React compiler analysis and implementation plan

/home/jm/data/ai/cloudlang-trees/react · 28 messages · 97 parts · · 511,566 tokens
Created Feb 10, 02:29:21 AM · Updated Feb 10, 05:17:24 AM · 196 syncs
userFeb 10, 02:29:21 AM

This branch, I want to basically attempt to steal as much of the react compiler as possible. I have just come upon it and it is at the very least doing something very similar to what I have in mind with this project.

If you read this, they are looking fro closures and have created a very nice multi stage compiler to really pull out of the code what they want.

# analyseFunctions

## File
`src/Inference/AnalyseFunctions.ts`

## Purpose
Recursively analyzes all nested function expressions and object methods in a function to infer their aliasing effect signatures, which describe how the function affects its captured variables when invoked.

## Input Invariants
- The HIR has been through SSA conversion and type inference
- FunctionExpression and ObjectMethod instructions have an empty `aliasingEffects` array (`@aliasingEffects=[]`)
- Context variables (captured variables from outer scope) exist on `fn.context` but do not have their effect populated

## Output Guarantees
- Every FunctionExpression and ObjectMethod has its `aliasingEffects` array populated with the effects the function performs when called (mutations, captures, aliasing to return value, etc.)
- Each context variable's `effect` property is set to either `Effect.Capture` (if the variable is captured or mutated by the inner function) or `Effect.Read` (if only read)
- Context variable mutable ranges are reset to `{start: 0, end: 0}` and scopes are set to `null` to prepare for the outer function's subsequent `inferMutationAliasingRanges` pass

## Algorithm
1. **Recursive traversal**: Iterates through all blocks and instructions looking for `FunctionExpression` or `ObjectMethod` instructions
2. **Depth-first processing**: For each function expression found, calls `lowerWithMutationAliasing()` which:
   - Recursively calls `analyseFunctions()` on the inner function (handles nested functions)
   - Runs `inferMutationAliasingEffects()` on the inner function to determine effects
   - Runs `deadCodeElimination()` to clean up
   - Runs `inferMutationAliasingRanges()` to compute mutable ranges and extract externally-visible effects
   - Runs `rewriteInstructionKindsBasedOnReassignment()` and `inferReactiveScopeVariables()`
   - Stores the computed effects in `fn.aliasingEffects`
3. **Context variable effect classification**: Scans the computed effects to determine which context variables are captured/mutated vs only read:
   - Effects like `Capture`, `Alias`, `Assign`, `MaybeAlias`, `CreateFrom` mark the source as captured
   - Mutation effects (`Mutate`, `MutateTransitive`, etc.) mark the target as captured
   - Sets `operand.effect = Effect.Capture` or `Effect.Read` accordingly
4. **Range reset**: Resets mutable ranges and scopes on context variables to prepare for outer function analysis

## Key Data Structures
- **HIRFunction.aliasingEffects**: Array of `AliasingEffect` storing the externally-visible behavior of a function when called
- **Place.effect**: Effect enum value (`Capture` or `Read`) describing how a context variable is used
- **AliasingEffect**: Union type describing data flow (Capture, Alias, Assign, etc.) and mutations (Mutate, MutateTransitive, etc.)
- **FunctionExpression/ObjectMethod.loweredFunc.func**: The inner HIRFunction to analyze

## Edge Cases
- **Nested functions**: Handled via recursive call to `analyseFunctions()` before processing the current function - innermost functions are analyzed first
- **ObjectMethod**: Treated identically to FunctionExpression
- **Apply effects invariant**: The pass asserts that no `Apply` effects remain in the function's signature - these should have been resolved to more precise effects by `inferMutationAliasingRanges()`
- **Conditional mutations**: Effects like `MutateTransitiveConditionally` are tracked - a function that conditionally mutates a captured variable will have that effect in its signature
- **Immutable captures**: `ImmutableCapture`, `Freeze`, `Create`, `Impure`, `Render` effects do not contribute to marking context variables as `Capture`

## TODOs
- No TODO comments in the pass itself

## Example
Consider a function that captures and conditionally mutates a variable:

```javascript
function useHook(a, b) {
  let z = {a};
  let y = b;
  let x = function () {
    if (y) {
      maybeMutate(z);  // Unknown function, may mutate z
    }
  };
  return x;
}

Before AnalyseFunctions:

Function @context[y$28, z$25] @aliasingEffects=[]

After AnalyseFunctions:

Function @context[read y$28, capture z$25] @aliasingEffects=[
  MutateTransitiveConditionally z$25,
  Create $14 = primitive
]

The pass infers:

  • y is only read (used in the condition)
  • z is captured into the function and conditionally mutated transitively (because maybeMutate() is unknown)
  • The inner function's signature includes MutateTransitiveConditionally z$25 to indicate this potential mutation

This signature is then used by InferMutationAliasingEffects on the outer function to understand that creating this function captures z, and calling the function may mutate z.



At the least I want to create some templated way of representing the transformations like in this documentation, with detailed algorithmic descriptions of how they are parsing  out objects of interest.

I think consider the high level work of the Compiler as outlined here:
https://codewiki.google/github.com/facebook/react#react-compiler

Then break up dissemination of the react codebase by first spending time partitioining the code base into relevant and not relevant, chunking the areas by size and semantic score (as a sign of its conceptual relatedness) and then delegate to as many subagents with a similar template, recursively potentially,making sure they can produce enought tokens to give a good summary of their reading always in the context of matching what works to our project, until you can construct a whole conceptualization of the work we need to do here.
assistantFeb 10, 02:29:21 AM
claude-opus-4.6 · github-copilot

I'll start by understanding both sides of this equation: your current CloudLang project and the React Compiler codebase. Let me kick off several parallel explorations.

The user wants me to: 1. Understand the React Compiler codebase (specifically the compilation pipeline) 2. Map it to their CloudLang project 3. Create a plan for extracting/adapting the React Compiler's multi-stage compilation approach 4. Break down the React Compiler codebase into relevant/not-relevant parts 5. Delegate analysis to subagents to build a comprehensive understanding Let me start by: 1. Understanding the current CloudLang project structure 2. Fetching the React Compiler overview from the provided URL 3. Exploring the React Compiler source code (it's likely in a local directory or we need to find it) Let me first look at the current project structure and the React Compiler codebase.
taskcompleted
Explore CloudLang project structure
Output
task_id: ses_3ba9e0f11ffeKKEPy7vH6THvjY (for resuming to continue this task if needed)

<task_result>
Now I have a complete picture. Here is the comprehensive analysis:

---

## Comprehensive Project Analysis: CloudLang (`react` tree)

### 1. Directory Tree Structure

