Forem: Abhishek Nayak

I Analyzed Every Vibe Coding Study From 2026. Here's What Nobody's Talking About.

Abhishek Nayak — Sat, 04 Apr 2026 18:43:55 +0000

You've heard the hype. AI writes 46% of all new code. 92% of developers use AI tools daily. Vibe coding is the future.

But I spent the last week diving into every major study, security audit, and productivity report from 2026. And the story everyone's telling? It's missing the most important parts.

Let me show you what I found.

The Number That Broke My Brain

METR, a nonprofit research organization, ran the most rigorous study on AI coding productivity to date. They took 16 experienced open-source developers. Real engineers working on real codebases they'd contributed to for years. 246 actual tasks.

Half the time, developers could use AI tools. Half the time, they couldn't.

The results?

Developers using AI were 19% slower.

Not faster. Slower.

But here's what broke my brain: before the study, these same developers predicted AI would make them 24% faster. After the study — after seeing the actual data — they still believed AI had helped them.

The subjective experience and objective reality completely diverged.

One developer in the study explained it perfectly:

"I think people overestimate speed-up because it's so much fun to use AI. We sit and work on these long bugs, and then eventually AI will solve the bug. But we don't focus on all the time we actually spent—we just focus on how it was more enjoyable."

This is the thing nobody wants to talk about. We're addicted to something that feels productive but might not be.

Wait, So AI Coding Is Useless?

No. That's not what the data says either.

Here's where it gets nuanced:

Senior developers (10+ years experience) report 81% productivity gains. They know what good code looks like. They catch AI mistakes fast. For them, AI handles the boring stuff while they focus on architecture.

Junior developers show mixed results. 40% admit to deploying code without fully understanding it. They can't evaluate what AI produces because they don't know what good looks like yet.

The codebase matters too. The METR study used massive, mature repositories — averaging 10+ years old and 1M+ lines of code. AI struggles with that complexity. For greenfield projects and prototypes, the productivity gains are real.

So here's the actual takeaway:

Scenario	AI Impact
Experienced dev + new project	Significant speedup
Experienced dev + mature codebase	Mixed to slower
Junior dev + any project	Dangerous without review
Prototyping/MVPs	Massive speedup
Production code	Requires heavy verification

The blanket "AI makes you 10x faster" narrative? It's marketing, not reality.

The Security Numbers Are Terrifying

Okay, productivity is complicated. But security? The data here is just bad.

45% of AI-generated code contains OWASP Top-10 vulnerabilities
AI co-authored pull requests show 2.74x higher rates of security vulnerabilities
Security firm Tenzai built 15 apps with popular vibe coding tools. Found 69 vulnerabilities. Six were critical.
CodeRabbit analyzed 470+ GitHub PRs. AI code had 1.7x more major issues than human code.

And the incidents are already happening:

Early 2026: A vibe-coded app suffered a massive data breach. 1.5 million API keys. 35,000 user emails. All exposed because of a misconfigured database. The developer admitted they hadn't written a single line of code manually.

May 2025: Security researchers scanned 1,645 apps built on Lovable (a popular vibe coding platform). 170 of them — more than 10% — had vulnerabilities exposing personal user data.

The honeypot hack: A security firm used AI to generate a honeypot (a tool to capture attacker traffic). During testing, attackers exploited a vulnerability in the AI-generated code itself. The AI had added logic that treated user-controllable headers as trusted data. A basic security violation that nobody caught because nobody wrote it.

That last one is the scariest. Security experts, building a security tool, using AI, still got burned.

The $4.7 Billion Market Nobody Trusts

Here's the paradox of 2026:

Metric	2023	2026
Developer AI tool adoption	~40%	92%
Trust in AI-generated code	77%	60%
AI-generated code share	~10%	46%
Market size	~$500M	$4.7B

Usage is up. Trust is down. The industry is hooked on something it doesn't believe in.

