Ventura Labs
@VenturaLabs • 2,271 subscribers
Intelligence For All
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Ep. 88 - Eyad Gomaa τroy is building Quasar Subnet 24 Timestamps 1:24 - Introduction 2:08 - Long Context & Decentralized MoE Training 4:05 - Why Long Context Remains Unsolved 5:18 - Quasar Attention: Beyond Transformers 5:55 - Why Decentralized MoE Training Is Hard 8:53 - The MoE Router Problem 9:51 - How Quasar Found Bittensor 11:26 - Why Open Source & Decentralized AI 12:43 - The Joseph Jacks Rejection 13:47 - Subnet Design: CUDA Kernels & Distillation 15:38 - Quasar vs. Other Distillation Subnets 17:05 - Decentralized MoE Design: Miners & Validators 18:34 - Training Data & Upcoming Partnership 19:36 - Quality Over Size: The Real Goal 22:26 - Value Capture: Chutes, API & Quasar Copilot 23:28 - Hardest Part: Building the Incentive Mechanism 24:17 - Bittensor as YC for Real AI 25:24 - Decentralized Advantage vs. Centralized Labs 26:44 - Const: Subnet Acquisition & Mentorship 28:42 - Youngest Subnet Owner & Crypto Background 29:48 - Biggest Personal Growth in Bittensor 30:57 - State-of-the-Art from Bittensor 32:10 - Infinite Context: 10M Effective Window 33:16 - Open Source Inspiration & Model Persona 34:21 - Building Persona via Reinforcement Learning 36:04 - Advice for AI Nerds: Set Goals Before Joining
Ventura Labs13,603 просмотров • 3 месяцев назад
Ventura Labs Ep. 62 - Mark Jeffrey & Rob Greer Mark Jeffrey (Mark Jeffrey) and Rob Greer (Rob Greer) are Partners at Stillcore Capital (Stillcore Capital) Timestamps 02:07 – Introduction 02:56 – Rob’s background and path to Bittensor 05:50 – Starting Stillcore Capital 07:25 – Why Mark and Rob teamed up 09:20 – Macro timing for crypto + AI 10:19 – Jason Calacanis joins as partner 13:24 – Stillcore’s composition and edge 15:23 – Stillcore’s investment thesis 17:25 – Timing, valuation deltas, and opportunity 20:37 – Liquid venture and raising strategy 22:39 – Fund target: 1% of liquid supply 25:11 – Timeline and accumulation approach 28:16 – Defining blue-chip subnets 33:25 – Framework for subnet evaluation 36:10 – Incentive alignment and alpha value 38:08 – Alpha vs. equity dynamics 41:54 – Sectors best suited for Bittensor 45:25 – Inter-subnet competition and capitalism 50:22 – Post-DynamicTAO innovation 53:50 – Halving impact on TAO and subnets 56:23 – Could TAO flip Bitcoin? 01:00:31 – Incentivization engine as Bittensor’s core 01:02:55 – Future of decentralized AI 01:05:07 – Advice for investors
Ventura Labs25,008 просмотров • 10 месяцев назад
Ventura Labs Ep. 61 - Shakeel Hussein Shakeel (Shakeel) is the core contributor to Ridges (Ridges AI | SN62) Timestamps 01:40 - Introduction 02:21 - Rapid rise 02:39 - Open miner transition 03:17 - Advice to younger self 03:56 - Typical day routine 04:33 - Product like Claude Code 05:22 - Human preferences incentives 07:32 - Next mixed benchmarks 08:49 - One product or suite 09:51 - Chutes inference dependence 11:10 - LLM performance difference 12:50 - Claude experiment results 13:19 - Competitors negative margins 14:30 - Judgment for cost/latency 15:41 - Problem scope full website 16:55 - Agent limits solve engineering 18:21 - Sandbox tools restricted 19:51 - Handcuffs boost web search 20:28 - Personal tool Claude code 20:54 - Product vs subnet challenges 22:13 - Product dev to miners 22:57 - No black box return 23:54 - R&D speed 80% benchmark 24:46 - Alpha to equity idea 32:58 - No other subnets 33:25 - Hiring from Waterloo 34:16 - Biggest challenge stability 35:38 - 100% owner structure 35:59 - Obsolete benchmark usage 36:13 - Catching revenue fast 37:33 - New jobs 10x tools 38:28 - Advice to subnet owners/miners 39:30 - Incentives distill capitalism
