Google is expanding access to digital IDs in Google Wallet in select countries, all built with advanced privacy features like selective disclosure to keep your data secure.

🇮🇳 In India, you’ll be able to save Aadhaar Verifiable Credentials directly on your device

🇸🇬 🇹🇼 🇧🇷 And in Singapore, Taiwan and Brazil you’ll be able to create a secure ID pass based on your passport information.

Source: Google

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Software testing has always been one of those necessary but grueling parts of development. Engineers spend hours writing scripts, hunting down flaky tests, and maintaining automation that breaks every time a developer changes a button's class name. Generative AI testing tools are quietly dismantling this entire workflow, and the shift is bigger than most teams realize.

The core difference between traditional automation and generative AI testing is intelligence. Traditional tools execute the exact instructions you give them. Generative AI reads your user stories, understands your application's structure, and creates test cases that reflect how real users would actually interact with your product. Where legacy automation runs on rigid instructions, generative AI understands context, reads user stories, and creates test cases that mimic real user behavior, transforming testing from a reactive process into a proactive quality approach. Testomat

This matters enormously for teams trying to ship faster. When tests are generated automatically from requirements, the time gap between writing a feature and validating it shrinks dramatically. Organizations are achieving up to 9x faster test creation as AI produces in hours what manual test authoring would require weeks to build. Virtuoso QA

Beyond speed, the maintenance burden is dropping. One of the biggest costs in traditional automation is keeping tests alive as the UI evolves. Self-healing capabilities in modern generative AI testing platforms allow tests to automatically adjust when elements move, attributes change, or layouts shift. Advanced platforms now offer up to 95% self-healing, where machine learning and generative AI autonomously maintain tests as applications change. Virtuoso QA

Tools like Testsigma, Katalon, Virtuoso QA, and Keploy are leading this space. Each approaches AI-powered testing from a slightly different angle, whether that's natural language test authoring, autonomous agent-based testing, or API-first coverage. Keploy, in particular, stands out for developers building backend services, offering a resource like its guide to generative AI testing tools that breaks down how these platforms actually work in practice.

If you haven't evaluated generative AI testing tools for your stack yet, the question is no longer whether you should. It's which one fits your pipeline best and how quickly you can get coverage running without adding manual overhead.

We are excited to announce that our UIHut Figma Plugin has just hit a major milestone with over 16,000 users on the Figma Community. It speaks volumes about the value it brings to designers and developers worldwide.

Try the plugin: https://www.figma.com/community/plugin/1401906007073649951/uihut-ui-kit-illustrations-3d-assets

Communities will have until May 30th to transition and migrate members to XChat, which has a limit of 500 members.

X's product head Nikita Bier today announced two product changes for organizing communities on X:

1. XChat now supports joinable links for groupchats. Create a public link & share direct to Timeline. With support for 350 members per chat (and growing), Groupchat Links are the fastest way to bring people together on X.
2. Due to declining usage, we're deprecating X Communities on May 6.

To migrate your Community's members, pin your groupchat link so people can join it over the next 2 weeks.

This is part of our broader effort to simplify the experience on X. Make no mistake: we are investing heavily in niche communities with the launch of Custom Timelines—and much more to come.

Source: X

APIs power everything from mobile apps to microservices architectures. Whenever applications communicate, APIs act as the bridge—and ensuring they work correctly is critical. That’s where API testing comes in.

In this guide, we’ll break down what API testing is, why it matters, types, benefits, and best practices to help you build reliable and scalable systems.

What Is API Testing?

API testing is a type of software testing that verifies whether an Application Programming Interface (API) works as expected. It focuses on validating functionality, performance, reliability, and security by sending requests and analyzing responses.

If you want a deeper explanation, check this guide on

👉 what is api testing in software

Unlike UI testing, which checks the front-end experience, API testing targets the business logic layer, ensuring that data flows correctly between systems.

Why API Testing Is Important

Modern applications rely heavily on APIs to connect services, databases, and third-party tools. If an API fails, the entire system can break.

API testing is important because it:

Detects issues early in development before they reach users

Ensures seamless communication between services

Improves performance and reliability

Prevents security vulnerabilities

Testing APIs early (shift-left testing) helps teams fix bugs faster and reduce development costs.

Types of API Testing

API testing includes multiple testing types, each targeting a specific aspect of the system:

1. Functional Testing

Ensures the API performs expected operations correctly.

2. Integration Testing

Validates how APIs interact with other services or components.

3. Performance Testing

Checks response time, scalability, and system behavior under load.

