The One Technologies Blog

AI Regulations in 2026: Navigating the Evolving Landscape for US Businesses

2026-11-05T06:13:44Z

If you've tried to keep up with AI regulation in the United States this year, you've probably felt whiplash. A federal executive order. A state law delayed and then rewritten. New rules taking effect on January 1, with more on the way. Compliance teams and founders alike are asking the same question: what actually applies to us?

The short answer is that the landscape is patchwork, and it keeps moving. Whether you build in-house tools or buy them through AI services, you carry responsibility for how AI affects customers and employees. This guide summarizes where things stand and how to prepare. It is general information, not legal advice.

The federal picture: no single AI law

Congress has not passed a comprehensive AI statute. In December 2025 an executive order opened a federal push to challenge state AI laws, directing federal agencies to review state laws and propose a national framework.

An executive order sits below a statute. As of this writing, no enacted federal law overrides state AI laws, and those laws stay in force until a statute or a court says otherwise. Treat preemption as something to watch while your compliance work carries on.

Existing federal laws still apply to AI. Consumer protection rules, anti-discrimination law, and privacy obligations cover AI-driven decisions just as they cover human ones. The Federal Trade Commission has pursued companies over misleading AI claims, so overstating what your product can do is a risk on its own.

The state patchwork

States are where most binding AI rules live. A few examples show the range.

Colorado passed a broad AI Act in 2024, then rewrote it in 2026. The new version, signed in May, focuses on automated decision tools that materially influence consequential decisions and takes effect January 1, 2027. It asks developers to share documentation with the companies using their tools, and asks those companies to give notice, explain adverse outcomes, and allow human review.

Texas passed its Responsible AI Governance Act, effective January 1, 2026, with enforcement handled by the state attorney general. California has added rules on training-data disclosure, AI transparency, and automated decision-making technology, with several provisions already in effect. Illinois now treats certain AI use in employment decisions as a civil rights issue, and New York has enacted a law aimed at frontier AI developers.

Details and dates keep changing, so confirm the current text for each state where you operate.

What triggers the obligations

Most rules focus on the stakes of a decision. Obligations tend to apply when AI helps decide something consequential: hiring and promotion, credit and lending, housing, insurance, healthcare, education, or access to essential services.

A chatbot that suggests blog titles faces little regulation. A tool that screens job applicants or sets loan terms faces a great deal. Map your AI uses against that scale.

Other triggers include interacting directly with consumers, which often requires disclosure that a person is talking to an AI, and generating synthetic media, which may require labeling.

Global rules can reach you too

US companies that serve customers in other markets may fall under foreign rules. The EU AI Act, for instance, can apply to businesses outside Europe when their AI systems are used there. If you sell internationally, check which regimes touch your products and what the timelines are.

A practical compliance checklist

You don't need a large legal department to make progress. Start with these steps.

Build an inventory. List every AI tool in use, who owns it, what data it touches, and what decisions it influences.
Classify by risk. Separate low-stakes tools from those affecting people's jobs, money, health, or housing.
Review vendors. Ask suppliers about training data, testing, known limits, and what documentation they can provide.
Add transparency. Tell people when they're dealing with AI and explain adverse decisions in plain language.
Keep humans in the loop. Offer a route to human review for consequential outcomes.
Test for bias and accuracy. Document the method and results and repeat after major changes.
Keep records. Many laws expect documentation to be kept for years.
Name an owner for AI governance, even if it is a part-time role. Unowned risks rarely get managed.

Build compliance into development

Retrofitting is expensive. It is far cheaper to design for logging, explainability, consent, and human review at the start. Add AI-specific tests to your release process, covering accuracy, bias, and misuse, and keep the results on file.

Training matters too. Staff who use AI tools should know what the tools can and can't do, which data is off limits, and when to escalate.

Stay current without losing your mind

Pick a small set of trusted sources, such as your legal counsel, industry associations, and state attorney general updates. Review your AI inventory against new developments each quarter. When a rule changes, you'll know quickly which tools it touches.

Regulators increasingly expect evidence that AI systems were checked before launch. Thorough AI software testing gives you that record, and it catches problems while they are still cheap to fix. Start with your highest-risk tools and work outward.

6 Best Mobile App Development Ideas for Startup Business

2026-11-03T11:50:57Z

If you have noticed, our lives have been constantly changed and influenced by technology ever since the industrial revolution. The brilliant ideas and the implementation of technology have made our lives much better and easier.

Therefore, every industry is focusing on the expansion of business with the implementation of technology. However, with various and unique ideas, startups have made their mark in the competitive world. In such cases, mobile application development is the perfect solution to reach out to users across the globe. And the mobile app is all about what your product is offering. When your app offers what everyone is looking for, it has a high chance of success.

As per the report, the global app market is projected to generate approximately US$810.2 billion in revenue in 2026 through paid downloads, in-app purchases, and advertising. The market is expected to reach US$1.18 trillion by 2031, growing at a CAGR of 7.81%.

So, as a startup, if you are looking to have a piece of that pie, here are going to give you some of the best app ideas for a startup business to make money in 2026-27.

1. Augmented Reality (AR)

AR - Augmented Reality uses cutting-edge technology to lay some information such as sounds, images, text over the real world.

The introduction of the AR application could be explained by giving an example of the Pokémon GO game. This was the game that has made everyone aware of the concept of Augmented Reality technology.

But still, people or many startup businesses are not making rich use of AR technology. Not many organizations have explored this field fully yet. However, if you implement this technology properly with your innovative ideas, you can make it huge for your business.

The worldwide market for Augmented Reality is expected to increase by $198 billion in 2025.

If you are looking to start your business in Interior Design, Automobile, Restaurant, Healthcare, Tourism, etc, you can implement AR app ideas into it. The best option is to develop an E-commerce store with an augmented reality feature as it will help your customers to try on clothes and accessories. It can be one of the best app ideas.

There are many big companies such as Housecraft, IKEA Place, Pokemon Go, YouCam Makeup, Inkhunter, and Euclidean Lands that have implemented AR in their business and apps.

