`// s4 // cpp // guidelines`

Strategy and Motivation

We use C++ in situations where we need to do something extreme along one or more dimensions: we are in a regime where no compromise is possible. Typically we do this by having low-friction access to efficient, ergonomic implementations of best-in-class algorithms. Sometimes, we have the opportunity to do something best-in-class ourselves; we consider such proposals with open minds and healthy skepticism. Our C++ codebase and the investment represented by maintaining it is the optionality premium on these degrees of freedom.

Much if not most excellent modern C++ code is proprietary because worthwhile C++ code is expensive and most contemporary projects don’t need it. This leads to a situation where it is difficult to learn well outside of an elite technology or finance company. For non-commercial examples of extreme requirements, consider people working at the frontiers of human knowledge: CERN has excellent code because they operate in regimes that would be daunting for any company.

This document is aimed at three audiences:

Experienced C++ programmers who have missed recent developments
Programmers new to serious C++ who want to skip learning curve friction
Agents with extensive informational resources who need clear guidelines

The Economics of Code in Agent-Heavy Development

In a codebase with heavy agent contribution, traditional economics invert:

Code is written once by agents in seconds
Code is read hundreds of times by humans and agents
Code is debugged when you’re under pressure by tired humans
Code is modified by agents who lack the original context

Every ambiguity compounds exponentially.

The Fundamental Principle

// this costs an agent 0.1 seconds to write, a human 10 seconds to debug:
auto e = edge{};
if (e.p > 0) process(e);

// this costs an agent 0.2 seconds to write, saves hours of cumulative confusion:
auto inference_configuration = s4::inference::config::engine{};
if (inference_configuration.batch_size > 0) {
  initialize_inference_engine(inference_configuration);
}

Optimize for disambiguation, not brevity.

Why Config Parsing Is Sacred

Configuration parsing is the most critical code in any system because:

Multiplication Effect: One config bug affects every component
Trust Boundary: External input that everything else trusts implicitly
Silent Corruption: Config errors manifest as business logic failures
Audit Trail: In regulated environments, you must prove correct configuration

Config parsing should be human-written, brutally simple, and fail-fast.

High Level Choices

Explicit Types over AAA (for agents) - Disambiguation beats brevity
Fully qualified names - No using namespace, absolute clarity
C++23 features - Use modern constructs maximally
Measure, don’t guess - Data-driven optimization
Name for grep - Every identifier must be globally searchable

Naming Conventions

The Disambiguation Imperative

In an agent-heavy codebase, names must be:

Globally unique within their semantic domain
Self-documenting without context
Searchable with basic tools

// BAD: Will create confusion at scale
class parser;
auto config = load();
int process(data& d);

// GOOD: Unambiguous even with 100 agents contributing
class tokenizer_engine;
auto inference_configuration = load_inference_configuration();
int process_tensor_batch(tensor_batch_data& batch);

Core Naming Rules

snake_case for everything: tensor_batch, model_weights, execute_inference()
Full words over abbreviations: configuration not config, connection not conn
Domain prefixes for common concepts: cuda_stream, device_memory, host_memory
member_suffix_ for members: tensor_shape_, latency_us_, device_id_
Preserve acronyms: NVFP4_quantizer not Nvfp4Quantizer

The Three-Letter Rule

If an abbreviation is less than 4 characters, it’s too short:

// BAD
auto cfg = load_cfg();
auto conn = db.get_conn();
auto res = process(req);

// GOOD
auto configuration = load_configuration();
auto connection = database.get_connection();
auto result = process_request(request);

Standard Abbreviations (Use Sparingly)

Only when the full name would be absurd:

idx/jdx - index (prefer descriptive names like row_index)
rxbuf/txbuf - receive/transmit buffer (domain-specific)
ctx - context (only when type makes it unambiguous)

Code Organization

Directory Structure Guidelines

s4/
├── core/           # Foundation utilities (exceptions, hash, workspace, nvtx)
│   ├── exceptions.h
│   ├── exceptions.cpp
│   ├── generator.h
│   └── workspace.h
├── cuda/           # CUDA primitives and utilities
│   ├── nvfp4/
│   │   ├── nvfp4.h
│   │   ├── nvfp4.cuh
│   │   └── nvfp4.cu
│   └── cccl_standard.h
├── attention/      # Attention mechanisms and kernels
│   ├── sage_attention_plugin.h
│   ├── sage_attention_plugin.cu
│   └── score_correction.h
├── tensor/         # Tensor abstractions
│   ├── device_tensor.h
│   └── view.h
├── dtypes/         # Data type system
│   ├── dtype.h
│   ├── cuda_types.h
│   └── dispatch.h
└── trt/            # TensorRT integration
  ├── affine_unary_plugin.h
  └── affine_unary_plugin.cu

