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UX designers vs. developers: Who really owns accessibility (A11y) in UX design?

Accessibility in UX design is not solely the responsibility of developers; designers play a crucial role from the beginning.
Designers must ensure that designs are accessible for all users, including those with disabilities like keyboard-only users.
Factors such as technical perception, lack of training, and poor team collaboration lead to accessibility issues in products.
Effective collaboration between designers and developers is essential to create accessible products.
Designers need to start integrating accessibility from the discovery and ideation stages throughout the product lifecycle.
Strategies include using tools like Stark and Figma plugins, implementing accessibility checklists, and prioritizing team education.
Shared tools for testing like Axe Devtools, Google Lighthouse, Silktide, and WAVE API help ensure ongoing accessibility compliance.
Fostering a culture of accessibility requires team education, collaborative workflows, and shared tools for testing at every stage of development.
Web accessibility is a necessity to ensure inclusive digital products that cater to diverse user needs.

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Javacodegeeks

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JSONObject Extract Keys Example

JSON (JavaScript Object Notation) is a lightweight data-interchange format commonly used for data transmission.
It can have flat key-value pairs or nested structures, affecting key access using dot or bracket notation.
Understanding both flat and nested keys is crucial for JSON data parsing and transformation in applications.
A Java example showcases extracting keys from JSON using JSONObject and recursive traversal.
The code traverses flat and nested keys, adding them to a list for subsequent processing.
Best practices include using notation for nested keys and handling JSONObjects and JSONArrays recursively.
Validating JSON structures before key extraction is advised for robustness, especially with dynamic data.
Extracting keys in Java involves thoughtful handling of JSON structures using recursion for effective data extraction.
The code output presents all keys, top-level and nested, in a readable dot and bracket notation format.
Developing skills in JSON processing is crucial, and subscribing to educational resources can aid in becoming proficient.

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Medium

336

Day 7: When Phone Detox Actually Works (And You Surprise Yourself)

The author discussed considering participating in a web development competition despite feeling rusty in frontend development skills.
The author acknowledged the need to refresh their JavaScript and CSS knowledge and catch up on new frameworks in web development.
Joining the competition could serve as motivation to dust off old skills and learn new ones, seen as 'networking' rather than distraction.
The author reflected on distinguishing intentional vs. compulsive phone use, noticing a difference during designated breaks compared to mindless scrolling.
Although not groundbreaking, these changes represent small steps toward personal progress and moving forward in the right direction.
The author acknowledged that these changes may not solve all productivity issues but are positive steps forward.

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Medium

243

Keycloak: The Modern Way to Secure Your Apps

Keycloak is an open-source IAM solution developed by Red Hat, offering authentication, authorization, SSO, social login, and user federation.
It is self-hosted, flexible, and powerful, similar to Auth0 or Firebase Auth.
Keycloak simplifies user, permissions, and roles management for web apps, mobile apps, or APIs.
Setting up Keycloak can be done using Docker or manual installation.
After installation, one can log in to the Keycloak Admin Console at http://localhost:8080 with admin/admin credentials.
Keycloak revolves around concepts like realms, clients, users, roles, and groups for configuration and security.
It supports OAuth2 and OpenID Connect by default.
Keycloak can be integrated with frontend applications for user authentication using various libraries like keycloak-js, react-keycloak, or passport-keycloak.
For apps requiring login and role-based access control, Keycloak is a valuable addition to the tech stack saving development time and supporting modern protocols.
Keycloak is an open-source tool that scales with the needs of developers.

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Medium

285

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Why 90% of Developer SaaS Tools Fail in Year Two (Analysis of 50+ Failed Startups)

A developer tools startup recently announced its closure despite positive feedback from users and significant funding.
Many developer tools startups fail to sustain their businesses despite having products loved by engineers.
An analysis of 50+ failed developer tool startups revealed that technical quality did not guarantee business success.
The main reasons for failure were identified as business model and go-to-market challenges.
These companies typically faced challenges around the 18–24 month mark, hindering their growth and sustainability.

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Dev

332

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Next.js Forgot to Design APIs. Tirne Didn’t.

Tirne aims to improve side effect tracing and performance in API development compared to Next.js.
Tirne offers declarative routes, explicit side effects, edge-native speed, and type-aligned logic.
Quick start with 'npx create-tirne-app' and performance benchmarks show Tirne's speed superiority.
Performance metrics like cold start, request latency, and requests/sec demonstrate Tirne's efficiency.
Tirne outperforms Next.js API Routes significantly in terms of speed and performance.
Examples of structured Tirne code for handling APIs are provided for easy understanding.
Tirne simplifies authentication handling and error management with clear structures and testability.
Error handling is built-in with Tirne, offering structured error responses and clean error boundaries.
Tirne encourages building APIs that are structured, testable, and explicit for better code understanding.