So, as an entrepreneur, try them for yourself to see the potential of modern AR apps.

2. Healthcare and Telemedicine

This COVID-19 pandemic has made everyone move online and therefore, the on-demand app ideas have become the moving spirit in every industry. And healthcare and telemedicine have left no stone unturned.

Telemedicine is a source of offering healthcare services to customers who wish to get treated and consulted by sitting at home. This allows customers to arrange appointments with a doctor and get the proper treatment.

A medical expert or doctor will examine your medical images, consult you via chatting or video call, prescribe medicines, and get the consultations done in a given time. This way, doctors can screen and monitor their patients remotely with utmost care.

This reduces the risk of exposure, both to the patients and the staff.

Telemedicine has the potential to make healthcare more affordable, personalized, and effective. This has helped many doctors to cover more patients in a much larger area.

As per the report, around 52% of people use their mobile apps for accessing health-related data.

Hence, it becomes one of the greatest app ideas for mobile app development services.

3. Chatbots

Chatbots are a better salesperson who helps customers to answer their questions accurately and quickly. It allows you to serve your customers with better customer service.

Chatbots can actually interact with your customers like humans on the basis of their queries. Developed with Artificial Intelligence technology, chatbots can actually behave like humans. Unlike humans, they never get exhausted or tired. They are capable of interacting with numerous queries as per the user’s requirements.

Let me give you an example of Starbucks.

Starbucks makes it easy for customers to order a drink or snack using a chatbot. It will notify you when your drink gets ready and how much rate it would be.

As per the research, more than 30% of eCommerce companies have implemented chatbots for their business.

So, a chatbot could be one of your great ideas to implement for your business these days. You can get in touch with an Android app development company for that.

4. Blockchain App

One of the well-known and disruptive technologies which has been silently shaping our future is Blockchain. With its high-secured and intense encryption features and decentralization of authority, many startup businesses have started adopting it.

Many people believe that blockchain is only used by the financial industry, but, well, that’s not completely right. As it’s concerned with storing data in a more secure way. It is already being used in different ways by various industries.

So, if you have an idea and plan to implement it using blockchain technology, we recommend you to get in touch with the blockchain development company. They are experts who have great hands-on experience in blockchain app development. The blockchain-driven app developed by them will be effective which will help your business grow. You can get a personalized blockchain app developed for your business needs.

5. Internet of Things (IoT)

The Internet of Things is taking its shape in every industry with its features and functionalities. By 2025, around 41 billion IoT devices are estimated to connect with the network.

IoT may be applied to develop smart security solutions, healthcare monitoring systems, automatic smart parking, smart roads, multiroom music players, wireless sensor networks, water-quality monitoring, etc.

IoT has invited endless opportunities for every business. So, hire an IoT development company and think about how it can benefit your industry.

6. On-demand Apps

The on-demand applications have shaken every industry by its storm with their mobile app development services. Every industry such as online food delivery, shopping, taxi, repair, cleaning and many more are implementing on-demand services for their better growth.

These services have turned out to be a lifesaver during the pandemic when we were forced to stay at home and isolate from the normal routine life.

Even our entertainment options have become on-demand. Hence, this is the right time to invest in on-demand applications. Both iOS and Android app ideas appear when you have a specific urgency in some service.

To run an on-demand development business, you need to have a tech infrastructure in place. To implement your idea, you can approach a website development company that would understand your on-demand business ideas and get an app built for you.

Choose the best idea and try to expand it.

Conclusion

Mobile is not a future but a present in the world of technology. It’s taking businesses to the next level from here and now.

Mobile apps are useful solutions that technology has gifted us. Many of these great app ideas can make people’s life easier. They can also become the backbone for a successful business. Moreover, there are around 3.8 billion people using smartphones, which makes this a flourishing industry.

So, as an entrepreneur, if you have an idea and are planning to invest in a mobile app, the above-mentioned app ideas will help you transform it into an excellent product by choosing the right mobile app development company.

So, got any idea for the next great app? Let us know your thoughts and share them with our team. We will help assist you and turn any of these intangible ideas into tangible revenue-generating machines.

So grab it before someone else steals the idea you’re dreaming of!

React Performance Optimization: 10 Issues That Slow Down Web Applications

2026-11-02T12:01:18Z

Most React performance problems aren't exotic. They trace back to the same handful of patterns, over and over, across completely unrelated codebases.

Teams building with React development services run into these same ten issues constantly, often without realizing which one is behind a sluggish interface. Here's what to look for.

1. Inline objects and functions breaking memoization

Passing a new object or arrow function as a prop on every render defeats React.memo entirely, since the component sees a new reference every time even when the actual values haven't changed. The fix is a stable reference, defined once outside the render path instead of recreating inside it.

2. Context providers holding too much state

Every consumer of a context re-renders when any value inside that context changes, not just the part a given component reads. A single context bundling user data, theme, and notification count means a notification badge update re-renders a component that only cares about the theme.

3. Unvirtualized long lists

Rendering a thousand rows when only twenty are visible on screen wastes render time on elements nobody can see. List virtualization renders only what's in or near the viewport, and the difference shows immediately on scroll performance for anything beyond a screenful of items.

4. One giant bundle instead of split ones

Shipping every route, every component, and every library in a single JavaScript file upfront means a user downloads code for pages they haven't visited yet before they can interact with the one they're on. Lazy loading routes and heavy components typically cuts initial bundle size by 40 to 70 percent on a typical app.

A bundle analyzer makes this visible fast. Most teams are surprised by what's actually eating the biggest share of the bundle, usually a dependency pulled in for one small feature that ended up importing far more than the project ever needed.

5. Expensive calculations repeated on every render

A heavy computation that runs fresh every time a component re-renders, instead of only when its actual inputs change, burns CPU time for no reason. Memoizing the result ties the recalculation to the inputs that matter, not to every unrelated state change nearby.

6. Sequential awaits that should run in parallel

Fetching data one request after another when the requests don't depend on each other creates a waterfall, each one waiting on the last to finish before it starts. Running independent requests together instead of one after the other often cuts that loading time dramatically.