Gartner predicts 60% of all new code will be AI-generated by end of 2026. The Sonar survey found 96% of developers don't fully trust the functional accuracy of AI code.

We're shipping code we don't trust at scale. That's the state of things.

What's Actually Working (The Honest Version)

After going through all this research, here's what the data actually supports:

Vibe Coding Works For:

1. Prototypes and MVPs
If you're validating an idea and the cost of bugs is low, vibe coding is genuinely transformative. Build it in a weekend. Throw it away if it doesn't work.

2. Internal Tools
IBM reports 60% reduction in development time for enterprise internal apps. Internal tools have higher bug tolerance and lower security stakes. Sweet spot.

3. Boilerplate and Documentation
75% of developers rate AI as effective for documentation. Nobody misses writing CRUD endpoints by hand.

4. Learning and Exploration
Using AI to understand new APIs, explore unfamiliar codebases, research solutions — this is where it shines without the downside risk.

Vibe Coding Breaks For:

1. Security-Critical Code
Authentication. Payments. Encryption. The data is clear: AI introduces more vulnerabilities than it prevents.

2. Complex, Mature Codebases
The METR study showed experienced developers were slower with AI in large repositories. AI misses implicit context that humans understand.

3. Anything You Can't Verify
If you can't evaluate whether the AI output is correct, you shouldn't be shipping it. Period.

The Skills That Actually Matter Now

The developer role is shifting. Not dying — shifting.

Old model: You write code. Quality depends on your coding ability.

New model: You direct AI. Quality depends on your ability to evaluate output.

The skills that matter in 2026:

Skill	Why It Matters
Systems thinking	AI can't design architectures. You need to see the big picture.
Security auditing	AI introduces vulnerabilities. Someone has to catch them.
Code review	Reading AI code critically is a core competency now.
Prompt engineering	Better prompts = better output. This is a real skill.
Knowing when NOT to use AI	The developers who thrive know when to turn it off.

The irony: the more AI writes code, the more valuable the humans who can evaluate code become.

The Tools Landscape (What People Actually Use)

Quick overview of what's dominating in 2026:

For Developers (Requires Coding Knowledge)

Tool	Price	Best For
Cursor	$20/mo	Most popular AI IDE. Deep codebase understanding. $9.9B valuation.
Windsurf	$15/mo	Large codebases. Recently acquired by OpenAI.
Claude Code	Usage-based	Terminal power users. Best at refactoring and cross-file changes.
GitHub Copilot	$10/mo	Most affordable. 20M+ users. Best GitHub integration.

For Non-Developers (No Code Required)

Tool	Price	Best For
Bolt.new	$20/mo	Fastest prototyping. $40M ARR in 4.5 months.
Lovable	$39/mo	Non-technical founders. Clean React output. $100M ARR in 8 months.
Replit	$25/mo	All-in-one for beginners. 75% of users never write code.
v0 by Vercel	$20/mo	Frontend UI components only. Production-ready React.

Most successful teams I've seen use 2-3 tools: a generator for prototyping, then an AI IDE for production work.

The Gap Nobody's Closing

Here's what keeps me up at night.

Development went AI-native. Testing mostly didn't.

Teams ship 3-5x faster with vibe coding. Their test suites are still written by hand. Maintained by hand. The math doesn't work.

41% of developers admit to pushing AI-generated code to production without full review.

The companies finding hardcoded API keys, disabled security checks, and logic bombs in production? They all have one thing in common: they automated the building but not the verification.

The winners of 2026 won't be the teams that vibe code the fastest. They'll be the teams that figure out how to verify at the speed of vibe coding.

My Takeaways After a Week in the Data

1. The productivity gains are real but conditional.
Senior devs on new projects: yes. Junior devs on anything: dangerous. Complex mature codebases: probably slower.

2. The security situation is bad.
45% vulnerability rate. 2.74x more security issues. Real breaches already happening. This isn't FUD — it's documented.

