In modern software development, creating and maintaining test cases can be time-consuming. AI test generator offers a way to streamline this process by automatically generating tests based on application behavior, requirements, or historical data.

These tools can analyze code, user flows, or APIs and produce relevant test scenarios, reducing manual effort and accelerating the testing cycle. This is especially useful in agile environments where features are added frequently, and maintaining traditional test suites becomes challenging.

AI test generator also helps improve test coverage. By identifying edge cases or scenarios that might be overlooked by humans, they ensure that critical paths and potential vulnerabilities are tested consistently. This leads to more reliable and robust applications.

Another advantage is adaptability. As applications evolve, AI-generated tests can update themselves based on changes in the system, helping teams maintain up-to-date validation without rewriting large portions of the test suite.

By integrating AI test generators into QA workflows, teams can reduce manual effort, enhance coverage, and accelerate delivery while maintaining high-quality standards.

I've been exploring AI video generation tools and recently came across Seedance 2.0. Here's what makes it stand out:

Key Features:

• Text-to-video and image-to-video generation

• Precise motion control with keyframe editing

• High-quality 1080p output

• Multiple aspect ratios support

• Fast generation - videos up to 10 seconds

What impressed me most is the motion control capability. You can actually guide how elements move in the video, which gives much more creative control compared to other AI video tools.

The output quality is solid for marketing content, social media, and product demos. It's particularly useful if you need consistent visual styles across multiple videos.

Check it out: https://www.xmk.com/seedance/seedance-2-pro

Has anyone else tried this? Would love to hear your experiences with AI video generation tools.

Google has officially upgraded the storage for its AI Pro (formerly AI Premium) plan from 2TB to 5TB at no additional cost. This change was announced on April 1, 2026, and is rolling out globally to all subscribers.

Plan Updates:

• Storage Increase: The plan now includes 5TB of cloud storage (a 3TB increase), which is shared across Google Drive, Gmail, and Google Photos.
• No Price Hike: The monthly subscription fee remains unchanged at $19.99/month (or ₹1,950/month in India).
• Availability: This upgrade is rolling out globally to all new and existing Google AI Pro subscribers as of April 1–2, 2026

Included Benefits:

• Gemini Advanced: Access to Google's most capable AI models for reasoning, coding, and creative projects.
• Workspace Integration: Use Gemini directly in Google Docs, Gmail, and other apps.
• Premium Features: Includes 10% back on Google Store purchases and enhanced video calling features like noise cancellation in Google Meet.

Source: shimrit ben-yair

Razorpay's biometric authentication delivers up to 95% transaction success rates. For businesses, that’s a great relief on the last mile.

Key Details of Razorpay Passkey:

• Reduced Friction: Aims to eliminate issues like OTP delays, wrong entries, and redirect loops, reducing checkout abandonment.
• Increased Security: Uses on-device tokenized authentication, enhancing security and reducing card payment failures by up to 95%.
• Compatibility: Supported for transactions with major card networks like Visa and Mastercard.
• RBI Compliance: Fully compliant with the RBI’s two-factor authentication framework.

This initiative is aimed at reducing the ~35% of online card transactions that fail due to OTP issues, offering a seamless and secure alternative for digital payments in India.

Source: Razorpay

GLM-5V-Turbo does native multimodal coding, balanced visual and programming capabilities, and deep adaptation for Claude Code and Claw Scenarios.

The model can understand design drafts, screenshots, and web interfaces to generate complete, runnable code, truly achieving the goal of "seeing the screen and writing the code.

GLM-5V-Turbo leads in benchmarks for design draft reconstruction, visual code generation, multimodal retrieval and QA, and visual exploration. It also performs exceptionally well on AndroidWorld and WebVoyager, which measure control capabilities in real GUI environments.

Regarding pure-text coding, GLM-5V-Turbo maintains stable performance across three core benchmarks of CC-Bench-V2 (Backend, Frontend, and Repo Exploration), proving that the introduction of visual capabilities does not degrade text-based reasoning.

