Загрузка видео...

Не удалось загрузить видео

На главную

Model Council is now available inside Computer. Run independent analysis across multiple frontier models, choose your analysis depth, and get a single cited report on where models agree, where they disagree, and what each found that others missed.

perplexity_ai's profile picture

Perplexity

499,543 subscribers

78,929 просмотров • 3 дней назад •via X (Twitter)

Наука и технологии
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

You can now run three frontier models at once and select your orchestrator model directly inside Perplexity Computer. Model Council automatically runs GPT-5.4, Claude Opus 4.6 and Gemini 3.1 Pro simultaneously. Three frontier models. One workflow. Best answer wins.
0:32

You can now run three frontier models at once and select your orchestrator model directly inside Perplexity Computer. Model Council automatically runs GPT-5.4, Claude Opus 4.6 and Gemini 3.1 Pro simultaneously. Three frontier models. One workflow. Best answer wins.

Computer

84,535 просмотров • 4 месяцев назад

New in M365 Copilot: Council. You can run multiple models on the same prompt at the same time, so you can see where they align and diverge, and understand what each adds.
0:51

New in M365 Copilot: Council. You can run multiple models on the same prompt at the same time, so you can see where they align and diverge, and understand what each adds.

Satya Nadella

465,944 просмотров • 4 месяцев назад

Introducing Model Council in Perplexity. Run three frontier models at once, compare outputs, and get a more accurate, higher‑confidence answer. Available now on web only for Perplexity Max subscribers.
0:41

Introducing Model Council in Perplexity. Run three frontier models at once, compare outputs, and get a more accurate, higher‑confidence answer. Available now on web only for Perplexity Max subscribers.

Perplexity

412,074 просмотров • 5 месяцев назад

DigitalOcean’s model synthesis tool is now available in public preview. 🆕 A new server-side tool: run multiple models on one inference request and get a single synthesized response, no custom orchestration. Just add it to your existing inference request.
3:22

DigitalOcean’s model synthesis tool is now available in public preview. 🆕 A new server-side tool: run multiple models on one inference request and get a single synthesized response, no custom orchestration. Just add it to your existing inference request.

DigitalOcean

2,030,708 просмотров • 8 дней назад

Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.
0:59

Today we're announcing that hybrid agentic inference is coming to Perplexity Computer. Computer can split tasks between a local model running on your machine and frontier models in the cloud. This keeps private data on your device and maximizes token efficiency. Coming soon.

Perplexity

352,555 просмотров • 1 месяц назад

Wave 9 is here: a frontier model built for software engineering. Introducing our new family of models: SWE-1, SWE-1-lite, and SWE-1-mini. Based on internal evals, it has performance nearing that of frontier models from the foundation labs. Available now, only in Windsurf!
1:40

Wave 9 is here: a frontier model built for software engineering. Introducing our new family of models: SWE-1, SWE-1-lite, and SWE-1-mini. Based on internal evals, it has performance nearing that of frontier models from the foundation labs. Available now, only in Windsurf!

Devin Desktop

742,703 просмотров • 1 год назад

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.
0:36

“Our compute nodes don’t run a full copy of the models we train on IOTA. They actually run a small sliver of the model. This means you can train really large frontier sized models using very small building blocks”. CTO crux unpacks how IOTA ・ SN9’s model parallel architecture allows us to train at scale by splitting our models across multiple machines, and then sewing them together. This is core to SN9’s architecture. See the full Eye On A.I. podcast below.

IOTA ・ SN9

15,465 просмотров • 2 месяцев назад

The best hedge fund PMs compare views from different research analysts. Now you can do that in 2 minutes with Equity Research Council in Perplexity Computer. This workflow pulls in research from brokers like GS, JPM, MS, and Evercore - and shows you where they agree vs. disagree.
0:31

The best hedge fund PMs compare views from different research analysts. Now you can do that in 2 minutes with Equity Research Council in Perplexity Computer. This workflow pulls in research from brokers like GS, JPM, MS, and Evercore - and shows you where they agree vs. disagree.

Jeff Grimes

90,308 просмотров • 3 месяцев назад

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”
0:41

Jason Calacanis: Anthropic, OpenAI and others are trying to kill OpenClaw Why? Because an open source agent platform is an existential threat to frontier model companies. @jason: “ People are going to say I'm a conspiracy theorist, but the number one goal, I believe, in the large language model, frontier model space, is to kill (OpenClaw). This is a giant movement to stop it, because this is the equivalent of having an open source Android-like player in the market, and that could be incredibly disruptive. Because, I believe, open source is going to win the day on the large language models and take 90% of the token usage, and I think the entire frontier model space could be undercut by open source. And I think they realize that SLMs, the smaller language models that are verticalized now, that will run on desktops and laptops and are even starting to run on the top ones, that is their biggest competitive threat, and I hope it happens.”

