Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Another big launch: Rippling AI Spend Console. Dashboards yes, but also a model router to control spend, and lineage analysis w/ Github data to directly measure engineering ROI. It really works! Our internal results in thread 🧵👇 ...

stanine's profile picture

Matt MacInnis

11,120 subscribers

17,368 görüntüleme • 1 gün önce •via X (Twitter)

Bilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

Nobody cares that you process 2B tokens. That’s not a flex, it’s a bill. Introducing Entelligence AI Agent Insights - Track not just dollars spent but actual ROI We built Agent Insights to map AI spend straight to engineering outcomes: features, fixes, and PRs Manage your budget based on actual company priorities Cut your spend in half with the Entelligence model router See what your AI spend actually shipped:
1:14

Nobody cares that you process 2B tokens. That’s not a flex, it’s a bill. Introducing Entelligence AI Agent Insights - Track not just dollars spent but actual ROI We built Agent Insights to map AI spend straight to engineering outcomes: features, fixes, and PRs Manage your budget based on actual company priorities Cut your spend in half with the Entelligence model router See what your AI spend actually shipped:

Aiswarya Sankar

63,974 görüntüleme • 14 gün önce

People have always dreamed of talking directly to their data, but business data lives in large databases accessible only by making manual requests to a small team of technical data specialists. Despite huge spend on data storage, ROI from analysis remains limited. Seek changes that. This week, we’re giving our first group of customers access to Deep Data Reasoning.
0:46

People have always dreamed of talking directly to their data, but business data lives in large databases accessible only by making manual requests to a small team of technical data specialists. Despite huge spend on data storage, ROI from analysis remains limited. Seek changes that. This week, we’re giving our first group of customers access to Deep Data Reasoning.

Sarah Nagy

12,074 görüntüleme • 1 yıl önce

you don’t need a master’s degree in AI to make these videos. just spend 5 minutes reading this thread and trying out the tips and prompts below 🧵👇
0:50

you don’t need a master’s degree in AI to make these videos. just spend 5 minutes reading this thread and trying out the tips and prompts below 🧵👇

TechHalla

77,928 görüntüleme • 10 ay önce

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi
5:34

Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

Andrew Bolis

36,504 görüntüleme • 10 ay önce

You don’t need to browse YouTube to understand what’s performing. The YouTube Agent lets you query videos and extract structured data directly from the Teneo Agent Console. Example: Search “AI agents” sorted by view count Instead of scrolling through results, you get ranked videos with key metadata like views, titles, and channel information. From discovery to analysis, in one query. Explore it ↓
0:41

You don’t need to browse YouTube to understand what’s performing. The YouTube Agent lets you query videos and extract structured data directly from the Teneo Agent Console. Example: Search “AI agents” sorted by view count Instead of scrolling through results, you get ranked videos with key metadata like views, titles, and channel information. From discovery to analysis, in one query. Explore it ↓

Teneo Protocol

35,491 görüntüleme • 4 ay önce

This is the most impressive tool I’ve tried in my 2 years working with generative AI. Kling AI gave me early access to their Custom Face Video Model, and yes, this is TEXT TO VIDEO. How does it work? I’ll break it all down in this thread (includes tutorial + prompts)🧵👇
0:48

This is the most impressive tool I’ve tried in my 2 years working with generative AI. Kling AI gave me early access to their Custom Face Video Model, and yes, this is TEXT TO VIDEO. How does it work? I’ll break it all down in this thread (includes tutorial + prompts)🧵👇

TechHalla

132,645 görüntüleme • 1 yıl önce

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.
1:56

AI spend is now one of the fastest-growing line items at every company, but here's the problem: it's invisible to traditional spend management. We experienced this firsthand at Brex, so we built Magpie: an internal tool that sources and normalizes AI spend across every vendor so we can break it down by model, customer, employee, and workflow. What Magpie has already unlocked: → ~85% cost reduction on an audit agent through prompt caching → Real customer cost-to-serve numbers that inform pricing and margin → Model routing governance we couldn't enforce before → A surprise: operational AI is much cheaper than we assumed So why is AI spend so hard to track? Because each one exposes usage data differently (some not at all). At Brex, tool-use costs weren't tracked alongside tokens, customer attribution was inconsistent, and a meaningful chunk of our daily AI spend was unattributable. Now with Magpie, we have reliable visibility. AI spend is more complex than anything finance teams have had to manage before, and it's exactly the kind of problem Brex is built to solve, shaping how we think about the future of our platform. More below.

Pedro Franceschi

28,492 görüntüleme • 2 ay önce

Introducing Oasis 2.0: Our most advanced AI model Transform game worlds & styles in real time. 1080p, 30fps Here’s how to play it live (and also a Minecraft Mod)! 🧵
0:28

Introducing Oasis 2.0: Our most advanced AI model Transform game worlds & styles in real time. 1080p, 30fps Here’s how to play it live (and also a Minecraft Mod)! 🧵

Decart

2,333,166 görüntüleme • 11 ay önce

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇
3:21

Introducing GEN-1. Our latest milestone in scaling robot learning. We believe it to be the first general-purpose AI model to master simple physical tasks. 99% success rates, 3x faster speeds, adapts in real time to unexpected scenarios, w/ only 1 hour of robot data. More🧵👇

