Hey, everyone! 👋
I wanted to share an idea that’s been brewing in my head: a brand-new programming language called Synapse. It’s designed from the ground up to be AI-first, focusing on formal program representation, code generation, verification, and transformation — all with the help of artificial intelligence. Let me walk you through what I’ve envisioned so far.
🌐 What is Synapse?
Synapse aims to be a language where AI doesn’t just help developers — it’s the primary driver of the development process. Instead of relying on traditional text-based code as the main source, Synapse uses an Abstract Syntax Graph (ASG) as its canonical representation. Textual code is secondary.
Here’s what that means:
Formal rigor: Every program is formally represented as an ASG, making it highly analyzable, verifiable, and transformable by AI tools.
Maximum static analyzability: Think of advanced type systems, effect systems, and resource management baked right into the language.
Cross-platform compilation and execution: Synapse would support compilation to native code (via LLVM) and WebAssembly out of the box.
🛠️ Key Ideas
🔹 ASG as the Core:
Programs are stored and processed as Abstract Syntax Graphs.
Each node has a type code and a binary payload.
Edges define data flow, control flow, and type relationships.
🔹 Binary Format (SYN1):
Compact, fast, and platform-independent.
Little-endian, VarInt encoding — efficient for both serialization and deserialization.
🔹 Advanced Type System:
Strong static typing with explicit type annotations.
Parametric polymorphism, effect systems, linear types, borrowing/lifetimes, and error handling with Result.
🔹 Effects and Verification:
Explicit effect tracking for I/O, state, etc.
Integrated proof nodes (Proof, Specification, Assert) enabling formal verification.
🔹 FFI Support:
Ability to interface with C/Rust and other languages through explicit foreign declarations.
🔹 AI Tooling:
Direct API for AI tools to access and transform the ASG.
Metadata and annotation support for advanced AI analysis.
🧩 Proof-of-Concept Roadmap
I’ve mapped out an initial plan to get a working prototype:
✅ Step 1: Build the Core ASG Data Structures
Implement ASG structures in Rust.
Write serialization/deserialization for the SYN1 binary format.
✅ Step 2: Create a Minimal Interpreter
Evaluate simple ASGs (literals, basic binary operations, conditionals).
✅ Step 3: Develop a Command-Line Tool
Load and execute .synapse files.
Print results or errors.
✅ Step 4: Type Checker
Validate basic type correctness: literals, binary operations, conditionals.
✅ Step 5: (Optional) Effect Analysis
Statically track effects like I/O and state.
💡 Why Synapse?
I believe this approach can change how we build software:
AI-First: The language is designed from the start to be machine-readable and machine-generatable.
Formal verification: Safety and correctness are first-class citizens.
Modular and extensible: The ASG representation allows plug-and-play analysis and transformation tools.
🔥 Is This Worth Pursuing?
Now, here’s where I’d love your feedback:
Does this idea resonate with you?
Do you think the ASG-first design makes sense for AI-assisted programming?
Which parts would you be most interested in seeing implemented first?
I’d love to collaborate or just get your thoughts on whether this is a crazy moonshot or something truly game-changing.
Let me know what you think! 👇
Top comments (29)
I think something like this will eventually be a thing
Believe me or not, but I was googling and found this article because I had exactly the same idea, an AI-first programming language, and... I thought of exactly the same name and wanted to check if it already exists or not. So, maybe your idea is not that crazy. :D
Interesting! Looks like we have a similar vision. Maybe the idea really has potential
that'a pretty good idea it wound come to a end finnally
Honestly, I wasn’t even sure if I should share this concept yet. It’s still unfinished, but I’m glad I did.
dude,that's cool isn't it ?
Trying to check my understanding. This is a high-level, declarative language to allow developers to more concisely create applications via AI? It would be interesting to see some pseudo code to see how you envision this working in practice.
I don't think it's crazy, but wonder if it's more beneficial than being able to speak to an AI in natural language and have it build in battle tested languages (like Rust).
Thanks for the feedback! I think creating such a programming language is a bit crazy for two reasons!
First, it’s very difficult for a person to read the code (the ASG itself). Second, the implementation of such a language would be challenging even for an experienced team. But yes — the idea is that we could ultimately build a programming language for AI that doesn’t make mistakes!
