Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

An audit of Anthropic customers’ bills uncovered invoicing errors, finding about $1.7 million in mistaken overcharges. The Information’s Laura Bratton explains how those errant changes could happen: "Maybe there was an AI agent that continued to retry a task that it was completing incorrectly and a customer didn't realize... show more

The Information

1,151,198 subscribers

21,557 views • 2 months ago •via X (Twitter)

News & Politics Science & Technology

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

A 2-person startup crossed $2M ARR with an AI agent doing the work of an ops hire. The agent was given read-only access to their codebase and database along with connected tools like Intercom, Stripe, CRM, and Fathom through CLIs. They routed Slack, email, and support requests into a task queue so the agent could pick up each task and run it in Claude Code. So when a customer asked about billing or product behavior, it could inspect how the business actually worked. Along with these tools, a coding agent was also provided. When the ops agent found a repeated task it could not do yet, the coding agent built a tool for it. That tool became permanent. Over time, this grew to 45+ internal tools. The agent also had an instruction.md where it stored the co-founder's feedback to avoid repeating its mistakes.

A 2-person startup crossed $2M ARR with an AI agent doing the work of an ops hire. The agent was given read-only access to their codebase and database along with connected tools like Intercom, Stripe, CRM, and Fathom through CLIs. They routed Slack, email, and support requests into a task queue so the agent could pick up each task and run it in Claude Code. So when a customer asked about billing or product behavior, it could inspect how the business actually worked. Along with these tools, a coding agent was also provided. When the ops agent found a repeated task it could not do yet, the coding agent built a tool for it. That tool became permanent. Over time, this grew to 45+ internal tools. The agent also had an instruction.md where it stored the co-founder's feedback to avoid repeating its mistakes.

rvivek

11,870 views • 2 months ago

Here’s an example of our internal Head of Growth AI Agent orchestrating work to a human: it creates a task, assigns it to a human to find leads, and pays them for completing it—working with and Payman to make it happen! If you're building AI Agents, happy to get you early access! Just send over a DM!

Here’s an example of our internal Head of Growth AI Agent orchestrating work to a human: it creates a task, assigns it to a human to find leads, and pays them for completing it—working with and Payman to make it happen! If you're building AI Agents, happy to get you early access! Just send over a DM!

tyllen

36,813 views • 2 years ago

Joe Rogan reveals to Tom Segura the one thing about January 6th that still disturbs him over five years later. ROGAN: “Here’s what’s f*cked...” “For sure...there were government people that were rabble-rousers.” “There were people that were trying to get people to go into the Capitol.” “That’s a fact. How many?” “They call them ‘agent provocateurs.’ So there’s people that your tax dollars pay that were trying to get people to commit crimes.” “We don’t know how many...” SEGURA: “We don’t know. Yeah.” ROGAN: “And supposedly, no, they were just there to monitor. Really?” “Okay, but we know that people have done that in the past, where they encourage people to commit crimes.” “And we do know that there was some knowledge that this was going to happen and that they wanted it to happen.” “They wanted it to happen exactly that way, and they encouraged people to do it so they could make it look like Donald Trump is a real threat and keep him from running for reelection again.”

Joe Rogan reveals to Tom Segura the one thing about January 6th that still disturbs him over five years later. ROGAN: “Here’s what’s f*cked...” “For sure...there were government people that were rabble-rousers.” “There were people that were trying to get people to go into the Capitol.” “That’s a fact. How many?” “They call them ‘agent provocateurs.’ So there’s people that your tax dollars pay that were trying to get people to commit crimes.” “We don’t know how many...” SEGURA: “We don’t know. Yeah.” ROGAN: “And supposedly, no, they were just there to monitor. Really?” “Okay, but we know that people have done that in the past, where they encourage people to commit crimes.” “And we do know that there was some knowledge that this was going to happen and that they wanted it to happen.” “They wanted it to happen exactly that way, and they encouraged people to do it so they could make it look like Donald Trump is a real threat and keep him from running for reelection again.”

