Joined April 2026
- Tweets 148
- Following 12
- Followers 83
- Likes 25
12 Photos and videos
Pinned Tweet
Apr 24
Introducing the Allora Forge Webinar Series hosted by @jefferythewind
A live session where we walk you through building, deploying, and optimizing prediction workers on Allora, from zero to on-chain.
First session: April 30
Link below:
5
3
18
3,498
May 21
We are live. @jefferythewind is walking through the CZAR loss function right now.
Join us.
ro.am/Allora/allora-forge-wo…
1
74
May 21
Going live in a couple hours.
Allora Forge Workshop, CZAR Loss Function
@jefferythewind breaks down how Allora evaluates prediction workers and shows you how to use it to train better models.
Come with questions.
1
67
May 21
23
May 20
Tomorrow.
Allora Forge Workshop with @jefferythewind.
We are diving into the CZAR loss function, the evaluation mechanism that decides which prediction workers the network trusts.
Links below
2
3
76
May 20
If you deployed a worker after Session 1, this is how you make it better.
If you missed Session 1, you can still join. The builder kit repo has everything you need to catch up.
github.com/allora-network/al…
11 AM EST / 4 PM UTC
ro.am/Allora/allora-forge-wo…
1
1
38
May 19
Here is what @jefferythewind is covering at the Forge Workshop on Thursday:
-- How CZAR loss works and why it was developed
-- How the network uses it to weight contributors
-- A live walkthrough using it to evaluate and train models
-- Example scripts in the builder kit
1
2
30
May 16
The problem with traditional loss functions: they treat every prediction the same.
CZAR loss doesn't. It evaluates your prediction relative to the state of the network, who else is predicting, how confident they are, and what the conditions look like.
Link below
1
25
May 16
That changes everything about how you build models for Allora.
May 21 we go deep on this. Live.
ro.am/Allora/allora-forge-wo…
12
May 15
Allora Forge Workshop — CZAR Loss
May 21 / 11 AM EST
@jefferythewind walks through the loss function the Allora research team developed to evaluate prediction workers on-chain.
Link below
1
2
10
983
May 15
You will leave understanding how the network decides who is right, and when.
ro.am/Allora/allora-forge-wo…
23
May 15
April was when the Forge opened its doors.
ML builders — the webinar series is live. The worker deployment path is documented. The CZAR loss function is being taught.
If you know how to build models, there is now a structured path into the network.
May is when you deploy.
1
113
May 14
Your model is only as good as how you measure it.
Session 1 showed people how to deploy their first workers. Session 2 goes deeper, into the math behind how Allora actually scores predictions.
1
1
37
May 13
Most prediction competitions score you on accuracy alone.
Allora is different. The CZAR loss function evaluates predictions in context, how they perform relative to other network participants, over time.
May 21 we break it down live
Link below
1
1
26
May 13
6
May 12
Allora Forge Workshop; May 21
@jefferythewind is going live to break down the CZAR loss function, a new way to evaluate prediction models developed by the Allora research team.
link below
2
41
