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How to find out if someone is a catfish by picture for free?

Catfishing, a deceptive practice online, can be detected by analyzing the person's pictures using free tools and methods.
Verifying a picture is crucial in protecting emotions, finances, and private information from scammers using fake identities.
Methods include reverse image search tools like Google, TinEye, and Yandex, analyzing photo metadata, examining image quality, checking social media footprint, and requesting video calls or real-time photos.
Tips to stay safe include trusting instincts, avoiding sharing personal information, not sending money, and reporting suspicious activity to the platform.

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Self-Learning-Java

326

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Partitioning by TIMESTAMP Column in BigQuery

BigQuery only supports partitioning by DATE, INTEGER, or RANGE, but not directly by a TIMESTAMP column.
To partition a table by a TIMESTAMP column in BigQuery, you should explicitly cast it to DATE.
Converting TIMESTAMP to DATE ensures all rows from the same UTC day are placed in the same partition.
Data inserted in a partitioned table must be consistent with the partitioning key to avoid errors.
Query 'INFORMATION_SCHEMA.PARTITIONS' can be used to verify the partitions created for a table in BigQuery.
Integer partitioning in BigQuery improves query performance and reduces costs by scanning relevant partitions only.
Partitioning a table by an integer column like order_year requires specifying start, end, and interval values.

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Self-Learning-Java

135

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How to Track Endpoint Usage with Prometheus Counters in Java?

Prometheus Counters are used to track metrics that only increase, like the total number of requests in an application.
Counters measures how many times something has happened, such as HTTP requests or errors occurred.
Naming conventions for Counters require unique names starting with a letter and ending with _total.
A sample Java application is created to expose endpoints like /health and /user-profile, while incrementing respective Counters.
Metrics are scraped by Prometheus from the /metrics endpoint using CollectorRegistry.defaultRegistry.metricFamilySamples() method.
The output from /metrics includes metadata comments describing the metric purpose, type (counter or gauge), and the current value.
Automatically added *_created metrics in Prometheus indicate the time a Counter was created, aiding in troubleshooting and monitoring.
Prometheus UI can be configured to display these metrics by updating the prometheus.yml file and attaching the target.
Using Prometheus queries like rate(user_profile_requests_total[5m]) helps analyze the rate of requests coming to specific endpoints.
Monitoring tools like Grafana can leverage timestamps to detect when a metric was first seen and calculate metric age.

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Dev

183

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🚀 Streamline Your Python & Django Development with Production-Ready Cookiecutter Templates

A collection of comprehensive cookiecutter templates has been created to generate production-ready Python apps, PyPI packages, Django REST APIs, and DRF packages with modern tooling and best practices.
The templates cover Python app, Python package, Django REST app, and DRF package scenarios, each with specialized configurations and tools for their specific use cases.
Features include modern tooling out of the box, production-ready architecture, and a zero-configuration philosophy for immediate project setup.
Intelligent dependency management with UV, dual type checking with mypy and pyright, and quality metrics automation are key aspects of the templates.
The templates adhere to modern Python practices, offer production deployment readiness, and provide real-world usage examples for building SaaS APIs, publishing libraries, and DRF extensions.
Key features include testing excellence, documentation standards, CI/CD pipelines, advanced testing features, and code quality enforced through pre-commit hooks.
Common questions regarding customization, database migrations, and choice of tools like UV over pip/poetry are addressed in the article.
Future roadmap plans include adding FastAPI template, Next.js + Django full-stack template, Kubernetes deployment configurations, more cloud provider integrations, and additional authentication providers.
Getting started is easy by installing cookiecutter, generating a project using the provided GitHub repository, choosing a template, configuring options, and starting development immediately.
The cookiecutter templates aim to save time spent on project setup, promote modern Python best practices, and improve the development experience for various types of projects.
Developers are encouraged to try the templates for their projects and provide feedback for continuous improvement.

