DEV Community: Abhay kumar

Your API client knows all your secrets. Where does it keep them?

Abhay kumar — Thu, 16 Jul 2026 10:40:36 +0000

A quick exercise. Open your API client right now and count:

How many auth tokens are saved in your environments?
How many requests contain real customer payloads?
How many internal hostnames does your collection reveal?

Now the uncomfortable question: whose server is all of that stored on?

For most developers the honest answer is "not mine." And nobody actually chose that — it happened to us.

How API clients quietly became cloud services

If you've been doing this for a few years, you watched the pattern:

Postman retired the offline Scratch Pad and reorganized everything around cloud workspaces. Monitors and mock servers? They run in — and bill through — Postman's cloud.
Insomnia shipped a cloud-first release in 2023 and the backlash was strong enough that community forks appeared basically overnight.
The community answered with Bruno and other file-based tools — a genuinely great move, but most of them cover only the request-builder slice of the job.

So here's the gap: the moment your work touches real traffic — a token, a payload, an internal URL — the most popular tools want it on someone else's infrastructure. And the moment you need more than a request builder (watch live traffic, monitor an endpoint overnight, mock a dead backend), you're buying a second tool or a higher tier.

I got tired of that gap. So I built for the opposite bet.

One app, one JSON file, zero cloud

Orbittest Client is a free desktop API client built on one hard rule: nothing leaves your machine unless you explicitly push it somewhere.

Your entire workspace — collections, environments, history, settings — is one plain JSON file on your disk. Back it up by copying a file. Delete it and it's gone. No account. No login. No telemetry.

The part I'm most proud of isn't the request builder (though it has OAuth 2.0 flows, scripting with a Jest-like assertion API, code generation, and full Postman import). It's the stuff that usually costs extra:

🔍 A traffic-capture proxy, built in. Charles/Fiddler-style HTTPS interception one click away — plus a dashboard that turns captured traffic into latency percentiles, Apdex scores, and an exposed-secrets panel. The first time I ran it, it flagged credentials in traffic from software I didn't even write. That feature alone changed how I audit my own machine.

⏰ API monitors without a subscription. Schedule any collection to run every minute-to-daily, get Slack/desktop alerts with debouncing, schema-drift detection, and SLO tracking — all running locally. There's a CLI that registers monitors with Task Scheduler/cron so they run while the app is closed.

👻 A mock server from recorded responses. Send a request once, flip a toggle, and it's served from http://127.0.0.1:4090 — with dynamic :id routes and realistic latency replay. Your frontend team stops being blocked by a dead backend.

🔐 Secrets treated as radioactive. Variables that look like credentials get auto-flagged, masked in the UI, and redacted from every export and Git push. Before anything leaves the machine, an audit lists anything credential-shaped — names only, never values.

🔀 Team sharing through your Git. A built-in source-control panel commits and pushes collections to your own GitHub repo. Versioned, reviewable API collections with the permissions you already have. No new vendor.

The honest part

Is it better than Postman/Insomnia/Bruno at everything? No — and I wrote the comparison honestly, including the case where Bruno is the right choice over my own tool.

The full breakdown — feature-by-feature table, how HTTPS decryption stays private, what the Pro license does and doesn't gate — is here:

👉 Orbittest Client: The API Client That Keeps Everything on Your Machine

And if you just want to poke at it: download, point it at an API, first request in under a minute — no signup, ever.

I'm a QA engineer building local-first testing tools (browser, Android, and API). If you've been burned by a cloud pivot — which tool was it? I'm collecting war stories in the comments. 👇

Claude Fable 5 Is Back — Here's What Broke, What Changed, and What Your Code Needs to Handle

Abhay kumar — Thu, 02 Jul 2026 05:38:29 +0000

The most capable AI model on the market went offline for 19 days. For everyone. Globally.

Not a bug. Not an outage. US export controls — triggered by a jailbreak discovered by Amazon researchers.