7. React.memo used without stable props

Wrapping a component in React.memo does nothing if the props it receives are still new objects or functions every render. The memoization and the stable references have to work together. One without the other is wasted effort that looks like an optimization but isn't.

This is one of the most common false fixes in a codebase. A team wraps a slow component in memo, sees no improvement, and concludes memoization doesn't work. When the real problem is an inline callback, a few lines up still breaking the comparison every time.

8. Unoptimized images dragging down load time

Full-resolution images served at a fraction of their display size are one of the most common and most overlooked bottlenecks on a slow page. Resizing, compressing, and lazy-loading offscreen images usually delivers a bigger visible speed improvement than any amount of component-level tuning.

9. Large context updates triggering cascading re-renders

A parent component's state change re-renders every child in that subtree by default, whether a given child's props changed. State localized to the component that needs it, instead of lifted unnecessarily high in the tree, keeps that cascade from spreading further than it has to.

10. Optimizing before profiling

Guessing at what's slow and memoizing everything defensively usually makes code harder to maintain without meaningfully improving performance. React DevTools Profiler shows exactly which components are re-rendering and why, before a single line gets changed. Optimizing without that data first is how a team spends a week optimizing the wrong component.

What this looks like fixed

A product list re-renders every row when one filter changes, because the filter buttons live in the same unmemoized parent as the list itself. Splitting that state, memoizing the row components, and virtualizing the list together turns a visibly janky scroll into one that feels instant, without touching a single business rule in the app.

Most of these fixes take hours, not weeks, once the actual bottleneck is identified correctly. If your React application has gotten sluggish and you're not sure which of these ten is the culprit, The One Technologies can profile it and find out before guessing at a fix.

How to Build a Scalable Test Automation Strategy

2026-10-29T08:31:28Z

Most teams start automating tests the same way. Someone records a few browser flows, the suite goes green, and everyone feels good. Six months later the suite takes two hours, fails on Tuesdays for no clear reason, and no one wants to touch it.

That fate is avoidable. Good automated software testing comes from a plan made before the first script, and many teams get that plan from outside test automation services when in-house experience runs thin. Here's how to build a strategy that keeps working as your product grows.

Start with goals, not tools

Ask what the suite is for. Faster releases? Fewer production bugs? Confidence during refactors? Each answer points to a different mix of tests. A team shipping weekly needs a fast smoke suite on every commit. A team with a fragile billing module needs deep coverage in that one area.

Write the goals down and attach a measure to each. Release frequency, escaped defects, and time to feedback are good starters. Tool debates come later.

Shape the suite like a pyramid

The classic test pyramid still holds. Many unit tests at the base, a medium layer of API and integration tests, and a thin top of end-to-end UI tests.

UI tests are slow and brittle, so they should cover only the journeys that make or lose money: sign-up, checkout, login, and the core workflow. Push everything else down. An API test that checks a pricing rule runs in milliseconds and breaks far less often than a browser script doing the same thing through three screens.

Teams that flip the pyramid, with hundreds of UI tests and a handful of unit tests, are the ones whose pipelines crawl.

Decide what to automate, and what to leave alone

Automate tests that run often, matter a lot, and behave the same way each time. Regression checks, data-driven validations, and cross-browser sweeps fit well.

Leave exploratory testing, usability judgment, and one-off checks to people. A test you run twice a year costs more to maintain than to run by hand. Be honest about this list, because every automated test is a small piece of software that someone must look after.

Design for maintenance from day one

Suites rarely die from lack of tests. They die from upkeep. A few habits keep that cost down.

Use a page object or similar pattern, so a changed button means one edit instead of forty. Pick stable locators, such as dedicated test IDs, rather than brittle XPath chains. Keep tests independent so one failure doesn't cascade. Make test data repeatable by creating it in setup and cleaning it up afterward, not borrowing records from a shared environment.

Flaky tests deserve special attention. A test that fails randomly teaches the team to ignore red builds. Quarantine flaky tests quickly, fix the cause, and bring them back only when they're steady.

Wire it into CI/CD

A suite no one runs protects nothing. Hook it into your pipeline so every pull request triggers the fast checks, and the heavier suites run on merge or on a nightly schedule.

Run tests in parallel across containers. Cache dependencies. Split the suite by tag so developers get an answer in minutes. Slow feedback pushes people to skip the checks, and then the investment is wasted.

Environments matter here too. Tests need stable, production-like setups with predictable data. Many mystery failures trace back to a shared staging server that three teams are changing at once.

Measure and prune

Track a few numbers over time: pass rate, flake rate, run time, and how many production bugs the suite missed. Review them every sprint or two and share the trend with the whole team so quality stays everyone's job.

Prune as often as you add. Delete tests that duplicate each other, cover retired features, or never catch anything. A smaller suite that people trust beats a huge one they argue with.

Scalable test automation comes down to this loop: add tests where risk is high, remove tests that stopped paying off, and watch the run time. Set a time budget for the pull request suite, say ten minutes, and treat a breach as a bug. When the budget is blown, the team splits, parallelizes, or deletes until it fits again.

Plan the team and the skills

QA automation sits between testing and engineering. The people doing it need to read code, design frameworks, and still think like a tester. That mix is hard to hire quickly.

Some teams grow it in house by pairing testers with developers. Others bring in a partner to set up the framework and train the team, then take over. Both work. What fails is giving the job to one person as a side task.

If your suite is already creaking, or you're about to start one, it may be faster to hire QA developers who have built frameworks before. They'll set up the structure, the pipeline hooks, and the reporting once, so your team can add tests without fighting the foundation.

Why Enterprises Are Investing in SQL ETL Automation for Scalable Growth

2026-10-26T11:24:23Z

Enterprise data is seldom static. Customer data can be in CRM platforms, financial records in databases, operational data in business applications, and reporting data in a cloud warehouse. As companies grow, it becomes harder to move and prepare this information manually.

Here, leveraging modern SQL ETL services and automation is becoming an important part of modern data architecture. The combination of ETL processing with SQL-based data transformation enables enterprises to automate data movement, reduce repetitive work, improve data quality, and build pipelines that can handle growing workloads. With modern ELT, the raw data can be loaded into cloud warehouses, and then SQL transformations are done inside the warehouse.

