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Griffin
Griffin

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I Built the First AI-Native Build System

#productivity #devops #opensource #programming

AI coding assistants are transforming how we write code. But our build systems? They're still stuck in 2015.

I built bldr – the first build system designed for the AI era. Here's why that matters and what makes it different.

The Problem: Build Systems Don't Speak AI

When you use Claude, Copilot, or Cursor to generate code, you're creating files dynamically. Protobuf definitions spawn multiple output files. Template engines generate code across languages. AI agents create entire modules on the fly.

Traditional build systems (Bazel, Buck2, CMake) demand you declare every dependency upfront in a static graph. But AI-generated code doesn't work that way – it's dynamic, emergent, unpredictable.

The result? You write preprocessing scripts. Hacky workarounds. Shell commands that run before the "real" build. Your AI writes code in seconds, but your build system takes hours to configure.

The Solution: Let AI Code in Any Language

bldr supports 28 languages with automatic detection. Your AI can generate Rust one moment, Python the next, then TypeScript – bldr figures it out automatically.

Compiled: C, C++, D, Rust, Go, Zig, Swift, Nim, OCaml, Haskell
JVM: Java, Kotlin, Scala
.NET: C#, F#
Scripting: Python, Ruby, JavaScript, TypeScript, and more

No configuration needed. No language-specific setup. Your AI writes polyglot code, bldr builds it all.

Auto-Generated Build Files

Here's the magic: bldr writes most of the Builderfile for you.

bldr init
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That's it. bldr scans your codebase, detects languages, discovers dependencies, and generates a working build configuration. When your AI adds new files or modules, bldr detects them automatically.

No more manually declaring every source file. No more dependency hell. The build system adapts to whatever your AI creates.

Runtime Dependency Discovery

bldr's build graph grows at runtime. When a protobuf action runs, it discovers its own outputs and registers them as new targets. When an AI agent generates a new module, bldr detects it automatically.

No preprocessing. No workarounds. The graph adapts to whatever your AI creates.

AI-Focused CLI Tooling

This is what makes bldr truly AI-native. The CLI is designed so that AI assistants who've never seen bldr before can learn it instantly:

bldr explain caching          # What is caching and how does it work?
bldr explain determinism      # Why does this matter?
bldr explain "remote execution"  # Fuzzy search across concepts
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Every feature has semantic documentation. Not man pages – actual explanations of why and how. When your AI assistant asks "how do I speed up this build?", it gets a meaningful answer it can act on.

The CLI describes itself. AI agents can explore capabilities, understand tradeoffs, and make intelligent decisions without prior training on bldr specifically.

Bayesian Intelligence for Flaky Tests

AI-generated tests can be flaky. bldr uses Bayesian statistical modeling to detect this automatically:

  • Beta distribution inference (not "failed twice = flaky" heuristics)
  • Temporal pattern analysis (time-of-day, load-based failures)
  • Confidence-based automatic quarantine
  • Adaptive retry strategies

Your AI writes the tests. bldr figures out which ones are actually broken vs. just flaky.

Lightning Fast & Optimized to the Bone

AI codegen produces massive outputs. bldr is built to handle it:

  • 98x faster parsing than alternatives
  • BLAKE3 hashing at 3.6 GB/s (AVX-512/NEON accelerated)
  • 50ns scheduler latency with lock-free Chase-Lev work-stealing
  • 5-tier caching from AST-level to distributed remote
  • Zero GC in hot paths via arena allocators and memory pooling

Built in D for C++ speeds with zero-cost abstractions. When your AI generates 10,000 files, bldr doesn't blink.

Economic Cost Optimization

Here's a question no other build system asks: what's your budget?

When you're running AI inference at scale, compute costs matter. A 2-minute build on 1000 cores costs more than an 8-minute build on 100 cores.

bldr computes Pareto-optimal build plans across cost and time dimensions. This is the economic thinking that AI-era infrastructure demands.

Built for AI Agents

If you're building AI coding agents, bldr provides:

  • Auto-detection – no configuration for new languages/files
  • Dynamic graph extension – agents can add targets at runtime
  • Self-documenting CLI – AI learns the tool by asking it
  • Hermetic sandboxing – safe execution of AI-generated code
  • Structured output – JSON/OpenTelemetry for agent consumption
  • Migration tools – move from 11 existing build systems automatically

Get Started 

# Install
brew tap GriffinCanCode/bldr && brew install bldr
# or
cargo install bldr

# Auto-detect everything and generate Builderfile
bldr init

# Build (bldr figures out what to do)
bldr build

# Let your AI learn about bldr
bldr explain caching
bldr help --verbose
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Links

The AI era needs infrastructure designed for it. Static build graphs, manual configuration, and cryptic error messages don't cut it anymore.

bldr is my answer: a build system that adapts to AI-generated code, explains itself to AI assistants, auto-detects everything, and runs lightning fast.

Your AI writes code in any language. bldr builds it. No configuration required.

Try it and let me know what you think.

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