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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

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21,395 Aufrufe • vor 20 Tagen •via X (Twitter)

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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.

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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.”

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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 LangChain 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 LangChain and Payman to make it happen! If you're building AI Agents, happy to get you early access! Just send over a DM!

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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. 👇

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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👇

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darkzodchi

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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!

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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 👇

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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)

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HotNewHipHop

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🚨 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

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Leshka.eth ⛩

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.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

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ben hylak

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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

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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.

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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

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90,885 Aufrufe • vor 7 Monaten

(Insert The Worst Person You Know Just Made a Great Point meme) Nancy Mace on Epstein: "You can't tell me that only one person was an accomplice in a sex trafficking ring with over a thousand girls. Didn't happen. There was way more than that. And those people that were involved, those predators, those child rapists, they need to be held accountable to the fullest extent of the law."

(Insert The Worst Person You Know Just Made a Great Point meme) Nancy Mace on Epstein: "You can't tell me that only one person was an accomplice in a sex trafficking ring with over a thousand girls. Didn't happen. There was way more than that. And those people that were involved, those predators, those child rapists, they need to be held accountable to the fullest extent of the law."

Blue Georgia

540,973 Aufrufe • vor 9 Monaten

I put together this analysis. This constitutes homicide. U.S. Immigration and Customs Enforcement Two facts are dispositive. First, the driver placed the vehicle in reverse to increase distance between herself and the agent positioned in front of the car. That action is inconsistent with an intent to strike or run over the agent. If the objective were to cause harm, reversing would have been unnecessary and counterproductive. Second, the driver turned the steering wheel to the right, away from the agent. At that point, the agent moved to the left side of the vehicle and was no longer in the vehicle’s path. The threat had dissipated. The agent then leaned over the hood, aimed, and fired. The use of deadly force was not reactive to an imminent threat. It was a discretionary act taken after the threat had ceased. This was not a justified shooting. It was an unlawful killing.

I put together this analysis. This constitutes homicide. U.S. Immigration and Customs Enforcement Two facts are dispositive. First, the driver placed the vehicle in reverse to increase distance between herself and the agent positioned in front of the car. That action is inconsistent with an intent to strike or run over the agent. If the objective were to cause harm, reversing would have been unnecessary and counterproductive. Second, the driver turned the steering wheel to the right, away from the agent. At that point, the agent moved to the left side of the vehicle and was no longer in the vehicle’s path. The threat had dissipated. The agent then leaned over the hood, aimed, and fired. The use of deadly force was not reactive to an imminent threat. It was a discretionary act taken after the threat had ceased. This was not a justified shooting. It was an unlawful killing.

Agent Self FBI

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There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

There is a massive advantage to hiring young people, and Shopify was the first company to figure it out, says Pragmatic Engineer's Gergely Orosz. "Shopify's Head of Engineering Farhan Thawar told me that he saw something interesting years back. Shopify was so early to AI. They got Copilot licenses for everyone, with no cost limit. But they didn't see many teams using it." "But there was this one team that was using it a lot more. There was an intern on the team. They onboarded the intern and gave the intern his two-week task and the intern was done in a day. And they're like, 'How did you do that?' He's like, 'Oh well I just used AI? What's next?'" "And suddenly the people on the team felt stupid, right? So they started to learn from the intern and use AI." "The next thing Farhan did was hire an intern for every single Shopify team. This is why Shopify had a thousand interns."

TBPN

135,300 Aufrufe • vor 5 Monaten