What Is SQL ETL Automation?

ETL means extract, transform, and load. The process consists of taking data from source systems, transforming it for use, and loading it into a target system such as a data warehouse or database. SQL is often used in this workflow to query, clean, join, filter, aggregate, and transform relational data.

SQL ETL automation takes it a step further and reduces the reliance on manual execution. Automated pipelines can be scheduled on defined schedules or business events instead of teams running queries or moving files over and over again. They may also include monitoring, error handling, retries, and validation.

For enterprises dealing with large data volumes, this approach creates a more consistent way to manage data across systems.

Growing Data Volumes Are Driving Automation

Usually, the data infrastructure becomes more complex as an organization grows. New applications are added, customer records grow, transactions become more frequent, and reporting requirements increase.

In this environment, manual data preparation does not scale well. Employees could spend hours exporting files, cleaning up records, running SQL queries, and checking whether information has reached the right place.

Implementing SQL ETL services for automation reduces this manual workload. Once you set up a pipeline, data operations can run automatically on a recurring basis. Then teams can spend more time working with the data instead of preparing it over and over again.

This is especially useful for enterprises that need regular reporting, operational dashboards, financial analysis, or customer data integration.

Better Data Quality and Consistency

Data from different systems often follow different formats and rules. Customer names may be stored differently across applications. Dates may use different formats. Duplicate records can also appear when information is collected from multiple sources.

SQL has the tools to make this information standard. Queries can eliminate duplicates, convert data types, apply business rules, join tables, and compute useful metrics. The ETL automation can then apply these transformations in a consistent manner every time there is new data coming into the pipeline.

Automated validation can also detect missing records, unexpected values, or changes in the source structure. This gives data teams a better way to maintain the reliability of their datasets as the organization grows.

SQL and Modern Cloud Data Warehouses

Cloud data warehouses have transformed how companies deal with ETL. Traditional ETL transforms data before loading it into the target system. The first step in ELT is to load raw data into the warehouse and then transform the data in the warehouse using SQL.

This model takes SQL to modern data engineering. Platforms like Snowflake, BigQuery, and Redshift can provide you the compute power for large-scale transformations.

This may facilitate enterprises to adapt data pipelines as reporting requirements evolve. “New transformation logic can be added without re-designing the whole process.”

Reduced Operational Effort

One of the main reasons enterprises invest in SQL ETL automation is the reduction in repetitive operational work.

A production data pipeline needs more than just SQL queries. It may include scheduling, dependency management, failure handling, monitoring, and alerts. These functions are not available with SQL alone, which is why enterprises generally combine SQL transformations with ETL platforms or workflow orchestration tools to achieve end-to-end SQL ETL automation.

Automation allows these processes to be performed with less human intervention. If a scheduled pipeline fails, monitoring systems can then alert the appropriate team. Some platforms can even automatically rerun the failed jobs.

This gives data teams more control over production workloads while reducing the chances of simple human errors.

Supporting Enterprise Growth

Scalability is a key component of any enterprise data strategy. If your organization processes millions of records every day, a pipeline that can process a few thousand records may not be the best pick.

SQL ETL automation allows organizations to create scalable, repeatable data workflows that can scale with the needs of their data. Another choice is incremental processing where only new or changed records are loaded, and the whole data set is not processed each time. It can make the pipeline perform better and reduce unnecessary processing.

The outcome is a data environment that can deal with higher workloads, without needing more manual work.

Building a Stronger Data Foundation

Reliable information is the engine of business growth. Data stored in separate system “silos” makes reporting slow and inconsistent because it requires manual preparation.

SQL ETL automation provides a systematic way to collect and prepare data for analytics, reporting, and business applications. With the right testing, monitoring, governance, and documentation, organizations can build pipelines that are easier to maintain as their technology environment changes.

For companies looking to scale their data infrastructure, automated SQL ETL workflows can be a practical way to build a more scalable data environment.

The Conclusion

SQL ETL process automation helps enterprises cope with growing data volumes, reduce repetitive data tasks, and improve consistency. With SQL and automated data pipelines, organizations can transform data to build a stronger foundation for analytics and long-term success.

If your enterprise needs a reliable way to integrate data and automate SQL ETL, The One Technologies can help you design and implement solutions that work with your existing systems. Contact our team to discuss your data needs and formulate an automation plan to help fuel your next phase of growth.

How to Hire Bubble Developers for AI-Powered Web Apps

2026-10-22T06:43:33Z

Bubble's own AI builder can now generate a working app, database, authentication, and workflows included, in about 5 to 7 minutes from a text prompt. That's genuinely impressive. It's also not the same thing as a production-ready application.

That gap between a fast demo and something real users can rely on is exactly why demand to hire Bubble developers has grown alongside the AI tooling, not shrunk because of it. The AI got better at scaffolding. What happens after the scaffolding still takes someone who knows the platform under real load.

What 'AI-powered' actually means on Bubble now

The AI app generator handles the first draft: database structure, basic workflows, a working interface. Bubble added a native mobile app builder in August 2025, so that first draft can target iOS and Android alongside the web version without a separate build.

Layered on top of that, the API connector lets a Bubble app call out to OpenAI, Anthropic, or other model providers directly, so the application genuinely embeds AI features, a support assistant, a content generator, a recommendation engine, into the product itself, not just a faster build process.

Why a fast AI-generated MVP still needs a developer

Generating an app is the easy ten percent. Data privacy rules, workflow logic that holds up under concurrent users, database relationships that don't fall apart as the record count grows, these are the parts an AI generator sketches but doesn't fully solve.

A five-minute MVP is a real starting point. It's rarely a finished product, and treating it like one is where a lot of first-time Bubble builds run into trouble a few months after launch.

The AI-specific risks most no-code guides skip over

Wiring an AI model into a Bubble app is straightforward for a proof of concept. Shipping it to real users with sensitive data involved is a different problem entirely, and it's architecture, not plugins, that closes the gap.

