CleanSlop vs RightNow CUDA Editor: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of CleanSlop and RightNow CUDA Editor — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
CleanSlop
CleanSlop
Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.
Key features
- Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
- System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
- Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
- Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
- Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
- Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
- GitHub App Integration: Scan only the repositories you select, read-only access.
- Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.
Best for
- A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
- A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
- A side-project builder verifies that one user's data cannot be read by another through database policies.
- A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
- A team lead gets a short prioritized list instead of hundreds of linter warnings.
- An indie hacker books an expert triage call to confirm an ambiguous finding.
RightNow CUDA Editor
RightNow AI (RightNow-AI team)
All-in-one AI-powered code editor for CUDA with hardware-aware agents, GPU emulation/virtualization, real-time profiling and enterprise benchmarking.
Key features
- Agentic Hardware-Aware Assistant: An AI agent that reasons about NVIDIA GPU architecture (memory hierarchy, warp scheduling, occupancy) to suggest kernel-level optimizations, launch configuration changes, and micro-architectural fixes tailored to the target GPU.
- GPU Emulation and CPU Simulation Mode: Built-in GPU emulator allowing developers to run and test CUDA code on machines without physical GPUs by simulating GPU behavior and validating kernel logic before deployment.
- GPU Virtualization Support: Virtualized GPU environments for remote testing and multi-tenant workflows, enabling developers to run GPU workloads in isolated virtual GPUs for reproducible experiments.
- Real-time Profiling with Smart Terminal: Live profiling integrated into the editor that surfaces hotspots, stalls, memory transfers, and kernel timelines in the terminal while code runs, allowing rapid iterative tuning.
- Line-by-Line Performance Analysis: Fine-grained cost annotations that attribute runtime and memory behavior to specific lines or blocks of CUDA code to pinpoint bottlenecks and inefficient constructs.
- Benchmarking Terminal with Sweep Configurations: Enterprise-grade benchmarking tooling that runs parameter sweeps (grid/search) across kernel launch parameters, inputs, and device targets and produces reproducible reports.
- Open-source CLI and Easy Install: Community-facing CLI (rightnow-cli) available via pip for quick setup, enabling a lightweight GPU-native AI code assistant and integration into developer workflows and CI.
- GPU Profiler Visualization: Web-based visualization transforms NVIDIA profiling data into timeline views, flame graphs, heatmaps and includes AI-powered bottleneck detection to accelerate root-cause analysis.
- Agentic hardware-aware assistants that reason about GPU architecture and propose optimizations
- GPU emulator and virtualization allowing code execution without physical GPUs (CPU simulation mode)
- Real-time profiling with line-by-line performance analysis
- Enterprise-grade benchmarking across NVIDIA GPUs (supports GTX 1060 to H100)
- Integrated debugging and code completion tailored for CUDA
- Open-source CLI (rightnow-cli) installable via pip
- Web-based gpu-profiler: timeline views, flame graphs, heatmaps, AI-powered bottleneck detection
- Multi-agent interactive tools and professional UI for GPU development
- Supports running and testing in GPU-native environments and simulated environments
- Community resources: GitHub repos, Discord, and documentation (INSTALLATION.md, CONTRIBUTING.md)
Best for
- CUDA Kernel Optimization: Iteratively tune kernels with the agentic assistant and line-by-line performance feedback to reduce execution time and increase occupancy on target NVIDIA GPUs.
- Developing Without Hardware: Use the GPU emulator/CPU simulation mode to write and validate CUDA code on developer laptops or CI runners that lack physical GPUs before running on real devices.
- Fleet Benchmarking and Regression Testing: Run sweep benchmarks across multiple GPU models (GTX 1060 through H100) to compare performance, detect regressions, and generate reproducible benchmarking reports for releases.
- Performance Debugging and Bottleneck Detection: Combine real-time profiling and profiler visualizations to trace memory transfer stalls, warp divergence, and synchronization issues, with AI-suggested fixes.
- Enterprise Workflows and CI Integration: Integrate the CLI and benchmarking terminal into continuous integration pipelines to automatically run performance sweeps, collect metrics, and gate commits on performance thresholds.
- Educational and Research Use: Provide students and researchers with a free, GPU-aware coding environment and visualization tools to learn CUDA programming and analyze kernel performance without needing physical GPUs.
- Developing and optimizing CUDA kernels with hardware-aware suggestions
- Profiling and diagnosing GPU performance issues using timeline views and flame graphs
- Benchmarking CUDA workloads across a range of NVIDIA GPUs for enterprise reporting
- Debugging and iterating CUDA code on machines without GPUs using CPU simulation/emulation
- Educational use for learning CUDA, performance analysis, and GPU programming patterns
- Integrating into CI or dev workflows via CLI for automated performance checks