The All-In Podcast

64,611 просмотров • 3 месяцев назад

Project Launch 🚀: mukh v0.1.14 - The Face Analysis Library Hello Everyone, I am happy to launch my first python package "mukh". Mukh (मुख, meaning "face" in Sanskrit) is a comprehensive face analysis library that provides unified APIs for various face-related tasks. It simplifies the process of working with multiple face analysis models through a consistent interface. --- 📌 Features: 🥸 DeepFake Detector: First Python package featuring an Ensemble of multiple models 🎯 Unified API: Single, consistent API for multiple face analysis tasks like face detection and reenactment 🔄 Model Flexibility: Support for multiple models per task 🛠️ Custom Pipelines: Optimized preprocessing and model combinations --- 📌 Currently Supported Tasks: 1️⃣ Face Detection 2️⃣ Face Reenactment with Source Image and Driving Video 3️⃣ Deepfake Detection for Image and Video 4️⃣ Deepfake Detection Pipeline - Ensemble of multiple models --- 📌 Open Source Contributions: I am opening up multiple tasks in the issues section of mukh including a bunch of `good-first-issues`. My goal is to help a number of beginners get started with their contributions, I will be personally guiding across completing these tasks so keep a check on the issues section. The link to the GitHub repository and detailed documentation is available below 👇 in the reply section, do give it a ⭐️ to support the project! --- Special Mentions in the video: Ayush Chaurasia adi unni hsr hacker house Nimisha Chanda @ElonMastikhor Ctrl+Vibe
12:07

Project Launch 🚀: mukh v0.1.14 - The Face Analysis Library Hello Everyone, I am happy to launch my first python package "mukh". Mukh (मुख, meaning "face" in Sanskrit) is a comprehensive face analysis library that provides unified APIs for various face-related tasks. It simplifies the process of working with multiple face analysis models through a consistent interface. --- 📌 Features: 🥸 DeepFake Detector: First Python package featuring an Ensemble of multiple models 🎯 Unified API: Single, consistent API for multiple face analysis tasks like face detection and reenactment 🔄 Model Flexibility: Support for multiple models per task 🛠️ Custom Pipelines: Optimized preprocessing and model combinations --- 📌 Currently Supported Tasks: 1️⃣ Face Detection 2️⃣ Face Reenactment with Source Image and Driving Video 3️⃣ Deepfake Detection for Image and Video 4️⃣ Deepfake Detection Pipeline - Ensemble of multiple models --- 📌 Open Source Contributions: I am opening up multiple tasks in the issues section of mukh including a bunch of `good-first-issues`. My goal is to help a number of beginners get started with their contributions, I will be personally guiding across completing these tasks so keep a check on the issues section. The link to the GitHub repository and detailed documentation is available below 👇 in the reply section, do give it a ⭐️ to support the project! --- Special Mentions in the video: Ayush Chaurasia adi unni hsr hacker house Nimisha Chanda @ElonMastikhor Ctrl+Vibe

Ishan Dutta

13,931 просмотров • 1 год назад

This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.
1:23

This Chinese AI model beat ChatGPT and Gemini Guess what? It's now available on AI Fiesta also. Now you can use up to 7 AI models simultaneously. We are probably the only AI platform in the world where you can do that.

Dhruv Rathee

1,059,372 просмотров • 8 месяцев назад

Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.
0:23

Ollama 0.2 is here! Concurrency is now enabled by default. This unlocks 2 major features: Parallel requests Ollama can now serve multiple requests at the same time, using only a little bit of additional memory for each request. This enables use cases such as: - Handling multiple chat sessions at the same time - Hosting code completion LLMs for your team - Processing different parts of a document simultaneously - Running multiple agents at the same time Run multiple models Ollama now supports loading different models at the same time. This improves several use cases: - Retrieval Augmented Generation (RAG): both the embedding and text completion models can be loaded into memory simultaneously. - Agents: multiple versions of an agent can now run simultaneously - Running large and small models side-by-side Models are automatically loaded and unloaded based on requests and how much GPU memory is available.

ollama

219,417 просмотров • 2 лет назад

Excited to introduce Scheduled Runs in @JuliusAI_ 🚀 You can now schedule any analysis to be run with just a single click, and have the results delivered straight to your slack or email. From daily metric updates, weekly KPI tracking, to monthly investor updates. Get your analysis on Autopilot to free up your and your teams bandwidth.
1:07

Excited to introduce Scheduled Runs in @JuliusAI_ 🚀 You can now schedule any analysis to be run with just a single click, and have the results delivered straight to your slack or email. From daily metric updates, weekly KPI tracking, to monthly investor updates. Get your analysis on Autopilot to free up your and your teams bandwidth.