3. Trust is falling while usage rises.
This is unsustainable. Something will break. Either the tools get dramatically better at security, or we'll see a major incident that changes the conversation.

4. The skill shift is real.
Writing code matters less. Evaluating code matters more. Architecture, security auditing, systems thinking — these are the premium skills now.

5. Testing is the missing piece.
Everyone automated the building. Almost nobody automated the verification. That's the opportunity.

What I'm Doing Differently

After digesting all this, here's how I'm approaching vibe coding now:

✅ Use AI aggressively for prototypes and exploration
The speedup is real here. Build fast, learn fast, throw away fast.

✅ Manually review anything security-related
Auth, payments, data access, encryption. AI doesn't get the final word on these.

✅ Track actual time, not perceived time
The METR study showed we can't trust our intuition. Measure.

✅ Treat AI output as untrusted by default
It's a very fast junior developer who makes confident mistakes.

✅ Invest in evaluation skills
The ability to read code critically is more valuable than the ability to write it.

The Bottom Line

Vibe coding won. That's not the debate anymore.

The debate is: how do we ship fast without shipping garbage?

The data says we're not there yet. 46% AI-generated code. 45% vulnerability rate. 19% slower in complex environments. Trust falling while adoption rises.

The teams that win 2026 will be the ones that figured out verification. Everyone else is building on sand.

That's what the data actually says. Not the hype. Not the marketing. The research.

If this breakdown helped, share it with your team. Everyone shipping AI-generated code needs to see these numbers.

So… Claude Code's Source Code Just Leaked. Here's What We Know.

Abhishek Nayak — Thu, 02 Apr 2026 12:29:04 +0000

If you've been anywhere near Dev Twitter (or X, whatever we're calling it now) in the last couple of days, you've probably already seen this. But if you haven't — buckle up, because this one's wild.

On March 31st, Anthropic accidentally shipped the entire source code of Claude Code in a public npm package. Yes, you read that right. The whole thing. Nearly 2,000 TypeScript files. Over 512,000 lines of code. Just… out there. For everyone to see.

And the internet did what the internet does — it went absolutely nuts.

Wait, How Did This Even Happen?

So here's the thing — this wasn't some sophisticated cyber attack. Nobody hacked into Anthropic's servers. There was no insider threat, no zero-day exploit. It was a packaging mistake.

When Anthropic pushed version 2.1.88 of the @anthropic-ai/claude-code npm package, someone accidentally included a source map file in the build. If you're not familiar, source maps are dev tools — they map compiled/minified code back to the original source. Super useful during development. Absolutely should never ship in production.

But it did. And security researcher Chaofan Shou caught it almost immediately and posted about it on X. That post? It blew up to over 28.8 million views.

Anthropic pulled the version from npm pretty quickly, but by then it was way too late. The code had already been extracted and uploaded to GitHub.

Anthropic's official response was basically: "This was a release packaging issue caused by human error, not a security breach. No customer data or credentials were exposed." They also said they're putting measures in place so this doesn't happen again.

Fair enough. But the code is out there now, and there's no putting that genie back in the bottle.

The GitHub Repo That Broke Records

Within hours, a GitHub repo called instructkr/claw-code popped up with the leaked source. And here's the crazy part — it hit 50,000 stars in under 2 hours. As of now, it's sitting at over 84,000 stars and 82,000 forks.

That's not just popular. That's one of the fastest-growing repos in GitHub history.

What's interesting is that the repo didn't stay as just a code dump. The maintainers turned it into something more — a clean-room reimplementation project. They've been porting the architecture to both Python and Rust, building it out as a research tool for studying AI agent systems. They've been pretty clear that it's not affiliated with Anthropic and that they deliberately avoided storing the original leaked snapshot.

The repo includes a Python workspace with modules for commands, tools, a query engine, and a CLI, plus a full Rust workspace with crates for everything from the API client to plugin systems to a compatibility layer for editor integration.