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PlanetPython

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Python Insider: Python 3.14.0 beta 3 is here!

Python 3.14.0 beta 3 is now available, as part of the Python 3.14 development cycle.
Beta releases like 3.14.0b3 allow the community to test new features and bug fixes before the final release.
Maintainers of third-party Python projects are encouraged to test with 3.14 and report any issues.
Python 3.14 is feature-complete in the beta phase, with minimal changes expected after the first release candidate.
Key features of Python 3.14 include support for free-threaded Python, deferred type annotation evaluation, and template string literals.
Other additions in Python 3.14 are multiple interpreters in the standard library, Zstandard compression support, and improved UUID module.
Python 3.14 also introduces enhancements in debugging, error messages, HMAC implementation, and remote attaching in the pdb module.
Build changes include using Sigstore for verifying release artifacts and providing an experimental JIT compiler in macOS and Windows binaries.
Incompatible changes, removals, and deprecations are documented for Python 3.14.
The Python installer for Windows is replaced by a new install manager, available from the Windows Store or FTP page.

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Dev

394

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# Leveraging BioChatter: A Conversational AI Backend for Biomedicine

BioChatter is an open source Python package released on June 6, 2025, aimed at integrating conversational AI into biomedical research workflows.
The package provides a backend interface for connecting large language models with biological knowledge graphs and common biomedical tasks using a unified API.
Users can install the package via pip and interact with models by sending prompts and receiving structured outputs.
BioChatter supports integration with BioCypher, evaluation metrics, and model agnostic chaining to compare different large language models.
The library simplifies the development of applications like gene disease extractors and dialog agents for molecular data by automating various components.
It facilitates reproducible benchmarks in biomedical NLP and provides researchers with an easier way to prototype and evaluate conversational AI pipelines.
Interested individuals can explore the documentation on the GitHub repository, experiment with custom prompts, and contribute to the community.
BioChatter enables the integration of large language models into scientific workflows, paving the way for more conversational and data-driven tools in biology.

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RealPython

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Exploring Python's list Data Type With Examples

The list class is a fundamental built-in data type in Python, offering a variety of features for organizing and manipulating data.
Understanding lists is essential for Python developers, given their versatility and usefulness in managing different types of data.
Lists are commonly utilized in various real-world coding scenarios due to their wide range of applications.
A video course is available to explore Python lists in depth and gain a comprehensive understanding of their functionalities.
The course covers creating new lists, accessing items in lists, copying, updating, resizing, and concatenating lists.
Additionally, sorting, reversing, and iterating through lists are explained to improve coding efficiency.
Other advanced features of Python lists are also discussed in the course.
The course aims to enhance Python skills through practical examples and exercises.

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Dev

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How to Use Mock Service Worker (MSW) and Storybook for Efficient UI Development

Efficient testing and development workflows are crucial for modern web applications, with Mock Service Worker (MSW) and Storybook being key tools for frontend developers.
MSW allows for mocking network requests, while Storybook provides a structured environment for developing and testing UI components individually.
Reasons to use MSW include decoupling frontend from backend, seamless testing, and the ability to control and mock various API response scenarios.
Storybook's advantages include component isolation, faster UI development, easy documentation, and enhanced collaboration among developers and designers.
Setting up Storybook in the project involves installing and starting Storybook to display components effectively in different states.
Creating stories in Storybook helps represent UI components, and integrating MSW with Storybook enables the simulation of component behavior with various responses.
Advanced use cases for MSW in Storybook involve handling different API responses and automating visual and interaction tests using tools like Chromatic.
Best practices for MSW and Storybook include using MSW for all network requests, creating comprehensive stories, combining with unit and integration tests, and documenting component behavior.
Using MSW and Storybook together streamlines UI development, testing, and documentation, enhancing frontend productivity and reliability.
Experiment with advanced MSW scenarios, share Storybook stories for collaboration, and automate testing processes for improved workflow efficiency.

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Medium

376

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What Shouldn’t We Ask ChatGPT About Software?