Access control is the first issue. Hiding a field in the interface doesn't protect it if the underlying API stays open, which is one of the most common security gaps in web applications generally. The second is prompt injection: any content a model reads, including something a user uploads, can carry instructions the model wasn't supposed to follow. The third is reliability. Independent testing from May 2026 found that even well-grounded AI summaries still introduce information absent from the source roughly 2 to 6 percent of the time. Building AI features with someone who's accounted for all three matters more than avoiding AI features altogether.

What a Bubble AI developer actually adds

Experienced Bubble app developers bring the parts a generator can't: database architecture that scales past a few hundred users, workflow logic for edge cases nobody thought to prompt for, and AI integrations built with access control and error handling from the start instead of bolted on after something breaks.

That's the difference between a working demo and a product a business can actually run on. The visual editor makes the platform approachable. It doesn't make production engineering optional.

Where Bubble genuinely fits, and where it doesn't

Bubble is a strong fit for an MVP, an internal tool, or a product validating demand with a modest user base. It's worth knowing upfront that Bubble doesn't export source code, so an application that outgrows the platform faces a real migration cost later, not a small one.

That tradeoff is worth making with eyes open rather than discovering it two years and thousands of users in. A developer who's shipped production Bubble apps before can tell you honestly whether a given project fits that tradeoff or not, before the build starts rather than after.

What hiring the right fit looks like

A team wants an internal tool with an AI-assisted intake form built in weeks, not months. A Bubble developer scopes the data structure first, wires the AI model through a properly access-controlled endpoint, and builds the workflow logic to handle a rejected upload or a malformed response instead of assuming every input arrives clean.

The AI wrote the first draft. The engineering discipline is what makes it hold up in production. If you're scoping a build that needs both speed and that discipline, Bubble AI developers at The One Technologies can take it from prompt to production.

How to Get Google App Client ID and Client Secret

2026-10-21T06:40:28Z

To connect any application to Google services whether you are building a Google Sign-In feature, integrating Google Analytics, or accessing Google Drive or the YouTube API you need two credentials from the Google Cloud Console: a Client ID and a Client Secret.

These credentials are the foundation of Google OAuth 2.0 authentication. They allow your application to securely identify itself to Google and request permission on behalf of users without ever exposing user passwords.

This guide walks you through every step of the process: creating a project, enabling the right APIs, configuring the OAuth consent screen, generating your credentials, and handling the common errors that trip most developers up along the way.

If you need expert help integrating Google APIs into your web application, you can hire an ASP.NET developer or hire PHP developer from us at an affordable rate.

What Are Google Client ID and Client Secret?

Before diving into the steps, here is a quick overview of what these two credentials actually are and why both are needed:

Credential	What It Is	How It Is Used
Client ID	A public identifier for your application	Tells Google which app is making the request. Safe to use in client-side code.
Client Secret	A private key that verifies your app's identity	Confirms to Google that the request is coming from your app. Must be kept server-side and never exposed publicly.

Together, they power the OAuth 2.0 authorization flow the industry-standard protocol Google uses to grant applications access to user data securely. Without both credentials correctly configured, your application cannot authenticate with any Google API.

Before You Begin

Make sure you have the following ready before starting:

A Google account with access to the Google Cloud Console
The base URL of your website or application (e.g., https://theonetechnologies.com)
The redirect URI the URL in your application where Google should send users after they log in

How to Get Google App Client ID and Client Secret (Step-by-Step)

Follow the steps below to generate your Google App Client ID and Client Secret from the Google Cloud Console.

Step 1: Log In to Google Developer Console

Step 2: Accept the Terms of Service Agreement

If you are new to the Google Developer Console, you will need to accept the terms of service agreement before proceeding.

Step 3: Select a Project

After accepting the agreement, or if you are already registered, you will see the Google Cloud Console dashboard. Click "Select a project" on the top header bar.

Step 4: Create a New Project

Click the "NEW PROJECT" button in the project selection popup to create a new project.

Step 5: Name Your Project and Create It

Enter your Project name and click the "Create" button. You can also edit the Project ID if needed.

Step 6: Enable the Required Google APIs

This step is essential and often missed. Before generating credentials, you need to enable the specific Google APIs your application will use.

From the left side menu, navigate to APIs & Services > Library. Use the search box to find and enable the APIs relevant to your use case, then click Enable for each one.

Here are the most commonly required APIs:

API	When to Enable It
Google Analytics API	Google Analytics data access
Google Analytics Reporting API	Historical report data
Google People API	Google Sign-In and user profile access
Google Drive API	Google Drive file integration
YouTube Data API	YouTube content and channel integration
Google Search Console API	Search query data and impressions

Why this matters: Without enabling the relevant API, your credentials will not have the permissions needed to make API calls even if the Client ID and Secret are correctly set up.

Step 7: Open the OAuth Consent Screen

Navigate to APIs & Services > OAuth consent screen from the left side menu.

Step 8: Choose User Type and Create

On the OAuth consent screen, select a User Type based on your requirement:

Internal Only available for Google Workspace accounts. Restricts login access to users within your organization.
External Available to any Google account. Use this for public-facing websites and applications.

Click the Create button to proceed.

Step 9: Fill In Your App Information

Complete the required fields on the OAuth consent screen:

Application name The name users will see when asked to grant your app permissions
Support email A contact email for users who have questions about the login flow
Authorized domain Your website's root domain (e.g., theonetechnologies.com)
Developer Contact Information Email address(es) for Google to contact you about your project

Then click Save and Continue.

Important: If you leave the App domain fields (homepage URL, privacy policy link, terms of service link) empty, users will see a "This app isn't verified" warning when logging in. See the Troubleshooting section below for how to resolve this.

Step 10: Complete the OAuth Consent Screen Setup

After completing all three stages of the OAuth consent screen App Information, Scopes, and Test Users click Back to Dashboard on the Summary step.

You do not need to change anything on the Scopes or Test Users pages. Simply click Save and Continue on each to move forward.

Step 11: Publish Your App

On the OAuth consent screen dashboard, click the "Publish App" button. When the confirmation popup appears, click Confirm.