```
/home/jm/data/ai/cloudlang-trees/react/
├── .claude/context/
│   └── capnweb-integration.md
├── .github/workflows/
│   └── ci.yml                          # CI: OCaml 5.2.0, fmt + build + test
├── .ocamlformat                        # v0.28.1
├── .opencode/
│   ├── .gitignore
│   ├── package.json
│   └── context/
│       ├── cloudlang-architecture-design.md
│       ├── typescript-ast-json-research.md
│       └── typescript-ast-summary.md
├── AGENTS.md
├── LICENSE.md
├── README.md
├── dune-project
├── cloudlang.opam                      # Generated from dune-project
├── bin/
│   ├── dune
│   └── main.ml                         # CLI entry point
├── lib/
│   ├── dune                            # library "cloudlang" (jsont, jsont.bytesrw, unix)
│   ├── ast.ml / ast.mli                # ESTree AST types
│   ├── ir.ml / ir.mli                  # Cloudflare IR types
│   ├── parse.ml / parse.mli            # JSON -> AST deserializer (jsont)
│   ├── scope.ml / scope.mli            # Scope/closure analysis pass
│   ├── decompose.ml / decompose.mli    # Closure decomposition -> Worker groups -> IR
│   ├── lower.ml / lower.mli            # Body lowering (AST rewriting for distributed exec)
│   ├── emit.ml / emit.mli              # AST -> TypeScript source emitter
│   ├── cloudlang.ml / cloudlang.mli    # Top-level pipeline facade + code generation
│   ├── string_list.ml / string_list.mli# Utility: dedup_preserve_order
│   ├── subprocess.ml / subprocess.mli  # Bun subprocess bridge
│   └── wrangler.ml / wrangler.mli      # Wrangler TOML + project file generation
├── test/
│   ├── dune
│   ├── fixture.ml                      # Shared test fixtures (AST builders)
│   ├── test_cloudlang.ml               # Test ru...
webfetchcompleted
https://codewiki.google/github.com/facebook/react (text/html; charset=utf-8)
Output
 Code Wiki
taskcompleted
Map React Compiler source tree
Output
task_id: ses_3ba9df4e5ffe1GBl9GEhKp16Vf (for resuming to continue this task if needed)

<task_result>
I now have a thorough understanding of the React Compiler source. Here is the comprehensive report.

---

## React Compiler Source Code: Comprehensive Exploration Report

### Location Status

**The React Compiler source is NOT available locally.** The working directory (`/home/jm/data/ai/cloudlang-trees/react`) is a git worktree of a CloudLang OCaml project, not the facebook/react repository. All data below was retrieved via the GitHub API (`gh api repos/facebook/react/...`).

To clone locally, run:
```
git clone --depth=1 --filter=blob:none --sparse https://github.com/facebook/react.git
cd react && git sparse-checkout set compiler
```

---

### 1. Full Directory Listing of the Compiler Source

**Top-level compiler packages** (`compiler/packages/`):
```
babel-plugin-react-compiler/     (THE core compiler)
eslint-plugin-react-compiler/
make-read-only-util/
react-compiler-healthcheck/
react-compiler-runtime/
react-forgive/
react-mcp-server/
snap/
```