Headers and implementations are adjacent - foo.h and foo.cpp live together
Test files live in separate tests/ directory: tests/unit/test_*.cpp
Property tests: tests/property/test_*_properties.cpp
Python hypothesis tests: tests/python/test_*_hypothesis.py
CUDA device code uses .cu extension, device-only headers use .cuh

Headers

#pragma once

#include <chrono>
#include <memory>
#include <span>

#include "s4/core/exceptions.h"
#include "s4/dtypes/dtype.h"
#include "s4/tensor/device_tensor.h"

namespace s4::inference {

class engine {  // Full descriptive names
public:
  engine();

  // full words in function names
  auto initialize_from_configuration(std::string configuration_path) noexcept
    -> s4::core::status;

  auto run_inference(std::span<const float> input_tensor) noexcept
    -> s4::core::result<tensor_batch>;

private:
  // clear member names with units where applicable
  std::unique_ptr<model_executor> executor_;
  std::chrono::microseconds inference_timeout_us_;
  int device_id_;
};

}  // namespace s4::inference

Implementation

#include "s4/inference/engine.h"

#include <format>

#include "s4/core/logging.h"
#include "s4/cuda/device.h"

namespace s4::inference {

auto engine::initialize_from_configuration(
  std::string configuration_path) noexcept -> s4::core::status {

  // Descriptive variable names throughout
  auto configuration_result = s4::core::fs::read_file_to_string(configuration_path);

  if (!configuration_result) {
    return s4::core::fail(
      std::format("[s4] [inference] [engine] failed to read configuration: {}",
                  configuration_result.error().what()));
  }

  auto parsed_configuration = parse_inference_configuration(configuration_result.value());
  // ...

  return s4::core::ok();
}

}  // namespace s4::inference

Modern C++23 Patterns

Core Hardware Realities

Modern GPUs and CPUs are not the abstraction models from your CS courses, they’re not even the ones you worked with a few years ago:

Cache lines are 64 bytes - This is the unit of memory transfer. Period.
Branches are heinously expensive - A mispredicted branch costs 15-20 cycles on modern CPUs
The prefetcher is your friend - Linear access patterns let it work magic
The compiler is your best optimizer - With -O3 -march=native, it knows tricks you don’t
This is even more true of Myelin - When attempting to go fast on a GPU, you will almost never outsmart Myelin except when it has a pathological failure.

Performance Anti-Patterns and Reality Checks

Write simple, clear loops. The compiler will optimize them:

// BAD: Hand-rolled "optimization" that confuses compiler and humans
for (; data_index + 8 <= data_length; data_index += 8) {
  auto chunk = *reinterpret_cast<const uint64_t*>(data + data_index);
  // Complex bit manipulation
}

// GOOD: Clear intent, compiler optimizes perfectly
for (auto data_index = 0; data_index < data_length; ++data_index) {
  if (data[data_index] == target_value) {
    match_count++;
  }
}

Error Handling Philosophy

We don’t throw exceptions. We use s4::core::result<T> and when something is truly unrecoverable:

// When failure is recoverable - return result
auto parse_configuration(std::string_view configuration_json) noexcept
  -> s4::core::result<server_configuration> {

  if (configuration_json.empty()) {
    return s4::core::fail<server_configuration>("empty configuration string");
  }

  // parse...
  return s4::core::ok(server_configuration{...});
}

// when failure is unrecoverable - fatal and we do the postmortem...
if (!critical_resource_handle) {
  s4::fatal("critical resource unavailable: {}", resource_name);
}

Error Handling Patterns

// DO: Use specific fail overloads
if (size > max_size) {
  return s4::fail<buffer>("buffer size {} exceeds maximum {}", size, max_size);
}

if (::listen(socket_fd, backlog) < 0) {
  return s4::fail_errno<socket>("failed to listen on socket");
}

// DON'T: Build error messages manually
if (size > max_size) {
  return s4::fail<buffer>(std::format("buffer size {} exceeds maximum {}", size, max_size));
}

Result Type Usage

// prefer explicit type parameters for fail() - aids readability...
auto parse_config(std::string_view json) -> s4::core::result<configuration> {
  if (json.empty()) {
    return s4::fail<configuration>("empty configuration string");
  }
  // ...
}

// for functions returning status, the type parameter can be omitted
auto validate_connection() -> s4::core::status {

  if (!is_connected()) {
    return s4::fail("not connected");  // T defaults to monostate
  }

  return s4::ok();
}

Const-Correctness

// DO: mark everything const that can be...
auto process_batch(const tensor_batch& batch_data) const noexcept -> s4::core::status;

// DO: use const for local variables that don't change...
const auto configuration = load_configuration();
const auto batch_count = batches.size();

// DON'T: forget const on method that doesn't modify state...
auto get_status() -> status_code;  // n.b. should be const, often [[nodiscard]]...

