Ken Jon

Ventura Labs Verified account @VenturaLabs · Nov 3

Ep. 107 - Dylan Uys & Ken Miyachi

Dylan @_dubm and Ken @kenjon are building @yugosai

Timestamps
01:00 Dylan Uys: From BitMind to Yugos
04:09 AI coding workflows and model choice
06:33 The harness versus the model
11:31 Balancing intelligence, cost and speed
13:21 Keeping the model and provider catalog current
16:43 Routing across Bittensor services
20:47 Routing versus multi-agent orchestration
26:01 Benchmark gaming and validation
30:03 Can a routing system build a moat?
31:44 The value of real-world traffic
33:37 Yugos roadmap and custom provider choices
35:55 Local models and privacy
38:24 Agents in the research workflow

40:24 Ken Miyachi
42:29 The future of agent harnesses
47:02 Building a business around the router
52:58 The human role in software development
55:24 Distribution and lessons from BitMind
58:35 Competing with well-funded AI companies
1:00:56 Vendor lock-in and changing models
1:04:01 Yugos go-to-market plans
1:04:57 Token spending and the value of AI work
1:08:51 Research versus production development
1:10:59 Training routers for different user needs
1:16:18 Consumer AI and agent business models
1:21:01 Provider incentives and customer trust
1:22:44 Why distribution matters for Yugos
1:25:12 Open source, monetization and defensibility
1:28:26 Bittensor development speed and security

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Dynamical Systems Verified account @dynamical__ · Nov 2

Can agents use experimental feedback to establish new material capabilities that hardware teams can reliably design around?

Today we’re releasing SDL-2, an open evaluation of how agents choose experiments and decide whether a material process will reliably meet a requirement.

We’re interested in the full process of scientific investigation. That means discovering which relationships matter, choosing experiments that distinguish competing explanations, revising an explanation when results surprise and knowing when the evidence is strong enough to act.

The experiments run in a digital twin of magnet processing, calibrated on measurements from our campaign with Rensselaer Polytechnic Institute (@rpi). Agents investigate unfamiliar powders with an uncertain physics model, a limited experimental budget and results that vary from batch to batch.

Across 32 simulated worlds, frontier models reached correct decisions supported by their own measurements in 44–50% of investigations, compared with 25–28% for other language models and classical experiment design. One in three of their correct answers were unsupported.

Our larger ambition is for materials development to keep pace with hardware design, so teams can design around the capabilities they request, not only those that already exist. SDL-2 makes one part of that measurable, how agents investigate.

We're releasing the evaluation code, harness, digital twin and all 256 investigation traces.

Technical Report: dynamicalsystems.ai/blog/sdl-2

Code: github.com/Dynamical-Syst…

Dataset: huggingface.co/datasets/Dynam…

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Yugos Verified account @yugosai · Oct 21

The last AI model you'll ever choose.

New models launch every week. Pricing changes. Intelligence improves. Providers compete on speed, cost, and performance.

Keeping up with which model to use, from which provider, and at what reasoning effort is becoming a full-time job.

Introducing Yugos.

One intelligent decision layer for AI inference that continuously optimizes for intelligence, cost, and speed.

Yugos automatically selects the best model, provider, and reasoning effort for every request.

As new models launch, pricing changes, and performance evolves, Yugos adapts automatically.

All with full transparency into model selection, usage, and costs.

Stop choosing models. Let Yugos optimize intelligence for you.

Live today. ↓

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Karia Samaroo Verified account @kariasamaroo · Oct 20

subnet valley: coming soon!

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Boomer Verified account @0xBoomz · Sep 28

Here is @const_reborn's full keynote at @ExploitSummit

It is worth the 36 minutes, but I'll give you the cliff notes.

> Affine 1, a model trained by anonymous miners entirely on Bittensor, matches the intelligence score of Anthropic's Opus 4.1.

> 23 of 128 subnets now have paying customers

> Gamma tokens are coming to turn subnet usage credits into something agents can buy from any chain.

> "We're not afraid of hyper-intelligent bot swarms on the internet. We use them to optimize our technology."

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