DEV Community: Sean

I Tested GPT-5.4 vs Claude Opus 4.6 vs Gemini 3.1 Pro on 5 Real Coding Tasks

Sean — Mon, 30 Mar 2026 02:38:52 +0000

Why I Ran This Test

I use all three models daily for coding. But I've never put them head-to-head on the exact same tasks. So I designed 5 real-world coding challenges and ran each model through them.

No synthetic benchmarks. No cherry-picked examples. Just everyday dev work.

The 5 Tasks

Refactor a 400-line Express router into a layered architecture
Debug an async race condition
Generate CRUD endpoints from an OpenAPI spec
Document a 2000-line legacy codebase
Write unit tests with edge case coverage

Each task was run 3 times per model; I picked the best output.

Deep Dive

Refactoring - Claude Wins

Claude didn't just split the code - it understood the architecture. It identified two circular dependencies I hadn't even noticed and proposed clean solutions. GPT's output was solid but missed a middleware injection edge case. Gemini got the job done but with inconsistent naming conventions.

Debugging - Claude Edges Ahead

All three found the race condition root cause. The difference was in the fix quality. Claude's solution included mutex locking, retry logic, and timeout handling. GPT pointed in the right direction but left boundary handling as an exercise. Gemini suggested a mutex but forgot about timeout scenarios.

Code Generation - GPT is King

Given an OpenAPI spec, GPT-5.4 produced complete CRUD routes, validation middleware, and error handlers in record time. The code was nearly copy-paste ready. Claude was slightly slower but marginally higher quality. Gemini was middle-of-the-road here.

Long Context - Gemini Shines

This is where Gemini's massive context window pays off. It generated documentation covering every major data flow in a 2000-line legacy module, and even flagged potential performance bottlenecks. Claude's docs were high quality but occasionally missed details in very long functions. GPT struggled with the global picture.

Unit Tests - Everyone's Got Moves

Claude wrote the most thorough edge cases. GPT was fastest with the most standardized templates. Gemini got creative with failure scenario coverage. No clear winner.

My Takeaway

There's no single "best" model in 2026. The smartest strategy is model routing - pick the right model for each task:

Refactoring / debugging: Claude
Fast prototyping / boilerplate: GPT
Large codebase analysis: Gemini

If you're switching between models frequently, consider using a unified API gateway to manage multiple providers through a single endpoint. It saves a ton of integration overhead.

What's your experience? Drop your own comparison results in the comments.

I Used 4 AI Coding Tools for 3 Months. Here's My Honest Take.

Sean — Fri, 27 Mar 2026 03:25:12 +0000

I've been using AI coding tools full-time since January 2026. Not just testing them for a weekend - actually building production software with them daily.

After 3 months of real-world usage across Cursor, Claude Code, Windsurf, and GitHub Copilot, here's what I actually think.

The TL;DR

Tool	Best At	Worst At	Monthly Cost
Cursor	Everyday coding + agent tasks	Gets expensive fast	$20-60
Claude Code	Complex refactoring	Learning curve	$20-200
Windsurf	Value for money	Less powerful agent	$15
Copilot	Low-friction integration	Weaker autonomy	$10

But rankings don't tell the real story. Let me explain.

Cursor: The Daily Driver

Cursor is where I spend 80% of my coding time. Two features make it irreplaceable:

1. Tab Completion That Actually Understands Context

Cursor doesn't just complete the current line. It predicts what you're about to do across multiple lines and shows it as a diff. One Tab press, and an entire function gets restructured.

This alone saves me 30-40 minutes per day. No exaggeration.

2. Agent Mode (Composer)

"Hey Cursor, add input validation to all the form handlers in this project."

It scans the codebase, identifies the relevant files, makes the changes, and shows you a diff. You review and accept. Done.

The catch? Cursor's credit system means heavy usage adds up. If you manually select premium models (like Claude Opus), you can burn through $60/month easily. The "Auto" mode is unlimited but uses cheaper models - which is fine 90% of the time.

Verdict: Best all-around tool if you're willing to pay.

Claude Code: The Heavy Lifter

Claude Code is strange. It's a terminal tool with no GUI, no autocomplete, no syntax highlighting. You type what you want in plain English, and it goes and does it.

And somehow, it's the most reliable tool for complex tasks.

Where it shines:

I asked each tool to migrate an Express.js backend from JavaScript to TypeScript. Full migration - interfaces, type annotations, tsconfig, the works.