**Core source tree** (`compiler/packages/babel-plugin-react-compiler/src/`):
```
index.ts                          # Package entry point
Babel/
  BabelPlugin.ts                  # Babel plugin wrapper (visitor pattern)
  RunReactCompilerBabelPlugin.ts
CompilerError.ts                  # (standalone file, not a directory)
Entrypoint/
  Gating.ts                       # Feature flag gating support
  Imports.ts                      # Import management
  Options.ts                      # Plugin configuration/options parsing
  Pipeline.ts                     # THE COMPILATION PIPELINE ORCHESTRATOR
  Program.ts                      # Program-level traversal (find components/hooks)
  Reanimated.ts                   # Reanimated integration support
  Suppression.ts                  # Suppression comment handling
  ValidateNoUntransformedReferences.ts
  index.ts
Flood/
  FlowTypes.ts
  TypeErrors.ts
  TypeUtils.ts
  Types.ts
HIR/...
19,776in / 830out
assistantFeb 10, 02:33:18 AM
claude-opus-4.6 · github-copilot
Excellent - I now have comprehensive information about both projects. The CodeWiki URL returned minimal content, but the agent exploration of the React Compiler source is extremely detailed. Let me now create a plan. Let me first understand the conceptual mapping between what the React Compiler does and what CloudLang needs. Then I'll partition the React Compiler codebase into relevant vs. not relevant, and plan the analysis work. Key observations: 1. **CloudLang** is a TypeScript-to-Cloudflare compiler that decomposes closures into Workers 2. **React Compiler** is a sophisticated multi-stage compiler that analyzes mutation, aliasing, and effects to auto-memoize React components 3. Both projects care deeply about closures, captured variables, mutation analysis, and effect tracking From the React Compiler, the most applicable pieces are the HIR design, SSA conversion, the inference pipeline for aliasing and effects, the recursive function analysis approach, type inference, and optimization passes like dead code elimination. The parts that won't transfer well are the JSX handling, React-specific validations, reactive scopes, and the codegen back to React. Now I need to map out a systematic analysis plan. Before diving in, I should clarify what's already available locally versus what I need to pull down, and then present the full strategy to the user for feedback rather than jumping straight into execution.

Good, I now have a thorough picture of both sides. Let me also check whether the React Compiler source is accessible locally or needs to be cloned.

bashcompleted
Check git remote and branch info
Output
origin	https://github.com/manno23/cloudlang (fetch)
origin	https://github.com/manno23/cloudlang (push)
---
react-inspiration
---
c90f10d3 Document Cap'n Web integration strategy for distributed iteration
0cb1b54f Update scope analysis and test suite with AwaitExpression support and new test calls
243d6384 Add reference HTTP server for verification testing
9faa548d Add verification framework validating Node.js vs Miniflare behavioral equivalence
8af6bb76 Add file-based workflow and --lower/--emit-project/--out-dir flags
bashcompleted
Check sibling worktrees
Output
react
16,154in / 582out
assistantFeb 10, 02:33:33 AM
claude-opus-4.6 · github-copilot
bashcompleted
Check for React repo locally
Output
total 0
drwxr-xr-x 4 jm jm  80 Feb 10 02:17 .
drwxr-xr-x 4 jm jm  80 Feb 10 02:17 ..
drwxrwxr-x 1 jm jm 382 Feb 10 02:10 cloudlang
drwxrwxr-x 1 jm jm  10 Feb 10 02:11 cloudlang-trees
783in / 88out
assistantFeb 10, 02:33:36 AM
claude-opus-4.6 · github-copilot
bashcompleted
Check if gh CLI available
Output
/usr/bin/gh
github.com
  ✓ Logged in to github.com account manno23 (GITHUB_TOKEN)
  - Active account: true
227in / 100out
assistantFeb 10, 02:33:40 AM
claude-opus-4.6 · github-copilot

Now I have the full picture. Let me lay out the plan.


Plan: Stealing from the React Compiler

Situation Assessment

Your project (cloudlang on branch react-inspiration) is a TypeScript-to-Cloudflare compiler. It already has:

  • A 19-node ESTree AST subset parsed via external bun bridge
  • A 3-pass scope/closure analysis (declarations, references, exports)
  • Closure decomposition into Worker groups based on shared mutable state
  • Body lowering (Map->KV rewriting, cross-group RPC generation)
  • A rich Cloudflare IR with Durable Object annotations
  • AST->TypeScript emitter, wrangler config generation

The React Compiler (facebook/react/compiler/) is a 70+ pass pipeline that:

  1. Lowers Babel AST -> a proper HIR (CFG with basic blocks, SSA, typed places)
  2. Runs deep mutation/aliasing/effect inference via abstract interpretation
  3. Recursively analyzes nested function expressions for their aliasing signatures
  4. Groups co-mutating values into "reactive scopes" for memoization
  5. Converts CFG -> tree-shaped ReactiveFunction -> codegen back to JS

The conceptual overlap is large. Both projects fundamentally care about:

  • Analyzing closures and what they capture
  • Understanding mutation vs. read-only access to captured variables
  • Tracking data flow through function boundaries
  • Making decisions (memoization boundaries / Worker boundaries) based on effect analysis

Relevance Partitioning

I've divided the React Compiler into 4 tiers:

Tier Relevance Directories/Files Reason
A: Core steal Direct adaptation HIR/HIR.ts, HIR/BuildHIR.ts, SSA/, Inference/ (all), HIR/Types.ts, HIR/ObjectShape.ts, Entrypoint/Pipeline.ts The HIR design, SSA, and mutation/aliasing inference are exactly what CloudLang needs to make smarter decomposition decisions
B: Architectural patterns Learn from ReactiveScopes/BuildReactiveFunction.ts, ReactiveScopes/InferReactiveScopeVariables.ts, Optimization/DeadCodeElimination.ts, Optimization/ConstantPropagation.ts, HIR/Dominator.ts, HIR/visitors.ts The pattern of "group co-mutating things into scopes" maps directly to "group co-mutating things into Worker groups"
C: Useful utilities Cherry-pick Utils/DisjointSet.ts, CompilerError.ts, HIR/PrintHIR.ts, HIR/HIRBuilder.ts Supporting infrastructure
D: Not relevant Skip ReactiveScopes/* (most pruning/alignment passes), Validation/*, Babel/*, Entrypoint/Gating.ts, Entrypoint/Reanimated.ts, Transform/TransformFire.ts, Optimization/InlineJsxTransform.ts, Optimization/OutlineJsx.ts, Flood/ React/JSX-specific, hook validation, memoization codegen

Proposed Analysis Work

I would dispatch 6 parallel subagent investigations, each producing a standardized template:

# Pass Name

## File
## Purpose  
## Input Invariants  
## Output Guarantees  
## Algorithm (detailed, step-by-step)
## Key Data Structures  
## Edge Cases  
## Mapping to CloudLang (what we'd adapt and how)
## Example (before/after)

The 6 work packages:

# Scope Files to Analyze Agent Focus
1 HIR Type System & Construction HIR/HIR.ts, HIR/Types.ts, HIR/ObjectShape.ts, HIR/BuildHIR.ts, HIR/HIRBuilder.ts The core data model: what is a Place, Identifier, Instruction, Terminal, BasicBlock. How AST lowers to HIR. What CloudLang's equivalent would be.
2 SSA & Type Inference SSA/EnterSSA.ts, SSA/EliminateRedundantPhi.ts, SSA/RewriteInstructionKindsBasedOnReassignment.ts, TypeInference/InferTypes.ts SSA construction, phi node handling, type inference approach
3 Mutation & Aliasing Model Inference/AliasingEffects.ts, Inference/InferMutationAliasingEffects.ts, Inference/MUTABILITY_ALIASING_MODEL.md, Inference/InferMutationAliasingRanges.ts The abstract interpretation engine. The 17-variant AliasingEffect type. How it determines what mutates what.
4 Function Analysis & Effects Inference/AnalyseFunctions.ts, Inference/InferReactivePlaces.ts, Inference/InferEffectDependencies.ts, Inference/DropManualMemoization.ts, Inference/InlineImmediatelyInvokedFunctionExpressions.ts Recursive nested function analysis, effect classification on context variables
5 Scope Grouping & Dependency ReactiveScopes/InferReactiveScopeVariables.ts, ReactiveScopes/BuildReactiveFunction.ts, HIR/PropagateScopeDependenciesHIR.ts, HIR/DeriveMinimalDependenciesHIR.ts, HIR/CollectHoistablePropertyLoads.ts How co-mutating values get grouped into scopes (analogous to our Worker group decomposition)
6 Pipeline Orchestration & Optimizations Entrypoint/Pipeline.ts, Optimization/DeadCodeElimination.ts, Optimization/ConstantPropagation.ts, Optimization/InstructionReordering.ts, HIR/MergeConsecutiveBlocks.ts, HIR/Dominator.ts Pipeline structure, pass ordering rationale, optimization patterns

What This Produces

A comprehensive .opencode/context/react-compiler-analysis/ directory containing:

  1. One detailed template document per work package (6 docs)
  2. A synthesis document mapping React Compiler concepts -> CloudLang equivalents
  3. A prioritized implementation roadmap

Key Architectural Questions for You

Before I dispatch:

questioncompleted
Asked 3 questions
Output
User has answered your questions: "The React Compiler source isn't available locally. Should I clone it (sparse checkout, just the compiler/ directory) so agents can read actual source code? Or should I have them work via the GitHub API (slower, token-limited)?"="It is checked out in this git worktree, at /home/jm/data/ai/cloudlang-trees/react/react", "Your current AST has 19 node types. The React Compiler's HIR has ~40 instruction value types plus 20 terminal types. How much of the TypeScript language do you actually need to support? This affects how much of the HIR design we should adopt."="Incremental expansion", "The React Compiler's 'reactive scopes' concept (grouping co-mutating values) maps closely to your 'worker groups' (grouping closures that share mutable state). Do you want to replace your current decomposition algorithm with something based on their scope inference, or layer their analysis on top of your existing decomposition?"="Research first, decide later". You can now continue with the user's answers in mind.
