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Reflecting on 18 Years at Google

Reflecting on 18 years at Google, former employees share their experiences and observations of the company's evolution over time.
In the early post-IPO days, Google was known for its 'don't be evil' ethos and a culture focused on doing the right thing for users and humanity.
Executives at early Google were transparent, with decisions made for the benefit of users rather than just profit.
However, over time, Google's culture eroded, transparency diminished, and decision-making shifted towards self-interest.
One former employee highlighted a time when Google prioritized employee preservation, valuing them as the most precious resource.
Despite changes, some still view Google as a great place to work, but acknowledge shifts towards a 'culture of limited resources.'
As Google navigates layoffs and changes in leadership, there is a sense of nostalgia for the company's earlier innovative and employee-centric approach.
Employees reflect on the transition from Google's heyday to a period where the company seems to have lost its edge, especially in search.
Former employees express concerns about Google's direction and corporate culture, citing shifts in leadership and priorities.
The dynamics at Google have changed significantly over time, with the company facing challenges in maintaining its innovative edge and corporate ethos.
As Google evolves, former employees reminisce about the past and grapple with the changes in the company's culture and priorities.

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Towards Data Science

148

Time Series Forecasting Made Simple (Part 1): Decomposition and Baseline Models

Time series analysis can be made simpler by starting from basics and focusing on intuition behind concepts.
Understanding time series involves identifying trends, seasonality, and noise to make informed predictions.
Baseline models like Naive Forecast, Seasonal Naive Forecast, and Moving Average provide simple yet effective forecasting.
Baseline models like Moving Average can provide around 80% accuracy for business planning, making them valuable.
Decomposing time series into trend, seasonality, and residuals is crucial for selecting appropriate forecasting models.
Additive model in time series decomposition assumes that trend, seasonality, and residuals combine linearly.
The stability of seasonal patterns over time indicates that an additive model is suitable for decomposition.
Multiplicative models are preferred when seasonal effects scale with trend, capturing proportional changes.
Implementing a Seasonal Naive model based on decomposition shows the forecasting accuracy and limitations.
Evaluation metrics like MAPE are used to assess forecasting model performance and set benchmarks for future improvements.

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Dev

342

Image Credit: Dev

Stitching Giant JSONs Together with JSON Patch

JSON Patch is a tool for making precise updates to JSON documents without rewriting everything from scratch.
It allows defining operations like adding a key, replacing a value, or removing a field in a compact, reusable way.
The json-patch library supports both JSON Patch (RFC 6902) for precise updates and JSON Merge Patch (RFC 7396) for whole-document changes.
JSON Patch is crucial for handling large JSON files, such as those generated by Language Models (LLMs) with output limits.
You can efficiently stitch together multiple JSON chunks from LLM outputs using JSON Patch.
Setting up and configuring the json-patch library involves adjusting global settings and using ApplyWithOptions for more control.
You can patch LLM output chunks into one JSON document by applying operations like add, replace, or remove.
JSON Patch is useful for scenarios like syncing client-server updates and combining multiple patches in a controlled manner.
Tips for smoother patching include validating patches, testing equality, handling missing paths, and using the json-patch CLI for testing.
By leveraging JSON Patch and understanding its operations, you can effectively manage and manipulate JSON data for various applications.

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Medium

238

Image Credit: Medium

Mitchell in the Box: A Scientific Exploration of Recursive Reality and Observer Dynamics

The concept of Mitchell in the Box reimagines reality as a recursive, observer-dependent system, integrating quantum mechanics, fractal dynamics, and feedback loops.
Reality is viewed as a flux of potential and actualized states, with Mitchell playing dual roles inside and outside the system.
Observation in this system is recursive, influencing future states based on present observations and potential states.
The framework unites classical and quantum physics perspectives, highlighting the role of the observer in shaping reality.
Fractal dynamics and recursive feedback loops create self-similar patterns in reality at all scales, mirroring neural systems and AI processes.
The simulation involves computational models that simulate recursive observer dynamics using parameters like temporal flux factor and observation strengths.
Mitchell in the Box presents a new paradigm for understanding reality as a dynamic, participatory process shaped by observation and interaction.
Future directions include developing computational simulations, AI systems based on Fractal Flux, and exploring observer-influenced state transitions in cosmology.
The paper explores philosophical implications of an observer-dependent reality, highlighting the dual role of the observer as both creator and participant.