Overton

460,432 views • 3 months ago

Honored to join @HF0 for S26 We're building Franklin : an AI agent with its own wallet — it auto-picks the best model for every task and pays per action in USDC. An AI agent that can pay and get paid. 👇

Honored to join @HF0 for S26 We're building Franklin : an AI agent with its own wallet — it auto-picks the best model for every task and pays per action in USDC. An AI agent that can pay and get paid. 👇

BlockRunAI

18,732 views • 2 months ago

An Anthropic team member broke his hand and was forced to code without a keyboard for 2 months. He could only talk to an AI agent that wrote and committed code for him. This 30-minute talk is free and teaches you more about skills and AI coding than any $500 course. Watch it, then read the breakdown below👇

An Anthropic team member broke his hand and was forced to code without a keyboard for 2 months. He could only talk to an AI agent that wrote and committed code for him. This 30-minute talk is free and teaches you more about skills and AI coding than any $500 course. Watch it, then read the breakdown below👇

darkzodchi

195,679 views • 4 months ago

They built an AI agent in 20 minutes and sold it to Anthropic for $100k - and at Stanford showed how to repeat it from scratch: 02:00 - how to build your first agent with Opus 5 in 20 minutes 22:34 - one agent replaces an Anthropic engineering team worth $200k a year 49:47 - how to make $1.2M a year selling agents to Anthropic and Google after watching I spent 20 minutes in Claude Code - the agent replaced 10 Anthropic engineers and they paid $100k and asked for more. save & watch - the article below shows how to build an agent in Claude Code in 20 minutes and reach $1.2M a year.

They built an AI agent in 20 minutes and sold it to Anthropic for $100k - and at Stanford showed how to repeat it from scratch: 02:00 - how to build your first agent with Opus 5 in 20 minutes 22:34 - one agent replaces an Anthropic engineering team worth $200k a year 49:47 - how to make $1.2M a year selling agents to Anthropic and Google after watching I spent 20 minutes in Claude Code - the agent replaced 10 Anthropic engineers and they paid $100k and asked for more. save & watch - the article below shows how to build an agent in Claude Code in 20 minutes and reach $1.2M a year.

Noisy

73,018 views • 1 day ago

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

Anthropic engineer: "Over 90% of Anthropic engineers use a self-improving agentic loop for coding. The key is to close the loop: give the agent a way to verify and improve its own work" In this 12-minute talk, an Anthropic engineer explains how to build an agent that continuously improves itself. Claude + routines + loops + CLAUDE.md - that's the foundation. Skip Netflix tonight. Save this post!

unicode

62,153 views • 2 months ago

Nicki Minaj reflects on the subject matter of her past songs, admitting she wishes she could go back and change certain things: "A lot of the stuff that I was rapping about, I was so young too, I didn't realize that there were young people listening to it and how it could probably make them make certain decisions... If I could do it all over again, I would've changed a lot of things that I said." (Via the Bryce Crawford Podcast)

Nicki Minaj reflects on the subject matter of her past songs, admitting she wishes she could go back and change certain things: "A lot of the stuff that I was rapping about, I was so young too, I didn't realize that there were young people listening to it and how it could probably make them make certain decisions... If I could do it all over again, I would've changed a lot of things that I said." (Via the Bryce Crawford Podcast)

HotNewHipHop

17,677 views • 2 months ago

I built an AI voice agent that called the DMV to schedule an appointment for me! ....and you can too. It took <30 minutes and two tools. (And, it was incredibly satisfying to hear my AI deal with the DMV's AI instead of sitting on the phone myself😂) How I did it 👇

I built an AI voice agent that called the DMV to schedule an appointment for me! ....and you can too. It took <30 minutes and two tools. (And, it was incredibly satisfying to hear my AI deal with the DMV's AI instead of sitting on the phone myself😂) How I did it 👇

Olivia Moore

62,403 views • 2 years ago

Close, the CRM, released an AI agent called Chloe that calls new leads, qualifies them and books them onto a calendar. I sent it to my fiancée without telling her what it was and she thought she was talking to a person. The catch is that it has to announce itself as an AI by the third sentence, which I think ends most of those calls. But still, it's an agent working your 6PM leads while your rep is logged off.

Close, the CRM, released an AI agent called Chloe that calls new leads, qualifies them and books them onto a calendar. I sent it to my fiancée without telling her what it was and she thought she was talking to a person. The catch is that it has to announce itself as an AI by the third sentence, which I think ends most of those calls. But still, it's an agent working your 6PM leads while your rep is logged off.