On July 1, Claude Fable 5 came back. And if you're building on frontier models, the way it came back matters more than the fact that it did:

🛡️ A new safety classifier blocking the reported technique in 99%+ of cases
🔁 Blocked requests auto-reroute to Opus 4.8 — but on the API, fallbacks are opt-in
⚠️ Refusals return HTTP 200 with stop_reason: "refusal" — code reading response.content[0] blindly will break
🎯 Deliberate false positives on security-adjacent prompts (yes, by design)

The big takeaway for devs: model availability is now a regulatory risk, not just an uptime risk. If your product hard-depends on one model with no tested fallback path, you just watched the precedent happen.

I broke down the full timeline, the new safeguards, pricing, and the exact API changes you need to handle:

👉 Read the full breakdown

What's your fallback strategy when a model disappears overnight? Drop it in the comments 👇

𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐢𝐬 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐂𝐥𝐚𝐮𝐝𝐞 𝐒𝐨𝐧𝐧𝐞𝐭 𝟓, 𝐛𝐮𝐭 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐢𝐬: 𝐰𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐜𝐡𝐚𝐧𝐠𝐞𝐝, 𝐚𝐧𝐝 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐟𝐨𝐫 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬?

Abhay kumar — Wed, 01 Jul 2026 07:12:06 +0000

From improved coding performance and stronger reasoning to updated API defaults, pricing changes, benchmark results, and a clear comparison with Opus 4.8 and Haiku 4.5, there's a lot to unpack before deciding where it fits in your AI stack.
I broke everything down into one practical, developer-friendly guide so you can understand what's new without spending hours reading release notes.
Read the full blog here: https://www.orbittest.dev/blog/claude-sonnet-5-explained
What do you think—will Claude Sonnet 5 become your default coding assistant, or are you sticking with another model?

I Tried to Design an Entire AI Software Testing Company. Here's the Architecture I'd Actually Build.

Abhay kumar — Tue, 30 Jun 2026 07:11:40 +0000

What happens when you stop building "an AI testing tool" and start designing an autonomous AI quality organization — and the engineering reality that forces you to think smaller to win bigger.

Every few months a new idea arrives that sounds less like a product and more like a small company you could hire. Mine was called TitanixAI, and the pitch was simple enough to fit on a napkin and ambitious enough to keep me up at night:

What if uploading a requirements document was like hiring an entire software testing company?

Not a chatbot. Not a "generate test cases" button. Not another automation framework with an AI sticker on it. A complete, autonomous AI Software Quality Organization — with a Business Analyst that reads your SRS, a Product Owner that builds the roadmap, a Scrum Master that plans the sprint, a QA Manager that chooses the strategy, Manual Testers that write scenarios, Automation Engineers that generate runnable code, an Execution Agent that runs it all, and a Bug Agent that files the defects. Thirty specialized agents. Every decision explainable. Every output reviewable. Every action traceable. Humans always in the loop.

It's a beautiful vision. It's also, as written, a five-year roadmap for a forty-person company described as a v1 spec.

This article is the story of how I'd take an idea that grand and turn it into something a small team could actually ship — and the architectural decisions that matter far more than the agents everyone gets excited about.

Part 1: The seductive trap of "more agents"

When you sketch an AI organization, the instinct is to list the org chart. CEO Agent. Project Director. System Architect. Scrum Master. QA Manager. Test Lead. Performance Tester. Security Tester. Accessibility Tester. Root Cause Agent. Meeting Agent. Knowledge Agent. Release Manager. Customer Success Agent.

It feels like progress. It isn't.

Here is the uncomfortable truth I had to sit with: agents are cheap to describe and brutal to make reliable. Writing "Performance Tester Agent" in a spec takes four seconds. Making an agent that produces a correct, runnable, trustworthy artifact — and knows when it's unsure — is the entire engineering problem.

A list of thirty agents isn't an architecture. It's a wish list. And the single biggest risk to a project like this isn't technical difficulty — it's that you try to build all of it and ship none of it.

So the first real decision wasn't "which agents?" It was: what is the smallest version that delivers the genuine wow, and earns the right to expand?

Part 2: The moat isn't the agents. It's the graph.

If you remember one thing from this article, remember this: in a system like TitanixAI, the agents are the replaceable part. The durable, defensible core is something far less glamorous — the artifact graph.

