DEV Community: Todd Henderson

Garbage Collection vs Deterministic Memory Regions in AiVM

Todd Henderson — Fri, 22 May 2026 02:51:30 +0000

Modern managed runtimes such as the JVM, CLR, Mono SGen, Go, and JavaScript engines rely heavily on sophisticated garbage collectors. These systems are optimized for large, long-running shared heaps with highly dynamic allocation patterns.

AiVM is taking a different path.

Rather than building a traditional “fully managed” runtime with increasingly complex concurrent garbage collectors, AiVM is moving toward a model based on:

deterministic memory regions
explicit lifetime boundaries
worker-local heaps
immutable message passing
safe-point compaction
bounded resource behavior

This document explains both approaches, their tradeoffs, and why AiVM is intentionally choosing a different direction.

Traditional Garbage Collection

The Goal

Traditional garbage collectors attempt to automatically reclaim memory that is no longer reachable by the application.

The runtime tracks references between objects and periodically identifies memory that can be freed.

Most modern systems use some variation of:

mark-sweep
mark-compact
generational collection
concurrent/incremental collection

Mark-Sweep Collection

The classic tracing collector works in two phases:

Mark all reachable objects
Sweep unreachable objects

Flow Diagram

                ┌────────────────────┐
                │   Heap Objects     │
                └─────────┬──────────┘
                          │
                          ▼
                ┌────────────────────┐
                │ Root Traversal     │
                │ (stack/globals)    │
                └─────────┬──────────┘
                          │
                          ▼
                ┌────────────────────┐
                │ Mark Reachable     │
                │ Objects            │
                └─────────┬──────────┘
                          │
                          ▼
                ┌────────────────────┐
                │ Sweep Unmarked     │
                │ Objects            │
                └─────────┬──────────┘
                          │
                          ▼
                ┌────────────────────┐
                │ Free Memory Holes  │
                └────────────────────┘

Advantages

Conceptually simple
Handles arbitrary object graphs
Automatic reclamation

Problems

Fragmentation

After repeated allocations and frees:

[A][_][C][_][E][_][G]

Memory becomes fragmented into holes.

Large allocations may fail even when total free memory exists.

Stop-The-World Pauses

Applications are often paused during collection.

Poor Locality

Live objects become scattered through memory.

Mark-Sweep-Compact

To solve fragmentation, compacting collectors move live objects together.

Flow Diagram

Before Compaction:

[A][_][C][_][E][_][G]

After Compaction:

[A][C][E][G][_][_][_]

Advantages

Solves fragmentation
Better cache locality
Stable long-running heaps

Problems

Object Movement Complexity

Moving objects requires updating:

pointers
stack roots
object references
interior references
handles

Longer Pauses

Compaction is expensive.

Runtime Complexity

The runtime must coordinate relocation safely.

Generational Garbage Collection

Modern runtimes optimize around one observation:

Most objects die young.

The heap is divided into generations.

Flow Diagram

             ┌────────────────────┐
             │  New Allocation    │
             └─────────┬──────────┘
                       │
                       ▼
             ┌────────────────────┐
             │ Young Generation   │
             │   (Nursery)        │
             └─────────┬──────────┘
                       │
                Minor Collection
                       │
        ┌──────────────┴──────────────┐
        │                             │
        ▼                             ▼
┌────────────────────┐   ┌────────────────────┐
│ Dead Objects Freed │   │ Survivors Promoted │
└────────────────────┘   └─────────┬──────────┘
                                    │
                                    ▼
                         ┌────────────────────┐
                         │ Old Generation     │
                         └─────────┬──────────┘
                                   │
                           Major Collection

Advantages

Excellent throughput
Fast allocation
Efficient for large applications

Problems

Massive Complexity

Requires:

promotion logic
write barriers
remembered sets
cross-generation tracking
moving collectors
concurrent synchronization

Reduced Determinism

Collection timing becomes heuristic-driven.

Shared Heap Coordination

Multiple threads must cooperate with the collector.

Concurrent / Low-Latency Collectors

Modern runtimes such as:

G1
ZGC
Shenandoah
CMS

attempt to reduce pauses by performing GC concurrently with application threads.