Here’s how I see a simple example:
Let’s say we wanted to add two numbers and print the result:
let x = 1 + 2;
perform IO.write_line(x);
In Synapse ASG form, this might look like:
A Node representing LiteralInt(1)
A Node representing LiteralInt(2)
A Node representing BinaryOperationAdd, with edges to the literals
A Node representing PerformIOWriteLine, with an edge to the result
This is not "crazy" because it's good. I honestly just don't see the point at all.
There are way too many limitations and points of failure.
Why not strive to improve AI to work better with existing languages that humans already understand?
Thanks! It’s just a concept that I wanted to share. And that third reason is exactly why it’s crazy)
you should tell everyone what you just did, that's awsome dude
I’ve started implementing Synapse for real —
the first open-source version is now live, with a Rust interpreter, graph generators, a JSON/SYN1 converter, and more.
Read the next post with code samples, a roadmap, and practical usage here:
👉 Implementing Synapse: From AI-First Language Idea to Executable Graphs
The code is on GitHub:
👉 github.com/Xzdes/synapse
Feel free to try it, fork it, ask questions, or suggest features!
Let’s build something new for programming and AI together.
If only there would be a language to tell a computer exactly what it should do...
Synapse sounds like a new programming language designed specifically to work closely with AI, making it easier for developers to collaborate with AI models in writing and improving code. It focuses on blending human intent with AI-generated solutions to create more adaptive and intelligent software. here's an example of Synapse in Action Imagine you need to build a system that automatically summarizes long documents. Traditionally, developers would write detailed code to handle the summarization logic, test various edge cases, and refine the solution over time. With Synapse, you simply describe your goal—creating a concise, accurate summary—and the AI integrated within the language interprets this intent. It generates the underlying implementation, continuously improving the summary quality based on user feedback and new data.This approach shifts the developer’s role from writing every line of code to guiding and collaborating with AI, resulting in faster development, smarter applications, and systems that evolve dynamically without extensive manual intervention.
Thanks again for the interest! If you'd like, I can share the full specification I've drafted so far — it's a work-in-progress but gives a deeper look at all the ideas behind Synapse (types, effects, ASG structure, and so on). Just let me know if you'd be interested!
Nice concept
Programming Language Specification "Synapse" (Version 0.2 - Draft)
I. Introduction
1.1. Name: Synapse
1.2. Purpose: A formal programming language designed for optimal analysis, generation, verification, and transformation of code by Artificial Intelligence (AI) systems. It is a target language for AI-assisted development environments.
1.3. Principles:
Formal rigor and verifiability.
Primary representation: Abstract Syntax Graph (ASG).
Maximum static analyzability.
Cross-platform compilation and execution.
1.4. Scope of Application: AI code generation, systems programming, high-reliability systems, verifiable computation.
II. Basic Representation: Abstract Syntax Graph (ASG)
2.1. Primary Form: ASG is the canonical representation of Synapse programs. Textual representation is secondary.
2.2. Node Structure:
NodeID: Unique node identifier.
NodeType: Node type code (see Section IV).
Payload: NodeType-specific binary data.
Edges: List of outgoing edges.
2.3. Edge Structure:
EdgeType: Edge type code (see Section V).
TargetNodeID: ID of the target node.
(Optional) EdgePayload: EdgeType-specific data.
III. ASG Serialization Format ('SYN1')
3.1. Purpose: A compact, fast, platform-independent representation of the ASG.
3.2. Specification:
Header: Identifier ('SYN1'), Version, Number of nodes (VarInt), Table offsets (optional).
Body: Sequence of node records.
Node Record: NodeTypeCode (VarInt), PayloadLength (VarInt), PayloadBytes, EdgeCount (VarInt), Edge list.
Edge Record: EdgeTypeCode (VarInt), TargetNodeID (VarInt), optional EdgePayload.
Encoding: Little Endian, VarInt, UTF-8.
IV. Node Types (NodeType)
(Preliminary codes, subject to finalization. List is not exhaustive.)