Think about what software testing actually is as a data problem. A requirement gives rise to epics, which give rise to user stories, which give rise to test cases, which give rise to automation code, which produces test runs, which produce bugs. Every one of those is connected to the things above and below it.

Requirement → Epic → Story → Test Case → Automation → Test Run → Bug

Now ask the question that makes this valuable: a requirement changes — what breaks?

If your system is just thirty agents passing JSON to each other, you have no answer. But if every artifact is a versioned node in a traceable graph, you can walk the edges: this requirement feeds these three stories, which feed these eleven test cases, which feed this automation suite. Mark them stale. Regenerate. That impact analysis is a killer feature — and it's essentially free if you model the graph correctly from day one, and nearly impossible to bolt on later.

So the rule I set was: build the graph before you build a single agent.

And the graph carries something most "AI agent" demos quietly skip: an approval lifecycle on every node.

DRAFT → PENDING_REVIEW → APPROVED | REJECTED | REVISION_REQUESTED

With one ironclad constraint: no agent may consume an artifact that isn't APPROVED. The Automation Engineer never writes code from test cases a human hasn't signed off on. The Bug Agent never files defects from an unapproved run.

That single rule is the difference between "an impressive demo" and "something an enterprise will actually trust with their quality process." Human-in-the-loop isn't a feature you sprinkle on top. It's a state machine you design first.

Part 3: Why I'd start with the least exciting thing — API testing

Here's where I had to disappoint my own ambition.

The vision covers web, mobile, desktop, microservices, IoT, even games. But for v1, I'd test exactly one thing: APIs. Not web UI. Not mobile. APIs.

Why pick the boring one? Because the whole thesis lives or dies on a chain of deterministic, verifiable steps, and API testing is the only domain where every link is clean:

Input is structured. An OpenAPI spec or Postman collection is machine-readable truth. The Business Analyst agent isn't guessing from prose — it's parsing a contract.
Output is runnable and checkable. Generated Pytest or REST Assured code either hits a real endpoint and asserts a real response, or it doesn't. No ambiguity.
The "last mile" is survivable. Running tests against a real system is the unglamorous 80% of any automation effort. With APIs, "connect to the system under test" means a base URL and an auth token. With web, it means fighting flaky DOM selectors, headless browser quirks, and timing races — and you'll burn all your credibility debugging selectors instead of proving your concept.

Web UI testing is v2. Mobile is v3. Starting with APIs isn't lowering the bar — it's choosing the battlefield where you can actually win, then expanding from a position of strength.

Part 4: The model strategy nobody plans for (and the bill that follows)

Modern AI architecture has a quiet financial trap. "Hundreds of AI employees collaborating on one upload" sounds magical right up until you realize it might mean thousands of LLM calls, and one upload costs $40 and takes 90 minutes.

So the model layer needs to be smart about which brain handles which job. I'd build a model router where every agent declares a task class, and the router picks the model:

Task class	Who needs it	Model choice	Why
Reasoning	Business Analyst, Root-Cause	Frontier (Claude)	Multi-step decomposition; quality compounds downstream
Code generation	Automation Engineer	Frontier (Claude)	Code that runs on the first try saves hours of debugging
Extraction	Spec parsing helpers	Local (Qwen/DeepSeek)	High-volume, structured, cheap
Bulk	Test data, boilerplate	Local (Llama/Mistral)	Low-risk, cost-sensitive

The mix matters. Local open models are fantastic for privacy and cost on bulk work — but on hard, multi-step reasoning like decomposing a messy requirement into correct test cases, the quality gap with frontier models is real and it shows up exactly where mistakes are most expensive. So: frontier brains for the hard thinking, local brains for the heavy lifting.

And the non-negotiable: every single call logs its token cost. Cost-per-project should be a number on a dashboard from day one — not a surprise on your inference bill in month three.

Part 5: Don't build an orchestration engine. You're not in that business.

It's tempting to write your own agent orchestration framework. Resist it.

The job here is state management, checkpointing, and — most importantly — pausing for human approval and resuming days later. That's exactly what mature graph-based orchestration frameworks already do well, including native human-in-the-loop interrupts. Use one. Build a thin, domain-specific layer on top. Revisit a custom engine only if the framework genuinely blocks you.