Flow Diagram

Application Threads
        │
        ├── Continue Running
        │
        ▼
Concurrent GC Threads
        │
        ├── Background Marking
        ├── Incremental Relocation
        ├── Barrier Tracking
        └── Heap Coordination

Advantages

Extremely low pauses
Large heaps
Better UI/server responsiveness

Problems

Extreme Runtime Complexity

Requires:

read barriers
write barriers
synchronization protocols
relocation safety
concurrent root scanning
race handling

Higher CPU Overhead

GC becomes part of normal runtime execution.

Harder Debugging

Behavior becomes timing-sensitive.

Why AiVM Is Taking a Different Direction

AiVM is not trying to become:

a desktop CLR replacement
a JVM clone
a general-purpose OS runtime
a giant adaptive managed heap

Instead, AiVM is optimized for:

deterministic execution
bounded resource behavior
portability
AI-generated code stability
cross-platform reproducibility
explicit architectural ownership
worker isolation
predictable runtime behavior

These goals change the memory strategy entirely.

The AiVM Direction: Deterministic Memory Regions

Rather than relying on runtime heuristics to decide object lifetimes, AiVM uses explicit lifetime regions.

Core Idea

Data survives only when it crosses explicit deterministic boundaries.

Flow Diagram

             ┌────────────────────┐
             │ Parse / Eval Work  │
             └─────────┬──────────┘
                       │
                       ▼
            ┌──────────────────────┐
            │ Scratch / Temp       │
            │ Region               │
            └─────────┬────────────┘
                      │
            Explicit Safe-Point Boundary
                      │
        ┌─────────────┴─────────────┐
        │                           │
        ▼                           ▼
┌────────────────────┐   ┌──────────────────────────┐
│ Temporary Data     │   │ Explicitly Promoted Data │
│ Destroyed/Reset    │   │ Survives Boundary        │
└────────────────────┘   └──────────┬───────────────┘
                                     │
                                     ▼
                         ┌──────────────────────────┐
                         │ Long-Lived Region        │
                         │ Module / Session / Blob  │
                         └──────────┬───────────────┘
                                    │
                         Explicit Release / Dispose
                                    │
                                    ▼
                         ┌──────────────────────────┐
                         │ Region Reset / Cleanup   │
                         └──────────────────────────┘

Deterministic Safe Points

AiVM cleanup and compaction occur only at explicit boundaries:

parse complete
module publish
worker completion
message freeze
task join
app shutdown
explicit runtime reset

No hidden background collector.

No heuristic object aging.

No concurrent relocation.

Worker-Local Heaps

AiVM concurrency is based on isolation.

Flow Diagram

Worker A ── Local Heap ─┐
                        │
Worker B ── Local Heap ─┼──► Deterministic Queue
                        │
Worker C ── Local Heap ─┘
                                  │
                                  ▼
                       UI / Semantic Thread
                           Applies Changes

Workers:

perform background computation
allocate local temporary memory
cannot mutate semantic/UI state directly

All observable state changes occur through deterministic queue dispatch.

Why This Fits AiVectra

AiVectra requires:

one UI/semantic thread
background workers
deterministic event ordering
cross-platform consistency
mobile-friendly execution

Traditional shared mutable heaps complicate:

iOS threading
Android UI safety
deterministic rendering
replay/testing
portable behavior

AiVM’s direction keeps UI semantics simple:

Workers do work.
UI thread applies deterministic results.

Resource-Bounded Execution

AiVM is also introducing deterministic cumulative resource accounting:

network bytes
blob memory
arena usage
worker counts
queue depth
file I/O
execution budgets

This supports:

sandboxing
mobile execution
reproducibility
AI agent safety
predictable hosting

Why Not Full Concurrent GC?

AiVM may eventually evolve toward more advanced memory management.

But before beta, the project is prioritizing:

simplicity
correctness
deterministic behavior
bounded execution
explicit ownership
predictable debugging

The current roadmap intentionally delays:

fully generational GC
concurrent tracing collectors
moving shared heaps
runtime heuristic aging

until after the core runtime architecture stabilizes.