Nick Abraham

10,385 views • 25 days ago

🚨 NEW: Anthropic released shocking revenue numbers. They now have over 1,000 enterprises paying $1M+ annually: "It turns out that the TAM for intelligence is radically different than anything that we've seen before... It's not that there was some great go-to-market in Anthropic that all of a sudden they snuck up and blew everybody away. No, it was companies demanding the product. They're getting throttled on the product. Why? Because it's so good. It makes them better at their business. We are all self-interested actors. And when millions of those people are all making the same decision, there's a huge tell. And the tell here is that the TAM is as big as Dario and Sam and others have been saying." -Brad Gerstner "This is truly mind-boggling when you think about it because those are the most coveted customers in the world... Slack dreamed of getting these million-dollar customers. Salesforce dreams of getting these million-dollar customers." -@jason

🚨 NEW: Anthropic released shocking revenue numbers. They now have over 1,000 enterprises paying $1M+ annually: "It turns out that the TAM for intelligence is radically different than anything that we've seen before... It's not that there was some great go-to-market in Anthropic that all of a sudden they snuck up and blew everybody away. No, it was companies demanding the product. They're getting throttled on the product. Why? Because it's so good. It makes them better at their business. We are all self-interested actors. And when millions of those people are all making the same decision, there's a huge tell. And the tell here is that the TAM is as big as Dario and Sam and others have been saying." -Brad Gerstner "This is truly mind-boggling when you think about it because those are the most coveted customers in the world... Slack dreamed of getting these million-dollar customers. Salesforce dreams of getting these million-dollar customers." -@jason

bg2clips

36,660 views • 4 months ago

🚨 ICE agents drove over protesters blocking them in San Francisco. The Antifa clowns surrounded the ICE van and continued to stand in front. Eventually the ICE agent floored it and ran over a few of them. The weird part, they seem upset and surprised that they were run over.

🚨 ICE agents drove over protesters blocking them in San Francisco. The Antifa clowns surrounded the ICE van and continued to stand in front. Eventually the ICE agent floored it and ran over a few of them. The weird part, they seem upset and surprised that they were run over.

Wall Street Mav

119,369 views • 5 months ago

My Hermes agent builds AI employees for me. Just closed another $5K‑a‑month customer today for an AI employee for a design agency. I'm not even building any of it. I have Dewey, my Hermes agent, building out the entire Hermes agent for this design agency and onboarding the agent into Slack for me. All I do is ask the questions, make sure it gets recorded in Granola (my ai notetaker), and then Dewey takes it over from there. Crazy world we live in.

My Hermes agent builds AI employees for me. Just closed another $5K‑a‑month customer today for an AI employee for a design agency. I'm not even building any of it. I have Dewey, my Hermes agent, building out the entire Hermes agent for this design agency and onboarding the agent into Slack for me. All I do is ask the questions, make sure it gets recorded in Granola (my ai notetaker), and then Dewey takes it over from there. Crazy world we live in.

Nick Vasilescu

148,117 views • 1 month ago

$🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it$

🚨🇨🇳 China could crash the U.S. stock market Not with tariffs With AI MODEL DUMPING The same playbook they used in SOLAR and EVs Make it almost as good Sell it much cheaper Take the market Now they are doing it with AI Open models are getting close to U.S. frontier models But some run at just a fraction of the cost If a company can cut a $100M AI bill to a few million It will switch And that is the real problem OpenAI and Anthropic are not priced like normal companies They are priced like they have unlimited PRICING POWER China is trying to destroy that The AI BUBBLE was never about whether AI works It was about how much companies can charge for it

Leshka.eth ⛩

11,737 views • 1 month ago

.Speak is an AI language learning app that serves over 15 million users. cto/co-founder Andrew Hsu discusses how speak approaches agent engineering, the importance of self-healing loops, and how they use Raindrop

.Speak is an AI language learning app that serves over 15 million users. cto/co-founder Andrew Hsu discusses how speak approaches agent engineering, the importance of self-healing loops, and how they use Raindrop

ben hylak

28,478 views • 3 months ago

Former Georgia Republican Lt. Gov. Geoff Duncan: “I went to my first democratic rally this week on Tuesday. The thing I kept hearing over and over and over again…it felt a lot like the Obama… energy in that room. There was something different than just an election. It was a movement…”

Former Georgia Republican Lt. Gov. Geoff Duncan: “I went to my first democratic rally this week on Tuesday. The thing I kept hearing over and over and over again…it felt a lot like the Obama… energy in that room. There was something different than just an election. It was a movement…”

Republicans against Trump

2,033,886 views • 2 years ago

Collins: Can you square that with Tom Cotton saying that they were trying to flip a boat that was loaded with drugs over? Smith: That's patently ridiculous. Admiral Bradley didn't even pretend that that was the case. There there was no way they were turning this boat back over. It appears that the boat was split at least in half

Collins: Can you square that with Tom Cotton saying that they were trying to flip a boat that was loaded with drugs over? Smith: That's patently ridiculous. Admiral Bradley didn't even pretend that that was the case. There there was no way they were turning this boat back over. It appears that the boat was split at least in half

Acyn

90,885 views • 8 months ago

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

122,548 views • 2 months ago