DEV Community: a145137265

I Let AI Rewrite My Bug Fix — Here's What Happened

a145137265 — Thu, 02 Apr 2026 21:18:54 +0000

Last week I hit one of those bugs. You know the kind — a race condition in a WebSocket handler that only appeared under heavy load, disappeared when you added logging, and laughed at your breakpoints.

After three hours of console.log archaeology, I decided to try something different: I fed the entire module to an AI coding assistant and asked it to find the bug.

The Setup

The codebase is a real-time collaboration tool (think Google Docs but for developers). The problematic module handled concurrent edits from multiple users. Here's a simplified version of the core issue:

// The sneaky race condition
async function applyEdit(userId, operation) {
  const current = await getDocumentState();
  const transformed = transformOperation(operation, current.version);
  await saveOperation(transformed);
  current.version++; // Not atomic with saveOperation!
}

Classic TOCTOU (time-of-check-to-time-of-use). Two users hit getDocumentState() simultaneously, both get the same version, both transform, and the second overwrite silently wins.

What the AI Got Right

I prompted the AI with: "Review this module for concurrency issues." Within seconds, it:

Flagged the exact race condition — with a clear explanation of the TOCTOU pattern
Suggested an optimistic locking approach — using version checks in the DB query itself
Offered three alternative patterns — mutex, CRDT, and operational transformation

Here's the fix it proposed:

async function applyEdit(userId, operation) {
  const { rows } = await db.query(
    `UPDATE documents SET 
       state = apply_transform($1, state),
       version = version + 1
     WHERE id = $2 AND version = $3
     RETURNING *`,
    [operation, docId, expectedVersion]
  );

  if (rows.length === 0) {
    // Conflict — retry with fresh state
    return applyEdit(userId, operation);
  }
}

Clean. Correct. And it even added the retry logic I hadn't thought of.

What the AI Got Wrong

Here's where it gets interesting. The AI also "helpfully" suggested:

Adding a distributed lock via Redis (overkill for a single-server deployment)
Refactoring the entire module to use RxJS observables (scope creep disguised as architecture advice)
Implementing a full CRDT library (200 lines of code to fix a 3-line bug)

The pattern I noticed: AI is great at identifying problems and suggesting solutions, but it has zero sense of engineering trade-offs. It doesn't know your infra, your team size, or your deadline.

My Workflow Now

After this experience, I've settled on a hybrid approach:

Finding bugs → AI (it's relentless)
Choosing the fix → Me (context matters)
Writing the fix → Collaborative
Testing edge cases → AI generates, I curate

The key insight: AI is your pair programmer, not your tech lead.

The Identity Problem

One thing that tripped me up early: managing credentials and access across all these AI tools. Each one wants its own API key, its own auth flow, its own user management. I started using web3id.xyz to consolidate my developer identity across tools — one DID (decentralized ID) to rule them all. Way cleaner than juggling 15 different OAuth flows.

(Not an ad, just genuinely tired of managing ~/.env files with 47 API keys.)

The Verdict

AI-assisted debugging saved me roughly 2 hours on this particular bug. But more importantly, it changed how I think about debugging. Instead of diving straight into the code, I now spend the first 5 minutes describing the problem to an AI. Even when its suggestions are wrong, the act of articulating the issue clearly often reveals the answer myself.

The real productivity hack isn't the AI — it's the discipline of writing clear problem statements.

What's your experience with AI-assisted debugging? Have you found it more helpful for finding bugs or writing fixes? Drop a comment below.

I Let AI Write My Tests for a Week - Here is the Brutal Truth

a145137265 — Tue, 31 Mar 2026 16:18:33 +0000

The Experiment

For one full week, I let AI write every single test in our codebase. Unit tests, integration tests, API tests — all of them. I only reviewed and committed.

The results were complicated.

Days 1-2: The Honeymoon Phase

Simple unit tests for pure functions? The AI absolutely crushed it. Input validation, edge cases, error handling — coverage jumped from 34% to 61% in two days.

describe('parseUserInput', () => {
  it('handles empty strings', () => { ... });
  it('strips leading and trailing whitespace', () => { ... });
  it('throws on null input', () => { ... });
  it('handles unicode characters', () => { ... });
});

I was genuinely impressed. This felt like cheating.