Claude Code: 12 files modified correctly on the first try
Cursor: 10/12 files, needed manual fixes
Windsurf: 8/12 files
Copilot: Needed 4+ rounds of prompting

The difference is Claude's reasoning ability. It doesn't just pattern-match - it understands what the migration requires and plans accordingly.

The catch? Rate limits are brutal. On the $20/month Pro plan, you get roughly 10-45 messages per 5-hour window. During peak hours, it's on the lower end. I've been mid-refactor when the rate limit hit, and there's nothing to do but wait.

Verdict: Best for complex, multi-file tasks. Frustrating rate limits.

Windsurf: The Underdog

Windsurf (formerly Codeium) doesn't get enough credit.

At $15/month, it offers:

Decent autocomplete (not Cursor-level, but solid)
Cascade - an agent feature that automatically indexes your codebase
A usable free tier (25 credits/month)

For 80% of daily coding tasks, Windsurf gets the job done. It won't blow your mind like Cursor's Tab completion, but it also won't blow your budget.

When I use it: Side projects, smaller codebases, and when I don't want to think about credit consumption.

Verdict: Best value. If budget matters, start here.

Copilot: The Safe Choice

GitHub Copilot is the Toyota Corolla of AI coding tools. It's reliable, affordable ($10/month), and works inside your existing IDE.

Pros:

Lowest friction to adopt (just install a plugin)
Great GitHub integration (auto PR descriptions, issue linking)
Decent autocomplete for common patterns

Cons:

Agent mode is weaker than Cursor or Claude Code
Less context-aware than dedicated AI editors
Multi-file changes often need manual guidance

When I use it: When I'm in a JetBrains IDE (where Cursor isn't available), or when I want completions without the overhead of a new editor.

Verdict: Best entry point. You'll probably outgrow it.

What I Actually Use Day-to-Day

Here's my real workflow:

Morning standup -> Plan tasks -> Small edits, bug fixes -> Cursor -> Large refactoring -> Claude Code -> Side project hacking -> Windsurf -> Quick PR reviews -> Copilot

I don't think there's a single "best" tool. The best setup is combining tools based on what each does best.

The Uncomfortable Truth About AI Coding Tools in 2026

After 3 months, here's what nobody talks about:

1. They're all expensive if you use them seriously.

$20/month sounds cheap until you realize that's the base price. Heavy usage pushes Cursor to $60+, Claude Code to $100-200. That's $1,200-2,400/year on AI coding tools alone.

2. Rate limits are the real differentiator.

The model quality across tools is converging. What actually matters day-to-day is: can I keep working without hitting a wall? Right now, Cursor (with Auto mode) and Copilot handle this best.

3. They make you faster but not necessarily better.

I ship features 2-3x faster now. But I've caught myself accepting AI-generated code without fully understanding it. That's a trap. The tools should amplify your skills, not replace your thinking.

Final Scores

Dimension	Cursor	Claude Code	Windsurf	Copilot
Autocomplete	9	N/A	8	8
Agent/Autonomy	9	10	8	7
Value for Money	7	6	9	9
Ease of Use	9	6	9	10
Ecosystem	9	7	8	9

What's your setup? I'm curious how others are combining these tools. Drop a comment below - especially if you've found a workflow that works better than mine.

6 Months of Building in Public: What Actually Worked (and What Didn't)

Sean — Thu, 26 Mar 2026 01:42:42 +0000

Six months ago, I started building Praka — an AI API gateway — in public.

Every week: real numbers, real problems, real decisions. No polish, no PR spin.

Here's what I learned.

What "Building in Public" Actually Means

Not this:

"Excited to share we've hit a major milestone! 🎉"

This:

"We hit 1,000 users this week. Lost 3 of them on day 1 because our onboarding is confusing. Here's what I'm doing about it."

The difference is specificity + honesty. Vague updates help no one. Specific, honest updates build community.

The Unexpected Benefits

1. Your users find you

I've gotten my best customers from technical Twitter threads where I was just... thinking out loud about an architecture decision.

Someone reads "I'm trying to figure out how to route AI API calls based on request complexity" and thinks "wait, that's exactly my problem."

They check out what I'm building. They sign up.

No ad spend. No SEO tricks. Just thinking publicly.

2. Community gives better feedback than surveys

I posted a thread asking "Should I add streaming support or multi-turn conversation memory first?"

Within 24 hours: 47 replies, 12 DMs. Not just votes — explanations. Use cases I hadn't thought of. Edge cases I'd have hit six months later.

