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We are announcing potpie AI $2.2M pre-seed fundraise to advance Spec-Driven Development for large enterprise codebases. The round is led by Emergent Ventures , with participation from All In Capital , DeVC , and PointOne Capital , along with the support of some amazing angel investors from companies including...

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Aditi Kothari

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New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

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Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

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In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.
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In 2025, it was rational to be skeptical about whether AI would change the future of software development. In 2026, it's not, anymore. With Charity Majors: Timestamps: 00:00 Intro 02:56 How Parse led to Honeycomb 06:00 The limits of individual productivity metrics 09:08 How Charity’s perspective on AI has evolved 13:50 Rewriting code vs. editing code 19:20 Production as a stage of development 22:14 Code reviews 26:56 Non-deterministic systems 31:11 Sensible uses of AI 37:41 The two AI camps 44:40 Why AI works so well for building software 49:42 DevOps 55:13 Modern observability 1:00:40 Handling context overload 1:01:56 What’s new in Observability Engineering’s 2nd edition 1:07:45 What effective leadership looks like 1:10:25 Engineering management: what is changing? 1:16:31 Junior engineers 1:18:01 AI fatigue 1:21:39 Book recommendations Brought to you by: • Antithesis — turbocharge testing of your systems by running your whole system under aggressive fault injection. Teams like Jane Street, and the etcd community rely on Antithesis. • Buildkite — the CI platform trusted by OpenAI, Anthropic, Cursor, Meta, Uber, NVIDIA, Airbnb and many more. Engineered to absorb whatever your coding agents throw at the build queue. • WorkOS — make your app and agents Enterprise Ready, with SSO, SCIM, RBAC, and more. 1. The question engineers need to answer: what would it take for you to be fully comfortable shipping code you have not read? Charity believes it is a “when” and not an “if” that professional software engineers will ship code they never looked at – and thus do not understand – to production. Engineering is building the systems that validate this code, and allow shipping with full confidence. 2. AI could have the software industry go through the “pets” to “cattle” change that compute infra went through in the 2010s. Up to now, writing software from scratch was far more expensive than editing existing software. But now, generating hundreds of variants of a function can be done faster than how long it would take you to hand-write it once. Charity believes that we might be at the beginning of the transition from “pets” to “cattle” that happened at the hardware infrastructure layer. Before the 2010s, configuring and repairing individual servers was commonly done. But with tools like Terraform and Kubernetes, individual servers having issues are no longer fixed up: they are re-created instead. Charity thinks the same might happen with code, sooner rather than later. When there’s an issue with the code, generate new code that solves it, and is verifyably correct.​ 3. Non-deterministic systems require more engineering discipline versus before. With code written by AI, we’re reducing the trust in the code (because we no longer wrote it), so we need to increase trust at the other part of the development process. Specifically, at validation: with things like tests, evals, and conformance testing.

Gergely Orosz

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Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic
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Strategy is proud to announce a strategic partnership with marking an important milestone in their AI journey. Strategy makes AI reliable on enterprise data, and is committed to helping organizations transform data into actionable insights and make faster, more informed decisions. Through this collaboration, we are enabling to further leverage data as a strategic asset across their organization, powered by Strategy Mosaic, our Universal Semantic Layer that gives AI the business context it needs to produce consistent, governed results. This partnership aligns with long-term vision of embedding AI and data-driven decision-making into every aspect of their business. By enhancing their analytical capabilities with Strategy, we are helping to build a stronger foundation for innovation, operational excellence, and customer value. As continues its AI-first journey, Strategy is proud to play a role in supporting their growth and shaping the future of their organization. #AI #SemanticLayer #InsuranceTech #StrategyMosaic

Strategy

55,060 Aufrufe • vor 1 Monat

Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich
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Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

Helen Yu

10,982 Aufrufe • vor 2 Monaten

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."
1:27

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."