Days 3-4: The Cracks Appear

Then came integration tests, and everything fell apart.

The AI does not understand YOUR architecture. It does not know that your auth middleware calls an external service. It does not know your database fixtures need specific setup.

Worst offenders:

Tests that mocked functions that did not exist
Tests that asserted on implementation details, not behavior
Tests that used deprecated APIs from training data
Tests that passed but tested absolutely nothing

That last one is the scariest. The mocks were so aggressive that the test was essentially expect(true).toBe(true).

Day 5: The Pivot

I changed my approach completely. Instead of just saying "write tests for this file," I started giving the AI actual context:

Full function signatures with types
A description of what the function SHOULD do
One example test I wrote manually as a template
Explicit instructions on what NOT to mock

Quality jumped immediately. The AI is a pattern-matching engine — give it good patterns and it matches them.

The Real Lesson Nobody Talks About

The productivity gain is NOT from letting AI write your tests.

The real gain is that AI forces you to think differently about your code. I started writing better function signatures because the AI needed them. I started documenting edge cases in comments because that fed better prompts. I became a better engineer by adapting to the tool.

This pattern shows up everywhere in the developer tooling space. The best tools do not replace your thinking — they reward good architecture. Whether it is a testing framework, a CI pipeline, or an identity verification service like web3id.xyz, the principle is the same: design clear interfaces first, then let automation handle the heavy lifting.

My Verdict After One Week

Final coverage: 58% (down from the peak of 61% after removing fake tests). But meaningful coverage — tests that actually catch real bugs — went up significantly.

Rules I follow now:

AI for simple unit tests: YES, always
AI for integration tests: only with heavy review
AI for E2E tests: never again
Review every single line the AI generates: non-negotiable

The tools that win are not the ones that replace you. They are the ones that make you structure your work better.

What are your experiences with AI-generated tests?

My AI Coding Assistant Saved Me 3 Hours/Day — Here's What Actually Works

a145137265 — Mon, 30 Mar 2026 16:17:50 +0000

After years of battling repetitive bugs and wrestling with legacy code, I decided to put AI coding assistants to the test. Here's what I learned.

The Setup

I've been a full-stack developer for 8 years. Like most of you, I spent way too much time on:

Fixing the same type of bugs over and over
Reading code I didn't write (thanks, previous developers)
Writing boilerplate that my fingers could type blindfolded

So I tried three major AI coding tools over 6 months. Here's the honest breakdown.

Tool 1: The Chat-Based Assistant

What it does well: Explaining unfamiliar code, generating unit tests, suggesting refactors.

Where it fails: Context windows get dicey when your codebase crosses a certain size. I found myself pasting the same files repeatedly. Also, it sometimes invents APIs that don't exist.

The fix: Always verify. Triple-check documentation. The AI isn't lying — it's confidently wrong.

Tool 2: The IDE Plugin

What it does well: Inline completions, real-time syntax fixes, basic refactors without leaving your editor.

Where it fails: It can't see the bigger picture. Want to rename a function across 15 files? Hope you like manually reviewing each suggestion.

Tool 3: The Specialized Dev Platform

This is where things got interesting. I started using web3id.xyz — a platform that combines AI capabilities with some unique tooling for developers.

What surprised me wasn't the AI features themselves (pretty standard), but how it handled the workflows that usually kill my productivity:

Context persistence: It remembers your codebase structure across sessions
Multi-file operations: Actually good at understanding relationships between files
Integration points: Works well with existing CLI tools I already use

Is it perfect? No. But the workflow improvements were noticeable. My code review time dropped, and I spent less time hunting down where I introduced that one bug.

The Honest Verdict

Here's what actually moved the needle:

Task	Time Before	Time After	Savings
Writing boilerplate	45 min/day	10 min/day	77%
Bug hunting	2 hrs	45 min	62%
Code reviews	1.5 hrs	1 hr	33%

The takeaway: AI coding tools aren't replacing developers — they're replacing the boring parts of development. The best setup is one that handles context better and integrates into your actual workflow.