Span Usage

// DO: use `span` or `mdspan` for non-owning array views...
auto process_batch(std::span<const inference_request> requests) -> s4::core::status;

// DON'T: use raw pointer + size
auto process_batch(const inference_request* requests, size_t count) -> s4::core::status;

// DO: use span for fixed-size buffers...
auto read_into(std::span<std::byte> buffer) -> s4::core::result<size_t>;

CUDA and GPU Computing Patterns

CCCL-Forward Modern CUDA

We use CUDA C++ Core Libraries (CCCL) for modern, standards-compliant CUDA code. As of March 2024, CCCL unifies Thrust, CUB, and libcudacxx.

Key principle: Always prefer cuda::std:: over std:: - it works in both host and device code, works with NVRTC, and is tested for CUDA.

#include <cuda/std/span>
#include <cuda/std/array>
#include <cuda/stream_ref>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>

// DO: Use cuda::std:: entities (not std::) for device compatibility
__global__ void process_kernel(cuda::std::span<float> input_data,
                         cuda::std::span<float> output_data) {
  int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
  if (thread_id < input_data.size()) {
    output_data[thread_id] = input_data[thread_id] * 2.0f;
  }
}

// DO: Use cuda::stream_ref for stream management
auto launch_inference_kernel(cuda::stream_ref stream,
                        std::span<const float> device_input) -> s4::core::status {
  constexpr auto threads_per_block = 256;
  auto block_count = (device_input.size() + threads_per_block - 1) / threads_per_block;

  process_kernel<<<block_count, threads_per_block, 0, stream>>>(
  cuda::std::span{device_input.data(), device_input.size()},
  // ...
  );

  return s4::cuda::check_last_error();
}

Thrust Vectors for Memory Management

Thrust provides STL-like containers for host and device memory:

#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/universal_vector.h>
#include <thrust/async/copy.h>

// DO: Use thrust::device_vector for device-side data
auto prepare_inference_batch(std::span<const float> host_data)
  -> s4::core::result<thrust::device_vector<float>> {

  // Host vector with STL-like interface
  auto host_batch = thrust::host_vector<float>(host_data.begin(), host_data.end());

  // Transfer to device (synchronous) - type deduced
  auto device_batch = host_batch;

  return s4::ok(std::move(device_batch));
}

// DO: Use thrust::async for non-blocking operations
auto prepare_batch_async(std::span<const float> host_data,
                   cudaStream_t stream)
  -> thrust::device_future<thrust::device_vector<float>> {

  auto host_batch = thrust::host_vector<float>(host_data.begin(), host_data.end());
  auto device_batch = thrust::device_vector<float>(host_batch.size());

  // Asynchronous copy
  return thrust::async::copy(thrust::device.on(stream),
                       host_batch.begin(), host_batch.end(),
                       device_batch.begin());
}

// DO: Use thrust::universal_vector for unified memory scenarios
auto shared_buffer = thrust::universal_vector<float>(batch_size);
// Accessible by both host and device without explicit transfers

// DON'T: Access individual device_vector elements in loops
// Each access requires cudaMemcpy!
for (auto idx = 0; idx < device_vec.size(); ++idx) {
  auto value = device_vec[idx];  // BAD: N cudaMemcpy calls
}

// DO: Transfer once, process in bulk
auto host_copy = device_vec;  // One transfer, type deduced
for (auto idx = 0; idx < host_copy.size(); ++idx) {
  auto value = host_copy[idx];  // GOOD: Local memory access
}

mdspan for Multidimensional Data (C++23)

mdspan provides non-owning views of multidimensional arrays. CUDA support is available via Kokkos implementation:

#include <mdspan>
// Future: #include <cuda/std/mdspan> when available in libcudacxx

// DO: Use mdspan for type-safe multidimensional indexing
template<typename T>
using matrix_view = std::mdspan<T, std::dextents<size_t, 2>>;

template<typename T>
using tensor3d_view = std::mdspan<T, std::dextents<size_t, 3>>;