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Dev

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Hands-On Selenium with Python: From Basics to Framework Building

Selenium, when combined with Python, becomes effective for automating web applications.
Selenium offers tools like WebDriver, IDE, and Grid for automating interactions, recording tests, and parallel testing.
To set up Selenium with Python, you need to install Python, Selenium package, and an IDE like PyCharm.
Writing your first Python Selenium script involves opening a browser, navigating to a page, capturing the title, and closing the browser.
Basic Selenium commands in Python include opening a web page, locating web elements, and interacting with elements.
Handling dynamic elements in Selenium involves using implicit and explicit waits to manage wait times for elements to appear.
Additional functionalities like handling alerts, frames, windows, and taking screenshots are also available in Selenium with Python.
PyTest can be integrated with Selenium for writing test cases and generating reports for efficient test execution.
Best practices for Selenium automation using Python include using explicit waits, optimizing WebDriver management, running tests in headless mode, and using Page Object Model.
By leveraging Selenium with Python and PyTest, you can build a robust test automation framework for efficient web task automation.

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Dev

147

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From Code to Defense: Understanding Zero Trust as a Developer

Zero Trust is a security model that assumes nothing and no one is trusted by default.
The core concepts of Zero Trust include strict identity verification, least privilege access, micro-segmentation, and continuous monitoring and analytics.
Zero Trust is especially powerful in today's world where teams are remote, apps are in the cloud, devices are everywhere, and cyber threats are more sophisticated than ever.
For software developers, tools like LiveAPI can help generate interactive API docs, saving time and improving documentation of codebases.

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Medium

194

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Why Your Technical Documentation Needs a UX Designer

Technical documentation has traditionally been treated as an afterthought, but it is becoming increasingly important as technology grows more complex.
Studies show that developers spend a significant amount of time dealing with bad code caused by poor documentation, resulting in lost productivity.
Confusing or incomplete documentation is a major reason why developers abandon new tools or APIs, leading to lost revenue and missed opportunities.
Companies with subpar documentation receive significantly more support tickets, but a UX designer can help reduce support requests through improved documentation.

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Dev

116

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Hi everyone, nice to meet you!

Liram is a 16.5-year-old undergraduate student studying Computer Science at the Academic College of Management.
Liram is passionate about learning and experimenting with programming languages, algorithms, and software development.
Liram is excited about integrating into the job market and continuing to grow personally and professionally.
Liram is looking forward to connecting with people in the tech industry for collaboration and innovation.

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PlanetPython

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Mirek D��ugosz: pytest: running multiple tests with names that might contain��spaces

You can run multiple tests in a suite by passing the full path as a command argument to pytest.
If the test names contain spaces, using command arguments directly can lead to failures.
To avoid this problem, you can use cat and xargs to pass the test names with spaces as input.
Alternatively, you can use the '@' symbol along with the file path containing the test names as a command argument to pytest.

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Dev

411

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10 hard truths EVERY DEV must learn in 2025

Every developer must learn some hard truths throughout their career, such as the importance of clean code principles over relying solely on AI code assistants.
Effort alone is not enough; true progress requires intentional decisions and valuing simplicity and purpose in development.
Focusing on the purpose of your projects rather than getting distracted by the latest coding tools leads to greater recognition and success.
Prioritizing simplicity and delivering powerful functionality with clean, simple code is key to standing out as a developer.
Learning from past mistakes and holding oneself accountable for past coding practices is crucial for future growth and improvement.
Being selective about the tools and frameworks you choose to learn and use based on their relevance and effectiveness is more valuable than chasing trends.
Independence in coding, understanding, and problem-solving skills are essential for becoming a competent developer.
Learning to set boundaries, make informed decisions, and not blindly say yes to every feature request is important for professionalism and work-life balance.
Prioritizing code clarity through naming, structure, and organization is more beneficial than relying on excessive comments for explanations.
Balancing work projects with personal growth, learning, and networking is crucial to avoid losing oneself in coding endeavors.