It's kind of become this hub for developers who want to understand how AI coding agents actually work under the hood.

OK But What's Actually In The Code?

This is where it gets really interesting. People have been tearing through the source and sharing their findings, and some of this stuff is genuinely fascinating. Let me walk you through the highlights.

The Self-Healing Memory Thing

You know how LLMs have a fixed context window, right? So when you're in a long coding session with Claude Code, it eventually has to decide what to keep and what to throw away. Well, turns out Claude Code has this really clever system for handling that.

It's not just doing dumb truncation. There's a four-stage context management pipeline that compresses and reorganizes information to keep the most relevant stuff around. Developers who've studied it are calling it a "self-healing memory architecture" — basically the system can reconstruct important context even after compaction. Pretty smart.

Multi-Agent Swarms

Claude Code isn't just one agent. When you give it a complex task — like a big refactor across multiple files — it can spawn sub-agents. Think of it like a team lead delegating tasks to specialists. These agents work on different parts of the problem and their results get coordinated back together.

This explains why Claude Code is weirdly good at large multi-file changes. It's not doing it all in one pass.

The Tools Under The Hood

At its core, Claude Code runs on a tool system that handles file reading, bash execution, API calls — all the stuff that makes it feel like it actually understands your project. There's a query engine that routes requests to the right model with the right context, and a bidirectional communication layer that connects IDE extensions to the CLI.

Nothing too surprising here if you've thought about how these tools must work, but it's cool to see the actual implementation.

KAIROS — The Background Agent

OK, this one is wild. There's a feature in the code called KAIROS that basically turns Claude Code into a persistent background process. It can fix errors, run tasks, and even send push notifications to you — all without you explicitly asking it to do anything.

Imagine you push some code, go grab coffee, and come back to a notification saying "hey, I noticed a bug in your last commit and fixed it." That's what KAIROS seems to be building toward.

"Dream" Mode

And if KAIROS wasn't enough, there's also this experimental thing called Dream mode. It lets Claude continuously think in the background — brainstorming ideas, iterating on your existing code, and preparing suggestions for when you come back.

It's like having a junior dev who never sleeps and is always noodling on improvements to your codebase. Whether that's exciting or terrifying probably depends on your perspective.

Undercover Mode (Yes, Really)

This one raised a lot of eyebrows. There's a mode called "Undercover Mode" that's designed for making contributions to open-source repos without revealing that Anthropic is behind them. The system prompt literally tells the agent not to include any Anthropic-internal information in commits or PRs.

People have a lot of opinions about this. Some see it as shady — Anthropic secretly contributing to open-source projects without disclosure. Others argue it's pretty standard for companies to contribute to open source without broadcasting it. Either way, it's not a great look when it gets discovered through a leak.

Fighting Model Distillation

Here's something the AI industry watchers will find interesting. The code includes a system that detects when competitors might be scraping Claude Code's outputs to train their own models (this is called model distillation). When it suspects scraping, it injects fake tool definitions into the API responses to poison the training data.

This makes sense given that Anthropic has publicly accused certain AI firms of using distillation attacks against Claude. Now we can see exactly how they've been fighting back.

The Security Mess

So far I've been talking about this mainly as an IP issue and a fascinating peek behind the curtain. But there's a real security problem here too.

AI security company Straiker put it bluntly: with the full source code available, attackers don't need to brute-force jailbreaks anymore. They can study exactly how data flows through the context pipeline and craft inputs designed to survive compaction. Basically, they can build persistent backdoors that stick around for an entire session.

That's… not great.

The Supply Chain Attack

To make things worse, the timing of the leak overlapped with a supply chain attack on the Axios npm package. If you installed or updated Claude Code via npm on March 31st between 00:21 and 03:29 UTC, you might have pulled a trojanized version of Axios that included a cross-platform remote access trojan.

