Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

"The most important thing about the intelligence layer is the model.” But what happens when developers can choose from hundreds of them? Vincent Weisser of Prime Intellect joined us for New Defaults to talk open models, model choice, routing, economics, and what happens as intelligence becomes increasingly open and competitive.

16,312 görüntüleme • 4 gün önce •via X (Twitter)

12 Yorum

stochasm profil fotoğrafı
stochasm4 gün önce

@vincentweisser @PrimeIntellect he's got a nice jacket there

OpenRouter profil fotoğrafı
OpenRouter4 gün önce

Watch the full New Defaults episode:

Prime Intellect profil fotoğrafı
Prime Intellect4 gün önce

@vincentweisser Thanks for having us!

Anastasia Crew profil fotoğrafı
Anastasia Crew4 gün önce

@vincentweisser @PrimeIntellect admire your mission and the way you build @vincentweisser. had a lot of fun producing this!

knivesysl profil fotoğrafı
knivesysl4 gün önce

@vincentweisser @PrimeIntellect why does everyone talk like a retard. notice how they start drooling when they use the word “scaling” and “agi”

Viber · fireply.ai profil fotoğrafı
Viber · fireply.ai4 gün önce

@vincentweisser @PrimeIntellect more models just means more ways to pick wrong faster honestly

MASA profil fotoğrafı
MASA4 gün önce

@vincentweisser @PrimeIntellect Dynamic switching matters way more than individual weights

Dasha profil fotoğrafı
Dasha3 gün önce

@vincentweisser @PrimeIntellect love it!

Ibesh profil fotoğrafı
Ibesh4 gün önce

@vincentweisser @PrimeIntellect model choice only becomes leverage when routing is invisible; otherwise developers inherit a new ops problem where every task starts with a benchmark spreadsheet

Nexqor profil fotoğrafı
Nexqor4 gün önce

@vincentweisser @PrimeIntellect how does routing survive when frontier labs ship their own cheap tiers?

James Camarota profil fotoğrafı
James Camarota4 gün önce

@vincentweisser @PrimeIntellect With hundreds of models, what should a router optimize first for a production agent: quality, latency, cost, or reliability over time?

Tony profil fotoğrafı
Tony4 gün önce

@vincentweisser @PrimeIntellect With hundreds to choose from, the hard part stops being access and becomes knowing which one to reach for.

Benzer Videolar

When Mudith Jayasekara and I met Gabe Pereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

Charlie O'Neill

92,869 görüntüleme • 2 ay önce

🦙 ollama is used by 9 million developers and 85% of the Fortune 500, giving co-founder and CEO Jeffrey Morgan (Jeffrey Morgan) a unique view into which AI models people are actually using and how that’s changing. Right now, the biggest shift he sees is toward open models, driven by coding agents, falling costs, and capabilities that are rapidly catching up to the frontier labs. On Ollama Cloud, that shift has driven a 150x increase in token usage since the start of the year. In this episode of Lightcone Podcast, Jeff joins Garry Tan, Jared Friedman, Diana, and Harj Taggar to talk about the future of open models and the story behind Ollama, from two years of searching for the right idea to building one of the most widely used AI developer tools in the world. 00:43 — The Shift to Open Models 03:03 — How AI Agents Are Driving Token Usage 05:31 — Are Open Models Catching Up? 08:26 — What Happens When a New Model Launches 11:31 — Ollama as an Operating System for AI 14:05 — The New Opportunities Above the Model Layer 18:19 — Why 80–90% of Enterprise Tokens Could Be Open 20:57 — The Future Is Local and Cloud 26:40 — Why AI Is Coming Back to Your Computer 28:56 — The Coming Era of Unlimited Tokens 32:30 — Do We Still Need a “God Model”? 33:41 — Open Models and Geopolitics 36:14 — The Origins of Ollama 40:36 — Two Years Lost in the Wilderness 42:39 — The Pivot That Changed Everything 47:02 — How Ollama Found a Business Model 49:43 — Why Second-Time Founders Did YC

Y Combinator

310,621 görüntüleme • 17 gün önce