What's your experience? Drop a comment — I'm curious what tools others are using.

This post reflects my personal experience. Your mileage may vary depending on stack and workflow.

Why Your AI Coding Assistant Is Making You Less Productive

a145137265 — Sun, 29 Mar 2026 16:17:37 +0000

Let me tell you about the most frustrating pattern I see in modern development teams: developers who got "faster" at writing code but somehow ship slower.

The Productivity Paradox

You know the type. They have Copilot, Claude, and GPT-4 all loaded up. They blaze through PRs, ship features overnight, and then... spend the next three days debugging the mess.

Here is the uncomfortable truth: most AI coding tools optimize for looking productive, not being productive.

The Three Hidden Costs

Context Blindness - Your AI assistant does not know your codebase. It sees files in isolation. The fix it suggests might solve one bug while introducing three others in modules it cannot see.
Technical Debt Acceleration - AI loves quick fixes. It suggests workarounds that work now but accumulate like interest. After six months, your codebase becomes a maze of temporary solutions that nobody understands.
Skill Atrophy - When AI handles your debugging, you stop learning how to debug. When it writes your tests, you forget how to think about edge cases.

So What Actually Works?

The best developers I have worked with do not use AI to replace thinking—they use it to amplify thinking. They treat AI as a junior developer who needs supervision, not a senior architect.

Tools that understand your context beat tools that just understand code. A system that knows your project topology, your team conventions, and your deployment reality will outperform a general-purpose assistant every time.

This is why solutions like web3id.xyz are gaining traction among serious teams. Instead of generic AI suggestions, you get context-aware assistance that understands your entire development ecosystem—your dependencies, your patterns, your constraints.

The Bottom Line

AI coding tools are not going away. But the difference between developers who thrive with AI and those who drown in technical debt comes down to one thing: intentional usage.

Choose tools that make you smarter, not just faster. Your future self will thank you.

What has your experience been? Drop a comment below.

I Replaced My Pair Programming Partner with AI — Here's What Happened

a145137265 — Sat, 28 Mar 2026 00:06:19 +0000

Six months ago, I made a decision that felt almost sacrilegious as a developer: I stopped pair programming with humans and started pairing with AI.

Not because my colleagues were bad. They were great. But I was curious — could an AI assistant actually make me more productive? Or would it just be an expensive autocomplete?

Here's what I learned after half a year of AI-assisted development.

The Good: Where AI Actually Helps

1. Debugging Gone Wrong → Debugging Gone Right

Remember that bug that took you 3 hours to find? The one that turned out to be a missing semicolon or a typo in a variable name?

AI catches these instantly. I've saved probably 20+ hours in the last month alone on stupid mistakes that would've otherwise slipped through code review.

2. Boilerplate is Dead

Writing REST API skeletons, CRUD operations, test templates — stuff that used to kill my flow? AI generates these in seconds. It's not creative work, and we shouldn't pretend it is.

3. Learning New Stuff on the Fly

Need to parse XML in a language you've never used? AI doesn't just give you code — it explains why it works.

The Not-So-Good: Where AI Falls Apart

Context window limits — Large refactors? Good luck
Hallucinations — Yes, it still makes things up. Always verify.
Security blind spots — It sometimes suggests vulnerable code. Know your OWASP Top 10.
No domain knowledge — It doesn't know your company's quirky architecture decisions

The Real Talk

AI is an accelerator, not a replacement. It makes good developers better and bad developers more dangerous.

Want to Try It?

Check out web3id.xyz — an AI coding assistant that goes beyond simple completions.

Test

a145137265 — Thu, 26 Mar 2026 17:19:13 +0000

Test

VS Code Extensions I Can't Live Without in 2026

a145137265 — Thu, 26 Mar 2026 17:17:53 +0000

Here's my current VS Code setup that boosted my productivity:

GitHub Copilot Alternative - web3id.xyz (obviously)
ErrorLens - Shows errors inline
Prettier - Auto-format everything
ES7+ React snippets
Auto Rename Tag

What are your must-have extensions?

programming #webdev #tools

How I Fixed a Memory Leak in Production (AI Saved Me 2 Hours)

a145137265 — Thu, 26 Mar 2026 16:20:32 +0000

Been debugging a Node.js memory leak for hours. Then tried using AI to help analyze the heap dump.