The Key Philosophical Difference

Traditional runtimes optimize for:

huge shared dynamic heaps

AiVM optimizes for:

deterministic lifetime regions
isolated workers
explicit boundaries
message passing
bounded resources

That dramatically reduces how much garbage collection complexity is actually required.

Conclusion

Traditional garbage collectors are extraordinary engineering achievements. They power modern browsers, enterprise servers, mobile runtimes, and massive applications.

But they solve a different problem.

AiVM is intentionally optimizing for:

deterministic execution
AI-generated code reliability
bounded runtime behavior
portable semantics
explicit architectural ownership
reproducible cross-platform execution

For those goals, deterministic memory regions and safe-point cleanup are currently a better fit than large concurrent tracing collectors.

The result is a runtime architecture that is:

simpler
more predictable
easier to reason about
easier to sandbox
easier to port
easier to make deterministic

while still supporting real multithreaded production workloads through isolated workers and deterministic message passing.

Learn More

To learn more about the AiLangCore ecosystem and follow development:

AiLangCore is actively evolving in the open, with ongoing work focused on deterministic execution, AI-oriented tooling, bounded resource behavior, portable runtime architecture, and vector-first cross-platform UI development.

AiLang: Exploring Deterministic AI-First Programming with Gemma 4

Todd Henderson — Fri, 22 May 2026 02:24:14 +0000

AiLang: Exploring Deterministic AI-First Programming with Gemma 4

What I Built

AiLang — A Deterministic AI-First Programming Language Ecosystem

https://ailang.codes

https://github.com/AiLangCore

AiLang is an experimental AI-first programming language ecosystem focused on deterministic execution, canonical structure, and spec-governed semantics.

The ecosystem currently includes:

AiLang — the language and SDK
AiVM — a deterministic virtual machine/runtime
AiVectra — a cross-platform UI framework

The project explores an important question:

What would a programming language look like if it were designed specifically for AI-assisted development and autonomous agents?

Most existing languages evolved around human-centric workflows and increasingly complex runtime behavior. AiLang instead prioritizes:

deterministic execution
canonical formatting
structured syntax
reproducible builds
thin replaceable runtimes
AI-oriented tooling workflows

The host runtime intentionally remains mechanical and replaceable, while semantic authority lives entirely inside the language specification itself.

The project originally started while evaluating AI-assisted software development tools for a client project. That experimentation eventually evolved into a much larger exploration of deterministic systems and AI-native software architecture.

Demo

Website

https://ailang.codes

GitHub Repositories

Example AiLang Program

Program#p1 {
  Export#e1(name=start)

  Let#l1(name=start) {
    Fn#f1(params=args) {
      Block#b1 {
        Call#c1(target=sys.stdout.writeLine) {
          Lit#s1(value="Hello from AiLang")
        }

        Return#r1 {
          Lit#i1(value=0)
        }
      }
    }
  }
}

Current Areas of Development

Deterministic execution
Canonical formatting
AI-assisted tooling workflows
Runtime portability
NativeAOT experimentation
Standard library expansion
Multi-agent orchestration concepts

Code

GitHub

https://github.com/AiLangCore

Primary repositories:

How I Used Gemma 4

Gemma 4 was used throughout development as part of the broader AI-assisted workflow surrounding the AiLang ecosystem.

The project itself explores deterministic architectures for AI-assisted software engineering, so using modern language models during development became a natural part of the experimentation process.

I primarily focused on AI-assisted:

architecture exploration
implementation iteration
parser experimentation
runtime design discussions
documentation generation
specification refinement
testing strategies
developer workflow analysis

One of the core ideas behind AiLang is that existing programming languages were largely designed around human-first workflows rather than autonomous or collaborative AI systems.

