How I Use Admix to Write Code 3x Faster

My Coding Workflow Before and After AI

I'm a full-stack developer working mostly with TypeScript, React, and Node.js. I've been coding professionally for seven years. AI hasn't made me a better programmer, but it has made me a faster one. The "3x faster" claim in the title isn't hype. I've tracked my output over the past year, and the numbers back it up for certain types of work.

Here's my actual workflow, not a theoretical best case. This is what I do every day.

The Key Insight: Different Models for Different Coding Tasks

Before I started using Admix, I used ChatGPT for everything code-related. It was fine, but I noticed that it was better at some tasks than others. Boilerplate generation? Great. Complex architecture decisions? Mediocre. Debugging obscure errors? Hit or miss.

When I switched to Admix and could easily try different models, I discovered that the quality of AI coding assistance depends heavily on matching the model to the task. This sounds obvious, but when switching models requires opening a different app, you don't bother. When it's a dropdown menu, you do it naturally.

Task 1: Scaffolding and Boilerplate

Model I use: GPT-4o

Why: It's fast, and scaffolding doesn't need the most capable model. When I need a new API endpoint, React component, or database migration, GPT-4o generates clean boilerplate in seconds.

Example prompt: "Create a REST endpoint in Express.js with TypeScript for a user profile update. Include input validation with Zod, error handling, and return types."

The output is usually 80-90% ready to use. I adjust naming conventions to match my project, add project-specific logic, and move on. This takes 5 minutes for something that used to take 20-30 minutes of typing.

Task 2: Debugging and Error Resolution

Model I use: Claude Sonnet

Why: Claude is better at understanding context and reading through larger code blocks. When I paste 200 lines of code with an error, Claude consistently identifies the issue more accurately than other models I've tried.

My debugging workflow:

1. Copy the relevant code and the error message
2. Paste into Admix with Claude Sonnet selected
3. Ask: "This code produces [error]. What's causing it and how do I fix it?"
4. Claude usually identifies the problem on the first try. If not, I provide more context and ask again.

The time savings here are huge. Some bugs that would take me an hour of console.log debugging get solved in 2 minutes. Not every bug, but enough of them that the overall debugging time has dropped significantly.

Task 3: Code Review

Model I use: Claude Opus

Why: For code review, I want the most thorough analysis possible. Claude Opus catches things that other models miss: potential race conditions, edge cases, security concerns, and performance issues.

Before submitting a pull request, I paste my changes and ask: "Review this code for bugs, performance issues, security concerns, and readability. Suggest specific improvements."

This doesn't replace human code review from my teammates. But it catches the easy stuff before they see it, which means their review time is spent on architecture and design decisions instead of pointing out that I forgot a null check.

Task 4: Complex Logic and Algorithms

Model I use: DeepSeek R1 or o3

Why: Both reasoning-focused models are strong at complex logic. When I need to implement a tricky algorithm, design a state machine, or figure out an efficient data structure, these models outperform general-purpose ones.

I don't use them to generate the final code directly. Instead, I describe the problem and ask for an approach. The model outlines the algorithm, I review the logic, and then I implement it myself (or ask a faster model to generate the implementation based on the approach).

Task 5: Writing Tests

Model I use: GPT-5

Why: Writing tests is where AI saves the most time for me. GPT-5 generates comprehensive test suites quickly. I paste a function and ask: "Write unit tests covering all edge cases, including error states and boundary conditions."

The tests usually need some adjustment for my testing framework's conventions and mocking setup. But getting from zero to a comprehensive test suite in 5 minutes instead of 30 minutes is a real productivity multiplier. And because the AI is good at identifying edge cases, the test coverage is often better than what I'd write manually in a hurry.

Task 6: Documentation

Model I use: Claude Sonnet

Why: Claude writes the most natural documentation. I paste a module and ask for JSDoc comments, README sections, or API documentation. The output reads well and covers the important details.

I used to skip documentation because it felt like a chore. Now it takes 2 minutes per module, so I actually do it. My codebase is better documented than it's ever been, which makes onboarding new team members much easier.

The Numbers

Over the past 6 months, here's what I've tracked:

• Pull requests per week: Went from about 4 to about 10
• Time to first commit on new features: Down roughly 60%
• Bug reports from QA: Down about 30% (probably due to AI-assisted code review and testing)
• Test coverage: Up from 65% to 82%

The "3x faster" applies to specific tasks like scaffolding and test writing. Overall productivity increase, accounting for tasks where AI doesn't help much (meetings, design discussions, deployment issues), is closer to 2x. Still significant.

What Doesn't Work

AI is not helpful for system architecture decisions on complex projects. It gives generic advice that doesn't account for your team's specific constraints, technical debt, and business requirements. I've learned to stop asking AI for architecture guidance and use it for implementation instead.

AI also struggles with project-specific context. It doesn't know your codebase's conventions, your team's preferred patterns, or your production environment's quirks. Including this context in every prompt helps, but it's still a limitation.

The Setup

My tools: VS Code, terminal, and Admix in a browser tab. I'm on the Starter plan ($10/month, or $8/month billed annually), which includes 500 credits per month. Switching between models takes one click, and the picker shows which models consume credits faster.

If you're a developer who hasn't tried using multiple models for different coding tasks, give it a shot. The free Admix plan with 20 free credits per day is enough for a couple of days of testing. You'll quickly see which models work best for your specific tech stack and coding style.