4. Security Testing

Ensures authentication, authorization, and data protection.

5. Validation Testing

Verifies correctness, usability, and compliance with requirements.

6. Load & Stress Testing

Evaluates API performance under heavy traffic conditions.

These testing types ensure APIs are robust, scalable, and production-ready.

Benefits of API Testing

API testing offers several advantages over traditional UI testing:

Faster Testing

API tests run quicker because they don’t rely on UI elements.

Early Bug Detection

Issues can be identified before the UI is even built.

Better Test Coverage

Directly tests business logic and backend functionality.

Cost Efficiency

Fixing bugs early reduces long-term development costs.

Automation-Friendly

API tests can be easily automated for CI/CD pipelines.

How API Testing Works

API testing typically follows these steps:

Understand API documentation (endpoints, request/response formats)

Create test cases for different scenarios

Send requests (GET, POST, PUT, DELETE)

Validate responses (status codes, data, performance)

Automate tests for continuous integration

Testers compare actual responses with expected results to ensure correctness and reliability.

Best Practices for API Testing

To get the most out of API testing, follow these best practices:

Test both positive and negative scenarios

Validate status codes and response data

Automate repetitive test cases

Include security and performance checks

Use mocking and contract testing for dependencies

Integrate API tests into CI/CD pipelines

These practices help maintain high-quality APIs in fast-moving development environments.

API Testing vs UI Testing

Feature API Testing UI Testing

Focus Business logic User interface

Speed Fast Slower

Stability More stable Prone to UI changes

Coverage Backend-heavy End-user experience

API testing is generally faster and more reliable, while UI testing ensures a smooth user experience.

Conclusion

API testing is a critical part of modern software development. It ensures that applications communicate correctly, perform efficiently, and remain secure. By focusing on the backend logic, API testing helps teams catch issues early, reduce costs, and deliver high-quality software faster.

Whether you're working on microservices, mobile apps, or enterprise systems, investing in API testing is essential for building scalable and reliable applications.

SpaceX said it has an agreement giving it the right to acquire artificial intelligence startup Cursor for $60 billion later this year or to pay $10 billion for the companies’ work together, part of the Elon Musk-run firm’s efforts to catch up with rivals in AI coding tools.

Musk’s rocket, satellite and artificial intelligence giant announced the deal in a post on X, saying the two companies are “now working closely together to create the world’s best coding and knowledge work AI.”

Source: SpaceX

I made ResumeTailor.ai.

It’s a tool that takes your resume and a job description, then automatically rewrites your resume to match that specific role.

It also gives you an ATS match score and shows what keywords you’re missing, so you know exactly why your resume might not be getting through filters.

You can edit the result, export it as a PDF, and save different versions for different jobs.

Basically, it removes the need to manually rewrite your resume for every application.

If you’re applying to jobs, try it:

https://resumetailor.ai/

If you're a founder thinking about building an AI product, you've probably already noticed that getting a straight answer on cost is nearly impossible. Agencies quote wildly different numbers, freelancers underscope, and everyone has an opinion on which model to use before anyone has defined what the product actually needs to do. 😅

Here's a grounded breakdown of what things actually cost — and more importantly, why estimates go wrong.

Start here before talking to anyone 🎯

The single most expensive mistake founders make is starting with a feature list. You end up paying for complexity that hasn't been validated, while the one workflow that actually matters gets buried under everything else.

Before any vendor conversation, define one workflow. One user type, one task, one measurable outcome. That single constraint will save you more money than any negotiation tactic. ✅

Real cost ranges for 2026 💰

These cover a first stable production version — not a demo, not an MVP that barely works.

🔹 Customer support and assistant tools — $40K to $120K. Works well when your data is organized and integrations are straightforward. Costs climb with multi-language needs or strict access controls.

🔹 Meeting intelligence and transcription — $80K to $200K. Audio processing, speaker identification, action extraction. Recurring inference costs scale fast — model this before committing to pricing.

🔹 Recommendation and personalization engines — $120K to $350K. Looks simple from the outside, significant backend complexity underneath. Data pipelines alone can consume a large chunk of this range.

🔹 Document automation and computer vision — $100K to $300K. Annotation work and QA drive costs well beyond the model training itself.

Not yet ready for custom development? No-code AI platforms can get a focused use case live for $5K to $20K. Less ownership, but a much faster path to learning what your users actually need. 💡

What every budget needs to cover 📋

Most proposals only price the build. Here are all seven areas that will cost you something:

1. Discovery and architecture — defining the problem, auditing your data, mapping dependencies. Skip this and you pay for it twice in rework.

