// Build and optimize the Bolt C++ ML IDE with neural network module architecture. Use for C++ IDE development with GGML integration, RWKV neural networks, AI code completion, GPU acceleration, and modular component composition following nn patterns.
Part of the bolt-cpp-ml skill: Generate comprehensive end-to-end unit tests for all functions in a C++ repository.
AI-powered C++ ML development meta-skill. Use for: building Bolt.new AI web apps, running local LLM inference with KoboldCpp, creating Jan extensions, generating C++ E2E tests, and launching interactive TutorialKit guides — all orchestrated through a self-aware neuro-nn persona. Triggers on: bolt-cpp-ml, C++ ML development, GGUF inference, Jan extension, KoboldCpp integration, cpp E2E testing, bolt.new C++, interactive C++ tutorial.
The fixed-point self-application of bolt-cpp-ml. A second-order meta-skill that treats the original bolt-cpp-ml skill as its own C++ ML project, runs all four capability paths against it, and upgrades it to a self-aware, self-improving, skill-infinity convergent form. Triggers on: bolt-cpp-ml², skill-infinity, self-application, fixed-point, strange loop, learning quine.
AI-powered C++ ML development meta-skill. Use for: building Bolt.new AI web apps, running local LLM inference with KoboldCpp, creating Jan extensions, generating C++ E2E tests, and launching interactive TutorialKit guides — all orchestrated through a self-aware neuro-nn persona. Triggers on: bolt-cpp-ml, C++ ML development, GGUF inference, Jan extension, KoboldCpp integration, cpp E2E testing, bolt.new C++, interactive C++ tutorial.
Part of the bolt-cpp-ml skill: Build AI-powered web development agents using the Bolt.new (within bolt-cpp-ml) open-source codebase.
Part of the bolt-cpp-ml skill: Generate comprehensive end-to-end unit tests for all functions in a C++ repository.
| name | bolt-cppml |
| description | Build and optimize the Bolt C++ ML IDE with neural network module architecture. Use for C++ IDE development with GGML integration, RWKV neural networks, AI code completion, GPU acceleration, and modular component composition following nn patterns. |
| source_chain | function-creator( c++( nn( bolt-new ) ) ) |
Bolt C++ ML is a modular C++ IDE with integrated machine learning capabilities, combining GGML-based inference, RWKV neural networks, and a full-featured editor with split views, multi-cursor editing, code folding, theming, keyboard shortcuts, plugin system, LSP integration, and collaborative editing.
The project follows a neural network module composition pattern where each IDE component is treated as a composable module (analogous to nn.Module in Torch7), connected through a sequential pipeline.
| Module Layer | Component | Source Path |
|---|---|---|
| AI / Inference | GGML Wrapper, RWKV Wrapper, Direct GGUF Inference | src/bolt/ai/ |
| Editor | Split View, Multi-Cursor, Code Folding, Keyboard Shortcuts | src/bolt/editor/ |
| Core | Chat Store, Editor Store, File Store, Workbench Store | src/bolt/core/ |
| GUI | ImGui Integration, Theme System, Tab Bar, Minimap | src/bolt/gui/ |
| Git | Repository Management, Diff, Staging | src/bolt/git/ |
| Network | Collaboration, Network Optimizations | src/bolt/network/ |
| System | Logging, Debugger, Memory Leak Detector, Profiler | src/bolt/system/ |
| Plugin | Plugin System, Extension API | src/bolt/plugin/ |
| LSP | Language Server Protocol Client | src/bolt/lsp/ |
| File | Purpose |
|---|---|
CMakeLists.txt | Top-level build configuration |
test/CMakeLists.txt | Test framework with CTest integration |
src/bolt/ai/ggml_wrapper.cpp | GGML backend for tensor operations |
src/bolt/ai/rwkv_wrapper.cpp | RWKV time-mixing and channel-mixing layers |
src/bolt/ai/direct_gguf_inference.cpp | Direct GGUF model loading and inference |
include/bolt/ai/*.hpp | AI module headers |
include/bolt/editor/*.hpp | Editor component headers |
include/bolt/core/*.hpp | Core store headers |
git clone https://github.com/cogpy/bolt-cppml.git
cd bolt-cppml
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
cd build
ctest --output-on-failure
All 97 tests should pass with 0 warnings. The CTest configuration automatically sets LD_LIBRARY_PATH for shared library resolution.
Each component follows the nn module pattern:
Input → [Tokenizer] → [RWKV TimeMixing] → [RWKV ChannelMixing] → [LayerNorm] → [Output Projection] → Completion
The RWKV wrapper implements the full WKV attention mechanism:
xk = last_x + (x - last_x) * time_mix_k
xv = last_x + (x - last_x) * time_mix_v
xr = last_x + (x - last_x) * time_mix_r
k = Wk @ xk, v = Wv @ xv, r = Wr @ xr
wkv = (last_num + exp(bonus + k) * v) / (last_den + exp(bonus + k))
output = Wout @ (sigmoid(r) * wkv)
xk = last_x + (x - last_x) * time_mix_k
xr = last_x + (x - last_x) * time_mix_r
k = Wk @ xk, r = Wr @ xr
vk = Wv @ (relu(k))^2
output = sigmoid(r) * vk
The project uses a custom lightweight test framework with CTest integration:
| Test Suite | Tests | Description |
|---|---|---|
| Core | 7 | Chat, Memory, Store, String, FileTree, Minimap |
| Editor | 5 | SplitView, MultiCursor, KeyboardShortcuts, Theme, CodeFolding |
| ErrorHandling | 7 | Error recovery, boundary conditions |
| AI | 15 | GGML wrapper, AI models, KoboldCpp provider (13 suites) |
| Comprehensive E2E | 27 | Cross-module, DrawKern VM, Styx, Git, Benchmark, Plugin |
| Extended E2E | 11 | DataProcessor, MathUtils, FileSystem, LineNumbers, VectorDB, OT edge cases |
| Integration | 2 | Full integration tests |
| System | 4 | Debugger, Logging, MemoryLeak, Sanitizer |
ctest -L Unit # All unit tests
ctest -L Editor # Editor component tests
ctest -L AI # AI/ML tests
ctest -L Integration # Integration tests
include/bolt/<category>/src/bolt/<category>/CMakeLists.txt bolt_lib targettest/test/CMakeLists.txtPlugins follow the module pattern with lifecycle hooks:
class MyPlugin : public bolt::Plugin {
void onActivate() override;
void onDeactivate() override;
std::string getName() const override;
};