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Self-Learning-Java

223

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Getting Started with Prometheus in Java: A Hello World Example Using client_java

To start working with Prometheus in Java, a Hello World example using client_java library is provided.
First step includes creating a new maven project 'prometheus-demos' and updating pom.xml with necessary Prometheus dependencies.
Second step involves creating a package 'com.sample.app' and defining the PrometheusHelloWorld application.
Once the application is running, metrics can be viewed by accessing the URL http://localhost:8080/metrics and configuration should be added to the prometheus.yml file.

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Insider

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Nvidia CEO Jensen Huang says programming AI is similar to how you 'program a person'

Nvidia CEO Jensen Huang describes programming AI as similar to programming a person.
At London Tech Week, Huang highlighted that AI could be programmed using plain language, terming it the 'great equalizer.'
He emphasized the shift towards using 'human' as the new coders' language, making technology more accessible to everyone.
Huang believes AI is transformative and envisions a future where computing technology no longer requires traditional coding, making everyone a 'programmer.'

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Insider

618

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Sundar Pichai says AI is making Google engineers 10% more productive. Here's how it measures that.

Google CEO Sundar Pichai reveals that the company is tracking how AI tools are increasing engineers' productivity.
Pichai estimates a 10% boost in engineering capacity due to artificial intelligence at Google.
The company measures this increased productivity by tracking the additional engineering capacity gained through AI-powered tools.
Google is not only focusing on boosting productivity but also on using AI to generate code, with over 30% of new code generated by AI.

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RealPython

109

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Python Hits the Big Screen and Other Python News for June 2025

A newly announced documentary, Python: The Documentary, offers a behind-the-scenes look at Python's history and culture, featuring key contributors like Guido van Rossum and generating buzz in the Python community.
Python 3.14.0 enters beta with features like template strings, deferred annotations, Sigstore for release verification, enhanced JIT compiler, and a safe external debugger interface, with the final release scheduled for October 2025.
Three accepted PEPs focus on typing, installation, and compression, aiming to improve Python's capabilities and user experience.
The Python community is energized with new leadership at the PSF, impactful updates, and ongoing conferences fostering inclusion, learning, and connection.

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Dev

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Let's talk about vibe coding.

Vibe coding, relying heavily on AI for coding, is not considered the best approach as it may impact one's expertise.
Overdependence on AI in coding can lead to a decrease in one's confidence in their own knowledge and skills.
While AI tools are improving in coding capabilities, developers still play a crucial role in coming up with new ideas and translating client needs into structured code.
Future in coding will emphasize understanding client needs over memorizing programming languages, making it important for developers to adapt to using AI tools for coding.

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Dev

284

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Stop What You're Doing: Angular 20's New Superpowers Will Change How You Code Forever

Angular 20 introduces game-changing features like Signals, Zoneless change detection, and Resource APIs for asynchronous data management.
Signals offer reduced boilerplate, improved performance, and easier state management in Angular development.
Zoneless change detection improves performance and debugging by relying on event-driven reactivity.
Resource APIs simplify asynchronous data management with built-in caching, error handling, and loading states tied to Signals.
Incremental Hydration and Route-Level Rendering enhance performance, SEO, and user experience in Angular applications.
Enhancements in Developer Experience include improved template syntax, Signal-Based Forms, and Type Checking support.
Angular 20 enforces new naming conventions, HMR for Templates, and introduces an experimental default test runner.
Required Versions & Deprecations include updates requiring TypeScript v5.8 and Node.js v20, along with deprecations like TestBed.flushEffects().
Performance insights in Chrome DevTools integrate Angular-specific profiling data for improved application performance analysis.
Framework additions like createComponent and Extended template expression syntax enhance dynamism and expressiveness in Angular.
Extended diagnostics, Style guide updates, and Improved host bindings contribute to a cleaner, more efficient Angular development experience.