2. Product and model implementation — the actual engineering work. Visible and usually well-scoped.

3. Data preparation — cleaning, labeling, permissions. Almost always takes longer than planned. Almost always left out of first estimates. 😬

4. UX and trust design — how users interact with outputs, what happens when the system is wrong. This drives retention, not just aesthetics.

5. Quality and compliance — testing, security controls, audit logging. Defer this and it returns as incident response at the worst possible moment.

6. Launch instrumentation — analytics, funnels, experiment setup. Without this, every post-launch decision is a guess.

7. Ongoing optimization — prompt tuning, model updates, cost controls. Not optional work. The product either improves or quietly degrades. There is no middle ground. ⚙️

The hidden costs that hit hardest ⚠️

Four things cause most budget overruns and almost never appear in a vendor proposal:

😬 Messy data — if your records are scattered across systems, you're paying for cleanup before the AI can do anything useful

😬 Integration complexity — connecting to your existing tools often takes longer than the AI work itself

😬 Usage-based cloud fees — cheap at low volume, potentially your largest monthly expense at scale

😬 Post-launch tuning — real users behave differently than test users, always

A simple formula for early planning 🧮

Total quarterly cost = delivery milestone budget recurring usage budget optimization reserve (15–30%)

Run three scenarios — conservative 🐢, expected 🚶, aggressive 🚀. Where small assumption changes create large cost swings, that's your real risk. Fix those levers architecturally before you scale.

For founders, the bottom line is this 💬

You don't need a large budget to start. You need a clear problem, a focused first version, and a realistic view of what it costs to keep running after launch. The founders who get this right start narrow, learn fast, and expand only what works.

Full planning guide and cost breakdown 👉 https://unicornplatform.com/blog/budgeting-ai-app-development-in-2026/

#AI #StartupIndia #TechFounders #AppDevelopment #Budgeting #ArtificialIntelligence #FounderLife #SoftwareCosts #ProductDevelopment #DigitalIndia

Apple announced that Tim Cook will become executive chairman of Apple’s board of directors and John Ternus, senior vice president of Hardware Engineering, will become Apple’s next chief executive officer effective on September 1, 2026.

Cook will continue in his role as CEO through the summer as he works closely with Ternus on a smooth transition. As executive chairman, Cook will assist with certain aspects of the company, including engaging with policymakers around the world.

Arthur Levinson, who has been Apple’s non-executive chairman for the past 15 years, will become its lead independent director on September 1, 2026. Ternus will join the board of directors, also effective September 1, 2026.

Ternus joined Apple’s product design team in 2001 and became a vice president of Hardware Engineering in 2013. He joined the executive team in 2021 as senior vice president of Hardware Engineering. Throughout his tenure at Apple, Ternus has overseen hardware engineering work on a variety of groundbreaking products across every category. He was instrumental in the introduction of multiple new product lines, including iPad and AirPods, as well as many generations of products across iPhone, Mac, and Apple Watch.

Source: Apple

A user was able to access another users source code, database credentials, AI chat histories, and customer data are all readable by any free account.

They accessed another user's profile, listed their public projects, and downloaded the source code of an admin panel for Connected Women in AI, a real danish nonprofit. the project was last edited 10 days ago. the developer has 3,703 edits this year. this is not abandoned. this is active.

They extracted the database credentials from the source code and queried it. got back real names, real companies, real linkedin profiles. speakers from Accenture Denmark and Copenhagen Business School. not test data. not "John Doe". real people at real companies who have no idea their information is exposed.

Lovable patched this for new projects. they never patched it for existing ones.

A project created in April 2026 returns 403 forbidden. The same developer's older project, actively edited 10 days ago, returns 200 OK with the full source tree. same API. Same endpoint. same free account. same session. one is protected. the other is wide open.

The first hackerone report was filed March 3, 2026. Lovable marked it triaged, then they shipped ownership checks for new projects and left every existing project exposed. 48 days later nothing has changed. He also claims that every conversation you have with lovable's AI is stored and readable through the same bug.

Source: weezerOSINT

A threat actor has listed their customers' data, source code, databases, and keys up for sale.

A security incident has been identified that involved unauthorized access to certain internal Vercel systems. Customers Vercel credentials were compromised.

As per Vercel, the incident originated with a compromise of Context.ai, a third-party AI tool used by a Vercel employee. The attacker used that access to take over the employee's Vercel Google Workspace account, which enabled them to gain access to some Vercel environments and environment variables that were not marked as “sensitive.”

Source: Vercel

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