MTS

13,046 Aufrufe • vor 4 Monaten

Mark Zuckerberg says the future of coding is closer than we think: AI agents inside Meta are being built not as generic developer tools, but to push Llama research forward — fully integrated into their workflow. He predicts that within 12–18 months, most of the code for these efforts could be written by AI itself — not simple autocomplete, but systems that take goals, run tests, fix bugs, improve performance, and even write higher-quality code than many skilled engineers. The real takeaway? This is only one part of a massive AI landscape, and no single company will dominate it all. Meta is building its own internal agents designed specifically to advance Llama research — deeply connected to their toolchain rather than built for enterprise customers. He expects that within the next year to year-and-a-half, AI will be writing most of the code in these projects, going far beyond autocomplete — understanding goals, running experiments, finding issues, and continuously improving systems. And according to him, this shift will become a core driver of how AI itself evolves. Meta is investing heavily in coding and research agents deeply integrated into their own systems, focused on advancing Llama rather than building broad enterprise tools. He expects AI will soon move beyond autocomplete to fully goal-driven coding — testing, debugging, improving, and producing high-quality results. But in his view, the AI revolution is too big for any single winner — multiple players and approaches will shape the future.
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Mark Zuckerberg says the future of coding is closer than we think: AI agents inside Meta are being built not as generic developer tools, but to push Llama research forward — fully integrated into their workflow. He predicts that within 12–18 months, most of the code for these efforts could be written by AI itself — not simple autocomplete, but systems that take goals, run tests, fix bugs, improve performance, and even write higher-quality code than many skilled engineers. The real takeaway? This is only one part of a massive AI landscape, and no single company will dominate it all. Meta is building its own internal agents designed specifically to advance Llama research — deeply connected to their toolchain rather than built for enterprise customers. He expects that within the next year to year-and-a-half, AI will be writing most of the code in these projects, going far beyond autocomplete — understanding goals, running experiments, finding issues, and continuously improving systems. And according to him, this shift will become a core driver of how AI itself evolves. Meta is investing heavily in coding and research agents deeply integrated into their own systems, focused on advancing Llama rather than building broad enterprise tools. He expects AI will soon move beyond autocomplete to fully goal-driven coding — testing, debugging, improving, and producing high-quality results. But in his view, the AI revolution is too big for any single winner — multiple players and approaches will shape the future.

Ian Miles Cheong

20,561 Aufrufe • vor 6 Monaten

The U.S. Department of Defense is moving toward large-volume procurement of one-way attack drones, with requirements reaching up to 300,000 units over the coming years. This is a different level of scale for institutional procurement. At Ondas Capital, this is the operating model we are preparing for, working with companies already aligned with scalable production, supply chain resilience, and deployment readiness. Real capability comes from systems that can scale together, not from isolated solutions. If you are currently building for this level of production, scale, and deployment readiness, Ondas Capital is open to discussion: #OndasCapital Department of War CTO #JamesAcuna #DroneDominance #DefenseTechnology #UnmannedSystems
0:37

The U.S. Department of Defense is moving toward large-volume procurement of one-way attack drones, with requirements reaching up to 300,000 units over the coming years. This is a different level of scale for institutional procurement. At Ondas Capital, this is the operating model we are preparing for, working with companies already aligned with scalable production, supply chain resilience, and deployment readiness. Real capability comes from systems that can scale together, not from isolated solutions. If you are currently building for this level of production, scale, and deployment readiness, Ondas Capital is open to discussion: #OndasCapital Department of War CTO #JamesAcuna #DroneDominance #DefenseTechnology #UnmannedSystems