The flow becomes a graph with human gates baked into the topology:

ingest → BusinessAnalyst → [HUMAN: approve requirements]
       → TestDesigner    → [HUMAN: approve test cases]
       → AutomationEng    → [HUMAN: approve code]
       → Execution        → BugReporter → done

Each [HUMAN] is a real pause. The graph checkpoints its state, the UI surfaces the proposed artifacts, and nothing proceeds until someone clicks approve. A project can sit paused for a week and pick up exactly where it left off. That's enterprise-grade — not the number of agents, but the discipline of the gates.

Part 6: The meta-problem everyone forgets — who tests the testers?

This is the part that should keep you honest. We're building a quality company. So here's the question that has to be answered before you ship anything:

How do you know the AI's output is actually correct?

A confidently-wrong test case that "passes" is worse than no test at all — it manufactures false assurance, which is the exact opposite of what a QA organization exists to provide. Hallucinated tests don't just fail to help; they actively erode trust in the entire system.

So the system needs to be measured like any other quality-critical software:

A golden dataset — a handful of hand-curated API specs with known-correct expected requirements and test cases.
An evaluation harness — run the agents against the goldens and score completeness and correctness on every prompt or model change.
A gate — no prompt ships if it regresses the evals.

Build this in week two, not month six. An AI quality product whose own quality is unmeasured is a contradiction.

Part 7: The interface IS the product

When people imagine an AI agent platform, they picture the agents doing clever things autonomously. But for a tool that humans must trust with their software quality, the most important screen isn't the agent activity feed. It's the review queue.

A v1 needs only three screens:

Ingest — upload the spec, set a base URL, start the run.
Review queue — the heart of the product. Each proposed artifact shown with the agent's confidence score, its reasoning, and three buttons: Approve, Reject, Request Revision. This is human-in-the-loop made tangible.
Traceability view — an interactive graph from requirement to test to automation to run to bug, color-coded by approval and pass/fail state. This is the "wow" that sells the whole thesis in one glance.

Every other dashboard — executive KPIs, sprint burndowns, velocity charts — can wait. They're views over data the first five agents produce. Build the data first.

Part 8: The actual build order

Here's how I'd sequence it — each milestone proving exactly one thing:

#	Milestone	What it proves
0	Dev environment up (Postgres, Redis, Ollama, storage)	The ground is solid
1	Artifact-graph schema + approval state machine	The moat exists
2	Model router with cost logging	Costs are under control
3	OpenAPI/Postman ingestion → requirement nodes	Input becomes graph
4	Business Analyst agent + review queue + traceability view	First full human-in-the-loop cycle
5	Test Designer agent → approved test cases	Real domain value
6	Evaluation harness + golden dataset	The AI can be trusted
7	Automation agent → runnable Pytest	Code generation quality holds
8	Sandboxed executor + run reports	Real results from real systems
9	Bug Reporter + end-to-end demo	The entire thesis, proven

Ship after milestone nine. Then — and only then — start adding agents, project types, and integrations. Every item from the original grand vision becomes an expansion of a working core instead of a slide in a pitch deck.

The lesson, beyond TitanixAI

I started wanting to build an AI company with thirty employees. I ended with a plan for five agents, one input type, and three screens — and I'm more confident in that than I ever was in the org chart.

The pattern generalizes far past testing tools. When you design with AI agents, the temptation is always to add more agents, because they're so easy to imagine. But the engineering reality keeps pointing the other way:

The data model is the moat, not the agents. Get the traceable, versioned, human-gated graph right, and the agents become swappable parts.
Human-in-the-loop is a state machine, not a feature. Design the approval lifecycle before the autonomy.
Pick the deterministic battlefield first. Win where every link in the chain is verifiable, then expand.
Measure your own AI's quality from day one. Especially if quality is what you're selling.
Scope is the enemy of shipping. The smallest convincing demo beats the grandest unfinished platform every single time.

The grand vision isn't wrong. It's the destination. But you don't get there by building the whole city at once. You build one street that works end to end, prove people want to walk down it, and earn the right to build the next one.

