The current enterprise software market is a minefield of artificial intelligence hype. Every legacy SaaS tool has slapped an “AI-powered” badge onto their sidebar menu, and venture capital keeps flooding into wrappers that charge $30 a month per user to do something a basic shell script could handle for free.
At Dynamic Build Machine, we test software tools ruthlessly. Because we function as a lean studio, we don’t have the time or the budget to tolerate productivity theater. If a tool saves us measurable hours during a development sprint, it stays in our stack. If it introduces friction or costs more than the manual labor it purports to replace, we drop it instantly.
Here is the brutally honest breakdown of the AI tools that have earned a permanent spot in our daily engineering stack, the ones we tested and discarded, and what our choices mean for software builders.
The Core Stack: What We Actually Use
1. Cursor (The Forked IDE)
We completely migrated our primary development environments from vanilla VS Code to Cursor. While standard GitHub Copilot is decent at inline autocomplete, Cursor treats the entire codebase as an active, indexable context layer.
Why it stays: It allows us to reference multiple distinct files simultaneously using simple @-rules. If we are changing a database relationship in our Supabase schema, we can ask Cursor to instantly update our React types and corresponding API endpoints across separate folders in a single pass. It reduces the mechanical friction of hunting through directories by 50%.
2. Claude 3.5 Sonnet (Via API and Anthropic Workbench)
For architectural brainstorming, complex script generation, and drafting boilerplate configuration files, Claude 3.5 Sonnet remains our default standard model.
Why it stays: It possesses a distinct edge in handling long, multi-file code contexts and outputting highly logical, well-structured code without skipping sections or resorting to pseudo-code shortcuts. We pipe it directly into our local setup for rapid prototyping sessions.
3. OpenAI API (Specifically for Structured Data Outputs)
When we are building production-grade integrations for clients — like the returns processing pipeline described in our AI audit post — we rely on OpenAI’s JSON Mode and Structured Outputs.
Why it stays: OpenAI’s system is incredibly reliable at enforcing strict schema validation. If we define an API schema requiring a string, an integer, and a specific enum array, their models consistently output compliant JSON that our backend scripts can parse without throwing uncaught exceptions.
The Discarded Stack: What We Tried and Dropped
1. Autonomous AI Software Engineers
We spent considerable time testing platforms that promise to take a natural language prompt and build an entire application from scratch independently.
Why we dropped it: They are excellent at generating basic isolated scripts, but they break down when introduced to large, real-world codebases with messy dependencies. We found ourselves spending more time debugging the strange architectural choices made by the autonomous agent than it would have taken an experienced engineer to write the code cleanly from scratch.
2. Automated AI Meeting Notes and Calendaring Agents
We experimented with AI note-taking bots that transcribe conversations and generate automated action items.
Why we dropped it: The summaries were consistently flooded with generic, low-priority fluff while frequently missing the highly nuanced, critical technical specs that actually dictate project scope. We went back to having a human write down three bullet points of actionable tasks at the end of a meeting.
3. AI Presentation and Slide-Deck Builders
We evaluated platforms that claim to generate professional business decks based on a text prompt.
Why we dropped it: The outputs are the definition of low-effort corporate filler — predictable layouts filled with generic stock imagery and empty buzzwords. As an agency that prides itself on direct, honest, and data-backed communication, using these tools felt antithetical to our entire brand philosophy.
What the Tool Gap Means for Clients
When an agency is caught up in the AI hype cycle, the client ultimately pays the price. Teams that over-rely on unproven, automated platforms end up delivering fragile, over-complicated software products that are difficult to maintain and scale.
By keeping our internal AI footprint lean and highly focused, we achieve immense development speed without sacrificing architectural integrity. We don’t use AI to escape the hard work of deep software design. We use it to wipe out the boring, repetitive administrative boilerplate so we can spend our energy solving the complex, high-stakes engineering problems that matter.
Build your stack around utilities that make your hands faster, not tools that try to do your thinking for you.