated.
- The sentiment analysis pipeline employs a pre-trained transformer model to classify text as negative, neutral, or positive, providing a sentiment score between -1 and +1.
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Dev
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72
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Why Does Python Raise SSLCertVerificationError with API Calls?
- SSL certificate verification errors, like SSLCertVerificationError, can occur when making API calls in Python despite working in tools like Postman.
- Python, especially with the requests library, enforces stricter SSL certificate validation compared to tools like Postman, leading to discrepancies.
- Reasons for SSLCertVerificationError include differences in certificate verification settings, certificate chain issues, and misconfigured CA certificates between Python and Postman.
- Solutions include temporarily bypassing SSL verification, updating CA certificates with certifi, or configuring custom CA certificates to address SSLCertVerificationError in Python.
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Medium
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259
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Is Python’s autoML capable of Handling complex Time series data?
- Time series forecasting is essential for predicting future values based on past temporal patterns in various domains like finance, energy, and healthcare.
- AutoML platforms, such as those in Python, aim to streamline the machine learning process by automating tasks like data preprocessing, feature engineering, model selection, training, and hyperparameter tuning.
- However, the critical question remains: Can AutoML effectively handle the complexities of complex time series data, including multivariate, irregular, noisy, seasonal, and non-stationary patterns?
- The discussion around leveraging AutoML for time series forecasting goes beyond academic curiosity to the practical implications of democratizing this forecasting capability.
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PlanetPython
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The Python Coding Stack: "AI Coffee" Grand Opening This Monday ��� A Story About Parameters and Arguments in Python Functions
- The article tells the story of Alex, who opens a new coffee shop, AI Coffee, and discusses parameters and arguments in Python functions within the context of the story.
- The story follows Alex's journey as he prepares for the grand opening of his coffee shop, AI Coffee.
- The article explains the concept of parameters and arguments in Python functions through five key sections: Parameters and Arguments, Positional and Keyword Arguments, Args and Kwargs, Optional Arguments with Default Values, and Positional-Only and Keyword-Only Arguments.
- The story intertwines Alex's experiences in running the coffee shop with coding examples and explanations related to Python functions.
- The article provides code snippets throughout to illustrate various concepts like using *args and **kwargs, default parameter values, and different argument types.
- It emphasizes the importance of distinguishing between parameters and arguments in Python functions, detailing how they are used and how they interact.
- The narrative follows Alex's challenges and successes as he navigates building a loyal client base and addressing complex coffee orders at his shop.
- The article delves into the complexities of function signatures, including rules for positional-only and keyword-only arguments, and the implications of parameter default values.
- Through Alex's story, readers gain a practical understanding of how to design and use Python functions effectively, considering different types of parameters and arguments.
- The article concludes by highlighting the benefits of using positional-only and keyword-only arguments in Python functions to improve clarity and prevent misuse in code.
- The narrative style of the article helps to make the technical content more engaging and relatable for readers interested in Python programming concepts.
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Dev
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295
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🧪 5 Weirdly Useful Python Libraries You’ve Probably Never Heard Of (But Will Love)
- Python's standard library is solid, but exploring quirky libraries can make coding fun again.
- Five quirky Python libraries like Faker, alive-progress, pyttsx3, termgraph, and pynput are highlighted.
- Faker generates fake data, alive-progress showcases an interactive loading bar, pyttsx3 enables sound output, termgraph plots charts in the terminal, and pynput controls mouse and keyboard.
- These libraries serve as teaching tools, each introducing a new aspect of Python development.
- Combining these libraries could lead to interesting projects like a talking bot interacting with the screen.
- Python enthusiasts can find more resources and discussions on python.0x3d.site.
- Building unusual projects using these lightweight yet powerful libraries can be a fun weekend activity.
- Various resources and kits for building side hustles and businesses are also mentioned in the article.
- The article encourages treating programming as play, combining libraries to create unique tools, and sharing creations with others.
- Additionally, it offers insights into a system for launching local business websites efficiently.
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