Building on bittensor - sn91 (@cascade_sn91)
Planum Temporale
Joined January 2025
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@numinous_ai is producing data that will give true edge in prediction markets, trading and beyond. This is a positive feedback loop - we help build better graphs they help train better TSFMs
Track how the world changes. Predict how it evolves.
We’ve partnered with @numinous_ai to combine knowledge graphs with time-series forecasting.
Numinous maps relationships between companies and events, and predicts how those relationships may change. Cascade brings forecasting models to assist in flagging future activity, pointing agents towards worthy developments.
In turn, Numinous’s graph will help us develop synthetic training data for our mixture-of-experts model, capturing how changes at one company can ripple through connected businesses.
Together, we’re working towards better forecasts of both changing relationships and the numerical trends they influence.
Chris(t)ensor retweeted
Eager to see the #USElection midterm results?
We’ve used state-of-the-art AI models to forecast the outcome.
With Ephemeris, our forecasting inference product, we’ve projected the Senate results:
Via zero-shot forecasts, our model tells us the Democrats are leaning to 53 seats.
Leans D: Kansas +4.6, Ohio +3.9, Maine +3.8, Texas +2.8, Alaska +0.8
Leans R: Iowa +0.8, South Carolina +1.0
Coin flip: Louisiana
We'll score them tonight.
Yeah, it should be part of their training to do internships with actual SMEs to see all the compliance you must consider and hats to wear
Chris(t)ensor retweeted
SN91, Cascade:
"We connected Ephemeris, our time-series forecasting API, with Decision Models (powered by @reaxlabs) to build a system that tackles this. Ephemeris forecasts how market conditions may develop."
Chris(t)ensor retweeted
Cascade Mixture-of-Experts competition is live.
Submissions are currently open.
Join us in training a 1.23B time-series foundation model, comparing against @MistralAI’s Timestral-209M - the current state-of-the-art.
Get your supported compute from @lium_io, @shadeformai, @vast_ai, and @runpod
Chris(t)ensor retweeted
“How many credits will this cost?”
Forecast your AI credit usage with @cascade_sn91 - directly inside @heydittoai.
Get spending estimates based on Ditto’s historical data about you, accessing time series inference via Ephemeris, to predict how much you’ll use in the future.
You can even forecast how many credits will be used for a particular task. Give Ditto the details, and it’ll run your history through Ephemeris, and output meaningful predictions.
nitter.cf/heydittoai/status/2113…
Chris(t)ensor retweeted
The Bittensor community is as strong as ever and gets stronger by the day.
Along my journey I've found many great people and @tensorlink_dev from @cascade_sn91 is one of those great people.
Massive thanks to Chris he has saved us weeks of guesswork.
We run Cascade's synthetic time-series generator as a research substrate. The Alphacast feed sits alongside it as forecasting infrastructure. Both have become core pieces of our engineering stack.
Research-stage, and one live deployment which we constantly update,the capability jump has been huge. I can't go into to much detail but I can say this,without the Cascade tech we wouldn't really have an edge. Not only is the tech been built on SN91 brilliant it's highly versatile. It's like giving an acromat artist color vision which really broadens your horizons Cascade is a truly remarkable technology.
gScade🫡
Get the MCP: ephemeris.cascade.industries
Join the Telegram: t.me/cascade_sn91
Time series is everywhere and this a great example on Ditto of how to use ephemeris for token efficiency
Extremely powerful approach to modelling, forecasting and knowledge distillation
Chris(t)ensor retweeted
Cashflow is one of the toughest problems for freelancers and startups. And if earnings come from multiple sources, then it gets even harder to manage.
Forecasting when you’ll have a surplus vs deficit is paramount.
Cascade’s inference platform solves this - Ephemeris allows you to access 7 leading time series foundation models, suited to predicting this.
Select SOTA models from @MistralAI, @GoogleResearch, and @IBM, or let Ephemeris automatically combine them and get the strongest results.
Chris(t)ensor retweeted
SN91 is now training a 1B parameter model.
If 1B sounds small: the new model will rank among the largest time series foundation models, ever.
And our current model - with only 4M params - already beats Salesforce's 91M model.
Which means: we're going after SOTA.
From 4M to 1B+. Cascade’s biggest upgrade yet.
We’re preparing to train Cascade-MoE: a mixture-of-experts time-series foundation model targeting 1B+ total parameters.
We’ve shown that synthetic data generated by Bittensor miners can train a 4M-parameter forecasting model from scratch, reaching parity with Toto 2.0 4M. Now we’re scaling up, with SOTA forecasting as our goal.
Cascade-MoE will use a learned router to select combinations of expert networks based on patterns in the input, alongside an always-active shared expert. This gives the model access to specialised capacity without activating every stored weight for every input.
We’re also evolving the competition so miners can generate synthetic data targeted at particular domains and time resolutions, such as hourly versus daily series.
The architecture is designed to support targeted training of selected expert subsets, while the subnet tests whether contributions improve forecasting performance.
The goal: SOTA forecasting accuracy, built through competition on #SN91.