TitanixAI might still become a full autonomous AI quality organization someday. But it'll get there one approved artifact at a time.

If you're building with AI agents and wrestling with the same "how do I scope this down without killing the vision" tension, I'd genuinely like to hear how you're drawing the line. The comments are open.

JSON to POJO and Java Entity to JSON: A Practical Guide for Java Developers

Abhay kumar — Mon, 29 Jun 2026 10:11:23 +0000

If you've worked with Spring Boot, REST APIs, or microservices, you've probably found yourself converting between JSON and Java objects more times than you can count.

Sometimes you receive a JSON response and need to create Java model classes. Other times, you already have Java entity classes and want to generate a JSON payload for API testing or documentation.

These are common tasks, but doing them manually can become repetitive, especially for larger projects.

In this article, we'll explore both directions of conversion and how to simplify the process.

Why JSON and POJOs Matter

JSON has become the standard format for communication between applications. Every REST API request and response typically uses JSON.

Java applications, however, work with Plain Old Java Objects (POJOs) and entity classes.

That means developers constantly switch between these two formats.

Typical scenarios include:

Consuming REST APIs
Creating request payloads
Testing APIs
Building Spring Boot applications
Creating mock data
Debugging API responses

Converting JSON to Java POJO

Imagine you receive the following API response:

{
  "id": 101,
  "name": "Abhay Kumar",
  "email": "abhay@example.com",
  "active": true
}

To use this response in Java, you'll typically create a POJO like this:

public class User {

    private int id;
    private String name;
    private String email;
    private boolean active;

    // Getters and Setters
}

For simple objects, this isn't difficult.

But what happens when your JSON contains:

Nested objects
Arrays
Multiple levels
Hundreds of fields

Creating everything manually becomes time-consuming.

Converting Java Entity to JSON

Now imagine you already have a Java entity:

public class Employee {

    private Long id;
    private String firstName;
    private String lastName;
    private String department;
    private Double salary;
}

During API testing, you may need the JSON version:

{
  "id": 1,
  "firstName": "John",
  "lastName": "Doe",
  "department": "Engineering",
  "salary": 75000
}

Developers often create this manually, even though the structure already exists in the Java class.

Common Challenges

Some of the most common problems include:

Large entity classes
Nested objects
Lists of objects
Optional fields
Maintaining consistency
Repeated manual work

The larger the project becomes, the more time is spent on these repetitive tasks.

Automating the Process

Instead of manually converting between Java entities and JSON, you can use dedicated tools that generate the structure instantly.

This is especially useful for:

Backend developers
QA engineers
API testers
Spring Boot developers
Students learning Java

Free Tools on OrbitTest

To simplify these tasks, I built two free tools:

Java Entity → JSON

Generate a JSON structure directly from your Java entity class.

JSON → Java POJO

Convert JSON into Java model classes that are ready to use in your project.

These tools are browser-based and require no installation.

You can also read the complete guide here:

https://www.orbittest.dev/blog/json-to-pojo-and-java-entity-to-json

When These Tools Are Most Useful

You'll likely find them helpful when you're:

Building REST APIs
Testing endpoints with Postman or OrbitTest Client
Creating mock payloads
Learning Spring Boot
Working with microservices
Creating API documentation
Preparing automation test data

Final Thoughts

Writing Java models and JSON payloads manually isn't difficult—but it quickly becomes repetitive as applications grow.

Automating these small tasks helps reduce mistakes, speeds up development, and lets you focus on solving real business problems instead of rewriting boilerplate code.

I'm continuously building free tools on OrbitTest that solve everyday problems for developers and testers.

If there's a repetitive task you wish could be automated, I'd love to hear your ideas.

Happy coding! 🚀

The Hidden Time Sink in API Testing (And How We Solved It)

Abhay kumar — Wed, 24 Jun 2026 09:44:05 +0000

Every QA engineer, automation tester, and backend developer knows the feeling.

You start the day planning to test a new API.

Two hours later, you're still switching between browser tabs.

One tab for formatting JSON.

Another for decoding JWT tokens.