The leading performance of GLM-5V-Turbo stems from systematic upgrades across four levels:

• Native Multimodal Fusion: Deep fusion of text and vision begins at pre-training, with multimodal collaborative optimization during post-training. We developed the next-generation CogViT visual encoder, reaching SOTA in general object recognition, fine-grained understanding, and geometric/spatial perception. We also designed an inference-friendly MTP structure to ensure high efficiency.
• 30 Task Collaborative RL: The RL stage optimizes over 30 task types simultaneously, covering STEM, grounding, video, and GUI Agents. This improves perception and reasoning while mitigating the instability often found in single-domain training.
• Agentic Data and Task Construction: To solve the challenge of scarce Agent data, we built a multi-level system ranging from element perception to sequence-level action prediction. We use synthetic environments to generate verifiable training data and inject "Agentic Meta-capabilities" during pre-training (e.g., adding GUI Agent PRM data to reduce hallucinations).
• Multimodal Toolchain Extension: Beyond text tools, the model supports multimodal search, drawing, and web reading. This expands the perception-action loop into visual interaction. Synergies with Claude Code and OpenClaw are enhanced to support full-loop task execution.

Source: Z AI

When applications evolve, even small UI changes can unintentionally affect layouts, styles, or user experience. This is where visual regression testing becomes valuable. It focuses on detecting changes in the appearance of an application after updates, ensuring that the interface remains consistent and user-friendly.

Instead of checking functionality alone, this approach compares visual elements—such as layouts, colors, fonts, and spacing—before and after changes. By identifying differences, teams can quickly spot issues like misaligned components, broken layouts, or unintended design changes that might otherwise go unnoticed.

In practice, teams capture baseline snapshots of the interface and compare them with new versions after updates. These comparisons can be done manually or with automated tools that highlight even minor visual differences. This is especially useful for applications with complex user interfaces or frequent design updates.

Visual regression testing is often integrated into development workflows alongside other testing methods. It adds an extra layer of validation by ensuring that the product not only works correctly but also looks as intended across different devices and environments.

By incorporating visual regression testing into regular workflows, teams can maintain design consistency, catch UI issues early, and deliver a more polished and reliable user experience.

2026 has many layoff in tech companies till date. Below is list of major tech layoffs for past three months.

• ASML 1,700 people
• Atlassian 1,600 people
• Amazon 16,000 people
• Salesforce 1,500 people
• Epic Games 1,000 people
• Block 4,000 – 5,100 people
• WiseTech Global 2,000 people
• Oracle: 20,000 – 30,000 people
• Meta (Reality Labs) 1,500 people

Sanity testing plays a crucial role in modern software development by ensuring that recent code changes, bug fixes, or minor enhancements do not introduce new issues into an application. In fast-paced development environments where continuous integration and frequent deployments are common, sanity testing provides a quick and focused method to validate that specific functionality works as expected before moving to more extensive testing phases. Unlike full regression testing, which evaluates the entire system, sanity testing concentrates only on the modified components, saving both time and effort while maintaining software stability.

This type of testing is typically performed after minor updates, patches, or bug fixes when developers need quick confirmation that the recent changes did not negatively impact existing functionality. It helps teams detect critical issues early, preventing unstable builds from progressing further in the Software Development Life Cycle (SDLC). Because sanity testing is limited in scope and quick to execute, it allows development teams to maintain productivity without sacrificing quality.

Sanity testing is especially valuable in agile and DevOps environments where rapid releases are frequent. It provides immediate feedback, reduces testing cycles, and improves collaboration between developers and QA teams. By focusing on affected modules, sanity testing minimizes unnecessary testing efforts and helps maintain release timelines. Additionally, it supports continuous delivery pipelines by ensuring that builds remain stable before deployment.

Modern tools such as Selenium, Postman, Cypress, Jenkins, and TestNG are commonly used to automate or assist sanity testing workflows. These tools help teams quickly validate UI components, APIs, and backend services after minor changes.

Overall, sanity testing acts as a safety checkpoint in the development process. By quickly validating recent updates and identifying potential risks early, teams can deliver reliable software faster and with greater confidence. Integrating sanity testing into the development workflow ultimately improves software quality, reduces debugging costs, and enhances the overall user experience.