OndasCapital

13,592 Aufrufe • vor 3 Monaten

We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!
2:12

We’re thrilled to share that Outset has raised $17M in Series A funding, led by 8VC to help companies better understand their customers. When michael hess and I founded Outset, we set out to solve a critical problem: truly understanding your customer is painfully slow, expensive, and at times, impossible. It shouldn’t be. We believed AI could change this by focusing on the human side of AI. So we built the world’s first AI-interviewer. An AI agent that asked questions, listened, and learned from real humans. By removing the barriers of traditional research and allowing companies to have deeper conversations with more customers more quickly, we could help teams infuse their products with genuine human understanding at a speed and scale previously impossible. So what was the moment we knew we were onto something? When WeightWatchers ran that first pilot back in early 2023. Participants shared incredible amounts of depth, all with the world’s first AI-interviewer. We got a call the next day from their head of research: "You built a thing. We want that thing. How do I pay you for it?" That validation has turned into explosive growth. We've doubled our revenue in just the last 4 months alone, with >50 enterprise customers like Nestlé, Microsoft, and WeightWatchers trusting us to unlock deeper customer insights. All of this built on the backs of a truly special early team with 0 employee turnover. This funding round was led by 8VC, with participation from an incredible group, including: Future Back Ventures by Bain & Company, Y Combinator / Garry Tan, Adverb Ventures /April Underwood / Jessica Verrilli ), Alt Capital / Jack Altman , Rebel Fund, Genius Ventures, Ritual Capital, and many more. So what's next? We're scaling our engineering and go-to-market teams to build the world's most powerful agentic customer research platform. All focused on one mission: helping companies better understand humans. To our customers, investors, and team - thank you for believing in this vision. Now check out the video to see Outset in action, testing the video itself with real people!

Aaron Cannon

34,386 Aufrufe • vor 1 Jahr

Before software engineers even begin writing code, they have to set the stage of the entire development process. This process requires engineers to make complex tradeoffs between requirements, system design, and implementations details. Current IDEs that rely on AI features, like chat and inline coding, can help engineers get the job done quickly on small development tasks. Still, engineers spend much more time on larger projects—even after the initial code is generated—by conducting rigorous testing and creating documentation. This is where today’s AI IDEs can do more to accelerate the development lifecycle—and this is why we built Kiro. Kiro is an AI IDE that helps you go from prototype to production with spec-driven development and agent hooks. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and test so what you build is exactly what you want and ready to share with your team. After a developer builds the code with Kiro, Kiro’s agent hooks help engineers solve challenging problems and automate tasks like generating documentation and unit tests. Kiro brings structure and mature engineering practices to AI coding, so you can go from concept to application while being in the driver’s seat every step of the way. Kiro is free during preview, and supports Mac, Windows, and Linux, and most popular programming languages. We're excited for you to try it out and let us know what you think ➡️
1:12

Before software engineers even begin writing code, they have to set the stage of the entire development process. This process requires engineers to make complex tradeoffs between requirements, system design, and implementations details. Current IDEs that rely on AI features, like chat and inline coding, can help engineers get the job done quickly on small development tasks. Still, engineers spend much more time on larger projects—even after the initial code is generated—by conducting rigorous testing and creating documentation. This is where today’s AI IDEs can do more to accelerate the development lifecycle—and this is why we built Kiro. Kiro is an AI IDE that helps you go from prototype to production with spec-driven development and agent hooks. From simple to complex tasks, Kiro works alongside you to turn prompts into detailed specs, then into working code, docs, and test so what you build is exactly what you want and ready to share with your team. After a developer builds the code with Kiro, Kiro’s agent hooks help engineers solve challenging problems and automate tasks like generating documentation and unit tests. Kiro brings structure and mature engineering practices to AI coding, so you can go from concept to application while being in the driver’s seat every step of the way. Kiro is free during preview, and supports Mac, Windows, and Linux, and most popular programming languages. We're excited for you to try it out and let us know what you think ➡️

Swami Sivasubramanian

154,343 Aufrufe • vor 1 Jahr

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.
1:52

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,950 Aufrufe • vor 1 Jahr

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.
3:40

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

Mike Fishbein

10,141 Aufrufe • vor 2 Monaten

Today, Michael Elabd, Arjun Karanam, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. Trajectory We’ve raised $15M from Conviction, Bessemer, Radical Ventures, Jeff Dean, Fei-Fei Li and more. We’re partnering with some of the best AI-native companies: @ClayRunHQ Harvey, Decagon, , Rogo to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.
1:28

