Decompiler for iOS and macOS binaries

The description states that this is a decompiler for ARM64 iOS and macOS binaries, specifically targeting Objective-C and Swift applications. The author, Ethan Arbuckle, describes building a specialized tool using Python and Capstone, with contributions from Codex and GPT-5.6 in architectural design and correctness validation.

Key claims include:

• It converts ARM64 Mach-O machine code into source-like C, Objective-C, and Swift pseudocode
• It recovers constructs like Objective-C message-send syntax, Swift metadata operations, and UIKit API calls
• It uses a multi-stage compiler-style pipeline with provenance tracking and correctness validation
• It has 8,480 tests and 95% line coverage across core package

The project appears to be a technical prototype or early-stage tool built by one person. No evidence of revenue, customers, or commercial traction is provided. The author emphasizes that it's not a general-purpose decompiler but a specialized tool for iOS/macOS environments.

The description states that this is a "decompiler that turns ARM64 iOS and macOS binaries into source-like Objective-C and Swift."

It is described as:

• A tool that converts ARM64 Mach-O machine code into source pseudocode
• Built specifically for Objective-C, Swift, Foundation, UIKit, blocks, and Apple-specific ABI patterns
• Written in Python using Capstone
• Using a multi-stage compiler-style pipeline with intermediate representations
• Focused on ARM64 Mach-O binaries containing Objective-C, Swift, Foundation, UIKit, blocks, and Apple-specific ABI patterns

The author states it does not aim to support every architecture or programming language, but instead focuses deeply on the iOS/macOS environment.

The description states that this is a decompiler built specifically for ARM64 Mach-O binaries containing Objective-C, Swift, Foundation, UIKit, blocks, and Apple-specific ABI patterns. The author claims it goes "much deeper on the binaries iOS and macOS researchers encounter every day."

Key positioning elements:

• Specialized focus on iOS/macOS ARM64 binaries
• Targeting Objective-C and Swift applications specifically
• Claims to go "deeper" than existing decompilers that expose compiler/runtime machinery instead of application logic
• Positions itself as a tool for iOS/macOS researchers who want to avoid manual reconstruction

The claim evolution shows:

• Initial inspiration: reverse engineering iOS/macOS apps still involves large amounts of manual reconstruction
• Current state: produces output that is "more source-like and easier to understand" than established decompilers
• Future goal: practical reverse-engineering tool that lets researchers begin with recognizable application logic instead of first spending hours manually removing compiler and runtime scaffolding

The description states that the target users are "iOS and macOS researchers" who encounter ARM64 binaries daily.

The author's own write-up indicates:

• The tool is built for developers working with iOS/macOS applications
• It addresses people who want to avoid manual reconstruction of reverse-engineered code
• It targets those who work with Objective-C, Swift, Foundation, UIKit, blocks, and Apple-specific ABI patterns

No specific customer segments or personas are identified beyond "iOS/macOS researchers." The description does not indicate whether this is intended for security researchers, app developers, or other specific user groups.

Not evidenced. The description provides no information about pricing, revenue streams, monetization strategies, or business model.

The description states:

• Built with Python and Capstone
• Uses a multi-stage compiler-style pipeline
• Parses Mach-O metadata, symbols, relocations, Objective-C runtime data, and Swift metadata
• Lifts ARM64 instructions into intermediate representation with explicit registers, memory accesses, flags, branches, calls, and side effects
• Performs control-flow graph construction, static single-assignment and dataflow analysis, stack and register variable recovery, type inference, interprocedural analysis
• Objective-C and Swift semantic recovery, block and closure reconstruction, source-level control-flow structuring
• Tracks instruction provenance and observable effects through transformations
• Uses Codex with GPT-5.6 for architectural design and correctness validation

The author states that the project is "still evolving" particularly around difficult Swift ABI cases, C++ cleanup regions, complex aggregate values, and unusual floating-point calling conventions.

Not evidenced. The description does not provide any information about:

• Revenue or monetization
• Customers or user base
• Adoption metrics
• Market traction
• Product usage statistics
• Commercial deployment

The author states that the project is "still evolving" and "already a practical specialized decompiler rather than a toy or syntax-only prototype."

Not evidenced. The description does not mention:

• Competitors in the reverse engineering/decompilation space
• Market positioning relative to existing tools like IDA, Hopper, Ghidra, Binary Ninja
• Competitive advantages or differentiators
• Market size or competitive landscape

The author mentions that existing decompilers "can process ARM64 binaries, but on Objective-C and Swift applications they often expose compiler and runtime machinery instead of the application logic a developer originally wrote."

Inferences based on self-reported information:

1. Single-person development: The project is built by one person (Ethan Arbuckle), which may limit scalability, maintenance, and feature development
2. Limited commercial evidence: No revenue, customers, or traction data provided - this appears to be a technical prototype rather than a commercial product
3. Narrow focus: While specialization can be an advantage, focusing only on ARM64 Mach-O Objective-C/Swift applications may limit market reach
4. Technical complexity: The author notes significant challenges in Swift ABI recovery, Objective-C blocks, and control-flow structuring, suggesting ongoing technical difficulties
5. AI dependency: Heavy reliance on Codex and GPT-5.6 for architectural design raises questions about long-term sustainability and proprietary dependencies
6. Unproven commercial viability: No evidence of market demand or business model

1. What is the actual current state of the tool - is it usable in practice, or still experimental?
2. How does this compare to existing decompilers like IDA, Hopper, Ghidra, Binary Ninja in terms of accuracy and usability?
3. What are the specific use cases where this tool provides value over existing alternatives?
4. Are there any commercial partnerships or customers currently using this tool?
5. What is the roadmap for expanding beyond the current ARM64 Mach-O Objective-C/Swift focus?
6. How does the tool handle edge cases and complex scenarios that might not be covered in the test corpus?
7. What are the technical limitations of the current implementation that prevent broader adoption?
8. How do you plan to monetize this tool or build a sustainable business around it?