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Foundation models for time series forecasting

Hacker News

Foundation models for time series forecasting

After months of brewing the perfect recipe in our AI kitchen, we're beyond excited to introduce Sulie - a fully managed (Model as a Service) platform for time series forecasting that actually works! From day one, we've had one mission: make powerful time series forecasting as easy as ordering your morning coffee. Now, businesses can make accurate forecasts from their data without the hassle of building complex models from scratch. We kept hearing the same frustrations from data teams trying to work with foundation models for time series forecasting: 1. "The zero-shot performance is about as reliable as a chocolate teapot!" 2. "Fine-tuning these models? Easier to teach a cat to bark!" 3. "And don't get me started on covariate support..." What makes Sulie special? • Automated model fine-tuning using LoRA - no PhD required! • Full covariate support for more accurate predictions. • Go from zero to production-ready forecasts in minutes (not weeks). • Zero ML complexity - we handle all MLOps heavy-lifting (you focus on the insights). We're already working with amazing customers who are: • Optimizing their supply chains • Making precise financial forecasts • Building custom models using Sulie's powerful embeddings And this is just the beginning! Stay tuned for deep dives into these use cases in the coming weeks Check us out: Python SDK: https://github.com/wearesulie/sulie Website: https://sulie.co

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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