// DO: Express tensor operations with clear dimensionality
auto quantize_weight_matrix(matrix_view<const float> weights_fp32,
                      matrix_view<uint8_t> weights_nvfp4,
                      float scale_factor) -> s4::core::status {

  if (weights_fp32.extent(0) != weights_nvfp4.extent(0) ||
      weights_fp32.extent(1) != weights_nvfp4.extent(1)) {
    return s4::fail("dimension mismatch: fp32[{},{}] vs nvfp4[{},{}]",
                    weights_fp32.extent(0), weights_fp32.extent(1),
                    weights_nvfp4.extent(0), weights_nvfp4.extent(1));
  }

  // C++23 bracket operator for multidimensional access
  for (auto idx = 0; idx < weights_fp32.extent(0); ++idx) {
    for (auto jdx = 0; jdx < weights_fp32.extent(1); ++jdx) {
      weights_nvfp4[idx, jdx] = quantize_value(weights_fp32[idx, jdx], scale_factor);
    }
  }

  return s4::ok();
}

// DO: Use mdspan for batch tensor layouts (N, C, H, W)
auto process_image_batch(tensor3d_view<const float> batch,  // [batch, height, width]
                   size_t channels) -> s4::core::status {

  auto batch_size = batch.extent(0);
  auto height = batch.extent(1);
  auto width = batch.extent(2);

  s4::info("[s4] [tensor] processing batch shape=[{},{},{}] channels={}",
     batch_size, height, width, channels);

  // Clear dimensional semantics
  return s4::ok();
}

CUTLASS cute::Tensor

CUTLASS cute::Tensor provides layout-aware tensor abstractions for high-performance kernels:

#include <cute/tensor.hpp>

using namespace cute;

// DO: Use cute::Tensor for layout-aware kernel code
template<class T, class Layout>
__global__ void gemm_kernel(Tensor<T, Layout> const& A,
                     Tensor<T, Layout> const& B,
                     Tensor<T, Layout>& C) {

  // cute::Tensor provides hierarchical operations
  auto tile_shape = make_shape(Int<16>{}, Int<16>{});

  // Access with logical coordinates
  for (auto idx = 0; idx < size<0>(A); ++idx) {
    for (auto jdx = 0; jdx < size<1>(B); ++jdx) {
      C(idx, jdx) = A(idx, 0) * B(0, jdx);  // Simplified GEMM
    }
  }
}

// DO: Create tensors with explicit layout control
auto create_row_major_tensor(float* device_ptr, size_t rows, size_t cols) {
  auto shape = make_shape(rows, cols);
  auto stride = make_stride(cols, Int<1>{});  // Row-major: stride by cols
  auto layout = make_layout(shape, stride);

  return make_tensor(device_ptr, layout);
}

// DO: Use cute for copy algorithms with optimal layouts
template<class TA, class ALayout, class TB, class BLayout>
__global__ void copy_kernel(Tensor<TA, ALayout> const& src,
                     Tensor<TB, BLayout>& dst) {

  // Generic copy that respects layout
  for (auto idx = 0; idx < size(src); ++idx) {
    dst(idx) = src(idx);
  }
}

// DO: Integrate with PyTorch via dlpack (Python API, 2025)
// Python: cute_tensor = cute.from_dlpack(torch_tensor)
// Access shape, stride, memspace, element_type attributes

NVFP4 Quantization Patterns

NVFP4 (4-bit floating point) requires careful handling for optimal inference performance:

namespace s4::quantization {

// Explicit quantization configuration
struct nvfp4_config {
  float scale_factor;
  float zero_point;
  bool use_symmetric_quantization;
  size_t block_size;  // Quantization block size in elements
};

// DO: Make quantization operations explicit and verifiable
auto quantize_tensor_to_nvfp4(cuda::std::span<const float> input_fp32,
                        cuda::std::span<uint8_t> output_nvfp4,
                        const nvfp4_config& config,
                        cuda::stream_ref stream)
  -> s4::core::result<quantization_metadata> {

  if (input_fp32.size() * 4 / 8 != output_nvfp4.size()) {
    return s4::fail<quantization_metadata>(
      "output buffer size mismatch: expected {} bytes, got {}",
      input_fp32.size() / 2, output_nvfp4.size());
  }

  // Launch quantization kernel with explicit block size
  constexpr auto threads_per_block = 256;
  auto block_count = (input_fp32.size() + config.block_size - 1) / config.block_size;

  nvfp4_quantize_kernel<<<block_count, threads_per_block, 0, stream>>>(
    input_fp32, output_nvfp4, config);

  if (auto error = s4::cuda::check_last_error(); !error) {
    return s4::fail<quantization_metadata>("quantization kernel failed: {}",
                                           error.error().what());
  }

  return s4::ok(quantization_metadata{config.scale_factor, config.zero_point});
}

}  // namespace s4::quantization

Myelin Tactics Integration

TensorRT Myelin tactics for fused kernel generation:

namespace s4::tensorrt {

// DO: Wrap Myelin tactics in type-safe interfaces
struct myelin_tactic_config {
  std::string tactic_name;
  std::vector<size_t> input_shapes;
  data_type precision;  // FP32, FP16, INT8, NVFP4
  size_t workspace_size_bytes;
};