If you're in that window, you need to:

Downgrade to a known safe version immediately
Rotate ALL secrets — API keys, SSH keys, tokens, everything Claude Code had access to
Check your systems for any suspicious activity

I'm not being dramatic here. This is a legit "drop everything and deal with this" situation if it applies to you.

Typosquatting Packages

And because things weren't bad enough already, attackers started registering npm packages with names similar to internal Claude Code dependencies. A user called "pacifier136" published packages like audio-capture-napi, color-diff-napi, image-processor-napi, and a few others. Right now they're empty stubs, but that's how dependency confusion attacks work — squat the name, wait for installs, then push a malicious update.

If you've been trying to build the leaked source, be extremely careful about what packages you're pulling in.

Oh, And There's More

Here's the kicker — this wasn't even Anthropic's only security incident that week. Just days before the Claude Code leak, details about an unreleased AI model (reportedly called "Mythos") and other internal data were found sitting on an unsecured CMS. Anthropic later confirmed they'd been testing this new model with early access customers and called it the most capable model they've ever built.

Two significant data exposures in one week. For a company that has built its entire brand around being the "safety-first" AI lab, that's a rough look. It doesn't mean their AI safety research isn't legit — it absolutely is — but operational security is clearly something they need to tighten up.

What This Means For The Rest Of Us

I think there are a few takeaways here, beyond the obvious "don't ship source maps in production."

AI coding tools have deep access. We're giving these tools access to our codebases, our terminals, our secrets. When something goes wrong — whether it's a leak, a supply chain attack, or a compromised build — the blast radius is huge. We need to think about these tools as critical infrastructure, not just developer convenience.

The packaging step is a security boundary. This whole thing happened because of a build configuration mistake. Your CI/CD pipeline isn't just about deployment speed — it's about security. Source maps, debug symbols, internal configs — all of these need to be explicitly excluded from production builds, and that exclusion needs to be verified automatically.

Open source benefits from transparency, even accidental transparency. The community has learned an enormous amount about how modern AI agent systems work from studying this code. The clean-room reimplementation projects are already advancing our understanding of agent orchestration, context management, and tool systems. There's real value here for the field.

Audit your dependencies. This isn't new advice, but it matters more than ever. Lock your package versions. Use integrity checks. Pay attention to what's in your node_modules. The typosquatting and supply chain attacks that followed this leak show how quickly bad actors can exploit confusion.

Final Thoughts

Look, mistakes happen. I'm not going to pile on Anthropic for a packaging error — anyone who's shipped software has made similar kinds of mistakes at some point. The difference is that when your tool is used by millions of developers worldwide and has deep access to their systems, the stakes are just higher.

What I am going to say is: if you use Claude Code (or any AI coding tool), take this as a reminder to treat it with the same security diligence you'd give to any other piece of infrastructure with privileged access. Check your installs, rotate your keys, and keep an eye on the follow-up advisories.

And if you're a developer who's curious about how these AI agent systems actually work — go check out the claw-code repo. It's a genuinely fascinating read. Just, you know, be careful what you npm install while you're at it.

What do you think about the leak? Too much panic, or are we not panicking enough? Drop your thoughts in the comments — I'd love to hear what other devs are thinking about this.

JsonMaster vs JSONFormatter vs JSONLint — Which JSON Tool Should You Use in 2026?

Abhishek Nayak — Fri, 27 Mar 2026 16:05:59 +0000

There are dozens of online JSON tools. Most developers just use the first result on Google and stick with it. But they are not all equal — especially when it comes to privacy, editor quality, and feature count.

I compared the four most popular tools so you don't have to.

