Hey, everyone! đź‘‹
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...
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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.
This is a bold and fascinating direction....really excited to see how synapse evolves...
Thanks! I’m glad the project is sparking interest. Let’s see where it goes!
honestly pretty insane idea, stuff like this always makes me step back and wonder - you think it’s stuff like consistency or energy that actually keeps big language projects alive past the buzz?
Yeah, I think consistency and energy are exactly what keep languages alive beyond the initial buzz. Without a clear vision and an active community, most languages fade out quickly.
Given its design towards easily AI-generatable code from plain human language prompts, Synapse sounds like it would end up requiring far less technical software engineering expertise from development teams using it if it were to become a fully mature, widely adopted programming language/platform (barring a couple of senior/lead roles).
Never forget the human factors in any software engineering project. Who might be negatively impacted by what you are building and to what degree? The concept of ethical software development (such as it is) should prompt us to always consider the benefits vs. harms of the software we create in terms of real individuals' lives and livelihoods.
This has the potential to put even more junior/mid-level developers out of work while the collective industry C-suite executives and shareholders start shopping for another yacht with the savings from not paying those folks' salaries anymore (less the costs associated with whatever AI model is being used and the lower paid "AI-Vibe" prompt generation code monkeys who will be hired to replace them).
To clothe this in a historical metaphor, why are so many employees of buggy-whip manufacturers devoting time, effort, and knowledge to the development of the internal combustion engine? The other buggy-whip factory workers would really like to know why some of their peers are actively working towards the continuing elimination of job opportunities on the production lines of the entire buggy-whip manufacturing industry as a whole.
I understand what you're saying, but that's not how the economy works! In this case, the programming field is evolving and transforming. Look, you yourself mentioned yachts: if people start buying them, more yachts will be needed—and that also means more jobs for people working in that industry.
Similarly, people who move out of IT will find opportunities in other fields, but those who have dedicated their lives to programming will still have their place. A true professional can't just be replaced overnight.
Now, imagine a world where anyone can bring to life anything they can dream up (within the framework of a program), without the risk of losing everything. That’s unlike what happens now with many small companies that want to showcase their products but find development costs prohibitively high.
If their idea doesn’t catch on, that small entrepreneur who’s saved up for their idea suddenly needs to hire several developers to make it real. They might invest everything, but if the idea fails to find a market, they’re left with nothing.
This is a very interesting idea—kudos! Let me share some thoughts. Is the description of ASG precise enough? Consider two extremes: if it’s overly precise, it becomes like another programming language; if less precise, resembling natural language, it requires extensive interaction for confirmation and iterative refinement. The current approach of using AI agents for programming aligns with this direction—progressively refining precision through natural language interaction. How AI can be applied in software engineering is indeed worth exploring. Kudos again for your idea!
Thank you for your thoughtful comment!
You’re right — finding the right level of precision for ASG is a big part of the experiment. The idea is to keep it precise enough to run and analyze graphs, but flexible enough for easy editing and even AI-assisted refinement.
I hope Synapse can be a platform for both — formal programming and natural, interactive, human-AI collaboration.
Pretty insane seeing stuff like this come together - honestly building out even half the roadmap would be a win already.
I’ve already started working on it, and I really hope it will be successful!