Working alongside modern AI models while developing AiLang helped reinforce several architectural priorities:

deterministic behavior
canonical formatting
structured syntax
explicit semantics
reproducibility
reduced ambiguity for tooling

For this project, the larger-context capabilities of modern models were especially valuable when reasoning about:

multi-repository architecture
runtime boundaries
language semantics
deterministic VM behavior
long-term ecosystem structure

Rather than replacing engineering decisions, AI tooling acted as an accelerator for experimentation and iteration while the core architectural constraints and deterministic guarantees remained specification-driven.

AiLang: An AI-First Language Focused on Deterministic Execution

Todd Henderson — Fri, 22 May 2026 02:20:31 +0000

AiLang — GitHub Finish-Up-A-Thon Challenge Submission

This is a submission for the GitHub Finish-Up-A-Thon Challenge

What I Built

AiLang — A Deterministic AI-First Programming Language

AiLang Website: https://ailang.codes

AiLangCore GitHub Organization: https://github.com/AiLangCore

AiLang is an experimental AI-first programming language ecosystem focused on deterministic execution, canonical structure, and spec-governed semantics.

The project currently consists of three major components:

AiLang — the language, compiler, and SDK
AiVM — a deterministic virtual machine/runtime
AiVectra — a cross-platform UI framework

Unlike many modern platforms where behavior is partially defined by the runtime or host platform, AiLang keeps semantic authority inside the language specification itself. The host runtime is intentionally thin, mechanical, and replaceable.

The goal is to explore what programming languages and tooling could look like when designed specifically for:

autonomous AI agents
reproducible builds
deterministic execution
canonical formatting
multi-agent development workflows

This project started as a collection of experiments around deterministic execution and AI-assisted development, but evolved into a much larger ecosystem focused on AI-native software engineering.

Demo

Website

https://ailang.codes

GitHub Repositories

Example AiLang Program

Program#p1 {
  Export#e1(name=start)

  Let#l1(name=start) {
    Fn#f1(params=args) {
      Block#b1 {
        Call#c1(target=sys.stdout.writeLine) {
          Lit#s1(value="Hello from AiLang")
        }

        Return#r1 {
          Lit#i1(value=0)
        }
      }
    }
  }
}

Current Focus Areas

Deterministic VM execution
Canonical formatting
Spec-governed semantics
AI-oriented tooling
Multi-file module support
Standard library expansion
NativeAOT runtime work
Future C-based VM portability

The Comeback Story

This project originally began while I was working for a client evaluating AI tools for software development workflows. During that process, I became increasingly interested in the limitations current programming languages present for AI-assisted development, deterministic execution, and autonomous tooling. What started as a small experimental language project gradually evolved into the broader AiLang ecosystem.

Over time, the scope grew into a complete ecosystem:

language
VM/runtime
tooling
UI framework
package system concepts
AI-agent workflows

Like many long-running side projects, parts of the architecture evolved organically and needed substantial cleanup and stabilization before they could realistically move toward public adoption.

For this challenge, I focused heavily on:

improving architectural consistency
refining deterministic execution guarantees
cleaning up repository structure
improving documentation
strengthening the specification-first workflow
hardening VM resource limit handling
improving the overall developer onboarding experience

One major focus was reinforcing a core design principle:

The language defines behavior — not the runtime.

That required revisiting assumptions across the compiler, VM, syscall boundaries, and tooling to ensure the ecosystem remained deterministic and spec-governed.

This challenge helped push the project from “experimental internal architecture work” toward something that is becoming increasingly usable and explainable to outside developers.

My Experience with GitHub Copilot

GitHub Copilot became an extremely valuable accelerator during development, especially while working across multiple repositories and architectural layers simultaneously.

Where it helped the most:

scaffolding repetitive infrastructure
generating test boilerplate
helping iterate on parser/runtime ideas
exploring alternative implementations
accelerating documentation work
reducing friction while refactoring

The biggest benefit was not replacing architectural thinking, but reducing the mechanical overhead around experimentation.

Because AiLang emphasizes deterministic behavior and specification-driven development, I still had to carefully validate generated code against the project’s architectural constraints and invariants.

In many ways, the project itself explores a question closely related to tools like Copilot:

What would a programming language look like if it were designed from the beginning for AI-assisted development?

That exploration became one of the driving motivations behind the entire AiLang ecosystem.