The Tools

JsonMaster → https://jsonmaster.netlify.app/
JSONFormatter.org → jsonformatter.org
JSONLint → jsonlint.com
JSON Editor Online → jsoneditoronline.org

Feature Comparison

Feature	JsonMaster	JSONFormatter	JSONLint	JSON Editor Online
JSON Formatter	✅	✅	✅	✅
JSON Validator	✅	✅	✅	✅
JSON Minifier	✅	✅	❌	✅
JSON to YAML	✅	✅	❌	❌
JSON to CSV	✅	✅	❌	❌
JSON to TypeScript	✅	❌	❌	❌
JSON to Zod Schema	✅	❌	❌	❌
JWT Decoder	✅	❌	❌	❌
Base64 Encoder/Decoder	✅	❌	❌	❌
Regex Tester	✅	❌	❌	❌
JSON Diff / Compare	✅	❌	❌	✅
Duplicate Key Detector	✅	❌	❌	❌
Monaco Editor (VS Code engine)	✅	❌	❌	❌
Dark Mode	✅	❌	❌	✅
100% Client-side	✅	❌	❌	❌
No ads	✅	❌	❌	❌

Privacy — The Biggest Difference

Most developers paste real API responses, JWT tokens, or production configs into these tools without thinking about it.

JsonMaster processes everything in your browser. Open DevTools → Network tab while using it — you will see zero outgoing requests for your data.

JSONFormatter.org, JSONLint, and JSON Editor Online all process data server-side or run third-party analytics that may capture your input. When you paste a JWT or a database export into those tools, it leaves your machine.

Editor Quality

JSONLint uses a plain <textarea>.
JSONFormatter.org uses CodeMirror.
JsonMaster uses Monaco Editor — the same engine that powers VS Code.

With Monaco you get inline error markers, code folding, find & replace (Ctrl+F), multi-cursor editing, and the keyboard shortcuts already in your muscle memory. For developers who spend hours in VS Code daily, this is not a small thing.

When to Use Each Tool

Tool	Use it when...
JsonMaster	You want privacy + multiple tools in one tab + VS Code-quality editing
JSONLint	You only need a one-off syntax check, nothing else
JSONFormatter.org	You need JSON→CSV and don't have privacy requirements
JSON Editor Online	You need to visually navigate a large JSON tree

Verdict

For day-to-day developer work — debugging API responses, converting formats, decoding JWTs, testing regex — JsonMaster is the most complete and private option.

The Monaco editor alone makes it worth switching from any textarea-based tool.

Try it free → https://jsonmaster.netlify.app/

No login. No ads. No server.

Stop Switching Tabs — One Free Tool Replaces All Your JSON & Dev Tools

Abhishek Nayak — Fri, 27 Mar 2026 16:02:38 +0000

Every day I was doing the same thing:

Paste API response into some random formatter
Open another tab to decode the JWT
Open another tab for Base64
Open another tab to test a regex

Four tabs. Same tools. Every single day.

So I built JsonMaster — a free developer toolbox that puts 12 tools in one tab.

👉 https://jsonmaster.netlify.app/

No login. No ads. Your data never leaves your browser.

What's Inside

JSON Formatter & Validator

Paste raw minified JSON and instantly see it beautified. Built on Monaco Editor — the same engine as VS Code — so you get syntax highlighting, inline error squiggles, code folding, and keyboard shortcuts you already know.

JSON Converters

Convert JSON to:

YAML — for Kubernetes, Docker Compose, GitHub Actions
CSV — for Excel, Google Sheets, data pipelines
XML — for legacy systems and SOAP APIs
TypeScript Interface — paste an API response, get a typed interface instantly
Zod Schema — instant runtime validation schema for TypeScript projects
.env file — flatten nested JSON config into environment variables

JWT Decoder

Paste any JWT. See header, payload, signature decoded instantly. Checks expiry and algorithm. Everything happens in your browser — nothing is sent to a server.

Base64 & URL Encoder / Decoder

Encode or decode Base64 and URL strings instantly. Handles Unicode correctly. Useful for decoding auth headers, query strings, and embedded images.

Regex Tester

Write a pattern, test it against your input in real time. Every match is highlighted. Supports all JS regex flags: g, i, m, s, u. Shows match groups and count.