Generalist

381,736 görüntüleme • 4 ay önce

Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇
4:07

Think of Model Context Protocol as a USB port for your AI. 🔌 You can plug in documentation, tools, and context directly into Copilot CLI. Scott Hanselman 🌮 shows how to set it all up. 👇

GitHub

44,363 görüntüleme • 6 ay önce

Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇
3:03

Seer, Sentry's AI debugger, analyzes your issues and finds the root cause. Now you can pass that analysis directly to a @GitHub Copilot agent which picks up the context, generates a fix, and opens a pull request. Learn more in our latest video with Paul Jaffré 👇

Sentry

12,356 görüntüleme • 1 ay önce

In our $KGN earnings call today, it sounded like it's David Shafer and I presenting but it wasn't. Our voices were AI generated and read the script. We uploaded several hours of our prior talks to train our voice model and the results were pretty incredible. This saved us a few hours preparation - valuable time we could spend working on other areas of the business. This is an example of the many one percenters across our business where AI is making us more efficient and helping the team be more productive. Our marketing team uses it to improve our content and make it more enaging. Our Engineering team uses it to get more context of our entire code base quicker and produce production ready code more rapidly. Our purchasing teams are using it for data and trend analysis to bring the latest products to our customers at even better prices. The list goes on. While the productivity gains are exciting, what's even more exciting is the pace at which things are changing and the technology is getting better!
25:12

In our $KGN earnings call today, it sounded like it's David Shafer and I presenting but it wasn't. Our voices were AI generated and read the script. We uploaded several hours of our prior talks to train our voice model and the results were pretty incredible. This saved us a few hours preparation - valuable time we could spend working on other areas of the business. This is an example of the many one percenters across our business where AI is making us more efficient and helping the team be more productive. Our marketing team uses it to improve our content and make it more enaging. Our Engineering team uses it to get more context of our entire code base quicker and produce production ready code more rapidly. Our purchasing teams are using it for data and trend analysis to bring the latest products to our customers at even better prices. The list goes on. While the productivity gains are exciting, what's even more exciting is the pace at which things are changing and the technology is getting better!

Ruslan Kogan

814,106 görüntüleme • 11 ay önce

this AI is like having a professional studio in your pocket Fotor launches their Agent that works for you by editing images, creating videos… you name it! Everything you need to know, prompts and tips, in this thread 🧵👇
0:42

this AI is like having a professional studio in your pocket Fotor launches their Agent that works for you by editing images, creating videos… you name it! Everything you need to know, prompts and tips, in this thread 🧵👇

TechHalla

18,268 görüntüleme • 11 ay önce

🤔What does it take to train an omni world model from scratch? 📽️Petabytes of video. 6⃣pipeline stages. And every improvement in data quality pays off twice when you're training a model that both generates and understands video. Our Reka Labs' data team on how it works → #PhysicalAI #WorldModels #AI #RekaLabs
0:38

🤔What does it take to train an omni world model from scratch? 📽️Petabytes of video. 6⃣pipeline stages. And every improvement in data quality pays off twice when you're training a model that both generates and understands video. Our Reka Labs' data team on how it works → #PhysicalAI #WorldModels #AI #RekaLabs

Reka

14,959 görüntüleme • 24 gün önce

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -
10:06

A client accidentally spent $500M in one month on Claude. $16.6M per day. No usage controls. Chamath Palihapitiya on The All-In Podcast EP275: a Fortune-level company failed to set token limits. The bill arrived at end of month. A separate Fortune 20 company is 6 months into a $1B AI-driven OPEX reduction program. $200M in tokens spent. Minimal results. Microsoft is already canceling Claude enterprise licenses. David Sacks calls it a "meme" forming - that token spend is wasteful and the market is overcorrecting. For enterprise AI buyers: the companies building spend-control and measurable-ROI infrastructure now are positioned to capture budget as the backlash unfolds. Full enterprise spend breakdown and what it means for AI vendor pricing power on The All-In Podcast: Source: All-In Podcast -

Podcast Alpha

31,016 görüntüleme • 2 ay önce

In the era of long-context LLMs, I needed a way to quickly convert a GitHub repo into a text file to feed as a prompt. Introducing RepoToTextForLLMs 📖 Not only does it return the full repo in one file, but it also appends a super prompt for analysis and understanding 👇
0:25

In the era of long-context LLMs, I needed a way to quickly convert a GitHub repo into a text file to feed as a prompt. Introducing RepoToTextForLLMs 📖 Not only does it return the full repo in one file, but it also appends a super prompt for analysis and understanding 👇

Pietro Schirano

104,920 görüntüleme • 2 yıl önce

Create compositions like a PRO by combining 3D with AI! I'll show how to take full control of your scene and generate images in any style you want. Breaking it all down for you in this thread. 🧵👇
0:21

Create compositions like a PRO by combining 3D with AI! I'll show how to take full control of your scene and generate images in any style you want. Breaking it all down for you in this thread. 🧵👇

TechHalla

158,085 görüntüleme • 1 yıl önce

Proud to announce alpha launch of our AI Character studio: Customise a character, provide it an AI personality and use it across different platforms. This is in active development and some of the most exciting features are coming soon 🧵👇 (1/8)
0:58

Proud to announce alpha launch of our AI Character studio: Customise a character, provide it an AI personality and use it across different platforms. This is in active development and some of the most exciting features are coming soon 🧵👇 (1/8)

HeyOz

85,573 görüntüleme • 3 yıl önce

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:
3:08

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 görüntüleme • 9 ay önce