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GitLoop
✓ verifiedFree trial
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
👁 11K/mo♥ 2.7K
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ApX Machine Learning
✓ verifiedFreemium
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
👁 355K/mo
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Softgen
✓ verifiedFreemium
AI app builder that turns plain-English prompts into deployable full-stack web apps with a managed database and full code ownership.
👁 103K/mo♥ 16K
Pricing
No public pricing
No public pricing
Free trial available
Basic: $0/mo (free forever)
Pro: $19/mo
Pro+: $59/mo
Starting plan: $25/mo (entry-level tier per pricing comparison)
Free trial available
Core features
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦Builds full-stack apps from plain-English descriptions
- ✦Choice of 12+ underlying AI models
- ✦Managed Supabase database included
- ✦GitHub code export on all plans
- ✦One-click Vercel deployment
- ✦Credits that don't expire while subscribed
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Non-technical founders shipping an MVP quickly
- →Agencies delivering client projects faster
- →Developers prototyping SaaS ideas without boilerplate setup
- →Solo builders launching small businesses without hiring engineers
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