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processing 2-hour long videos used to mean massive token bills. not anymore !!! we are announcing agentic video understanding with Gemini today, that cuts token usage by up to 88% & reduces costs by up to 66% 🔥 another fun launch I got to work with the team. with...

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Vamsi Batchu

9,179 subscribers

35,681 Aufrufe • vor 13 Tagen •via X (Twitter)

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25 Kommentare

Profilbild von Sandeep Singh
Sandeep Singhvor 12 Tagen

looking forward to see it analyzing full YouTube video or other videos (with frames not just transcript) inside @Gemini_Notebook ... would be great for long lectures which needs on screen context

Profilbild von Tam
Tamvor 12 Tagen

cutting video processing tokens by 88% is huge for long form content analysis and video search tools

Profilbild von wend 🇧🇷
wend 🇧🇷vor 12 Tagen

Amazing!!! People keep complaining that you don't have an updated Pro model, because they don't understand that you're resolving all the bottlenecks and building the foundations from AGI.

Profilbild von Adam Rosler
Adam Roslervor 13 Tagen

the power of video embedding

Profilbild von AI Mastery Guide
AI Mastery Guidevor 12 Tagen

2 hour videos, no problem now

Profilbild von Rudy
Rudyvor 13 Tagen

This is an absolutely stunning development. Y'all should be proud. SOTA models out there can't even process basic 20 min videos

Profilbild von ahmad ghoniem
ahmad ghoniemvor 13 Tagen

will it be available inside antigravity cli?

Profilbild von Ryan
Ryanvor 12 Tagen

Any good deals to try this out Vamsi?

Profilbild von Ismail/ Tech
Ismail/ Techvor 13 Tagen

88% fewer tokens and 66% lower costs for 2-hour videos? 🔥 That’s a massive leap in efficiency. Gemini is making long-form video understanding seriously practical. 🚀🤖

Profilbild von interplato
interplatovor 12 Tagen

Is there a tutorial for noobs on trying this out

Profilbild von Vamsi Batchu
Vamsi Batchuvor 12 Tagen

I gotchu, it also explains how agentic video understanding works :)

Profilbild von interplato
interplatovor 12 Tagen

Thank you - apart from the understanding of subject matter, does it analyse video style, motion graphics animations etc?

Profilbild von Prathmesh
Prathmeshvor 12 Tagen

cool

Profilbild von 90S KID
90S KIDvor 12 Tagen

Should be a riveting demo, given the launch image is an entirely black screen. 🙄

Profilbild von Math Afta
Math Aftavor 13 Tagen

gemini for youtube is coming

Profilbild von Nicku
Nickuvor 13 Tagen

Can this do video editing soon?

Profilbild von Leo Messias
Leo Messiasvor 12 Tagen

Pular direto pro frame relevante via agente sem queimar a janela inteira de contexto ⚡ é onde a inferência de vídeo finalmente fica viável em escala.

Profilbild von julian darelli
julian darellivor 12 Tagen

Leer

Profilbild von Rahim
Rahimvor 12 Tagen

That's quite interesting. Building a workflow for the movie industry and working with some studios, is there anyone we can be in touch with?

Profilbild von rob
robvor 12 Tagen

ysss

Profilbild von hanqing
hanqingvor 12 Tagen

Gemini视频理解，88%省令牌，66%降成本，太给力！

Profilbild von tab
tabvor 12 Tagen

The dynamic scrubbing part is what stands out to me. Finding exact moments in long files is usually such a headache. Nice work!

Profilbild von LinearUncle
LinearUnclevor 12 Tagen

陈本降低 66% 还是巨额成本😂

Profilbild von A Pale Blue Dot
A Pale Blue Dotvor 12 Tagen

So i can make clips from my long form podcast automatically now?

Profilbild von Vedant
Vedantvor 12 Tagen

The only thing gemini needs rn is a true SOTA model Other than that I think they are doing fine

