Python SDK
forecasting with foundation time-series and tabular models
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We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
HN · launched Dec 18, 2025 · 43 upvotes (top 20%) · 0 views
Niche: machine learning developer tools · 303 launches, 11 in the last 7 days ▼ -45% · Explore this niche →
Category and tags
- Horizontal
- Analytics & BI
- Developer
- AI-native
- Commercial product
Visit website ↗ Source listing ↗
More like this: all 303 launches in machine learning developer tools →
Could you build this?
No While an SDK client wrapper is easy to write, the actual product is an inference layer for state-of-the-art foundation time-series and tabular neural network models, which require deep ML research and specialized architecture.
What it would actually take: Building real foundation models for zero-shot time-series and tabular prediction requires large-scale architectures (such as patch-based transformers or state-space models like Mamba) pre-trained across massive heterogeneous multi-domain datasets. It requires specialized expertise in tabular embeddings, variable-length context tokenization, numerical representation, and distributed GPU cluster training pipelines.
Discussion
18 comments analyzed.
Competitors mentioned: Chronos-2 (state of the art in time-series modeling), TabPFN v2, Limix, Darts library (unit8co), Traditional statistical/nonparametric models
Concerns raised: Black box nature makes it hard to understand performance deterioration, Unclear if models work well across different domains/data types, Requires sufficient pretraining data with relevant characteristics, Limited evidence of real-world success vs. simpler approaches, Uncertainty about transfer learning effectiveness for specialized time-series (EEG/fMRI)
Feature requests: Case studies demonstrating real-world performance, Documentation on what data models were trained on, Clarity on SaaS vs. open-source deployment options
Competitors
Other products that read as similar to this one : 39 launches clear the similarity bar, closest 8 shown.
Attention rank: #9 of 40 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 42 days after the earliest competitor.
- TabPFN-2.5 · hn · 2025-11-06 · 73 upvotes · similarity 0.53
- Kumo Tabular · ph · 2026-10-01 · 1 upvotes · similarity 0.47
- causilo · github · 2026-09-13 · 64 upvotes · similarity 0.45
- TabPFN MCP, gives LLMs tools for predictions on tabular data (beta) · hn · 2026-02-05 · 11 upvotes · similarity 0.43
- T0: We trained a 256M SOTA open-weights time-series forecasting model · hn · 2026-10-14 · 14 upvotes · similarity 0.41
- TabPFN Scaling Mode · hn · 2025-12-03 · 5 upvotes · similarity 0.41
- Modeleon · hn · 2026-05-01 · 6 upvotes · similarity 0.39
- quant-models · github · 2026-09-20 · 10 upvotes · similarity 0.38
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