Sharing entire codebases or significant portions with external tools like ChatGPT in enterprise environments can pose serious risks due to potential exposure of sensitive business logic, API keys, and internal details.
Loading code into AI tools may violate internal policies, privacy agreements, or data protection laws and may lead to unintentional breaches.
Developers should avoid pasting full or sensitive project code into AI tools to prevent potential security and privacy risks, especially in commercial or custom applications.
Asking ChatGPT to perform unethical or illegal activities like exploiting systems through SQL injections or password cracking can pose risks and is not advisable.
ChatGPT lacks access to local development environments, logs, or system configurations, hindering its ability to diagnose real-time issues accurately.
Requesting completion of full-scale projects from ChatGPT is a common misuse as it may result in hypothetical answers, promoting incorrect processes.
Developers should strive to understand the code they write rather than relying solely on ChatGPT, utilizing it as a learning aid rather than a substitute for effort.
Vague or incomplete code questions to ChatGPT may yield poor-quality responses, necessitating clear, detailed input for effective assistance.
Using ChatGPT judiciously as a programming aid can enhance skills in software development, emphasizing the importance of understanding and learning rather than blind reliance.

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PlanetPython

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Mike Driscoll: Python 201 �� All About the TypedDict

Python supports type hinting but does not enforce it, requiring external tools like Mypy.
TypedDict allows adding type hinting to heterogeneous dictionaries with values of different types.
Type hinting a regular dictionary involves using dict[key_type, value_type].
Python versions before 3.9 required 'from typing import Dict' for type hinting.
TypedDict was introduced in Python 3.8 for dictionaries with different value types.
TypedDict helps in situations where a dictionary has values of various types.
Using TypedDict ensures better type checking and prevents errors.
Creating a TypedDict involves defining a class that inherits from TypedDict.
Dealing with keys containing spaces in a TypedDict requires using TypedDict constructor instead of subclassing.
TypedDict is useful for type-hinting complex dictionaries, even within other data structures like lists or tuples.

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Dev

274

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You Can Write Code — But Can You Read It? 🧩

Reading code, especially code you didn’t write, is often harder than writing it.
Challenges in reading code include decoding decisions without context, uncertainty on what to focus on, unfamiliarity with naming conventions, and the inability to rewrite everything to match personal understanding.
Developing the skill to read code is crucial for bug fixes, adding new features, and code reviews on a team.
Improving code-reading skills requires tracing data flow, exploring open-source projects, asking 'why' about coding decisions, refactoring old code, and engaging in code reviews.
Being proficient at reading code involves asking the right questions, following the trail of logic, and maintaining curiosity.

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Medium

203

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Roadmap Awareness

To stay aware of shifts in organizational direction, new Engineering Managers can monitor quarterly or half-year roadmaps shared in various formats like slide decks or documents.
Identifying patterns in roadmaps can provide insights, such as increased projects in areas like document extraction hinting at upcoming data ingestion needs.
Attending cross-functional planning reviews or roadmap readouts can offer valuable information without the need to actively participate.
Asking simple but insightful questions during interactions, like 'What's on the roadmap next quarter?' in 1:1s, can provide early awareness of upcoming changes and projects.
Consistent inquiry about future roadmaps can lead to learning about changes and launches in advance, enhancing planning skills and partnership abilities.
Adopting a simple quarterly forecasting framework can help in anticipating organizational shifts and being better prepared.
Developing the habit of reading early signals of change is crucial for engineering teams to be proactive and resilient.
Proactive monitoring and awareness of roadmap shifts can prevent unexpected surprises and better prepare for upcoming changes in the organization.
Monitoring roadmap changes is a beneficial practice for Engineering Managers to enhance foresight and planning abilities.
Simple strategies like asking relevant questions and paying attention to roadmap patterns can significantly improve an Engineering Manager's awareness of organizational direction shifts.

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Siliconangle

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Apiiro report reveals industry differences are shaping generative AI risk profiles

A new report from Apiiro Ltd. shows differences in how industries adopt and secure generative AI in software development.
Retail organizations are quickly integrating generative AI into customer-facing systems compared to financial institutions.
Financial institutions are moving more cautiously with older codebases, leading to greater risk exposure.
Apiiro's analysis found a seven-fold increase in secrets exposure in generative AI repositories in finance.
Retail codebases are 1.8 times more likely to contain sensitive data compared to finance.
Retail developers mainly use OpenAI's Python software development kits, while financial teams experiment with various tools.
Generative AI in retail is customer-facing and wired into sensitive data, while in finance, it's older and layered onto legacy systems.

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