Large Language Models (LLMs) have significantly transformed the way developers write, debug, and maintain software in 2026. What once started as simple autocomplete suggestions has now evolved into intelligent AI-powered coding assistants capable of understanding complex codebases, generating production-ready code, and helping developers solve challenging programming problems faster. These advanced coding LLMs are becoming an essential part of modern development workflows, improving productivity, reducing errors, and accelerating software delivery.

Modern coding LLMs can assist developers in multiple ways, including generating code snippets, debugging errors, explaining unfamiliar code, refactoring legacy systems, and even creating technical documentation automatically. With the ability to understand natural language prompts, developers can now describe what they want to build, and AI models can generate structured, clean, and optimized code across multiple programming languages. This makes LLMs especially useful for startups, enterprise teams, and individual developers looking to increase efficiency and reduce development time.

Choosing the best LLM for coding in 2026 depends on several important factors such as accuracy, context window size, supported programming languages, integration with development tools, pricing, and privacy requirements. Proprietary models like GPT-5, Claude, and Gemini are known for their strong reasoning abilities, large context windows, and enterprise-grade integrations. These models often deliver highly accurate results and are widely used by professional development teams.

On the other hand, open-source alternatives such as DeepSeek-Coder, Code Llama, StarCoder, and Mistral Codestral are gaining popularity due to their flexibility, cost-effectiveness, and self-hosting capabilities. These models allow developers to maintain privacy, customize workflows, and avoid vendor lock-in.

As AI continues to evolve, coding LLMs are becoming powerful AI pair programmers that help developers build better software faster. This guide explores the best LLMs for coding in 2025 and helps developers choose the right AI coding assistant based on their specific needs and development workflows.

Mistral AI has released Voxtral TTS, a high-performance, 4-billion parameter open-weights text-to-speech model that competes directly with proprietary tools like ElevenLabs.

It runs on 3 GB of RAM locally and is free. It supports nine languages, offers 3-second voice cloning with high similarity, and delivers sub-second, low-latency performance suitable for on-device applications.

Key Features of Voxtral TTS:

• Performance: Achieved high win rates in human evaluation against top competitors, with superior speaker similarity.
• Efficiency: The 4B model is lightweight enough to run on consumer hardware (laptops, GPUs).
• Voice Cloning: Requires only 3-5 seconds of reference audio for voice cloning and supports cross-lingual voice adoption.
• Capabilities: Generates highly emotive, expressive, and natural-sounding speech across nine languages including English, German, Spanish, and Hindi.
• License: Released under an open-source, permissive license (Apache 2.0), making it available for developers to deploy freely.

This release is part of Mistral's strategy to move into audio and provide open-source alternatives to premium voice AI services.

Source: Mistral

The Sora team will share more soon, including timelines for the app and API and details on preserving your work.

The move signals a major shift in OpenAI’s strategy as it focuses on core AI development amid changing partnerships.

The Sora decision means the end of a blockbuster $1 billion deal between Disney and the ChatGPT maker that was announced a little more than three months ago. As part of the three-year deal, Disney said it would invest $1 billion in OpenAI and lend more than 200 of its iconic characters to be used in short, AI-generated videos. But the transaction between the companies never closed, two other people familiar with the matter said, and no money changed hands. OpenAI executives have been debating Sora's fate for some time.

The studio giant, Disney will no longer move forward with its OpenAI investment, as the AI company exits the video generation business.

Running the AI video app required significant computational resources, a fourth person with knowledge of the matter said, and left other teams with less firepower. Even so, some OpenAI staffers on the Sora team were surprised when they were informed of the changes Tuesday morning, one of the people and another source said. The announcement was made just a day after OpenAI published a blog post about Sora safety standards.

"We're saying goodbye to Sora ... we know this news is disappointing," the Sora team said in a post on X, adding that timelines for the app and API, as well as details on preserving user work, would be shared later.

Source: Sora

A user username is a unique, optional name that WhatsApp users can set in order to display their username instead of their phone number in the app. Usernames can be used in lieu of profile names when personalizing message content for individual users.

Usernames are an optional feature for users and businesses. If a username is adopted by a WhatsApp user, their username will be displayed instead of their phone number in the app. Business usernames are not intended for privacy, however. If you adopt a business username, it will not cause your business phone number to be hidden in the app.