// DO: Make tactic selection explicit and logged
auto select_myelin_tactic(const model_layer& layer,
                   const execution_context& context)
  -> s4::core::result<myelin_tactic_config> {

  auto available_tactics = query_available_tactics(layer, context);

  if (available_tactics.empty()) {
    return s4::fail<myelin_tactic_config>(
      "no myelin tactics available for layer: {}", layer.name);
  }

  // Select based on measured performance
  auto selected_tactic = profile_and_select_best(available_tactics, context);

  s4::info("[s4] [tensorrt] [myelin] selected tactic '{}' for layer '{}' "
      "(workspace: {} MB, precision: {})",
      selected_tactic.tactic_name, layer.name,
      selected_tactic.workspace_size_bytes / (1024 * 1024),
      to_string(selected_tactic.precision));

  return s4::ok(selected_tactic);
}

}  // namespace s4::tensorrt

Stream Management Patterns

namespace s4::cuda {

// DO: Use RAII for stream management
class scoped_stream {
public:
  scoped_stream() {
    if (auto result = create_stream(); !result) {
      s4::fatal("failed to create CUDA stream: {}", result.error().what());
    }
  }

  ~scoped_stream() noexcept {
    if (stream_handle_) {
      cudaStreamDestroy(stream_handle_);
    }
  }

  // Non-copyable, movable
  scoped_stream(const scoped_stream&) = delete;
  scoped_stream(scoped_stream&& other) noexcept
  : stream_handle_(std::exchange(other.stream_handle_, nullptr)) {}

  auto get() const noexcept -> cudaStream_t { return stream_handle_; }
  auto ref() const noexcept -> cuda::stream_ref { return cuda::stream_ref{stream_handle_}; }

private:
  cudaStream_t stream_handle_ = nullptr;
};

// DO: Use stream ordering for complex pipelines
auto execute_inference_pipeline(const model& model_instance,
                         std::span<const float> input_data)
  -> s4::core::result<tensor_batch> {

  scoped_stream preprocessing_stream;
  scoped_stream inference_stream;
  scoped_stream postprocessing_stream;

  // Launch preprocessing (independent)
  preprocess_input_async(input_data, preprocessing_stream.ref());

  // Synchronize and launch inference
  cudaStreamWaitEvent(inference_stream.get(), preprocessing_done_event);
  run_inference_async(model_instance, inference_stream.ref());

  // Synchronize and launch postprocessing
  cudaStreamWaitEvent(postprocessing_stream.get(), inference_done_event);
  postprocess_output_async(postprocessing_stream.ref());

  return s4::ok(/* result */);
}

}  // namespace s4::cuda

Device Memory Management

namespace s4::cuda {

// DO: Use typed wrappers for device memory
template<typename T>
class device_buffer {
public:
  explicit device_buffer(size_t element_count) : count_(element_count) {
    auto alloc_result = allocate_device_memory(element_count * sizeof(T));
    if (!alloc_result) {
      s4::fatal("failed to allocate device memory: {}", alloc_result.error().what());
    }
    data_ = static_cast<T*>(alloc_result.value());
  }

  ~device_buffer() noexcept {
    if (data_) {
      cudaFree(data_);
    }
  }

  // Non-copyable, movable
  device_buffer(const device_buffer&) = delete;
  device_buffer(device_buffer&& other) noexcept
  : data_(std::exchange(other.data_, nullptr))
  , count_(std::exchange(other.count_, 0)) {}

  auto data() noexcept -> T* { return data_; }
  auto data() const noexcept -> const T* { return data_; }
  auto size() const noexcept { return count_; }
  auto size_bytes() const noexcept { return count_ * sizeof(T); }

  auto span() noexcept -> cuda::std::span<T> { return {data_, count_}; }
  auto span() const noexcept -> cuda::std::span<const T> { return {data_, count_}; }

private:
  T* data_ = nullptr;
  size_t count_ = 0;
};