Another for checking timestamps.

Another for testing regex patterns.

Then someone sends a cURL command and asks:

"Can you convert this into Java code?"

By lunchtime, you've spent more time dealing with API data than actually testing the API.

The funny thing is that most API failures aren't caused by complicated bugs.

They're caused by small things:

An expired token
A missing JSON field
A changed response structure
A timestamp issue
A malformed payload
A regex validation mistake

Small problems.

Big debugging time.

After working on API automation and testing for years, I noticed the same pattern repeating over and over.

The issue wasn't the APIs.

The issue was the workflow.

The Real API Testing Workflow Nobody Talks About

Let's say a login API suddenly starts failing in your test environment.

The first thing most testers do is inspect the response.

You receive something like this:

{"token":"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...","expires":1750741800}

At first glance, it looks fine.

But now the investigation begins.

Questions start appearing:

Is the JWT valid?
Has the token expired?
Is the response structure different from yesterday?
Did the backend team remove a field?
Is the timestamp correct?
Are my assertions still valid?

None of these questions require complex testing.

They require visibility.

And visibility is usually where time gets wasted.

The Problem With Most Developer Toolkits

Most engineers already have tools for these tasks.

The problem is that they're scattered everywhere.

A typical debugging session looks like this:

Open one website to format JSON.
Open another website to decode JWT.
Open another website to convert timestamps.
Open another website to compare responses.
Open another website to test regex.
Open another website to convert XML.

At that point, half your browser tabs have nothing to do with the application you're testing.

You're managing tools instead of solving problems.

That's exactly why we started building a collection of lightweight developer utilities inside OrbitTest.

Not because these tools are revolutionary.

Because they remove friction.

When JSON Responses Become a Nightmare

If you've ever worked with large APIs, you've seen responses that look like this:

{"user":{"profile":{"address":{"city":"London","country":"UK"}}},"permissions":["admin","editor"]}

Technically valid.

Practically unreadable.

The first thing most developers do is format it.

That's why the JSON Formatter exists:

https://www.orbittest.dev/tools/json-formatter

Nothing fancy.

Paste JSON.

Get readable JSON.

The amount of debugging time saved by proper formatting is surprisingly large.

JWT Tokens Are Usually the First Suspect

Whenever authentication breaks, JWT tokens become the prime suspect.

A tester receives:

eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...

Now the investigation begins.

Who issued the token?

When does it expire?

What roles does it contain?

Which user does it belong to?

Instead of manually decoding pieces of the token, the JWT Debugger provides immediate visibility:

https://www.orbittest.dev/tools/jwt-debugger

When authentication issues occur, visibility matters more than complexity.

Timestamps Cause More Bugs Than Expected

One of the most common support conversations looks like this:

"The token expired."

"No, it didn't."

"Yes, it did."

Then someone copies a Unix timestamp into Google.

Timestamps are simple until you're dealing with multiple environments, time zones, expiration windows, and audit logs.

That's why the Timestamp Converter became one of our most-used utilities:

https://www.orbittest.dev/tools/timestamp-converter

Because nobody wants to manually calculate whether a token expires in five minutes or five days.

The Silent Killer: Response Changes

Many API bugs aren't caused by failed responses.

They're caused by changed responses.

Yesterday:

{
"status": "active"
}

Today:

{
"status": "inactive"
}

Or worse:

{
"accountStatus": "inactive"
}

The API still works.

But every consumer breaks.

This is where JSON Diff becomes invaluable:

https://www.orbittest.dev/tools/json-diff

Instead of manually comparing hundreds of lines, differences become obvious immediately.

For regression testing, this tool alone can save hours every week.

Finding JSON Paths Shouldn't Feel Like Archaeology

Automation engineers often spend more time locating data than validating data.

Imagine receiving:

{
"users": [
{
"profile": {
"address": {
"city": "London"
}
}
}
]
}

Now you need the JSONPath.

Instead of manually digging through nested objects, JSON Path Finder generates it instantly:

https://www.orbittest.dev/tools/json-path-finder

This becomes especially useful when building assertions in automation frameworks.