Why this step matters: Without publishing, your app stays in Testing mode. In Testing mode, only accounts you have manually added as test users can authenticate. Publishing makes your app available to all Google account users which is required for a live website or application.

Step 12: Go to Credentials and Create OAuth Client ID

From the left side menu, click "Credentials" to open the Credentials screen. Click "Create credentials" and select OAuth client ID from the dropdown menu.

Step 13: Configure the OAuth Client ID

On the Create OAuth Client ID screen, fill in the following:

Application Type Select Web Application from the dropdown
Name Enter a name for your OAuth 2.0 client. This is for internal reference only and will not be shown to users during login
Authorized JavaScript origins Enter your website's base URL (e.g., http://theonetechnologies.com)
Authorized redirect URIs Enter the full URL where Google should redirect users after login. If you are using the Microsoft.Owin.Security.Google library, the endpoint is /signin-google. For example: http://theonetechnologies.com/signin-google

Click the Create button.

Important: The redirect URI you enter here must exactly match the one used in your application code including http vs https, trailing slashes, and the full path. Even a minor difference will cause a redirect_uri_mismatch error.

Step 14: Copy Your Client ID and Client Secret

A popup will appear displaying your Client ID and Client Secret. Copy both values immediately and store them in a safe, secure location.

You have now successfully generated your Google App Client ID and Client Secret.

Troubleshooting Common Errors

Even with the correct steps, a few errors are frequently encountered when setting up Google OAuth 2.0 credentials. Here is how to identify and fix each one.

Error 400: redirect_uri_mismatch

This is the most common error developers run into after setting up OAuth credentials.

What causes it: The redirect URI your application sends to Google does not exactly match any of the Authorized Redirect URIs registered in the Google Cloud Console.

How to fix it:

Go to your OAuth 2.0 Client ID in the Google Cloud Console
Under Authorized redirect URIs, verify that the URI matches your application exactly including the protocol (http vs https), any trailing slashes, and the full path
Save the changes and wait a few minutes for Google's configuration to update
Copy the URI directly from your application logs rather than typing it manually this eliminates the risk of typos

"This App Isn't Verified" Warning

Users may see this warning when logging in through your application. It appears when the App domain fields on the OAuth consent screen are left incomplete.

To continue using the app without verifying:

Click Advanced on the warning screen
Click Go to [your domain] (unsafe)
Click Allow when prompted

This approach is acceptable during development. For a live production application, proceed with verification below.

To remove the warning permanently:

Go to APIs & Services > OAuth consent screen
Click Edit App
Under App domain, fill in:
Application home page URL
Privacy policy link
Terms of service link
Click Save and Continue through all steps and return to Dashboard
Submit for Google verification

Note: After submitting, Google's Trust and Quality team will email you to verify property ownership via Google Search Console. Verification typically takes a few days.

invalid_client Error

What causes it: The Client ID or Client Secret in your application code does not match what is stored in the Google Cloud Console.

How to fix it: Return to your OAuth 2.0 Client ID in the console and re-copy the credentials carefully, checking for missing characters or extra spaces.

403 Forbidden After Authentication

What causes it: Your application is trying to access a Google API that has not been enabled for your project.

How to fix it: Go to APIs & Services > Library and enable the specific API your application is requesting. Refer to the table in Step 6 for the most common APIs.

Security Best Practices for Your Client ID and Client Secret

Once you have your credentials, handling them correctly is just as important as generating them. Here are the key security practices to follow:

Never hardcode credentials in your source code. If your repository is ever made public even accidentally exposed credentials can be misused immediately.
Use environment variables to store your Client Secret in your application. This keeps sensitive values out of your codebase entirely.
Do not store secrets in configuration files that are committed to version control. Use a dedicated secrets manager such as Google Cloud Secret Manager for production applications.
Keep your Client Secret server-side at all times. Unlike the Client ID, which can appear in client-side code, the Client Secret must never be sent to a browser or included in front-end files.
Rotate your Client Secret periodically. Google Cloud Console supports secret rotation you can generate a new secret, update your application to use it while the old one is still active, and then revoke the old secret with zero downtime.
Create separate credentials for each environment. Use distinct Client IDs for development, staging, and production. This limits the impact if one set of credentials is ever compromised.
Delete OAuth clients you no longer use. Unused credentials are an unnecessary security risk. Remove them from the Google Cloud Console when they are no longer needed.

Frequently Asked Questions

What is a Google App Client ID and Client Secret?

A Google App Client ID and Client Secret are OAuth 2.0 credentials that identify and verify your application when it connects to Google APIs. The Client ID is your app's public identifier, while the Client Secret is a private key that must be kept secure at all times.

Is it okay to use the same Client ID and Secret for multiple applications?

No. Each application should have its own unique Client ID and Client Secret. Sharing credentials across apps creates security risks and makes it impossible to revoke access for one app without affecting the others.

Where should I store my Client ID and Client Secret?

Store your Client Secret in environment variables or a dedicated secrets manager never in source code or configuration files that are committed to a repository. The Client ID can be used in client-side code, but the Client Secret must remain server-side only.

How can I get a Google App Client ID and Client Secret?

Log in to the Google Cloud Console, create a new project, enable the required APIs, set up the OAuth consent screen, publish your app, and create an OAuth Client ID credential. Your Client ID and Client Secret will be displayed in a popup after the credential is created.

Why do I need a Google App Client ID and Client Secret?

These credentials are required to integrate your application with Google services such as Google Sign-In, Gmail, Google Drive, Google Analytics, or the YouTube API. Without them, your app cannot authenticate with Google or request access to user data.

Can I change or regenerate my Google App Client ID or Client Secret later?

Your Client Secret can be regenerated at any time from the Google Cloud Console this is useful if a secret has been exposed or for routine security maintenance. The Client ID, however, remains fixed for the lifetime of the OAuth client. If you need a new Client ID, create a new OAuth client entirely.

Conclusion

Getting your Google App Client ID and Client Secret is a straightforward process once you know every step from enabling the right APIs and publishing your app to configuring redirect URIs and securing your credentials. Following the steps in this guide, you can complete the full setup in under 15 minutes and have your application connected to any Google service.