// DO: Make host-device transfers explicit
auto copy_to_device_async(std::span<const float> host_data,
                   device_buffer<float>& device_buffer,
                   cuda::stream_ref stream) -> s4::core::status {

  if (host_data.size() != device_buffer.size()) {
    return s4::fail("size mismatch: host {} elements, device {} elements",
                    host_data.size(), device_buffer.size());
  }

  auto result = cudaMemcpyAsync(device_buffer.data(),
                                host_data.data(),
                                device_buffer.size_bytes(),
                                cudaMemcpyHostToDevice,
                                stream);

  if (result != cudaSuccess) {
    return s4::fail_errno<void>("cudaMemcpyAsync failed");
  }

  return s4::ok();
}

}  // namespace s4::cuda

Error Handling for CUDA Operations

namespace s4::cuda {

// DO: Check every CUDA call
auto check_cuda_error(cudaError_t error, std::string_view operation) -> s4::core::status {
  if (error != cudaSuccess) {
    return s4::fail("CUDA operation '{}' failed: {} (code: {})",
                    operation, cudaGetErrorString(error), static_cast<int>(error));
  }
  return s4::ok();
}

// DO: Macro for inline error checking (use sparingly)
#define S4_CUDA_CHECK(call) \
  do { \
    if (auto _error = (call); _error != cudaSuccess) { \
      return s4::fail("CUDA call '" #call "' failed: {} at {}:{}", \
                      cudaGetErrorString(_error), __FILE__, __LINE__); \
    } \
  } while (0)

// DO: Check for asynchronous errors after kernel launches
auto check_last_error() -> s4::core::status {
  if (auto error = cudaGetLastError(); error != cudaSuccess) {
    return s4::fail("CUDA kernel launch failed: {}", cudaGetErrorString(error));
  }
  return s4::ok();
}

}  // namespace s4::cuda

Kernel Launch Guidelines

// DO: Document kernel launch parameters
namespace s4::kernels {

struct launch_config {
  dim3 grid_dimensions;       // Number of blocks
  dim3 block_dimensions;      // Threads per block
  size_t shared_memory_bytes; // Dynamic shared memory
  cudaStream_t stream;
};

// DO: Provide clear launch configuration calculators
auto calculate_1d_launch_config(size_t total_elements,
                          size_t threads_per_block = 256)
  -> launch_config {

  auto block_count = (total_elements + threads_per_block - 1) / threads_per_block;

  return launch_config{
    .grid_dimensions = dim3(block_count),
    .block_dimensions = dim3(threads_per_block),
    .shared_memory_bytes = 0,
    .stream = nullptr
  };
}

// DO: Log kernel launches in debug builds
template<typename KernelFunc, typename... Args>
auto launch_kernel(const char* kernel_name,
            const launch_config& config,
            KernelFunc kernel,
            Args&&... args) -> s4::core::status {

#ifndef NDEBUG
  s4::debug("[s4] [cuda] [kernel] launching '{}' with grid({},{},{}) block({},{},{})",
       kernel_name,
       config.grid_dimensions.x, config.grid_dimensions.y, config.grid_dimensions.z,
       config.block_dimensions.x, config.block_dimensions.y, config.block_dimensions.z);
#endif

  kernel<<<config.grid_dimensions, config.block_dimensions,
           config.shared_memory_bytes, config.stream>>>(
    std::forward<Args>(args)...);

  return check_last_error();
}

}  // namespace s4::kernels

Agent-Human Collaboration Patterns

The Comment Convention

This convention helps identify code provenance at a glance:

Agents: Properly capitalized comments
Humans: lowercase comments (straylight tradition)

// This is agent-generated code with standard patterns
auto tokenizer = create_tokenizer(configuration);

// human intuition: special handling needed for rope positional encoding
if (model_type == "llama") {
  apply_rope_encoding(tokenizer);
}

Agent-Specific Guidelines

Agents should:

Use explicit types instead of auto except where awkward
Fully qualify all names even when seemingly redundant
Generate descriptive names that tell the complete story
Add domain prefixes to prevent namespace collisions

// Agent style - explicit and unambiguous
std::vector<s4::inference::request> pending_requests = load_pending_requests();
s4::core::result<s4::inference::batch_result> inference_result =
  execute_inference(pending_requests.front());

// Human style - can use auto where type is obvious
auto pending_requests = load_pending_requests();
auto inference_result = execute_inference(pending_requests.front());

Critical Path Marking

Identify code requiring human review:

// CRITICAL PATH: Model quantization - human review required
namespace s4::quantization {
  // Config parsing errors here corrupt inference results
  auto parse_quantization_config(std::string_view config_json)
  -> s4::core::result<quantization_config> {
  // Human-written parser with aggressive validation
  }
}

// AUXILIARY: Metrics collection - agent generation acceptable
namespace s4::metrics {
  // Agent can generate this boilerplate
}

Working with Legacy APIs

When core APIs can’t be changed without breaking everything:

Add better-named aliases alongside existing functions
Use the new names in new code to model good patterns
Document the preferred style in comments
Gradually migrate during other refactoring

// Example: result.h evolution
// Old API (keep for compatibility):
auto ok(T value) -> result<T>;
auto fail(string msg) -> result<T>;

// New aliases (use in new code):
auto make_success(T value) -> result<T>;
auto make_error(string message) -> result<T>;

Testing Philosophy

The Five-Minute Rule

If you can’t understand what agent-generated code does in 5 minutes, regenerate it with better structure.