154in / 2,225out
userFeb 10, 02:33:43 AM

I am cloning it now into cloudlang-trees

assistantFeb 10, 02:46:41 AM
claude-opus-4.6 · github-copilot

Got it -- you're cloning it now. Let me verify the path once it's ready and then kick off the parallel analysis.

bashcompleted
Check if React repo is cloned
Output
CHANGELOG.md
CLAUDE.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
MAINTAINERS
README.md
ReactVersions.js
SECURITY.md
babel.config-react-compiler.js
babel.config-ts.js
babel.config.js
compiler
dangerfile.js
fixtures
flow-typed
flow-typed.config.json
package.json
packages
react.code-workspace
scripts
yarn.lock
37,434in / 138out
assistantFeb 10, 02:46:46 AM
claude-opus-4.6 · github-copilot
bashcompleted
Verify React Compiler source tree
Output
Babel
CompilerError.ts
Entrypoint
Flood
HIR
Inference
Optimization
ReactiveScopes
SSA
Transform
TypeInference
Utils
Validation
__tests__
index.ts
604in / 108out
assistantFeb 10, 02:46:50 AM
claude-opus-4.6 · github-copilot

The React Compiler source is fully available at /home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/. Now I'll dispatch all 6 analysis agents in parallel, each reading actual source files and producing structured documentation.

todowritecompleted
8 todos
Output
[
  {
    "id": "1",
    "content": "WP1: HIR Type System & Construction analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "2",
    "content": "WP2: SSA & Type Inference analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "3",
    "content": "WP3: Mutation & Aliasing Model analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "4",
    "content": "WP4: Function Analysis & Effects analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "5",
    "content": "WP5: Scope Grouping & Dependency analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "6",
    "content": "WP6: Pipeline Orchestration & Optimizations analysis",
    "status": "in_progress",
    "priority": "high"
  },
  {
    "id": "7",
    "content": "Synthesize findings into CloudLang mapping document",
    "status": "pending",
    "priority": "high"
  },
  {
    "id": "8",
    "content": "Create prioritized implementation roadmap",
    "status": "pending",
    "priority": "high"
  }
]
174in / 419out
assistantFeb 10, 02:46:57 AM
claude-opus-4.6 · github-copilot
taskerror
Tool execution aborted
taskcompleted
WP2: SSA & type inference
Output
task_id: ses_3ba8d7d77ffes9z6ryA5Jau40f (for resuming to continue this task if needed)

<task_result>
I've successfully created a comprehensive analysis document at `/home/jm/data/ai/cloudlang-trees/react/.opencode/context/react-compiler-ssa-analysis.md`. 

The document contains detailed analysis of:

## Document Contents

### 1. **SSA Construction (EnterSSA.ts)**
- **Algorithm**: Braun et al. (2013) with incomplete phi handling
- **Phi placement**: At join points (blocks with multiple predecessors) using the `getIdAt` recursive resolution
- **Scope handling**: Differentiates block, function, and catch scopes with special back-edge handling for nested functions
- **IdentifierId numbering**: Globally unique IDs allocated from `env.nextIdentifierId`, with `declarationId` preserved across all SSA versions
- **Name preservation**: The `name` field is preserved unchanged after SSA conversion

### 2. **Phi Node Elimination (EliminateRedundantPhi.ts)**
- **Redundancy criteria**: Phis where all operands are the same, or all operands are the same OR the phi result itself
- **Algorithm**: Iterative rewriting in reverse postorder with fixpoint iteration for loops
- **Effect**: Reduces IR size and simplifies downstream analysis

### 3. **Instruction Kind Rewriting (RewriteInstructionKindsBasedOnReassignment.ts)**
- **Purpose**: Classify variables as `Const`, `Let`, or `Reassign` after SSA
- **Algorithm**: Tracks first declarations and subsequent reassignments
- **Note**: This is React-specific (code generation concerns) and can be skipped for CloudLang

### 4. **Type Inference (InferTypes.ts)**
- **Type lattice**: Hindley-Milner unification with:
  - Primitive, Function, Object, Array, Map, Set, TypeVar, PhiType, PropertyType
  - Limited union support (MixedReadonly with Primitive/Array)
- **Algorithm**: Generate equations → Unify → Apply substitutions
- **Phi handling**: Phi types resolved to LUB (Least Upper Bound) of operands with cycle detection
- **React-specific patterns*...
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userFeb 10, 02:52:46 AM

Is everything alright

userFeb 10, 03:10:19 AM

Try that again

assistantFeb 10, 03:10:19 AM
claude-opus-4.6 · github-copilot
The user wants me to retry the 6 parallel analysis agents. 4 of them were aborted last time. But I'm in Plan Mode now - READ ONLY. I can still dispatch research/exploration agents since those are read-only tasks. The agents are doing analysis (reading files, producing summ
userFeb 10, 03:15:26 AM

Lets try that again. If you know what happend let me know quick and lets start again

assistantFeb 10, 03:15:26 AM
claude-opus-4.6 · github-copilot

4 of the 6 agents got aborted -- likely hit concurrent agent limits or token budget caps. Only WP2 (SSA & Type Inference) completed successfully and wrote its output. WP1, WP3, WP4, WP5, and WP6 all got killed.

The fix is straightforward: don't run 6 heavy analyzer agents in parallel. Each one is reading thousands of lines of source code and producing long-form output -- that's a lot of concurrent work. I should run them in batches of 2-3, or sequentially for the heaviest ones.

Here's my revised plan:

Execution Strategy

Batch 1 (the two most important, run in parallel):

  • WP3: Mutation & Aliasing Model -- the heart of what you want to steal
  • WP1: HIR Type System & Construction -- the foundation everything builds on

Batch 2 (next priority pair):

  • WP4: Function Analysis & Effects -- AnalyseFunctions, the pass you already documented
  • WP5: Scope Grouping & Dependencies -- the analog to your Worker group decomposition

Batch 3 (supporting work):

  • WP6: Pipeline Orchestration & Optimizations -- how it all fits together
  • WP2 is already done -- saved to .opencode/context/react-compiler-ssa-analysis.md

Final synthesis (after all batches complete):

  • Mapping document: React Compiler concepts -> CloudLang equivalents
  • Prioritized implementation roadmap

Each batch waits for the previous one to finish before dispatching. This avoids the abort issue while still parallelizing where possible.

Should I proceed with this batched approach? Also -- do you want me to keep the same depth/thoroughness per agent (they're each reading 1000+ lines of source and producing detailed algorithmic documentation), or would you prefer shorter summaries to get through faster?

46,976in / 456out
userFeb 10, 03:17:20 AM

OK

assistantFeb 10, 03:17:20 AM
claude-opus-4.6 · github-copilot

Starting Batch 1 -- the two most critical work packages in parallel. Both are read-only research.

The user said OK, so I should proceed with Batch 1. But I'm in Plan Mode - READ ONLY. I cannot edit files, but I CAN dispatch read-only research agents. The agents are doing analysis/research only, not editing files. Let me dispatch Batch 1: WP1 and WP3. Wait, let me re-read the plan mode instructions. It says I can "think, read, search, and delegate explore agents to construct a well-formed plan." So I can delegate agents for research. The agents themselves should only be doing reads and analysis, not edits. Let me make sure my prompts are clear about being read-only. Let me dispatch WP1 and WP3 in parallel.
todowritecompleted
7 todos
Output
[
  {
    "content": "WP1: HIR Type System & Construction analysis",
    "id": "1",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP2: SSA & Type Inference analysis",
    "id": "2",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP3: Mutation & Aliasing Model analysis",
    "id": "3",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP4: Function Analysis & Effects analysis",
    "id": "4",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "WP5: Scope Grouping & Dependency analysis",
    "id": "5",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "WP6: Pipeline Orchestration & Optimizations analysis",
    "id": "6",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Synthesize findings into CloudLang mapping document",
    "id": "7",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Create prioritized implementation roadmap",
    "id": "8",
    "priority": "high",
    "status": "pending"
  }
]
taskcompleted
WP1: HIR types & construction
Output
task_id: ses_3ba71dbe9ffegKnbOA2Pmpdl6M (for resuming to continue this task if needed)

<task_result>
Based on my analysis of the React Compiler's HIR system and CloudLang's architecture, here is my complete research documentation:

---

# React Compiler HIR Type System and Construction Analysis

## 1. HIR Core Model (HIR.ts - Core Types)

### File
`/home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/HIR/HIR.ts`

### Purpose
Defines the core data model for React Compiler's High-level Intermediate Representation (HIR). This is an SSA-based CFG intermediate form between Babel AST and reactive scope analysis.

### Ownership/Containment Hierarchy