If you run into issues or would prefer to have the entire Google API integration handled professionally, you can always reach out to a trusted software outsourcing company in India for a quick and cost-effective solution.

Continuous Testing in CI/CD: How Teams Catch Bugs Faster

2026-10-19T09:47:30Z

A team shipping multiple times a day is still running a testing strategy built for one release a sprint. That mismatch is where bugs slip through, not because the tests are bad, but because they run too late to matter.

Continuous testing closes that gap by turning quality checks into something that runs on every commit instead of a phase that happens right before release. Teams working with software testing and QA services built around this model catch problems while the code is still fresh in a developer's head, not weeks later during a pre-release scramble.

What continuous testing actually replaces

The old model treated testing as a gate at the end: write the code, hand it to QA, wait for a pass or fail. That worked fine when releases happened monthly. It falls apart completely once a team is deploying several times a day, because nobody has weeks to spare for a manual verification pass anymore.

Continuous testing turns quality into an always-on signal instead. Every commit, every pull request, every deploy gets validated automatically, and feedback comes back in minutes rather than at the end of a sprint.

The testing pyramid, mapped to real pipeline stages

Unit tests run first and fastest, ideally under five minutes, catching broken functions before code goes anywhere near a shared branch. Static analysis runs alongside them, flagging code smells and obvious security issues before a human even opens the pull request.

Integration and contract tests come next, checking that services, APIs, and database calls behave correctly once combined rather than in isolation. Full end-to-end suites, security scans, and load tests run against a staging environment before anything reaches production. Once it's live, testing narrows to non-destructive smoke tests and synthetic monitoring, just enough to confirm the application is actually working without simulating real load against real users.

Quality gates: enforcement, not a judgment call

A quality gate is a pass or fail check on a specific metric, coverage above a set threshold, zero high-severity vulnerabilities, every test passing, no new type errors. If a build fails the gate, the pipeline stops. No human has to review it, and nobody gets to quietly wave it through because a deadline's close.

That automatic enforcement is what separates a continuous testing pipeline from a delivery mechanism that just happens to run some tests along the way. One catches bugs at the cheapest possible stage. The other ships whatever gets pushed to it.

A concrete example of a gate doing its job

A team sets a non-negotiable rule: no response time over 200 milliseconds, no exceptions. A build ships with a database query that quietly regresses that number to 400 milliseconds under load. The performance gate catches it before deployment, and the pipeline stops automatically, before a single real user notices anything slow.

Nobody had to remember to check response times manually. Nobody had to escalate it to a senior engineer for a judgment call. The rule caught the problem because the rule was built into the pipeline instead of living in someone's head.

The finding that surprises most teams

Speed and stability look like a tradeoff on paper. Ship faster, break more things, that's the intuition most engineering leaders start with. Google's DORA research consistently finds the opposite among elite-performing teams: they deploy far more frequently than low performers while also maintaining lower change-failure rates.

That combination only happens when automated testing validates every change continuously, instead of quality getting checked in occasional big batches. Once testing runs on every commit instead of once a sprint, going faster and staying stable stop competing with each other.

Where teams get this wrong

Running a full four-hour test suite on every single commit defeats the entire purpose. Fast feedback needs a staged strategy: smoke tests first, a broader regression pass next, the full suite reserved for pre-deploy. Skip that staging and developers just stop waiting for results, which quietly kills the whole point of testing early.

Flaky tests do similar damage. A test that fails intermittently for reasons unrelated to the actual code teaches a team to ignore failures generally, and once that habit sets in, a genuine bug starts looking exactly like background noise.

What this looks like in a real pipeline

A developer pushes a commit. Unit tests and static analysis run in under three minutes and catch a broken function before the pull request even opens for review. Once merged, integration tests confirm the new code plays correctly with the existing API layer. A staging deploy runs the full suite overnight, and only a build that clears every gate reaches production the next morning.

Nobody manually verified any of that. The pipeline did, automatically, at every stage. If your team is still shipping fast but testing slow, The One Technologies can help you build a pipeline where the two stop working against each other.

Bubble MVP Development: How Startups Can Go from Idea to Launch Faster

2026-10-15T08:38:30Z

You have a product idea. You have probably thought about features, target users, and how the product could make their lives easier.

But there is one question that comes up quickly:

How do I actually build it without spending months and a huge budget before knowing if people will use it?

This is where an MVP can help.

MVP gives you a working version of your product with only features needed to test main idea. Instead of trying to build everything at once, you build what matters most, put it in front of real users, and learn from what happens.

For startups, Bubble MVP development can be a practical way to do this. Bubble lets you build web applications visually, including interface, database, workflows, and integrations, without traditional coding.

Start With the Problem, Not Every Feature

When you have new idea, it is easy to make a long list of things your product should do. You may want user accounts, dashboards, notifications, payments, reports, chat, analytics, and much more.

But do you need all of that for first launch? Probably not. Start by asking just one simple question:

What is the main problem my product needs to solve?

For example, imagine you want to build a platform that connects homeowners with local repair professionals. First version may only need user registration, professional profiles, search, booking, and notifications.

You do not need to build an advanced recommendation engine or ten different dashboard views before finding out whether people actually want the service.

That is the thinking behind MVP development.

Why Bubble Makes MVP Development Practical

Building a product traditionally can involve separate work for frontend, backend, database, hosting, and integrations. For a startup working with limited time and resources, managing all of this from the beginning can slow things down.

Bubble brings many of these parts into one visual development environment. You can create user interface, set up your database, build workflows, and connect external services through APIs.

That makes it easier to focus on product itself.

You can build feature, test it, notice something is not working as expected, and change it. You do not have to treat every change as major rebuild.

This is one reason Bubble is often considered for early-stage products where the idea is still being tested.

What Can You Build as a Bubble MVP?

An MVP does not have to look like a rough demo. Depending on idea, you can build working application with features such as:

User registration and login
Profiles and dashboards
Forms and search
Database-driven content
Payments
Notifications
User roles and permissions
Third-party API integrations
Automated workflows

Bubble's platform supports databases, workflows, APIs, payments, and other application functionality, so an MVP can be a real product that users interact with rather than just a clickable prototype.