The Everyday Utilities That Save More Time Than You Think

Some tools don't sound exciting.

Until you need them.

Regex Tester

For validating emails, phone numbers, URLs, passwords, and custom validations.

https://www.orbittest.dev/tools/regex-tester

Base64 Encoder / Decoder

For authentication headers, encoded payloads, and integration debugging.

https://www.orbittest.dev/tools/base64-encoder

Encrypt / Decrypt

For validating secured payloads and testing encrypted data exchanges.

https://www.orbittest.dev/tools/encrypt-decrypt

cURL Converter

For turning API requests into actual code examples.

https://www.orbittest.dev/tools/curl-converter

XML ↔ JSON Converter

Because legacy systems still exist.

And unfortunately, many of them still speak XML.

https://www.orbittest.dev/tools/xml-json-converter

Small Utilities. Big Productivity Gains.

The interesting thing about developer productivity is that it rarely comes from massive breakthroughs.

Most productivity gains come from removing tiny frustrations.

Five minutes here.

Ten minutes there.

A few unnecessary browser tabs.

A few manual conversions.

A few repetitive debugging steps.

Over time, those small improvements add up.

The goal of these tools isn't to replace Postman, Orbittest_Client, or your automation framework.

The goal is simpler:

Help engineers solve everyday API problems faster.

Final Thoughts

Good testing isn't just about writing better test cases.

It's about reducing the time between:

"Something is wrong."

and

"I know exactly what's wrong."

That's where most engineering time gets lost.

The OrbitTest utility collection was built around that idea.

Simple tools.

Real problems.

Less friction.

If you're spending part of every day debugging APIs, validating payloads, inspecting tokens, comparing responses, or searching through JSON structures, you'll probably find at least one tool here that saves you time.

Because API testing is hard enough already. The tools around it shouldn't be.

If OpenAI released your exact AI model tomorrow, would your product still survive?

Abhay kumar — Wed, 24 Jun 2026 07:38:46 +0000

If OpenAI released your exact AI model tomorrow, would your product still survive?

That's the question every AI founder and builder should be asking.

Everyone talks about AI models.

Very few talk about what actually creates a lasting competitive advantage.

After looking at how successful AI products evolve, one pattern keeps appearing: many of the strongest products started as internal tools built to solve real problems. The technology matters, but technology alone gets copied.

The real moat comes from combining three things:

• Deep domain knowledge
• Proprietary data
• The talent to turn ideas into production-grade systems

A prototype proves something is possible.

A production system proves it is valuable.

The companies that win won't necessarily have the best model. They'll have the best combination of technology, data, and execution.

I shared my thoughts on why technology, data, and talent together—not individually—decide who wins in AI.

Read here:
https://www.orbittest.dev/blog/ai-competitive-advantage-technology-data-talent

What's your take? If you had to choose one, which is harder to replicate: technology, data, or talent?

I Built a Collection of Free Developer Tools Because I Was Tired of Opening 10 Browser Tabs

Abhay kumar — Tue, 23 Jun 2026 07:45:20 +0000

As developers and testers, we spend a surprising amount of time doing small repetitive tasks.

Formatting JSON.
Decoding JWT tokens.
Comparing API responses.
Testing regex patterns.
Converting timestamps.
Encoding and decoding Base64 strings. None of these tasks are difficult, but constantly switching between different websites breaks focus and slows down the workflow.

A few months ago, while working on OrbitTest and testing APIs daily, I noticed that I was repeatedly opening the same set of utility websites. Sometimes I had more utility tabs open than actual project tabs.

That became the motivation behind creating a dedicated tools section on OrbitTest.

The goal wasn't to build something revolutionary.

The goal was simple:

Keep frequently used developer utilities in one place and make them fast, clean, and accessible.

Some of the tools currently available include:

JSON Formatter & Validator
JWT Decoder
Base64 Encoder / Decoder
JSON Compare Tool
Regex Tester
Timestamp Converter

- XML ↔ JSON Converter

JSON Schema Generator
Everything runs directly in the browser and is designed to be lightweight and easy to use.

One thing I've learned while building products is that not every feature has to be a massive innovation. Sometimes removing small daily frustrations creates the most value.