```
HIRFunction (top-level unit)
  └── body: HIR
        ├── entry: BlockId
        └── blocks: Map<BlockId, BasicBlock>
              └── BasicBlock
                    ├── kind: BlockKind
                    ├── id: BlockId
                    ├── instructions: Array<Instruction>
                    ├── terminal: Terminal
                    ├── preds: Set<BlockId>
                    └── phis: Set<Phi>

HIRFunction
  ├── params: Array<Place | SpreadPattern>
  ├── context: Array<Place> (captured vars)
  ├── returns: Place
  └── env: Environment
```

**Key Data Structures:**

```typescript
// HIR - The control flow graph
export type HIR = {
  entry: BlockId;
  blocks: Map<BlockId, BasicBlock>;
};

// BasicBlock - Single entry, single exit sequence of instructions
export type BasicBlock = {
  kind: BlockKind;
  id: BlockId;
  instructions: Array<Instruction>;
  terminal: Terminal;
  preds: Set<BlockId>;
  phis: Set<Phi>;
};

// HIRFunction - A compiled function
export type HIRFunction = {
  loc: SourceLocation;
  id: ValidIdentifierName | null;
  nameHint: string | null;
  fnType: ReactFunctionType;
  env: Environment;
  params: Array<Place | SpreadPattern>;
  returnTypeAnnotation: t.FlowType | t.TSType | null;
  returns: Place;
  context: Array<Place>;
  body: HIR;
  generator: boole...
taskerror
Tool execution aborted
userFeb 10, 03:18:17 AM

We need someone to record all the info, so maybe we need @researcher agents as well to record

Use the above message and context to generate a prompt and call the task tool with subagent: researcher

userFeb 10, 03:34:00 AM

We need someone to record all the info, so maybe we need @researcher agents as well to record

Use the above message and context to generate a prompt and call the task tool with subagent: researcher

userFeb 10, 04:54:51 AM

Is everything alright

assistantFeb 10, 04:54:51 AM
claude-opus-4.6 · github-copilot

Yes, we're fine. Here's the current state:

Completed:

  • Full exploration of your CloudLang project structure (in memory, not written)
  • Full exploration of the React Compiler source tree (in memory, not written)
  • WP2 (SSA & Type Inference) -- completed and saved to .opencode/context/react-compiler-ssa-analysis.md

Aborted (need to re-run):

  • WP1: HIR Type System & Construction
  • WP3: Mutation & Aliasing Model
  • WP4: Function Analysis & Effects
  • WP5: Scope Grouping & Dependencies
  • WP6: Pipeline Orchestration & Optimizations

What happened: I launched all 6 heavy analyzer agents simultaneously. Each was tasked with reading thousands of lines of React Compiler source and producing detailed write-ups. 4 of them got killed -- most likely a concurrency or resource limit.

The plan going forward is to run them in smaller batches (2 at a time) instead of all 6 at once. The pending work packages, their dependencies, and ordering:

Batch Work Packages Why together
1 WP1 (HIR types) + WP3 (aliasing model) Foundation + core analysis -- everything else depends on understanding these
2 WP4 (function analysis) + WP5 (scope grouping) Both build on WP1/WP3 concepts, directly map to your closure decomposition
3 WP6 (pipeline orchestration) Ties everything together, benefits from context of all prior WPs
Final Synthesis + roadmap Maps React concepts to CloudLang, produces implementation plan

Want me to proceed with this batched approach? Or would you prefer any adjustments -- e.g., different prioritization, more/less depth on specific work packages?

The user is asking if everything is alright. They're in plan mode, so I can only read/inspect. Let me check if the WP2 output was actually saved, and confirm the state of things.
47,440in / 518out
userFeb 10, 04:55:27 AM

Yes go ahead, this should be good

assistantFeb 10, 04:55:27 AM
claude-opus-4.6 · github-copilot

Starting Batch 1: the two foundational work packages.

The user wants me to proceed with the batched approach. However, I'm in plan mode - I can only read, search, and delegate explore agents. I cannot edit files. But I CAN dispatch research/analysis agents since those are read-only operations. The agents themselves should only read and analyze, not write files. Wait, but the system reminder says I'm in READ-ONLY phase and cannot make file edits. The task agents I'm dispatching are analyzers doing research - they read files and produce analysis. But WP2 apparently wrote a file to `.opencode/context/`. In plan mode, I should have the agents return their analysis to me rather than writing files. Let me dispatch Batch 1 (WP1 + WP3) with instructions to return analysis as text rather than writing files.
todowritecompleted
7 todos
Output
[
  {
    "content": "WP1: HIR Type System & Construction analysis",
    "id": "1",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP2: SSA & Type Inference analysis",
    "id": "2",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP3: Mutation & Aliasing Model analysis",
    "id": "3",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP4: Function Analysis & Effects analysis",
    "id": "4",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "WP5: Scope Grouping & Dependency analysis",
    "id": "5",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "WP6: Pipeline Orchestration & Optimizations analysis",
    "id": "6",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Synthesize findings into CloudLang mapping document",
    "id": "7",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Create prioritized implementation roadmap",
    "id": "8",
    "priority": "high",
    "status": "pending"
  }
]
taskcompleted
WP1: HIR types & construction
Output
task_id: ses_3ba18014dffemx376URE8AtfMo (for resuming to continue this task if needed)

<task_result>
Based on my analysis of the React Compiler's HIR system and the CloudLang types, here is the complete analysis:

---

# React Compiler HIR Type System and Construction - Complete Analysis

## HIR Core Model

### File
`/home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/HIR/HIR.ts`

### Purpose
The HIR (High-level Intermediate Representation) is the core data model for the React Compiler. It represents TypeScript/JavaScript code as a Control Flow Graph (CFG) with:
- SSA-style instructions for expressions
- Basic blocks with terminal nodes for control flow
- Effect and mutation tracking via AliasingEffects
- Reactive scope tracking for memoization analysis

### Input Invariants
- Babel AST is well-formed (valid TypeScript/JavaScript)
- Variables are properly scoped according to JavaScript scoping rules
- No duplicate identifiers in same lexical scope (after renaming)

### Output Guarantees
- All instructions have unique IDs
- Blocks are in reverse postorder for forward dataflow analysis
- Every variable reference maps to a unique Identifier with consistent `id` and `declarationId`
- Phi nodes at join points for SSA form
- ReactiveScope boundaries are properly nested

### Algorithm (detailed, step-by-step)
The HIR construction follows a multi-stage process:

1. **Environment Setup**: Create HIRBuilder with Environment tracking bindings, scopes, and exception handlers
2. **Function Entry**: Process parameters, create entry block, handle hoistable bindings
3. **Statement Lowering**: For each statement in AST body:
   - If `IfStatement`: Create consequent/alternate blocks + continuation, branch terminal
   - If `WhileStatement`: Create conditional block, loop body, test branch terminal
   - If `ForStatement`: Create init/test/update blocks, loop body, branch chain
   - If `ForOfStatement`: Create GetIterator + IteratorNext + loop...
taskcompleted
WP3: Mutation & aliasing model
Output
task_id: ses_3ba17b1e3ffe3lRYQgBNtCC7GS (for resuming to continue this task if needed)

<task_result>
Now I have all the necessary information. Let me produce my complete analysis:

---

# React Compiler Mutation and Aliasing Model Analysis

## Overview

The React Compiler's mutation and aliasing model is a sophisticated abstract interpretation system that tracks how values flow through a program, how they mutate over time, and which operations are safe/unsafe. This analysis serves as the foundation for reactive scope inference, memoization, and validation of React rules.

---

# Component: The Aliasing Model (MUTABILITY_ALIASING_MODEL.md)

## File
`/react/compiler/packages/babel-plugin-react-compiler/src/Inference/MUTABILITY_ALIASING_MODEL.md`

## Purpose
Design document explaining the fundamental concepts of the mutability and aliasing model. It defines the conceptual framework that both `InferMutationAliasingEffects` and `InferMutationAliasingRanges` implement.

## Input Invariants
- HIR (High-level Intermediate Representation) is already built
- Control flow graph (CFG) is constructed with blocks and terminals
- Types are inferred for all identifiers

## Output Guarantees
- Clear mental model for reasoning about mutation and aliasing
- Formal rules for effect transitivity
- Distinctions between different data flow mechanisms

## Algorithm (detailed, step-by-step)

### 1. Fundamental Concepts

**Values**: Abstract entities with a "kind" (Mutable, Frozen, Primitive, Global, Context, MaybeFrozen)

**References/Places**: Pointers to values with an `IdentifierId`. Each place has:
- `identifier.id`: Unique SSA identifier
- `identifier.declarationId`: Original variable declaration
- `mutableRange`: {start, end} instruction range where value is mutable
- `effect`: How this reference uses the value (Read, Store, Mutate, Freeze, etc.)

**Aliasing**: When multiple places point to the same underlying value (e.g., `b = a`)

**Capturing**: When a value stores a reference to a...
30,319in / 2,512out
assistantFeb 10, 05:01:04 AM
claude-opus-4.6 · github-copilot
Both WP1 and WP3 completed successfully. The outputs were very large and got truncated in the display, but the full outputs are saved to files. Let me now proceed with Batch 2 (WP4 and WP5).

Batch 1 complete -- both WP1 (HIR Type System) and WP3 (Mutation & Aliasing Model) succeeded with comprehensive outputs. Let me update todos and dispatch Batch 2.

todowritecompleted
5 todos
Output
[
  {
    "content": "WP1: HIR Type System & Construction analysis",
    "id": "1",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP2: SSA & Type Inference analysis",
    "id": "2",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP3: Mutation & Aliasing Model analysis",
    "id": "3",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP4: Function Analysis & Effects analysis",
    "id": "4",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP5: Scope Grouping & Dependency analysis",
    "id": "5",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "WP6: Pipeline Orchestration & Optimizations analysis",
    "id": "6",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Synthesize findings into CloudLang mapping document",
    "id": "7",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Create prioritized implementation roadmap",
    "id": "8",
    "priority": "high",
    "status": "pending"
  }
]
taskcompleted
WP4: Function analysis & effects
Output
task_id: ses_3ba12d1bbffe6c8jsVfS7PiGxO (for resuming to continue this task if needed)

<task_result>
Now I have all the necessary information. Let me produce my complete analysis:

---

# Complete Analysis: React Compiler Function Analysis and Effect Inference Passes

---

## 1. AnalyseFunctions

### File
`/home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/Inference/AnalyseFunctions.ts`

### Purpose
The key pass that performs recursive nested function analysis. It processes each inner function (function expressions and object methods) with a mini-pipeline of effect inference passes, then classifies context variables as either `Capture` or `Read` based on the function's behavior. This enables the outer function to understand what inner functions do without needing to re-analyze their bodies later.

### Input Invariants
- Input function (`HIRFunction`) must have its `body.blocks` map populated
- `instr.value.loweredFunc.func.context` is populated for each FunctionExpression/ObjectMethod
- Each context variable has a `mutableRange` and `scope` that can be reset

### Output Guarantees
- Each `fn.aliasingEffects` contains the externally-visible effects of that function
- Each context variable has its `operand.effect` set to either `Effect.Capture` or `Effect.Read`
- Context variable `mutableRange` is reset to `{start: 0, end: 0}` and `scope` is set to `null`
- Apply effects are replaced with more precise effects (assertion enforced)

### Algorithm (detailed, step-by-step)

**Outer Pass (`analyseFunctions` function):**
1. Iterate over all blocks in the function body
2. For each instruction in each block:
   - If instruction is a `FunctionExpression` or `ObjectMethod`:
     a. Call `lowerWithMutationAliasing` on the inner function
     b. Reset `mutableRange` for each context variable to `{start: 0, end: 0}`
     c. Reset `scope` for each context variable to `null`

**Inner Mini-Pipeline (`lowerWithMutationAliasing` function):**...
taskcompleted
WP5: Scope grouping & deps
Output
task_id: ses_3ba128f6fffeyCGnj0vMh2i3Eo (for resuming to continue this task if needed)

<task_result>
# Complete Analysis: React Compiler Reactive Scope Grouping vs CloudLang Decomposition

---

# InferReactiveScopeVariables

## File
`/home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/ReactiveScopes/InferReactiveScopeVariables.ts`

## Purpose
First of 4 passes determining how to break a function into discrete reactive scopes (independently memoizeable units of code). Groups operands that mutate together and assigns them a unique reactive scope.