Property-Based Testing for Invariants

Agents generate thorough unit tests but miss semantic invariants:

// Agent-generated test - thorough but mechanical
TEST_CASE("tokenizer handles empty input") {
  auto tokenize_result = tokenize_input("");
  REQUIRE(!tokenize_result.has_value());
}

// Human-written property test - catches semantic violations
TEST_CASE("quantizer preserves tensor shape") {
  check_property([](const tensor_fp32& input_tensor) {
    auto quantized_tensor = quantize_to_nvfp4(input_tensor);
    if (!quantized_tensor) return true;

    return quantized_tensor->shape == input_tensor.shape &&
           quantized_tensor->rank == input_tensor.rank;
  });
}

Testing Error Handling

// Check error content
REQUIRE(!result.has_value());
CHECK(!result.error().what().empty());
CHECK_THAT(result.error().what(), ContainsSubstring("expected text"));

// Check error codes
if (auto code = result.error().code()) {
  CHECK(code->value() == ENOENT);
}

// Check formatted errors work
auto error = s4::fail<int>("failed at position {}", 42);
CHECK_THAT(error.error().what(), ContainsSubstring("failed at position 42"));

Fuzz Testing for Parsers

// Add fuzz tests for any parser handling external input
FUZZ_TEST(configuration_parser, random_input) {
  auto result = parse_configuration(fuzz_input);
  // Should never crash, only return error
  if (result) {
    validate_configuration_invariants(*result);
  }
}

Debugging Patterns

The Grep Test

Every function should be globally unique and searchable:

# BAD: Too many results
grep -r "process(" .          # 500 matches
grep -r "handler::" .         # 200 matches

# GOOD: Finds exactly what you need
grep -r "process_tensor_batch(" .     # 3 relevant matches
grep -r "quantization_handler::" .    # 10 specific matches

State Machine Clarity

Make states explicit for debugging:

// BAD: Implicit state machines become agent debugging nightmares
if (flags & 0x04 && !error_flag && counter > threshold) {
  // What state is this?
}

// GOOD: Self-documenting states
enum class connection_state {
  disconnected,
  connecting,
  authenticated,
  active,
  draining
};

if (current_state == connection_state::authenticated &&
  error_count == 0 &&
  retry_counter > max_retries) {
  transition_to_state(connection_state::draining);
}

Performance Guidelines

Start with clear, simple code - The compiler optimizes clarity
Measure with production flags: -O3 -march=native
Small types belong in registers - pass by value
Profile before optimizing - Data always surprises

// Let the compiler work
for (const auto& request : pending_requests) {
  process_inference_request(request);
}

// Not this cleverness
for (auto idx = 0; idx < pending_requests.size(); idx += 4) {
  // Unrolled loop that's probably slower
}

Constexpr Usage

// DO: Use constexpr for compile-time constants
constexpr size_t max_batch_size = 1024;
constexpr std::string_view model_architecture = "transformer";

// DO: Mark functions constexpr when possible
constexpr auto calculate_tensor_size(uint64_t batch, uint64_t seq_len, uint64_t hidden_dim) -> uint64_t {
  return batch * seq_len * hidden_dim;
}

// DON'T: Force constexpr when it complicates implementation
constexpr auto complex_quantization() {  // Requires contortions
  // ...
}

Logging

Hierarchical tagging for structured logs:

s4::info("[s4] [inference] [engine] [batch] executing batch id={} device={}",
   batch_id, device_id);
s4::error("[s4] [inference] [engine] [error] inference failed: {}",
    error_description);