If you're a developer, tester, QA engineer, or anyone working with APIs, I'd love to know:

What's the developer tool you use almost every day?

You can explore the tools here:

https://www.orbittest.dev/tools

Feedback is always welcome.

Why "Log in with Google" never sees your password (PKCE, explained)

Abhay kumar — Tue, 23 Jun 2026 06:59:35 +0000

Ever wondered how "Log in with Google" works without the app ever touching
your password? That's OAuth 2.0 — and on mobile apps and SPAs, the piece that
makes it safe is PKCE (Proof Key for Code Exchange).

The problem PKCE solves: a public client (a mobile app or SPA) can't keep a
secret. So an attacker who intercepts the authorization code could exchange it
for a token.

PKCE fixes this with a simple trick:

The app generates a random "code verifier"
It sends a hashed version (the "code challenge") when starting login
To redeem the code, it must present the original verifier

An intercepted code is useless without the verifier that only the real app has.

I broke down the whole flow step by step — what each value does and the exact
attack it prevents:

👉 https://www.orbittest.dev/blog/oauth-authorization-code-flow-pkce

Are you using PKCE in your SPA/mobile auth today?

Stop waiting for the backend — mock any API in seconds

Abhay kumar — Tue, 23 Jun 2026 06:57:16 +0000

Frontend devs lose so much time waiting on backend APIs that aren't ready.

The usual "fix" — hand-writing JSON mock files and wiring up routes — just
trades one chore for another, and the mocks drift from reality over time.

A faster pattern: record a real API response once, then replay it from a local
mock server. Your app points at http://127.0.0.1:4010 instead of the real
backend and keeps working — even offline, even when the backend is down.

Great for:
• Building UI before the API exists
• Stable, repeatable test data
• Demos that don't depend on the network
• Avoiding third-party rate limits during dev

I wrote up the record-once/mock-instantly approach (with a short demo):

👉 https://www.orbittest.dev/blog/ghost-mock-server-local-api-mocking

How do you handle "API isn't ready yet" on your team?

Is your JWT encrypted? (No — and that trips up a lot of devs)

Abhay kumar — Tue, 23 Jun 2026 06:50:07 +0000

Common misconception: "JWTs are encrypted, so I can store data in them."

Reality: a standard JWT's header and payload are only Base64-encoded —
fully readable by anyone. Paste one into any decoder and the claims fall right
out. The signature proves the token wasn't tampered with; it does NOT hide
the contents.

So: never put secrets in a JWT payload.

While we're clearing up auth confusion, three things that look similar but
aren't:
• Encoding (Base64) → representation, reversible, no key
• Encryption (AES) → protection, reversible with a key
• Hashing (SHA-256) → one-way, can't be reversed (why passwords are hashed)

I wrote a from-scratch guide to API authentication — Basic Auth, API keys,
bearer tokens, JWT, and OAuth 2.0 — plus how to actually test each one:

👉 https://www.orbittest.dev/blog/api-authentication-oauth-jwt-tokens

What auth method does your current project use?

401 vs 403, 400 vs 422 — the status codes everyone mixes up

Abhay kumar — Tue, 23 Jun 2026 06:47:58 +0000

Quick quiz — which status code is correct?

• You're not logged in → ?
• You're logged in but not allowed → ?
• The JSON is malformed → ?
• The JSON is valid but the email is invalid → ?

Answers:
• 401 Unauthorized → "who are you?" (missing/invalid auth)
• 403 Forbidden → "I know you, you still can't"
• 400 Bad Request → malformed request
• 422 Unprocessable → valid request, failed validation

The #1 mistake I see: returning 200 OK for a login failure, or 500 for bad
user input. Both break monitoring and make debugging miserable, because the
first digit of a status code is supposed to tell you whose fault it is:

2xx = success · 3xx = go elsewhere · 4xx = your fault · 5xx = server's fault

I put together a complete guide with real examples, a cheat-sheet table, and a
testing checklist for every category:

👉 https://www.orbittest.dev/blog/http-status-codes-explained

What's the most misused status code you've seen in the wild?