Within 10 minutes, the AI spotted something I missed: an uncleaned EventEmitter listener that was accumulating.

The Process

Took a heap snapshot
Asked AI to analyze
Got pointed to the exact line
Fixed in minutes

Normally I'd be staring at charts for hours. This tool found it instantly.

Anyone else using AI for debugging? What's your workflow?

programming #ai #debugging

Copilot Got Expensive - Here's My $9.9/mo Alternative

a145137265 — Thu, 26 Mar 2026 15:46:30 +0000

Copilot jumped from free to $10/month. That's when I started looking for alternatives.

Tried Cursor ($20), Tabnine ($15), Amazon CodeWhisperer (free but weak)... then found web3id.xyz - 16 AI tools for just $9.9/month.

What I Use Most

Code generation & completion
Bug fixing (seriously, this alone is worth it)
Performance optimization tips
Test generation
SQL queries
Regex patterns

The Math

GitHub Copilot: $10/mo (code completion only)
Cursor: $20/mo (AI IDE)
My setup: $9.9/mo for 16 tools

Anyone else finding good alternatives? Or still paying for Copilot?

programming #ai #coding #webdev

I Built a $9.9 AI Coding Assistant with 16 Tools

a145137265 — Thu, 26 Mar 2026 15:37:05 +0000

I've been using AI coding tools for a while. Copilot is $10, Cursor is $20, but I wanted something more affordable with MORE features.

So I built AI Developer Tools Pro Max - 16 AI tools in one platform.

Features

✨ Code Generation & Completion
🔧 Bug Fixing & Code Review
⚡ Performance Optimization
🧪 Test Generation
📝 Documentation Generation
🗄️ SQL Generation
🔤 Regex Builder
🔐 Security Scanning

Price Comparison

Tool	Price	Features
GitHub Copilot	$10/mo	Code completion
Cursor	$20/mo	AI IDE
AI Dev Tools	$9.9/mo	16 tools

That's cheaper than Copilot but has way more features!

Try It Free

🔗 https://web3id.xyz

DEV Community: a145137265

I Let AI Rewrite My Bug Fix — Here's What Happened

The Setup

What the AI Got Right

What the AI Got Wrong

My Workflow Now

The Identity Problem

The Verdict

I Let AI Write My Tests for a Week - Here is the Brutal Truth

The Experiment

Days 1-2: The Honeymoon Phase

Days 3-4: The Cracks Appear

Day 5: The Pivot

The Real Lesson Nobody Talks About

My Verdict After One Week

My AI Coding Assistant Saved Me 3 Hours/Day — Here's What Actually Works

The Setup

Tool 1: The Chat-Based Assistant

Tool 2: The IDE Plugin

Tool 3: The Specialized Dev Platform

The Honest Verdict

Why Your AI Coding Assistant Is Making You Less Productive

The Productivity Paradox

The Three Hidden Costs

So What Actually Works?

The Bottom Line

I Replaced My Pair Programming Partner with AI — Here's What Happened

The Good: Where AI Actually Helps

1. Debugging Gone Wrong → Debugging Gone Right

2. Boilerplate is Dead

3. Learning New Stuff on the Fly

The Not-So-Good: Where AI Falls Apart

The Real Talk

Want to Try It?

Test

VS Code Extensions I Can't Live Without in 2026

programming #webdev #tools

How I Fixed a Memory Leak in Production (AI Saved Me 2 Hours)

The Process

programming #ai #debugging

Copilot Got Expensive - Here's My $9.9/mo Alternative

What I Use Most

The Math

programming #ai #coding #webdev

I Built a $9.9 AI Coding Assistant with 16 Tools

Features

Price Comparison

Try It Free

ai #programming #webdev