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24 Likes

Medium

199

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How to Use Elevenlabs for FREE (Forever!)

Create an account either by signing up with Google or by entering an email and password in the space provided. In my case, I’m signing in to Elevenlabs with Google.
With a free Elevenlabs account, you have 10,000 credits every single month. If we click on “Subscription,” you’ll be able to see what the free forever Elevenlabs plan gives you.
This includes, as I mentioned, 10,000 credits per month (which is about 10 minutes of audio), 15 minutes of conversational AI per month with up to four concurrent conversations, as well as the ability to:
Select any of these options from the inside panel, such as “Text to Speech,” and use your 10,000 credits for free every single month.

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Medium

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How Eigenfaces Got Me Hooked on Machine Learning

The tutorial on Eigenfaces and machine learning was taken on as a side project by someone still learning ML.
The project was done without using scikit-learn to maintain a beginner-friendly approach.
Google Colab was preferred for its suitability for such projects.
The dataset used was 'AT&T Database of Faces' from Kaggle.
Libraries used included os, cv2 (OpenCV), and numpy for file system interaction and image processing.
Principal Component Analysis (PCA) was explained as a method for dimensionality reduction.
Important steps included finding eigenfaces, centering images, computing covariance matrix, and projecting faces into eigenface space.
The recognize_face() function was replaced with a new function for improved performance.
The predict_face() function was detailed, highlighting how face recognition works in eigenface space.
A threshold was explained as a confidence level for face recognition to prevent false positives.
The tutorial concluded with visualizing the results and uploading a personal image for testing.

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Dev

182

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Clarity in Architecture: Not OOP vs FP

The article discusses the importance of architectural clarity in software development, emphasizing the need for clear boundaries and reasoning in system design.
It highlights that the debate should shift from OOP vs FP to focusing on practical boundaries and decision-making.
Classes are recommended for coordinating shared logic, establishing clear lifecycles, and separating dependency construction from behavior.
Internal state is deemed acceptable when owned by objects with clear lifecycles and behavior and should not leak into the system.
Dependency Injection (DI) should be explicit to avoid global singletons, ensuring traceability and testability in the codebase.
Functional principles such as immutability, composability, and isolation of effects are essential in software systems, even in the presence of necessary side effects.
The series focuses on practical architectural patterns rather than blind loyalty to a specific paradigm, aiming to help developers make informed decisions for scalable and maintainable codebases.
The upcoming parts of the series will delve into topics like when to use classes, avoiding unnecessary internal state, and maintaining a clear boundary between dependencies and behavior.

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10 Likes

Medium

238

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Building Powerful AI Agents with LangChain and LangGraph — Part 1

LangChain and LangGraph are frameworks that enable the development of powerful Agentic AI agents.
LangChain simplifies the integration of Large Language Models (LLMs) and provides modular components for reasoning, using tools, maintaining memory, and executing workflows.
LangChain offers interfaces for different model types and provides tools for prompts, memory, chains, data retrieval, and interaction with the world.
LangChain Expression Language (LCEL) allows developers to compose chains with a clean and intuitive syntax, enhancing readability and maintainability.

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14 Likes

Medium

229

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5 Shortcuts That Make Me Write Python Code 2x Faster

Using custom snippets in VS Code helps the writer type less and think more, leading to faster coding.
Tools like TabNine or Cursor IDE offer AI-assisted suggestions, making autocomplete more useful and tailored to the writer's style.
Using ChatGPT for code generation and microtasks is efficient and saves time.
Using a quick script and copy-pasting the output is faster than referring to documentation or Stack Overflow.

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