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It IS the tools
Last time, we observed that minor differences, no matter how fiercely debated, have little to do with a project's success or failure.
But it's indisputable that tools make a tremendous difference in productivity, developer experience, and, because of that, project outcome. So what's going on?
No tool will fix a broken strategy, but the right tool within your chosen strategy can amplify momentum when things are going well. Look for tools that
reduce friction
lighten the cognitive load
preserve optionality
Let's talk about the first one today.
Fast is better than slow.
Friction is the silent killer of projects, a death by a thousand paper cuts. Prioritize tools that do the same thing but faster, with fewer context switches and minimal setup. These tools might cost more upfront, but the savings in avoided mistakes and reclaimed time often outweigh the price tag.
This principle applies across every phase of a project:
Your IDE should make finding what you need quick and seamless.
Your test/build pipeline: The faster it runs, the less likely you lose focus or momentum while waiting for results.
Your code review process: Keep it swift. Bottlenecks from slow reviews can kill progress. Better yet, use non-blocking reviews whenever possible.
Your project management approach: It should make deciding what to work on next fast and intuitive, without endless meetings
Your MLOps setup: Rapid feedback is critical. Tools that streamline training, model evaluation, and deployment save hours of manual work and debugging.
Your CI/CD system: Deployments should be fast and reliable so you can focus on delivering value instead of firefighting pipelines.
Your data workflows: ETL pipelines that are slow or opaque make exploring data and iterating on features an uphill battle. Invest in tools that make it easy to preprocess and validate data.
Your communication tools: Team collaboration should be fast and frictionless. A slow or cumbersome chat, documentation, or ticketing system can sap time and energy.
As engineers, we’re encouraged to be scrappy and do more with less. That’s laudable, but if it makes us spend time on things tangential to the problem we’re solving, it’s a false economy.
It’s not the tools!
Many AI initiatives fail from the start. Some sources put the number at 80% or higher. We'll talk plenty about the why and how in this newsletter, but today, I want to talk about one thing that's not it: the tools.
Practitioners in any discipline have strong opinions about their preferred tools and tech stacks at just about any altitude:
Which editor/IDE is best?
AWS, GCP or Azure?
MySQL or PostgreSQL?
Pytorch versus Tensorflow
React versus Angular versus whatever the new sexy frontend framework is (Svelte?)
Our engineering mindset encourages us to look for the elusive "optimal" choice, but...
Here's the thing
There is often an inverse relationship between the fierceness of the debate over which tool is best and its ultimate impact on the project outcome. That's because the things that matter can be discussed (relatively) objectively. The critical decision lies in the category:
Microservices or monolith
Relational database or NoSQL
Classical ML, special-purpose neural network, or GenAI LLM?
Once those have been made, the rest does come down to personal preferences and tastes.
Keep it simple
Your project won't go up in smoke just because you selected Django over Ruby on Rails, your infrastructure runs on Azure rather than AWS, or you picked TensorFlow instead of PyTorch. So choose one, move forward, and concentrate your energy on the choices that truly matter.