JSON Diff & Compare

Paste two JSON objects. See exactly what was added, removed, or changed — down to the specific key path. Useful for comparing API responses before and after a deploy.

Why Client-Side Matters

Most JSON tools send your data to a server. You paste a JWT or production API response — it travels over the network to someone's backend.

JsonMaster is 100% client-side. Open DevTools → Network tab while using it. You will see zero outgoing requests for your data.

This matters when you're working with JWT tokens, API responses with user data, or config files with credentials.

Try it free → https://jsonmaster.netlify.app/

Drop a comment if you find a bug or want a new tool added. I read everything.

What is webhook ?

Abhishek Nayak — Wed, 23 Jul 2025 20:35:33 +0000

Why Were Webhooks Introduced?

Before diving into the concept of webhooks, let’s explore some foundational terms to understand why webhooks are important, how they differ from APIs, and the problems they solve.

What is an API?

An API (Application Programming Interface) is a way for a client (like a frontend app) and server (like a backend service) to communicate using the HTTP protocol. The client sends an HTTP request to the server, and the server responds—usually with data in JSON or XML format.

📘 Want to know more about APIs? Check this out: API Docs

What is Polling?

Sometimes, servers can’t immediately respond with data because they need to run complex algorithms or perform background processing that takes time. A simple HTTP request may timeout in such cases, especially when real-time feedback is needed (e.g., status updates or background job completions).

To solve this, polling was introduced.

Polling is the process where the client repeatedly sends requests to the server at fixed intervals to check whether new data is available or a specific event has occurred. This keeps the client in sync with backend state changes over time.

Think of polling as the client asking: “Is it ready yet? …How about now?”

While polling works, it isn’t always efficient. For example, consider an online payment system—polling the payment status endpoint every few seconds wastes bandwidth, increases server load, and isn't scalable.

That’s where webhooks come in.

What is a Webhook?

A webhook is a lightweight, event-driven HTTP callback that allows the server to push data to the client, rather than the client pulling it continuously.

In simple terms:

A webhook is the server saying, "Hey! That thing you care about just happened. Here’s the data!"

Webhooks are often called reverse APIs or push APIs because they shift the responsibility of making the request from the client to the server. Instead of polling an API endpoint over and over, the server automatically sends an HTTP POST request to a URL you (the client) specify—only when a specific event occurs.

For example, in a payment system, you can register a webhook URL for payment_success. Once the payment is processed, the server will automatically send a POST request with the payment details to your webhook URL.

See this diagram :

How Do Webhooks Work?

The client registers a webhook URL with the server.
The client specifies the event(s) it wants to listen to (e.g., "order placed", "payment successful").
The server stores this webhook configuration.
When the event occurs, the server sends a POST request with the payload (data) to the client’s webhook URL.

Webhook vs API – What’s the Difference?

Feature	API (Pull)	Webhook (Push)
Trigger	Client sends requests	Server sends request on event
Direction	Pull from server	Push to client
Efficiency	Less efficient (polling needed)	More efficient (event-based)
Use case	General data fetching	Real-time event notifications

Benefits of Using Webhooks

Real-time updates

No more waiting or polling for updates. Get notified the moment something happens.

Reduced server load

No repeated API calls. Saves bandwidth and computing resources on both sides.

Simple and lightweight

Only one request is made—when it’s actually needed.

Event-driven design

Makes your app more responsive, especially for workflows involving third-party services like Stripe, Razorpay, or GitHub.

Better user experience

End users don’t have to refresh or wait unnecessarily to see changes.

Summary

Webhooks are a powerful mechanism to build real-time, responsive systems. They help reduce overhead, improve scalability, and deliver faster feedback loops in modern web applications.

Whether you’re building a payment system, integrating with external APIs, or handling background jobs, webhooks are a smart solution for triggering actions based on events.