Ventura Labs24,328 просмотров • 11 месяцев назад
Ventura Labs Ep. 59 - James Altucher James (James Altucher) is the Digital Asset Treasury Strategist at TAO synergies (TAO Synergies Inc.) and creator of Timestamps 02:15 - Introduction 02:58 - What is Bittensor 06:24 - AI background and Deep Blue 09:34 - Chess interest and journey 11:46 - $TAOX inspirations and TAO-pilled 14:09 - TAO Synergies origin story 16:57 - Reaching 100M and playbook 19:41 - Treasury strategies and validators 22:02 - TAO Daily introduction 25:51 - Bitcoin adoption and involvement 29:32 - Subnet investing and analysis 30:59 - Token warnings and TAO demand 35:46 - Subnet proposals and increases 37:36 - Successful sectors and examples 40:04 - Yanez and Metanova 43:17 - Decentralization benefits 46:13 - AI jobs and countering fears 49:34 - Beneficial sectors: AI and stablecoins 53:59 - Bryan Johnson documentary 55:17 - Starting podcast and interviews 57:14 - Interests and obsessions 01:00:56 - Daily writing obsession and origins 01:03:47 - Confidence and opinions 01:07:33 - Company failures and lessons 01:10:42 - HBO series and 3AM show 01:14:26 - Hesitancy, regrets, pivotal points 01:17:38 - Advice on time and experiments
Ventura Labs23,500 просмотров • 11 месяцев назад
Ventura Labs Ep. 38 - Maciej Kula Maciej (Maciej Kula) is the Co-Head of education at Latent Holdings (Laτenτ Holdings) and Founder of (Learn Bittensor) Timestamps: 2:02 - Introduction 2:57 - Maciej Kula meets Bittensor 6:00 - The Journey into Bittensor 8:43 - Understanding Bittensor: A Decentralized AI Network 11:55 - TAO and Alpha Halving: Key Concepts Explained 14:52 - The Dynamics of Miner Registration and Price Effects 18:07 - Exploring the Sum of Prices and Market Dynamics 20:47 - The Impact of Inactive Subnets on Pricing 24:08 - The Future of Bittensor and Community Insights 39:07 - Understanding Complexity in Bittensor 40:28 - Navigating Changes Post-dTAO 42:28 - Optimal Strategies for Alpha 48:39 - Exploring Proxy Wallets and Their Risks 52:40 - Future Use Cases for Proxy Wallets 55:11 - Common Questions and Community Engagement 59:40 - Advice for Newcomers to Bittensor 1:02:16 - Liquid Alpha: Understanding Its Impact
Ventura Labs17,124 просмотров • 1 год назад
Ventura Labs Ep. 57 - Christopher Subia-Waud Christopher (Wandering Weights) is the primary contributor to Gradients (Gradients) Timestamps 01:11 - Introduction 02:05 - First contact with Bittensor 04:51 - Subnet 19’s verification problem 05:14 - Next-token probability checks 07:44 - Verifying images vs. text (diffusion nuances) 08:33 - How Bittensor reshaped his view 12:13 - NeurIPS credentials vs. real-world impact 14:00 - Why Bittensor moves faster than academia 15:51 - Gradients (Subnet 56) overview 18:14 - Tasks covered: Instruct, DPO, GRPO, Diffusion 20:34 - Winner-take-all incentives & emissions design 22:37 - Hyperparameters and search space 26:04 - Benchmarks vs. HF/Databricks/GCP/Together 31:10 - Best-performing sizes (7B–8B) 33:23 - Iterative training on large datasets 35:18 - Model merging vs. sequential approach 36:09 - Customer demand 38:20 - Gradients 5.0: open-source tournament 43:29 - Biggest surprise from open-sourcing 47:59 - Attracting and retaining top miners 50:20 - Miner stories and preserving the hobbyist spirit 52:57 - Rayon Labs partnership (Chutes + Gradients) 55:53 - Thriving under dTAO; emissions growth and why 58:01 - Bittensor-suited problems ahead 01:00:36 - Building toward “best AI” 01:04:58 - Risks of embedded ads in model outputs 01:07:30 - Advice to AI devs skeptical of crypto
Ventura Labs12,832 просмотров • 1 год назад
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