Flutter’s Cloudless AI Revolution
summary
Claims a workable cloudless Flutter stack that embeds INT4/INT8 quantized GGUF SLMs into apps and calls native runtimes like llama.cpp and whisper.cpp via FFI through a C/C++ wrapper and Dart API package (repo not yet public). Shows tokenization, mutexed model lifecycle, output-buffer safety, and that quantized models shrink to ~300 MB–4 GB but still need 6–8 GB device RAM to run reliably; demonstrates offline STT/TTS and on-device inference to cut latency, cost, and data exfiltration.
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chapters (8)
- 0:00Introduction
- 1:33Offline AI for sensitive data
- 9:30On-device AI evolution
- 13:45Cloudless AI architecture
- 18:22FFI bindings and runtime
- 29:28Popular SLMs
- 32:25Constraints and applications
- 33:10Summary
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