## Input Invariants
- HIRFunction with properly initialized `identifier.mutableRange` for all identifiers
- Phi nodes have been lowered with correct operand linking
- Global variables have `mutableRange.start = 0` (to be excluded from scopes)

## Output Guarantees
- Each `Identifier.scope` is assigned to a `ReactiveScope`
- Each scope has a `range` that spans from the first mutable instruction affecting any member to the last
- Scopes have disjoint sets of identifiers (no identifier belongs to two scopes)
- Scope ranges are valid within the function's instruction span

## Algorithm (detailed, step-by-step)
1. **Initialize DisjointSet** for tracking co-mutating identifiers

2. **Build mutable identifier groups** (in `findDisjointMutableValues`):
   - For each block in function:
     - **Handle PHI nodes**: If phi is mutated after creation, union all its operands (phi result + declaration + all phi operands). This handles cases where SSA phi values need same scope due to interleaved mutations.
     - **For each instruction**:
       a. Collect operands that are "mutable":
          - Lvalue is mutable if `range.end > range.start + 1` OR `mayAllocate(instr)`
          - RHS is mutable if `isMutable(instr, operand)` AND `operand.mutableRange.start > 0`
       b. Call `scopeIdentifiers.union(operands)` to merge all mutable operands

3. **Assign ScopeIds** (main function):
   - Iterate e...
82,286in / 2,460out
assistantFeb 10, 05:08:36 AM
claude-opus-4.6 · github-copilot

Batch 2 complete -- both WP4 (Function Analysis & Effects) and WP5 (Scope Grouping & Dependencies) succeeded. Now dispatching Batch 3 (WP6, the final analysis work package).

todowritecompleted
3 todos
Output
[
  {
    "content": "WP1: HIR Type System & Construction analysis",
    "id": "1",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP2: SSA & Type Inference analysis",
    "id": "2",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP3: Mutation & Aliasing Model analysis",
    "id": "3",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP4: Function Analysis & Effects analysis",
    "id": "4",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP5: Scope Grouping & Dependency analysis",
    "id": "5",
    "priority": "high",
    "status": "completed"
  },
  {
    "content": "WP6: Pipeline Orchestration & Optimizations analysis",
    "id": "6",
    "priority": "high",
    "status": "in_progress"
  },
  {
    "content": "Synthesize findings into CloudLang mapping document",
    "id": "7",
    "priority": "high",
    "status": "pending"
  },
  {
    "content": "Create prioritized implementation roadmap",
    "id": "8",
    "priority": "high",
    "status": "pending"
  }
]
taskcompleted
WP6: Pipeline & optimizations
Output
task_id: ses_3ba0bfe24ffeeoW75Fkjvt6prG (for resuming to continue this task if needed)

<task_result>
# Complete React Compiler Pipeline Orchestration Analysis

---

# Pipeline.ts

## File
`/react/compiler/packages/babel-plugin-react-compiler/src/Entrypoint/Pipeline.ts`

## Purpose
Main pipeline orchestrator that compiles individual React components/hooks from Babel AST to optimized JavaScript. Coordinates ~70 compilation passes in a specific order with conditional execution, error handling, and debugging integration.

## Input Invariants
- Valid Babel NodePath pointing to a FunctionDeclaration, ArrowFunctionExpression, or FunctionExpression
- Valid Environment configuration with all feature flags set
- Function must pass initial validation (not already marked for skipping)
- Source code must be available (for source location tracking)

## Output Guarantees
- Returns CodegenFunction with optimized function body
- All passes execute in the specified order (or skip if conditional)
- Logging/debugging events are recorded via logger callbacks
- Invariants are checked at strategic points in the pipeline
- Error handling follows panic threshold configuration

## Algorithm (detailed, step-by-step)

### Phase 1: Initial Setup & HIR Construction
1. `run()` creates Environment with config, scope, function type, context
2. `findContextIdentifiers()` - identifies React context references
3. Log EnvironmentConfig for debugging
4. Call `runWithEnvironment()` (main pipeline - config out of scope)
5. **`lower()`** - Convert Babel AST to HIR (High-level Intermediate Representation)
6. Log HIR state
7. **`pruneMaybeThrows()`** - Remove `maybeThrows` annotations
8. Log PruneMaybeThrows state
9. **`validateContextVariableLValues()`** - Validate context variable assignments
10. **`validateUseMemo()`** - Validate useMemo/useCallback usage

### Phase 2: Early Simplification
11. **Conditional**: `dropManualMemoization()` if:
    - `enableDropManualMemoization` is true
    - `!enablePreserv...
108,202in / 1,224out
userFeb 10, 05:16:19 AM
assistantFeb 10, 05:16:19 AM
claude-opus-4.6 · github-copilot

Summary for Continuation

Project Context

CloudLang (/home/jm/data/ai/cloudlang-trees/react/) is an OCaml compiler that decomposes TypeScript closures into Cloudflare Workers. It lives at github.com/manno23/cloudlang, branch react-inspiration. The React Compiler source is cloned at /home/jm/data/ai/cloudlang-trees/react/react/ (the full facebook/react repo).

What Was Done

We completed a comprehensive 6-work-package research analysis of the React Compiler (compiler/packages/babel-plugin-react-compiler/src/) to understand its multi-stage compilation pipeline and determine what to steal/adapt for CloudLang. All 6 work packages completed successfully:

WP Topic Status Output Location
WP1 HIR Type System & Construction ✅ Complete Returned in agent output (truncated in display, full saved to /home/jm/.local/share/opencode/tool-output/tool_c45eb4b73001NDPdqSd4K3SycE)
WP2 SSA & Type Inference ✅ Complete Written to .opencode/context/react-compiler-ssa-analysis.md
WP3 Mutation & Aliasing Model ✅ Complete Returned in agent output (truncated, full saved to /home/jm/.local/share/opencode/tool-output/tool_c45ecae13001lNuqihZetd3cPQ)
WP4 Function Analysis & Effects ✅ Complete Returned in agent output (full)
WP5 Scope Grouping & Dependencies ✅ Complete Returned in agent output (truncated, full saved to /home/jm/.local/share/opencode/tool-output/tool_c45f393f3001jYDnFmwHg1ivz7)
WP6 Pipeline Orchestration & Optimizations ✅ Complete Returned in agent output (truncated, full saved to /home/jm/.local/share/opencode/tool-output/tool_c45faa433001HjyVftbX3qXSHb)

What Was NOT Yet Done

The two remaining todo items:

  1. Synthesize findings into a CloudLang mapping document — consolidate the 6 WP outputs into a single document mapping React Compiler concepts → CloudLang equivalents
  2. Create a prioritized implementation roadmap — what to build first, in what order, with OCaml type sketches

Key Findings from the Research

CloudLang's current pipeline (5 steps):

parse (bun/ESTree JSON → 19-node AST) → scope_analyze → decompose → lower → emit/generate

What the React Compiler has that CloudLang lacks and needs:

  1. A proper HIR — CloudLang works directly on a flat AST (19 node types). React has a CFG-based HIR with ~40 instruction types, ~20 terminal types, basic blocks, phi nodes, SSA form. CloudLang needs an intermediate representation between its ESTree AST and its Cloudflare IR.