Format: [project] [system] [component] [detail] message

Configuration Philosophy

Parse Everything Up Front

// Parse and validate entire config at startup
auto load_system_configuration(std::string_view config_path)
  -> s4::core::result<system_configuration> {

  auto file_content = s4::core::fs::read_file_to_string(config_path);
  if (!file_content) {
    s4::fatal("Cannot read configuration file: {}", config_path);
  }

  auto parsed_config = parse_toml_configuration(file_content.value());
  if (!parsed_config) {
    s4::fatal("Invalid configuration: {}", parsed_config.error().what());
  }

  auto validation_result = validate_configuration(parsed_config.value());
  if (!validation_result) {
    s4::fatal("Configuration validation failed: {}",
              validation_result.error().what());
  }

  return s4::core::ok(parsed_config.value());
}

Configuration Errors Are Fatal

If configuration is wrong, nothing else can be trusted:

if (!model_config.has_valid_weights_path()) {
  s4::fatal("Model configuration missing weights path");
}

if (inference_config.max_batch_size <= 0) {
  s4::fatal("Invalid max_batch_size: {}", inference_config.max_batch_size);
}

API Evolution Guidelines

When core APIs need updates:

Start with backwards compatibility - Keep old functions working
Fix fundamental issues - Like string lifetime problems
Add better alternatives - New overloads following style guide
Constexpr where reasonable - Don’t force it if it complicates
Document breaking changes - Even minor ones like error_code() → code()

Incremental Improvement Strategy

For widely-used modules like s4::core::result:

Never break existing code - Aliases are cheap
Model better patterns in new functions
Update documentation to prefer new patterns
Consider [[deprecated]] only after wide adoption

Anti-Patterns to Avoid

The Abbreviation Cascade

// Starts innocent...
auto cfg = load_config();

// Spreads like a virus...
auto conn = create_conn(cfg);
auto mgr = conn_mgr(conn);
auto proc = mgr.get_proc();

// Ends in debugging hell
if (!proc.is_valid()) {  // What is proc again?
  // ...
}

Context-Dependent Names

// BAD: "decoder" means different things in different places
namespace tokenizer {
  class decoder;  // Decodes tokens
}
namespace model {
  class decoder;  // Transformer decoder layer
}

// GOOD: Names carry their domain
namespace tokenizer {
  class token_decoder;
}
namespace model {
  class transformer_decoder_layer;
}

Implicit State Machines

// BAD: State spread across booleans
bool is_connected;
bool is_authenticated;
bool is_active;
bool has_error;

// GOOD: Explicit state
enum class session_state {
  disconnected,
  connected_unauthenticated,
  authenticated_inactive,
  active,
  error_recovery
};

Summary

In an agent-heavy codebase:

Every name must be globally unambiguous
Every abbreviation creates exponential confusion
Every implicit assumption becomes a debugging nightmare
Every configuration error multiplies across the system

Write code as if 100 agents will be pattern-matching against it tomorrow, and a tired human will be debugging it at 3am next month. Because both will happen.

The Unix authors optimized for scarce memory. We optimize for scarce human comprehension. In 1970, every character cost bytes. In 2025, every ambiguity costs hours.

Required Reading/Watching

Performance

Modern C++

Living List of Great Code

Tier 1 (Perfection - Study every line)

simdjson - SIMD JSON parsing, exemplary modern C++
Abseil - Google’s foundation library, production-hardened
fmt - The formatting library that became std::format

Tier 2 (Domain Excellence - Best-in-class for their problem space)

DuckDB - Analytical database, zero dependencies, clean architecture
RocksDB - LSM storage engine, battle-tested at scale
DPDK - Kernel bypass networking, when microseconds matter
ClickHouse - Columnar database, SIMD everywhere

Tier 3 (Specific Excellence - Outstanding implementations of focused problems)

parallel-hashmap - Swiss tables with parallel access
concurrentqueue - Lock-free queue that actually works
mimalloc - Microsoft’s superb allocator
liburing - io_uring done right (see kernel code too)

Study Specific Files/Techniques

Facebook’s F14 - Vector instructions in hash tables
Google’s SwissTable - The hash table design that conquered all
Lemire’s streamvbyte - SIMD integer compression
Aeron - Reliable UDP messaging, mechanical sympathy exemplar

Controversial but Instructive

Seastar - Futures done differently, polarizing but educational
EASTL - EA’s STL replacement, different tradeoffs
Boost.Asio - The async model that influenced networking TS

Required Reading (Papers/Docs)

What Every Programmer Should Know About Memory
- Drepper’s classic
Can Seqlocks Get Along With Programming Language Memory Models?
- Hans Boehm on the hard stuff
There is No Fork
- Microsoft Research on process creation

What Makes Code “Great” for This List

Clarity despite complexity - Solving hard problems with readable code
Performance without compromise - Fast but not at the expense of correctness
Teaching value - You become a better programmer by reading it
Battle-tested - Used in production at serious scale
Influential - Changed how we think about the problem

What Doesn’t Belong

Clever for cleverness’ sake
Template metaprogramming gymnastics without purpose
“Look how few lines!” code golf
Abandoned experiments (unless historically important)