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Dev

271

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I Used AI to Debug My Code and Here's What Happened (Spoiler: Mind = Blown 🤯)

AI-powered code analysis and explanation tools have revolutionized debugging for a developer with 8 years of experience.
AI quickly identified and explained a state mutation issue in the code that caused UI update problems.
Various debugging scenarios, like performance issues, async/await problems, and logic bugs, were effectively addressed by AI suggestions.
Using AI tools for code explanation, bug detection, and code review significantly improved debugging efficiency and code quality.
AI debugging accelerated learning, boosted confidence, assisted in documentation, and enhanced the debugging process.
Despite its benefits, AI debugging has limitations such as false positives, context restrictions, security concerns, and the risk of over-reliance.
Integrating AI into the debugging workflow with specific steps like initial analysis, AI consultation, verification, documentation, and knowledge sharing proved to be effective.
Tips for better AI debugging include being specific, providing context, and asking follow-up questions for thorough understanding.
Since implementing AI for debugging, there has been a significant reduction in debugging time, fewer bugs in production, and enhanced problem-solving skills.
Future trends in AI debugging include IDE integration, project-specific learning, collaborative debugging, and visual debugging.
AI debugging is not about replacing developers but enhancing their effectiveness in debugging and learning processes.

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Medium

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Junior Developers Have No Chance in 2026

In 2026, it is predicted to be the toughest year for junior developers to enter the tech industry.
AI tools like ChatGPT and GitHub Copilot can now write boilerplate code rapidly, making it challenging for junior developers to compete.
Companies are questioning why they should hire junior developers when AI tools can perform coding tasks efficiently and without the need for breaks or salaries.
The tech industry landscape is changing, and junior developers are facing increasing challenges in securing entry-level positions.

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Medium

845

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Best AI Training in Electronic City, Bangalore — Become an AI Expert & Launch a Future-Proof Career!

The AI Certification Course in Electronic City, Bangalore offered by eMexo Technologies covers in-demand tools and techniques with sessions conducted by certified professionals.
eMexo Technologies stands out as the Best AI Training Institute in Electronic City, Bangalore with certified trainers, fully equipped labs, custom learning paths, career services, and flexible learning modes.
The course focuses on providing real skills desired by employers to ensure job readiness and offers placement support with a high demand for skilled AI professionals in the industry.
Enroll in the AI Training Institute in Electronic City, Bangalore by eMexo Technologies to kickstart a successful career in AI, Machine Learning, and Data Science with limited seats available.

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7 Likes

Medium

131

What If a Framework from a Garage Actually Solved Everything?

The history of technology showcases instances where superior solutions failed to gain traction while inferior alternatives succeeded due to effective marketing and adoption strategies.
Breakthrough technologies face challenges between innovation and adoption, referred to as the 'valley of death,' where excellent solutions can disappear.
Institutional resistance can hinder the adoption of new frameworks due to established players and professionals having vested interests in existing complex systems.
The expectation for extensive documentation and complexity in developer tools can work against simpler solutions that may actually be more effective.
Large organizations favor established solutions due to risk aversion, leading to a circular dependency where widespread adoption is necessary for enterprise uptake.
Successful framework adoption requires coordinated marketing efforts, which garage innovators often lack compared to frameworks backed by large companies.
Simplicity in a technically superior framework can hinder adoption as developers may struggle to see its value compared to more complex solutions.
Network effects play a significant role in adoption, with established frameworks benefiting from well-developed ecosystems that new frameworks lack.
Successful new frameworks find a niche, build a passionate community, and offer bridge strategies for gradual adoption to overcome barriers.
System thinking is crucial for successful framework adoption, understanding technology adoption as a social process beyond just technical excellence.

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Johndcook

407

Naively computing sine

A developer attempts to compute the sine function using the Taylor series method, encountering issues with accuracy and overflow for larger arguments.
The developer realizes the limitations of the Taylor series approach and the importance of considering range reduction to improve accuracy in calculations.
By implementing range reduction techniques, the developer is able to improve accuracy in computing the sine function for larger arguments.
While naive attempts at range reduction have limitations, with clever strategies and range reduction algorithms, precise computation of trigonometric functions is achievable.

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