Launches 
Preflights 
Most coding AIs fall apart the moment your project stops being a toy. They lose the plot, hallucinate changes, and waste your day with “almost right” code. Giga AI fixes that by keeping your assistant anchored to your goals and your real codebase context.
Your AI isn’t dumb. It’s blind.
You’re not “bad at prompting”. Your tool just doesn’t know what matters in your app once the codebase gets big.
That’s when the errors pile up: wrong files, wrong patterns, broken imports, half-refactors that leave landmines for tomorrow.
Here’s the deal: Giga AI sells context engineering - a way to feed your assistant the right project context so it stops guessing and starts acting like it has been in the repo longer than you have.
The problem: big repos break most AI workflows
Chat tools and basic IDE copilots work fine on small snippets.
Then your app grows.
Now every change touches multiple folders, shared types, old decisions, and invisible rules your team follows but never wrote down. Your AI can’t see that. So it “helps” by shipping random code that looks legit and fails in the spots you won’t notice until CI screams.
What Giga AI actually does
Giga AI focuses on keeping the model pointed at:
- Your goals (why you’re building the feature, not just what file you’re in)
- Your codebase (the structures and constraints that make your app yours)
- The right scope (so the assistant stops wandering into side quests)
Result: fewer AI errors, fewer back-and-forth retries, and more first-pass output you can ship.
Fits your setup (no weird workflow tax)
Giga AI positions itself as compatible with the tools builders already use: VSCode, Cursor, Claude, Codex, and more.
That matters because nobody wants “yet another editor”. You want your current setup to stop lying to you.
Why this matters if you ship for a living
When AI fails, it fails loud:
- You waste an hour debugging code you didn’t even want
- You lose trust and go back to manual work
- Your velocity tanks right when complexity ramps up
Giga AI aims at the root cause: missing context, not missing tokens.
Who should care
If you’re building anything bigger than a weekend app - multi-folder projects, shared components, real production code - this is for you.
If your AI keeps “helping” by breaking things, gigamind.dev is trying to make that stop.
The catch
Context quality decides output quality. If your project has no structure, no naming discipline, and no clear goals, no tool can save you.
But if you run a real codebase and you want AI that acts like it belongs there, this is the bet.