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taskdistill

Distil an expensive LLM API call on a narrow task into a small local model: capture traffic, curate, LoRA fine-tune with MLX on Apple Silicon, evaluate against the teacher, and serve an OpenAI-compatible cascade that escalates low-confidence requests.

Details

External ID
1390038957
Source
GITHUB
Company
—
Product
taskdistill
Website domain
pypi.org
Launched
Sept. 27, 2026
Cohort
—
Upvotes
10
Upvotes percentile
0.28183448629259544
Tags
apple-silicon, calibration, cost-optimization, fine-tuning, information-extraction, knowledge-distillation, llm, llm-evaluation, llmops, lora, mlx, model-cascade, openai-compatible, qwen, text-classification
Fetched at
Oct. 1, 2026, 1:02 a.m.
Updated at
Oct. 1, 2026, 1:02 a.m.

Enrichment

Theme
ML inference and model optimization
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
llm distillation and local fine-tuning tool for apple silicon
Manually corrected
False

Could you build this?

Partial The UI and CLI orchestration can be vibe-coded, but setting up a reliable distillation pipeline with Apple Silicon MLX LoRA training, confidence calibration, and fallback cascading requires ML engineering expertise.

What it would actually take: The stack involves Python, MLX/LoRA on Apple Silicon, and an OpenAI-compatible FastAPI gateway. The hard parts are automated dataset curation/filtering from logged traffic, tuning hyperparameter schedules for small student models, and calibrating logit probabilities or confidence thresholds to safely trigger cascade escalations.

Competitors

Other products that read as similar to this one — 126 launches clear the similarity bar, closest 8 shown.

Attention rank: #89 of 127 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 332 days after the earliest competitor.

Other launches for this product

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