Julius AI

15,048 просмотров • 10 месяцев назад

WOW! OPENAI DID IT! They released their first TWO open weights models! This is HUGE news for the entire AI industry. It means you can RIGHT NOW run cutting edge AI models locally on your computer Here's all the benefits and how to set up the models locally yourself:
7:40

WOW! OPENAI DID IT! They released their first TWO open weights models! This is HUGE news for the entire AI industry. It means you can RIGHT NOW run cutting edge AI models locally on your computer Here's all the benefits and how to set up the models locally yourself:

Alex Finn

71,875 просмотров • 1 год назад

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.
1:14

., Co-founder of fal, on where AI image & video generation stands today: “Image and video models haven’t really generalized yet. There’s still a lot of model chaining and workflow generation." "Models are getting bigger and better, but unlike LLMs where you can write a prompt and expect it to handle various tasks, that doesn’t happen for media yet.” He notes that while chaining multiple models is still the norm, the opportunity lies in building hybrid systems that stitch workflows together reliably until true generalization arrives.

TBPN

36,675 просмотров • 11 месяцев назад

Sam Altman says today's models are still dumb because they barely understand your life The next shift is a model that knows your context, your work, your goals, your people, and the way you use your computer "that is gonna be a complete change to what it feels like to use AI"
0:52

Sam Altman says today's models are still dumb because they barely understand your life The next shift is a model that knows your context, your work, your goals, your people, and the way you use your computer "that is gonna be a complete change to what it feels like to use AI"

Haider.

117,873 просмотров • 3 месяцев назад

A full and free version of this week's programme is now available wherever you get your podcasts, so if you want unbiased, intelligent, independent analysis of the news - check it out! The BigLight Stuart Cosgrove Eamonn O'Neill *
0:58

A full and free version of this week's programme is now available wherever you get your podcasts, so if you want unbiased, intelligent, independent analysis of the news - check it out! The BigLight Stuart Cosgrove Eamonn O'Neill *

Talk Media (Weekly Podcast)

16,877 просмотров • 3 лет назад

One of the major challenges of Traders is inability to do a well-detailed analysis on the Trade they want to take. Therefore, I will release the full video of this tomorrow where I did a well-detailed analysis on EURUSD and also ended it with a live analysis of what I expect next on EURUSD. You will be doing a lot of people a great good by sharing this. Subscribe and on your notifications for my YouTube channel where I will drop it (Link in the comment section below)
0:48

One of the major challenges of Traders is inability to do a well-detailed analysis on the Trade they want to take. Therefore, I will release the full video of this tomorrow where I did a well-detailed analysis on EURUSD and also ended it with a live analysis of what I expect next on EURUSD. You will be doing a lot of people a great good by sharing this. Subscribe and on your notifications for my YouTube channel where I will drop it (Link in the comment section below)

HENDRAY

35,701 просмотров • 1 год назад

I always wondered which Ollama model my machine could run properly. Now you can easily do that using llms-checker It's a CLI tool that checks your system specs and recommends the best models to run on Ollama based on what your machine npm install -g llm-checker
0:21

I always wondered which Ollama model my machine could run properly. Now you can easily do that using llms-checker It's a CLI tool that checks your system specs and recommends the best models to run on Ollama based on what your machine npm install -g llm-checker

Jaydeep

112,309 просмотров • 5 месяцев назад

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:
0:52

Introducing AI Lab Monetization Play. AI Lab Monetization Play tracks the revenue run-rate of major AI laboratories in real time using public and alternative data signals. The Play provides a continuously updated view of commercial scale across the frontier AI landscape. ARR Dashboard The dashboard estimates annual recurring revenue for Anthropic, OpenAI, and other frontier labs. Each estimate is broken down by revenue stream, growth trajectory, and implied valuation multiple. It surfaces which labs are scaling fastest and where the commercial momentum is concentrated. Model Routing and Market Share The Play tracks real-time model usage share across inference providers. It measures routing volume through aggregators to identify which models are capturing production workloads. Shifts in routing patterns signal adoption inflections before they appear in reported financials. Model Pricing and Capabilities The Play maintains a comprehensive pricing table across all major foundation models. Input and output token costs, context windows, and capability tiers are mapped side by side. It serves as the reference layer for competitive positioning and margin structure across the model ecosystem. Available to all paid clients on our full-stack research platform. Start here:

FUNDA

30,511 просмотров • 11 дней назад