That's a free product design session.

3. It actually makes you ship faster

Sounds counterintuitive, right? Doesn't thinking-in-public take time away from building?

Here's what I found: knowing I'm going to post an update on Friday makes me scope work differently. Instead of "I'll finish this big feature next week," it becomes "what can I ship this week that's worth talking about?"

Smaller batches. Faster feedback loops. Classic agile, accidentally enforced by social accountability.

What Didn't Work

Hot takes don't build trust

Early on, I posted some "hot take" style tweets about AI APIs to get engagement. They worked — for a day. Lots of retweets, zero meaningful follows.

The posts that actually converted readers to users? The ones where I shared a specific technical decision and why I made it.

Vanity metrics lose you credibility

"We're growing 20% week-over-week!" sounds impressive until someone does the math and realizes you started from 10 users.

Be specific. "We went from 800 to 960 users this week" is more credible than percentages — especially when the numbers are small.

Daily posting burns you out

I tried posting every day for a month. By week 3, I was manufacturing content. It showed, and engagement dropped.

Now I post meaningful updates when I have something real to say. Weekly is my natural rhythm.

My Actual Weekly Update Format

Here's the template I've settled on:

Week [N] of building [product]:

📈 This week:
• [Specific metric with real number]
• [Feature shipped]
• [Problem encountered]

🤔 Hardest decision this week:
[One specific tradeoff I made and why]

💡 What I learned:
[One concrete, transferable insight]

Next week I'm working on: [specific thing]

---
[link]

The constraint of this format forces me to actually think, not just report.

A Technical Example: Our Model Routing Decision

Here's the kind of thing I post. This is from week 14:

Spent 3 days debating how to handle model routing in Praka.

Option A: Let users specify which model per request (simple, explicit)
Option B: Auto-route based on request complexity (smart, but who decides "complex"?)

We went with A. Reason: developers hate magic. They want predictability.

Added a recommendation UI so users can see what we'd suggest, but they always have final say.

Lesson: when in doubt, give developers control.

This kind of post:

Demonstrates technical thinking
Shows you value user feedback
Gives other devs something to agree/disagree with
Humanizes the product

The Numbers After 6 Months

I'm not going to fake-humble my way through this. Here's real data:

Metric	Month 1	Month 6
Weekly Twitter impressions	~400	~18,000
Profile visits from build-in-public posts	—	~1,200/week
Users who mentioned finding me via Twitter	—	~30%
Time spent on "marketing"	0 hrs	3-4 hrs/week

The compounding effect is real. Early posts get found months later. A thread I wrote in month 2 still brings in 2-3 signups a week.

Should You Build in Public?

It's not for everyone. You need to be comfortable with:

Being wrong publicly
Sharing numbers that might seem small
Criticism from strangers
The discipline to be consistent

But if you're an indie developer or small startup trying to grow without a marketing budget, I don't know a better way.

The barrier to building in public is almost zero. A Twitter account. A commitment to honesty. One update a week.

Start before you're ready.

I'm building Praka — one API endpoint for 100+ AI models, 70% cheaper than going direct. If you've been thinking about building in public and want to talk shop, drop a comment below.

Build Your Own OpenClaw AI Assistant in Just 5 Minutes (Two Ways)

Sean — Wed, 25 Mar 2026 02:10:49 +0000

Tired of AI that only chats but never actually does stuff?

Meet OpenClaw — your open-source personal AI lobster 🦞 that lives in WhatsApp, Telegram, Discord, Slack, and more. It remembers everything about you, runs tasks autonomously, clears your inbox, schedules meetings, browses the web, runs commands, and stays completely private on your machine.

The crazy part? You can have a fully working OpenClaw assistant in under 5 minutes — and there are now two super easy ways to do it.

Why OpenClaw is getting popular fast in 2026

Runs locally (Mac, Windows, Linux) → your data never leaves your control
Persistent memory → it actually learns you
Works in the messaging apps you already use
Autonomous mode + community skills/plugins
Supports OpenAI, Claude, Gemini, local models (Ollama), etc.

Method 1: Traditional Deployment (Full Control)

Perfect if you love tinkering and want maximum customization.