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Ramp

29,662 Aufrufe • vor 5 Monaten

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.
2:50

🚨 The Silvia team just announced our latest engineering advancement. Every business wants access to the highest level of intelligence, but at the lowest cost possible. The rise of LLMs has made intelligence abundant, yet one of the hardest problems across startups and corporate America is predicting the compute cost associated with this intelligence. I have been dealing with this personally as we build Silvia and the problem comes up in almost every conversation I have with CEOs, founders, and executives. Every business embraced AI about 18 months ago and things seemed great until the compute bills started to show up. The bills for internal compute usage were difficult to swallow, but things got outrageous if you had an AI product that allowed your users to consume compute without limits. I know this problem intimately because that is the situation that Silvia was in. Every question that was asked meant higher compute costs for our company. But we didn’t want to limit usage because users were getting genuine value out of the product. This challenge sent our team down a deep rabbit hole of cutting costs, while improving the experience for users. The second part was really important: we did not want to degrade the user experience by simply taking away access to the highest quality models. Thankfully, resource constraints breed innovation. We aren’t the biggest company, nor do we have the largest balance sheet, but we came up with a very novel solution that we are announcing today. The Silvia engineering team built a model router that cut costs by up to 29%, decreased latency, and improved the quality of answers for users. Trifecta! The way we do this is by reading the first 500 characters of a query and then predicting the level of effort that will be needed by a model to answer the query. The highest effort needs are routed to the most powerful models. The lowest effort needs are routed to different, better models for the query. A good example of this would be “what is the date?” You don’t need to use the latest Anthropic model to answer this query. In fact, sending a simple query like this to the most powerful model will make your compute costs increase and will actually increase the latency, which means a worse user experience for the Silvia user. By implementing the model router, the user gets a better experience and we get lower costs. Win-win. One of the interesting aspects of the implementation is that our model router runs on CPUs instead of GPUs. This allows us to read the query and predict the level of effort needed in less than 1 millisecond. This CPU implementation is why latency is not affected, nor is cost significantly increased by any potential additional GPU consumption. Another important point is that many of you have probably seen the news that OpenRouter is being purchased by Stripe for around $7 billion. This is a great outcome from what appears to be a very smart, capable team. Their model routing API is related (their product and our internal implementation both touch model routing), but you should think of OpenRouter as making it possible to do model routing for companies, while Silvia’s model router is a custom, intelligent system that specifically routes Silvia queries to the right model. They give access to the functionality of model routing to many companies, while our internal product does the real decision-making specific to our use case. Lastly, our implementation of a model router is a strategic bet that will allow us to become model-agnostic over time. We don’t care who created the different models, we just want to route a query to the model best positioned to answer. The large model labs will never allow their users to be model agnostic, but that would require the lab to potentially route a query to a competitor’s model. No bueno in their eyes. Instead, Silvia being an independent AI research lab gives us the power of being agnostic. We simply want the best experience for our users. Last week we announced that Silvia is now the most accurate AI tax product on the market, including beating OpenAI, Anthropic, Google, and xAI. Today we are announcing a custom, in-house model router that rivals the best technology anyone else has built. There will be many more engineering announcements to come. I truly believe we have assembled one of the best AI teams and we are currently the best AI research lab in finance. If you are interested in learning more about the technical details of the model router, you can read the engineering blog post here: Everyone wants the best intelligence and the lowest cost. Silvia just showed the world what is possible in this pursuit. I anticipate many other companies will build this custom solutions to achieve the same benefits.

Anthony Pompliano 🌪

76,176 Aufrufe • vor 26 Tagen

$TITAN presale details 🔸September 26th, 16:00 PM UTC 🔸48 hours 🔸No WL & no private sales 🔸The presale will follow a first-come, first-served model, giving everyone an equal chance to participate. The CNT prices will fluctuate dynamically based on ADA value. The presale will be hosted by our partners at Yepple, and we are approaching it differently from typical token launches. Here’s how it works: ADA can be used to purchase tokens throughout the entire presale, but we’re also offering optional participation via IAG, WMT, NVL & DEDI: 🔸 Up to 20% of the presale can be purchased with IAG. 🔸 up to 10% can be purchased with DEDI, NVL & WMT. 🔸 up to 100% can be purchased with ADA. Once the allocations for IAG, WMT, NVL & DEDI tokens are filled, ADA will be the only option available for purchasing the remaining tokens.
0:53

$TITAN presale details 🔸September 26th, 16:00 PM UTC 🔸48 hours 🔸No WL & no private sales 🔸The presale will follow a first-come, first-served model, giving everyone an equal chance to participate. The CNT prices will fluctuate dynamically based on ADA value. The presale will be hosted by our partners at Yepple, and we are approaching it differently from typical token launches. Here’s how it works: ADA can be used to purchase tokens throughout the entire presale, but we’re also offering optional participation via IAG, WMT, NVL & DEDI: 🔸 Up to 20% of the presale can be purchased with IAG. 🔸 up to 10% can be purchased with DEDI, NVL & WMT. 🔸 up to 100% can be purchased with ADA. Once the allocations for IAG, WMT, NVL & DEDI tokens are filled, ADA will be the only option available for purchasing the remaining tokens.

House Of Titans

18,501 Aufrufe • vor 2 Jahren

$Lexi AI Fam, Our bot upgrades are now live with enhanced conversational AI features! What is Conversational AI? Conversational AI refers to technologies that enable machines to interact with humans in a natural, human-like manner. This includes understanding and processing natural language, enabling more fluid and intuitive interactions. New Features - Token Trading: Trade tokens by simply typing out the token contract and amount. No need for a fixed syntax! - Multilingual Support: You can now use any language to trade tokens. Check out the attached video demonstrating these new capabilities.
1:14

$Lexi AI Fam, Our bot upgrades are now live with enhanced conversational AI features! What is Conversational AI? Conversational AI refers to technologies that enable machines to interact with humans in a natural, human-like manner. This includes understanding and processing natural language, enabling more fluid and intuitive interactions. New Features - Token Trading: Trade tokens by simply typing out the token contract and amount. No need for a fixed syntax! - Multilingual Support: You can now use any language to trade tokens. Check out the attached video demonstrating these new capabilities.

Lexi AI

140,056 Aufrufe • vor 2 Jahren

🔵 BIG ANNOUNCEMENT 🟣 Yes, you guessed it right: we are launching our TOKEN and we want to give YOU the opportunity to snag some new token allocations! 🌟 How does it work? It's super simple! Just head over to JumpTask, where you can create, share & engage with our content to earn points. These points will then transform into tokens! Whether you're an early supporter or new to the game, this is your moment to shine and reap the rewards. 💫 Don’t miss out! Tap the link and start earning your points today ➡️
0:27

🔵 BIG ANNOUNCEMENT 🟣 Yes, you guessed it right: we are launching our TOKEN and we want to give YOU the opportunity to snag some new token allocations! 🌟 How does it work? It's super simple! Just head over to JumpTask, where you can create, share & engage with our content to earn points. These points will then transform into tokens! Whether you're an early supporter or new to the game, this is your moment to shine and reap the rewards. 💫 Don’t miss out! Tap the link and start earning your points today ➡️

JumpTask

69,644 Aufrufe • vor 2 Jahren