4.1. Literals: LiteralInt, LiteralFloat, LiteralBool, LiteralString, LiteralUnit.
4.2. Operations: BinaryOperation[Op], UnaryOperation[Op], Conditional, RecordFieldAccess, Dereference.
4.3. Variables and Functions: VariableDefinition, VariableReference, Lambda, Application.
4.4. Types: TypeInt, TypeFloat, TypeBool, TypeString, TypeUnit, TypeFunction, TypeVariable, ForAll, TypeRecord, FieldDefinition, TypeADT, ADTVariant, TypeLinear, TypeSharedRef, TypeMutableRef, TypeLifetime, TypeResult, TypeErrorUnion, TypeTrait, TraitMethodDecl, ForeignTypeDecl.
4.5. Effects: EffectIO, EffectConsole, EffectFSRead/Write, EffectNetwork, EffectState, EffectRandom, EffectExcep, EffectNonTerm, EffectPure.
4.6. Data Constructors: DataRecordInit, DataADTInit, DataOk, DataErr.
4.7. Effect Operations: Perform[EffectOperation].
4.8. Pattern Matching: MatchResult, MatchADT.
4.9. Metaprogramming: MacroDefinition, MacroInvocation.
4.10. Modules: ModuleRoot, ImportDeclaration, ExportDeclaration, ImportAlias.
4.11. FFI: ForeignFunctionDecl, ForeignBlock.
4.12. Verification: Proof, Specification, Assume, Assert.
4.13. Testing: TestCase, TestSuite, Assertion[Type], PropertyDefinition, InputGenerator.
4.14. Helpers: MatchCase, ImplMethodDef, TraitImpl.
V. Edge Types (EdgeType)
(Preliminary codes, subject to finalization. List is not exhaustive.)
5.1. Data/Control Flow: DataInput, ControlFlowNext, Condition, ThenBranch, ElseBranch.
5.2. Scope/Binding: ScopeLink, LambdaParameter, LambdaBody, DefinitionLink, BindsVariable.
5.3. Function Calls: ApplicationFunction, ApplicationArgument.
5.4. Typing: TypeAnnotation, FunctionParamType, FunctionReturnType, TypeVarBinding, TypeBody, Constraint, LinearInnerType, RefInnerType, RefLifetime, LifetimeBound, ResultOkType, ResultErrType.
5.5. Data Structures: RecordField, FieldName, FieldType, FieldValue, FieldTarget, HasVariant, VariantName, VariantParam, VariantArgValue, VariantTarget.
5.6. Effects: ProducesEffect.
5.7. Pattern Matching: MatchInput, MatchOkBranch, MatchErrBranch, MatchBranch, MatchesVariant, CaseBody.
5.8. Modules: ImportsFromModule, ImportsSymbol, ImportsAll, ExportsSymbol, ModuleContains.
5.9. FFI: HasFFISignature, UsesABI, LinksToLibrary.
5.10. Verification: ProvesSpec, SpecifiesCode, ProofStepDependsOn, ReliesOnAssumption.
5.11. Testing: TestsFunction, ProvidesInput, MakesAssertion, ChecksProperty, InputForProperty.
5.12. Metaprogramming: MacroBody, MacroInputAST, InvokesMacro.
5.13. Traits/Implementations: HasMethod, ImplementsTrait, ForType, ProvidesImpl, ImplementsMethod.
5.14. Structural: RootExpression.
VI. Execution Semantics
6.1. Model: Formal operational semantics via graph rewriting.
6.2. Reduction Rules: A set of rules describing the transformation of ASG subgraphs into their value or next state.
6.3. Strategy: Default applicative order (call by value). Mechanisms for lazy evaluation should be provided.
6.4. Effects: Effect operations interact with the Runtime API.
VII. Type System
7.1. Foundation: Static, strong typing with explicit annotations (TypeAnnotation).
7.2. Components:
Basic Types and Constructors: Numbers, bool, strings, Unit, functions, records, ADTs.
Polymorphism: Parametric (TypeVariable, ForAll) and constrained (TypeTrait, Constraint). Compilation via monomorphization.
Effect System: Explicit tracking (Effect*, ProducesEffect). Purity guarantees (EffectPure).
Linear Types: (TypeLinear) Static resource management.
Borrowing: (TypeSharedRef, TypeMutableRef, TypeLifetime) Temporary access. Static compiler checks. (Detailed lifetime specification required.)
Error Handling: Result type, exhaustive MatchResult.
