India is hosting its first major AI event — the India AI Impact Summit 2026 — in New Delhi between February 16 and 20, bringing together global technology leaders, including Open AI CEO Sam Altman, Nvidia CEO Jensen Huang, Google CEO Sundar Pichai, Bill Gates, and Anthropic CEO Dario Amodei alongside key Indian business figures like Reliance Industries chairman Mukesh Ambani, Nandan Nilekani, and more, as per the summit’s website.

The Indian government is hopeful that the upcoming summit will cement India’s status as a destination for large-scale AI investment. The country’s IT minister said in a recent interview that the event could help attract as much as $100 billion in investment. The federal government is also pushing domestic startups to build smaller models for local use cases, eventually reducing reliance on U.S.-based systems.

Baseline testing is a testing practice that establishes a stable reference version of an application against which future changes can be evaluated. This baseline represents an accepted level of functionality, performance, and reliability that the system must continue to meet as it evolves.

By comparing new test results with the baseline, teams can quickly identify regressions, performance drops, or unexpected behavior introduced by recent changes. This makes baseline testing especially valuable in environments with frequent releases or continuous integration, where changes are introduced regularly.

Baseline testing is often used alongside regression and performance testing to provide meaningful context for test results. When the baseline is updated thoughtfully after major improvements or releases, it helps teams track quality trends over time.

Overall, baseline testing supports data driven decisions, reduces risk during releases, and ensures that progress does not come at the cost of stability or user experience.

• AI companies like OpenAI and Anthropic are hiring social media creators to post sponsored content on apps like Facebook, Instagram, YouTube and LinkedIn.
• Companies including Microsoft and Google have paid creators between $400,000 and $600,000 for long-term partnerships spanning several months, CNBC has learned.
• AI companies have increased advertising considerably, spending more than $1 billion on digital ads in the U.S. in 2025, according to Sensor Tower, up 126% from 2024.

Source: CNBC

Test automation is a core practice in modern software development that uses tools and scripts to execute tests automatically. It helps teams validate functionality, integrations, and regressions efficiently, reducing the time and effort required for repetitive manual testing.

By integrating test automation into CI and CD pipelines, teams can run tests on every code change and receive immediate feedback. This early detection of defects shortens development cycles and improves overall product stability. Test automation also supports consistent execution, ensuring the same test scenarios are validated across different environments.

Effective test automation focuses on automating stable, high value test cases while leaving exploratory and usability testing to manual efforts. Well designed automation frameworks and regular maintenance are essential to keep test suites reliable and scalable over time.

When applied strategically, test automation improves testing efficiency, increases release confidence, and enables teams to deliver high quality software at speed.

Regression testing focuses on reducing the risk that new changes will negatively impact existing functionality. Whenever code is updated—whether to add features, fix defects, or improve performance—there is a chance that something previously stable may break. Regression testing helps identify these issues before they reach end users.

In fast-paced development cycles, regression testing provides a layer of confidence that core workflows continue to behave as expected. By prioritizing high-impact and business-critical test cases, teams can quickly validate system stability without slowing down delivery.

Automation is commonly used to support regression testing at scale, enabling tests to run automatically as part of continuous integration pipelines. This allows teams to catch regressions early and fix them when changes are still fresh and easier to resolve.

By consistently applying regression testing, organizations can maintain product quality, minimize release-related surprises, and support continuous improvement without compromising reliability.

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You can now also take full-length mock JEE Main tests in Gemini at no cost. Google is partnering with Physicswallah and careers360 for vetting the content.

Source: Gemini

TDD (Test-Driven Development) and BDD (Behavior-Driven Development) are both popular approaches in modern software testing, but they focus on different aspects of quality and development. Understanding the differences can help teams choose the right methodology for their workflow.

Test-Driven Development (TDD) is a coding practice where developers write tests before implementing the actual functionality. The process follows a simple cycle: write a failing test, implement the minimum code to pass the test, and then refactor. TDD emphasizes correctness at the code level and helps ensure that each unit of functionality behaves as intended. It improves test coverage, reduces defects early, and encourages cleaner, modular code. However, TDD tests are often technical and may not directly reflect user behavior.

Behavior-Driven Development (BDD), on the other hand, focuses on the expected behavior of the system from a user or stakeholder perspective. BDD uses natural language constructs and scenarios, often in “Given-When-Then” format, to define requirements and tests. This makes tests easier to understand for non-technical stakeholders and ensures that development aligns closely with business expectations. BDD encourages collaboration between developers, testers, and business analysts, promoting shared understanding of system behavior.

While TDD targets technical correctness and code quality, BDD emphasizes communication, collaboration, and user-focused outcomes. Many teams combine the two: using**tdd vs bdd** for high-level acceptance tests. Together, they provide a comprehensive approach that improves both code reliability and alignment with user expectations.

Meta plans to test new subscriptions that give people access to exclusive features on it apps. The new subscriptions will unlock more productivity and creativity, along with expanded AI capabilities.

In the coming months, Meta said it will offer a premium experience on Instagram, Facebook, and WhatsApp that gives users access to special features and more control over how they share and connect, while keeping the core experiences free. Meta doesn’t appear to be locked into one strategy, noting that it will test a variety of subscription features and bundles, and that each app subscription will have a distinct set of exclusive features.

Source: TechCrunch

Email sending is being introduced to the already existing email routing, and together forming as Email Services.

Source: Cloudlflare

Black box testing is a widely used testing method that focuses on verifying the external behavior of software without examining internal code or architecture. It is designed to validate whether the system produces the expected outputs when given specific inputs, based purely on functional requirements and user expectations.

One of the key strengths of black box testing is its user-centered approach. Since the tester does not rely on implementation details, the testing process aligns closely with real-world usage scenarios. This makes it especially effective for validating APIs, user interfaces, and system workflows where external behavior matters most.

Black box testing is also valuable for regression and release validation. Because the tests are based on behavior rather than code structure, they tend to remain stable even when internal implementations change. When combined with automation, black box tests can be run consistently across environments, providing reliable validation and faster feedback. Over time, this approach helps teams maintain stability and ensure that changes do not impact core functionality.

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#1 Anthropic - 40%

#2 OpenAI - 27%

#3 Google - 21%

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