WhatsApp users are limited to 1 username, but are able to change them periodically. Changing a username does not affect the user’s phone number or business-scoped user ID, and does not affect the user’s ability to communicate with other WhatsApp users or businesses on the WhatsApp Business Platform. User usernames have the same format restrictions as business usernames.

Source: META

X is testing a "dislike" or downvote button, likely styled as a broken heart icon, specifically for replies to improve conversation ranking.

Recent code discoveries in the X iOS app suggest this feature is designed to demote low-quality or irrelevant replies, acting as a private sentiment tool rather than a public dislike count.

Key Details on the X Downvote Button:

• Targeting Replies: Unlike previous tests that considered broad downvoting, current development focuses on ranking replies to posts.
• Broken Heart Icon: Evidence spotted in the iOS app points to a broken heart icon next to the "Like" button, allowing users to express disapproval.
• Private Functionality: Similar to the previously tested "dislike" mechanism in 2021, the new downvotes are expected to be used to improve content ranking behind the scenes rather than displaying a public tally, according to Storyboard18.
• Previous Testing: This initiative follows tests from 2021 and 2022 and aligns with current efforts to manage spam and improve reply visibility, notes

Black box testing methods are techniques used to validate software functionality by focusing on inputs and expected outputs without analyzing the internal code. These methods help testers design effective test cases based on requirements and user behavior, ensuring comprehensive functional validation.

One widely used method is equivalence partitioning, where input data is divided into groups that are expected to behave similarly. Instead of testing every possible value, testers select representative values from each group, improving efficiency while maintaining coverage. Another important method is boundary value analysis, which focuses on testing values at the edges of input ranges where defects are more likely to occur.

Decision table testing is another useful approach, especially for systems with multiple conditions and rules. It helps testers evaluate different combinations of inputs and their corresponding outcomes in a structured way. Additionally, state transition testing is used to validate how a system behaves when moving between different states based on user actions or events.

By applying these black box testing methods, teams can systematically design test cases that cover a wide range of scenarios. This structured approach improves defect detection, ensures better requirement validation, and enhances the overall reliability of the software.

my.WordPress.net gives you a complete, private WordPress environment with no sign-up and no hosting plan needed.

Everything stays on your device. You can use it to write, learn, experiment, or build with pre-configured apps like a personal CRM or RSS reader.

Built on WordPress Playground, my.WordPress.net takes the same technology that powers instant WordPress demos and turns it into something permanent and personal. As you don’t need to choose a hosting provider, your WordPress belongs entirely to you. In a publishing environment, you’d briefly interact with WordPress as you prepare your next post. In a personal setting, it becomes a place you shape and return to.

Because sites on my.WordPress.net are private by default and not accessible from the public internet, they don’t behave like traditional websites. They aren’t optimized for traffic, discovery, or presentation, and they don’t need to be. Instead, WordPress becomes a personal environment where ideas can exist before they are ready to be shared, or where they may never be shared at all.

my.WordPress.net includes an App Catalog with pre-configured experiences designed specifically for personal use, built with WordPress plugins. Contacts can be grouped, enriched with personal details, and paired with reminders to reconnect.

What you should know:

• Storage starts at roughly 100 MB
• The first launch takes a little longer while WordPress downloads and initializes
• All data stays in your browser and is not uploaded anywhere
• Each device has its own separate installation
• Backups should be downloaded regularly

Source: WordPress

As per a forbes report, cost remains an ever present challenge which does not seem to pass over to the user yet.

Cursor’s larger rivals are willing to subsidize aggressively. According to a person familiar with the company’s internal analysis, Cursor estimated last year that a $200-per-month Claude Code subscription could use up to $2,000 in compute, suggesting significant subsidization by Anthropic. Today, that subsidization appears to be even more aggressive, with that $200 plan able to consume about $5,000 in compute, according to a different person who has seen analyses on the company’s compute spend patterns.

Cursor also subsidizes some users, though it appears it doesn’t do so as much as Anthropic. Cursor has negative margins for consumer subscriptions, but its business plans operate on positive margins, according to a person familiar with its finances. Businesses that use Cursor can use the Teams plan, which is targeted at startups and is easy to cancel, or negotiate an enterprise contract, which is targeted at larger organizations.

Source: Forbes