VIII. Module System
8.1. Structure: Program as a module graph (ModuleRoot). ASG file = module.
8.2. Visibility: Private by default, explicit export (ExportDeclaration).
8.3. Import: Explicit import (ImportDeclaration) of symbols/namespaces.
8.4. Name Resolution: Compiler resolves references with module awareness.
8.5. Compilation: Supports monolithic and separate compilation. (ABI specification required.)
IX. Metaprogramming (Macros)
9.1. Approach: Homoiconic (ASG as data). Macros (MacroDefinition) operate on ASG.
9.2. Types: Declarative (pattern/substitution) and Procedural (Synapse code execution).
9.3. Safety: Hygiene, macro type-checking or result verification, effect restrictions at compile time.
X. Concurrency and Parallelism
10.1. Purpose: Safe and efficient parallel execution.
10.2. Approach: (Proposed start) Library model (std::thread, std::sync) with type-based safety (linearity, borrowing, Send/Sync).
10.3. Safety: Static analysis for data races (via types and Send/Sync) and potential deadlocks.
XI. Formal Verification
11.1. Integration: Specification, Proof, Assume, Assert nodes in the ASG.
11.2. Language: (To be chosen/developed) Formalism integrated with Synapse (e.g., Hoare logic + SMT).
11.3. Tools: Proof Checker required (internal/external). Possibility of automatic proof via SMT.
XII. Interoperability with Other Languages (FFI)
12.1. Mechanism: Declaration (ForeignFunctionDecl, ForeignTypeDecl) with ABI specification ("C").
12.2. Safety: FFI calls require an UnsafeBlock. Maintaining Synapse invariants within the block is the responsibility of the programmer/AI. Recommended safe wrappers.
12.3. Export: Attributes (#[no_mangle], extern "C") for exposing Synapse functions to external code.
XIII. Testing
13.1. Approach: Integrated into ASG (TestCase, TestSuite, Assertion*, PropertyDefinition, InputGenerator).
13.2. Methods: Formal verification, AI-generated tests, property-based testing, etc.
13.3. AI Tooling: Test generation, execution, analysis; structured reports.
XIV. Compilation and Runtime Environment
14.1. Compiler:
Architecture: Frontend (ASG analysis/verification, High-Level IR) / Backend (code generation).
Backends: Priority to LLVM (native code), WebAssembly (portable bytecode).
14.2. Runtime:
Minimalist and portable: Implemented in C/Rust.
Provides Runtime API: Stable, platform-independent interface for basic OS operations.
OS Abstraction: Conditional compilation for platform specifics.
XV. Standard Library (stdlib)
15.1. Implementation: Written in Synapse (ASG modules).
15.2. Structure: Module hierarchy (core, alloc, std). The core::prelude module is imported by default.
15.3. Content: Basic types (Option, Result), traits (Error, Eq, Ord, Read, Write, Send, Sync, Fn*), collections (Vec, String), I/O, synchronization, filesystem. Provides safe wrappers over the Runtime API. (Detailed API design required.)
XVI. Tooling and AI Interface
16.1. ASG Analysis API: Stable programmatic interface for AI tools to access ASG information.
16.2. Metadata: ASG annotation format for AI.
16.3. AI Mediator: The main interface for generating Synapse ASG, considering target platform features.
XVII. Open Questions and Further Development
Detailed lifetime system specification.
Finalization of concurrency model and its API.
Selection/development of the specification and proof language.
Detailed design of the standard library API.
ABI specification for separate compilation.
Finalization of NodeType and EdgeType codes.
Development of node attributes/annotations specification in ASG.
Note: This document is a draft specification. It covers core concepts and architectural decisions, but many details require further formalization before implementation can begin.
Why not to grab ASG representation for any of existing languages? What benefits are coming from inventing the wheel?
Existing languages are not designed with ASG (Abstract Syntax Graph) as the primary representation. In most modern languages (Rust, C++, Python), the program’s primary form is textual. This means:
There is a lot of “syntactic sugar” (constructs convenient for humans but inconvenient for machines).
The semantics of the language (the meaning of the code) are often hidden under layers of implicit conventions or implementation details.
Graph representations (AST → ASG) can be built afterward, but they are not native to the language and remain just a byproduct.