Step-by-step (still under 5 minutes)

Clone the repo

   git clone https://github.com/openclaw/openclaw.git
   cd openclaw

Install dependencies

   npm install

Create .env and add your keys

   OPENAI_API_KEY=sk-...
   # or ANTHROPIC_API_KEY=...
   TELEGRAM_BOT_TOKEN=your_bot_token_here   # get from @BotFather

Run it

   npm run dev

Done! Send a message to your bot like “Summarize my unread emails” and watch it work.

Method 2: Praka One-Click Deployment (Zero Config)

If you hate setup and just want it to work instantly, this is the winner.

Praka now supports OpenClaw one-click deployment — literally no manual configuration needed.

Just head over to Praka, choose the OpenClaw template, click Deploy, and your assistant is live in seconds. It automatically handles the environment, API routing, and basic connections, while still giving you full control later if you want it.

This is perfect for developers who want to test OpenClaw fast or run multiple instances without the usual .env file dance.

Which one should you choose?

Want full control + custom skills? → Traditional way
Want it running in < 60 seconds with zero hassle? → Praka one-click

I personally used the Praka method last week and had my assistant texting me useful stuff before my coffee got cold ☕

Pro Tips Once It's Running

Connect it to multiple chat platforms at once
Enable autonomous heartbeat for proactive tasks
Feed it your personal context so the memory becomes insanely useful
Explore the growing ecosystem of community skills

Final Thoughts

OpenClaw turns the “AI assistant” hype into something you can actually use every day — and with both the traditional and Praka one-click options, there’s zero excuse left.

I set mine up recently and already rely on it more than most SaaS tools.

Huge thanks to the OpenClaw team and especially Praka for making deployment so smooth.

Have you tried OpenClaw yet? Which deployment method did you (or will you) use?

Drop your experience or wildest use-case in the comments! 👇

OpenClaw Deployment Guide: Two Ways to Quickly Run a Computer-Use Agent

Sean — Tue, 24 Mar 2026 09:39:56 +0000

I. What Is OpenClaw?

As AI Agents evolve, more models are gaining Computer Use capabilities — AI doesn't just chat, it can operate a computer:

Browse web pages automatically
Fill out forms automatically
Execute tasks automatically

OpenClaw is an open-source framework for this: See the screen → Understand the interface → Operate the computer.

II. Why Do Many Deployments Fail?

Common pain points: complex dependencies, GPU requirements, difficult model config, long toolchain. A typical setup covers Python env → install deps → download model → configure API → configure Agent tools — high barrier for beginners.

This article covers two approaches: manual local deploy + online platform.

III. Method 1: Local Manual Deployment

Best for: developers and those who want full control.

1. Requirements

OS: macOS, Linux, or Windows (WSL2/Ubuntu strongly recommended for Windows)
Runtime: Node.js 22.x+
RAM: 2 GB min, 4 GB+ recommended

2. Deploy

Option A — Official one-click script (macOS/Linux/WSL2):

curl -fsSL https://openclaw.ai/install.sh | bash
# Then start with:
openclaw

Option B — Manual NPM install (developers):

mkdir my-openclaw && cd my-openclaw
npx webclaw

⚠️ On a VPS with < 2 GB RAM, add at least 4 GB Swap before npm install to avoid OOM kills.

3. Configure the LLM ("Brain")

Open dashboard at http://localhost:18789:

☁️ Cloud API: Add API key for Claude / OpenAI / DeepSeek / Kimi. Recommended: Claude 3.5 Sonnet or DeepSeek-V3.
🖥️ Local model: Use Ollama or LM Studio, point base URL to http://localhost:11434/v1.
SOUL.md: Write a detailed system prompt with the agent's identity and SOP — don't just say "you are a helpful assistant."

4. Connect a Channel

Telegram (recommended for beginners):

Find @BotFather → /newbot → copy HTTP API Token
Dashboard → Channels → Telegram → paste token → save
Message your bot — it responds instantly

IV. Method 2: Online Platform Deployment

Platforms like Tencent Cloud, MiniMax, Prakasa, SetupClaw pre-configure the environment. You just: Register → Select OpenClaw template → Deploy — up in minutes. No dependency headaches.

V. Comparison

	Local Deploy	Online Platform
Difficulty	High	Low
Speed	Slow	Fast (minutes)
Cost	Free (own hardware)	Usually paid
Privacy	High (data stays local)	Platform-dependent
Customization	Full control	Limited
Best for	Developers	Beginners

VI. Summary

Computer Use is becoming the next major AI capability. OpenClaw lets AI operate software, execute tasks, and automate work. Quick experience? → Online platform. Deep research? → Local deployment.