i-have-adhd vs AirLLM
A side-by-side comparison of features, pricing, and characteristics.
i-have-adhd
An open-source skill that makes coding-agent responses concise, actionable, and ADHD-friendly.
Read the full reviewAirLLM
Open-source Python library that runs very large language models on low-memory GPUs by streaming model layers one at a time.
Read the full review| Attribute | i-have-adhd | AirLLM |
|---|---|---|
| Pricing type | Free | Free |
| Korean support | No | No |
| Platforms | Claude Code, OpenAI Codex, Gemini CLI, GitHub Copilot, Cursor, Zed | Linux, macOS, CUDA-enabled NVIDIA GPUs, Apple Silicon |
| Open source | Yes | Yes |
| API available | - | - |
| SDK | - | - |
| LLM-based | - | - |
| Multimodal | - | - |
| AI model | - | Llama, Qwen, DeepSeek, Mistral, Mixtral, Phi, Gemma |
| GitHub Stars | 27.4K | 33.8K |
| Vendor | - | Anima AI LLC |
| Category | Developer Tools | Developer Tools |
| Details | View | View |
i-have-adhd key features
- Presenting answers and next actions first
- Structuring multi-step tasks into numbered lists
- Redisplaying progress at every turn
- Providing specific time estimates
- Explaining errors focusing on location, cause, and solution
- Suppressing unnecessary introductions and conclusions
AirLLM key features
- Reducing GPU memory usage through layer-wise model streaming
- Supporting inference of 70B-class models on a single 4GB GPU
- AutoModel interface based on Hugging Face model IDs
- 4-bit and 8-bit block-wise model compression
- Supporting CPU inference and Apple Silicon macOS
- Supporting various model families including Llama, Qwen, DeepSeek, and Mistral