  2. SSA conversion — React uses Braun et al. (2013) algorithm. SSA gives precise per-version tracking of variables, critical for knowing exactly which SSA version of a variable is captured by each closure.

  3. Mutation/aliasing analysis via abstract interpretation — This is the crown jewel. React's InferMutationAliasingEffects uses a 17-variant AliasingEffect type (Capture, Alias, Assign, MaybeAlias, CreateFrom, Mutate, MutateTransitive, Freeze, etc.) with an abstract value lattice (Primitive < Frozen < MaybeFrozen < Mutable, plus Context and Global). It iterates to fixpoint over the CFG. CloudLang currently detects mutable state by checking new Map/Set/Array — vastly less precise.

  4. Recursive function analysis (AnalyseFunctions) — Processes nested functions depth-first, running a mini-pipeline on each to produce an aliasing signature. Classifies each captured context variable as Capture (mutated/aliased) or Read. This maps directly to CloudLang's need to understand what each closure does with its captured variables to determine Worker bindings.

  5. Union-find based scope grouping — React groups co-mutating values using DisjointSet with overlapping mutable ranges. CloudLang uses O(n²) iterative merging by shared variable names. Union-find would be more efficient and more precise.

  6. Dependency propagation — React's PropagateScopeDependenciesHIR computes exactly what each reactive scope depends on (with property paths). This maps directly to "what does each Worker group need from outside?"

CloudLang's Current Architecture (key files)

lib/
  ast.ml / ast.mli          — 19-node ESTree subset
  ir.ml / ir.mli            — Cloudflare IR (bindings, workers, DO annotations)
  parse.ml / parse.mli      — JSON → AST via jsont
  scope.ml / scope.mli      — 3-pass scope/closure analysis (240 lines)
  decompose.ml / decompose.mli — Worker group formation + DO inference (411 lines)
  lower.ml / lower.mli      — AST rewriting for distributed execution (329 lines)
  emit.ml / emit.mli        — AST → TypeScript source (122 lines)
  cloudlang.ml / cloudlang.mli — Pipeline facade + code generation
  wrangler.ml / wrangler.mli   — Wrangler TOML generation

User Preferences & Constraints

  • Language: OCaml with algebraic data types
  • Design approach: Incremental expansion — start with current 19 AST node types, design the HIR to be extensible so new constructs can be added without redesigning
  • Decomposition strategy: Research first, decide later — the user hasn't committed to replacing vs layering on top of the current decomposition algorithm
  • Dependencies: jsont, bytesrw, unix. No test framework (bare assert).
  • External bridge: TypeScript parsing done via bun + @typescript-eslint/typescript-estree

Proposed Next Steps

  1. Write the synthesis document to .opencode/context/ — consolidate all 6 WPs into a single reference mapping React concepts to CloudLang equivalents with OCaml type sketches

  2. Write the implementation roadmap — the WP6 analysis proposed a ~20-pass CloudLang pipeline:

    • Phase 1: HIR types + AST→HIR lowering
    • Phase 2: SSA conversion + type inference
    • Phase 3: Mutation/aliasing inference (abstract interpretation)
    • Phase 4: Function analysis (AnalyseFunctions equivalent)
    • Phase 5: Improved decomposition (union-find + mutable ranges)
    • Phase 6: Dependency propagation
    • Phase 7: Existing lowering/emit/generate (adapted for new IR)
  3. Begin implementation — likely starting with the HIR type definitions in OCaml since everything else depends on them

Prompt for New Session

I'm working on CloudLang (github.com/manno23/cloudlang, branch react-inspiration), 
an OCaml compiler that decomposes TypeScript into Cloudflare Workers. The project is 
at /home/jm/data/ai/cloudlang-trees/react/.

We completed a thorough research phase analyzing Facebook's React Compiler 
(cloned at /home/jm/data/ai/cloudlang-trees/react/react/compiler/packages/babel-plugin-react-compiler/src/)
to steal its multi-stage compilation approach for our purposes. 

Six detailed analysis documents were produced covering:
1. HIR type system & construction (HIR.ts, BuildHIR.ts, Types.ts, ObjectShape.ts)
2. SSA & type inference (EnterSSA.ts, EliminateRedundantPhi.ts, InferTypes.ts)  
3. Mutation & aliasing model (AliasingEffects.ts, InferMutationAliasingEffects.ts, InferMutationAliasingRanges.ts, MUTABILITY_ALIASING_MODEL.md)
4. Function analysis & effects (AnalyseFunctions.ts, InferReactivePlaces.ts)
5. Scope grouping & dependencies (InferReactiveScopeVariables.ts, PropagateScopeDependenciesHIR.ts, DisjointSet.ts)
6. Pipeline orchestration (Pipeline.ts, Program.ts, DeadCodeElimination.ts, ConstantPropagation.ts)

One analysis was saved to .opencode/context/react-compiler-ssa-analysis.md.
The other 5 are saved as tool outputs at /home/jm/.local/share/opencode/tool-output/:
- tool_c45eb4b73001NDPdqSd4K3SycE (WP1: HIR types)
- tool_c45ecae13001lNuqihZetd3cPQ (WP3: aliasing model)
- tool_c45f393f3001jYDnFmwHg1ivz7 (WP5: scope grouping)
- tool_c45faa433001HjyVftbX3qXSHb (WP6: pipeline)
WP4 (function analysis) was not truncated and is lost unless re-run.

The next steps are:
1. Synthesize all findings into a single mapping document (React concepts → CloudLang OCaml equivalents)
2. Create a prioritized implementation roadmap
3. Begin implementation, starting with HIR type definitions in OCaml

Key design decisions:
- Incremental expansion: start with current 19 AST nodes, grow the HIR
- OCaml algebraic data types for all IR types
- Research-first approach to decomposition (don't commit to replacing vs layering yet)
- The HIR needs: basic blocks, SSA, effect annotations, mutable ranges, extensible instruction types

Read the existing CloudLang source (lib/*.ml, lib/*.mli) and the saved analysis 
documents to understand context before proceeding.
106,821in / 2,556out