Main part is deciding what belongs in first version. If feature does not help users solve main problem or help you learn something important about the product, it may be better left for later.

The First Launch Is About Learning

Launching your MVP does not mean your product is finished. In fact, first launch can tell you things that planning alone cannot.

You may discover that users love one feature but ignore another. They may find a signup process confusing. They may ask for a feature you never considered. Or you may realize that, problem you were trying to solve needs to be approached differently.

That information is valuable. Instead of guessing what to build next, you can use actual user feedback to guide the next version.

Bubble supports this kind of ongoing iteration. You can continue changing design, data, workflows, and integrations as your product develops.

When Should You Hire Bubble Developers?

Bubble makes application development more accessible, but that does not mean every MVP should be built without technical expertise.

Once your product involves several user roles, complex workflows, payment systems, external APIs, data privacy rules, or other custom requirements, the way application is structured starts to matter. This is when it can make sense to hire Bubble developers.

A developer can help you turn your idea into practical product plan, decide how database should be structured, build workflows, connect APIs, and handle technical details that may become difficult as product grows.

More importantly, experienced developers can help you avoid building MVP in a way that creates unnecessary problems later.

The goal is not to make first version overly complicated. It is to give you a solid starting point that can be improved as you learn.

A Good MVP Should Leave Room to Grow

Speed is important for startup, but building quickly does not mean rushing through every decision. Before starting no-code MVP development, it helps to be clear about:

Who will use the product
What problem it solves
What the main user journey looks like
Which features are essential for the first version
What you want to learn after launch
Which features can wait

This keeps the project focused. Without this clarity, MVP can easily become full product before it even launches. More features get added, timeline keeps moving, and original reason for building an MVP gets lost.

From Idea to a Product People Can Try

The biggest benefit of a Bubble MVP is not simply getting a product online faster. It is getting something real in front of real people sooner.

You can test your idea, hear what users have to say, find problems early, and make better decisions about where to spend your next round of development time and money.

That is why right MVP development services should focus on more than just building features. They should help turn your idea into a focused first version that you can actually test and improve.

If you are ready to move from an idea to a working product, The One Technologies can help with Bubble app development, from planning and MVP development to integrations and further product improvements.

The first version does not have to do everything. It just needs to do the right thing well enough for your users to tell you what should come next.

AI-Ready Custom Software: What to Build for the Future

2026-10-12T10:10:22Z

97 percent of enterprises are running some kind of AI initiative. Depending on whose 2026 survey, somewhere between 5 and 7 percent of enterprises have data that's ready for AI.

That gap is where many AI projects run into problems, and it's rarely the model's fault. Gartner projects that 60 percent of AI initiatives will be abandoned through 2026 because of data readiness issues rather than model quality. The software most businesses are running today wasn't built with any of this in mind, which means the fix isn't a better model. The Solution is custom software development that treats AI access as a design requirement from day one, rather than a feature bolted on after launch.

What 'AI-ready' Actually means

It's not a feature you add. It's a property of how the software is built underneath. Rigid architecture, siloed data, and no clean way for systems outside the software to access its data cannot be fixed simply by adding a language model later.

Three things have to be true at once: the architecture must support real-time, flexible data movement; the data itself is clean and governed rather than scattered across inconsistent formats. And the operations around it must support ongoing monitoring, rather than a one-time launch and walk away approach.

The data layer comes first, not last

AI-ready data should be discoverable, accessible in real time, governed by a consistent permission model, and high quality enough that an AI system using it doesn't have to guess. Most enterprise data estates fail at least one of those requirements, usually more than one.

This is the layer businesses skip because it's the least visible one. Nobody demos a clean data pipeline. But a model fed inconsistent, siloed data can produce inconsistent and unreliable answers, no matter how capable it is.

The interface layer is where governance actually lives

Don't give an AI system direct database access. Expose controlled interfaces instead, such as retrieval endpoints, tool APIs, semantic search services, and specific workflow commands. That interface layer is what turns an AI system into something a business can actually govern, set limits on, test, and revoke access from, without rewriting every system underneath it.

Logging matters as much as the interface itself. Every request, response, downstream action should be recorded. When an AI-driven decision turns out to be wrong, someone should be able to trace what data the system used and what actions it took, rather than reconstructing the story afterward from scattered logs across multiple systems.

Skip this and the first serious incident becomes unanswerable. A customer disputes a decision an AI system made, and nobody can say with confidence what data informed it, what permission boundary it operated under, or whether a human ever had the chance to review it before it went out. That's not simply a hypothetical risk. It's the exact gap regulators and auditors are starting to ask about directly.

Building for the agent you haven't built yet

AI-ready software doesn't need to be rebuilt every time a business adds a new AI capability. A modular, API-first architecture allows a new agent or workflow to use the same interface layer as existing AI capabilities, rather than requiring custom integration of work from scratch.

That's the actual payoff of building this way now. Not that it makes today's chatbot marginally better, but that next year's use case, the one nobody's scoped yet, has somewhere to plug in without tearing up the system apart to make room.

What this looks like in a real build

Imagine a logistics company building a new dispatch system with a clean event log, standardized APIs for core function, and role-based access baked in from the start. Eighteen months later, the company adds an AI agent that can reroute deliveries around traffic in real time. It plugs into the existing event stream and API layer, live in six weeks.

Now compare that with a company running a decade-old dispatch system with no API layer and data scattered across multiple formats. The same AI capability there means months of integration work before the agent can even see reliable data, let alone act on it.

Start the right foundation

None of this requires building around AI capabilities that don't exist yet. It requires building software that doesn't have to be torn apart when they arrive. Clean data, controlled interfaces, and built-in logging can reduce the need for costly rework later.

If you're planning a new system or rebuilding an old one, the AI-readiness questions are worth asking before the first line of code gets written, not after the first AI pilot stalls. The One Technologies AI services team can help you plan an AI-ready architecture from the start.