Today, Michael Elabd, Arjun Karanam, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. Trajectory We’ve raised $15M from Conviction, Bessemer, Radical Ventures, Jeff Dean, Fei-Fei Li and more. We’re partnering with some of the best AI-native companies: @ClayRunHQ Harvey, Decagon, , Rogo to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

Ronak Malde

1,836,790 Aufrufe • vor 3 Monaten

Anthropic CEO Dario Amodei just revealed the hidden bottleneck that will kill most AI companies in the next 18 months (Save this). The insight comes from a principle in computer science called Amdahl's Law. Dario's argument is simple when something starts working really well inside an organization, you have to immediately ask what isn't working well around it. Amdahl's Law states that the maximum speedup of any system is capped by the fraction you haven't improved and that applies to companies just as brutally as it applies to processors. If you can suddenly write three or four times as many pull requests as before, you don't get three or four times the output but you rather get a pile of code no one can review, verify, or trust. The data makes this impossible to ignore. Teams with heavy AI coding adoption are merging 98% more pull requests but PR review time has ballooned 91%, deployment velocity is effectively flat and 96% of developers don't fully trust AI-generated code reaching production. AI generated code produces 1.7x more issues per pull request than human written code, 0.83 issues per PR versus 6.45. Veracode's 2026 State of Software Security report found that 82% of organizations now carry security debt, up 11% year over year, with critical security debt surging 36% in a single year driven directly by AI-generated code reaching production faster than security teams can handle. What Dario is describing is a systems problem, not a software problem and coding is roughly 20% of the software delivery cycle. Even at infinite coding speed, you're still bottlenecked by review, security, verification, testing, and deployment which make up the other 80%. The enterprises that win are the ones that identify which part of their system is the new constraint after AI accelerates the old one and fix that next. This is why Anthropic's Claude Code focuses on the full development loop, not just generation, and why the verification and security layer of the AI stack is where the next wave of enterprise value gets created. This is also why Anthropic as a company is positioned differently than most people realize. Anthropic's 2026 Agentic Coding Trends Report found that organizations using full-loop agentic coding workflows where AI handles not just generation but testing, review, and deployment validation reduced their software defect rates by 43% while increasing velocity by 2.8x. Claude Code now authors 4% of all GitHub commits and is on track to hit 20%+ by year-end, with the full-loop use case growing 3x faster than pure code generation. Dario has been building Anthropic around the exact insight he's describing publicly ,the constraint isn't writing code but rather everything that has to happen after.
0:48

Anthropic CEO Dario Amodei just revealed the hidden bottleneck that will kill most AI companies in the next 18 months (Save this). The insight comes from a principle in computer science called Amdahl's Law. Dario's argument is simple when something starts working really well inside an organization, you have to immediately ask what isn't working well around it. Amdahl's Law states that the maximum speedup of any system is capped by the fraction you haven't improved and that applies to companies just as brutally as it applies to processors. If you can suddenly write three or four times as many pull requests as before, you don't get three or four times the output but you rather get a pile of code no one can review, verify, or trust. The data makes this impossible to ignore. Teams with heavy AI coding adoption are merging 98% more pull requests but PR review time has ballooned 91%, deployment velocity is effectively flat and 96% of developers don't fully trust AI-generated code reaching production. AI generated code produces 1.7x more issues per pull request than human written code, 0.83 issues per PR versus 6.45. Veracode's 2026 State of Software Security report found that 82% of organizations now carry security debt, up 11% year over year, with critical security debt surging 36% in a single year driven directly by AI-generated code reaching production faster than security teams can handle. What Dario is describing is a systems problem, not a software problem and coding is roughly 20% of the software delivery cycle. Even at infinite coding speed, you're still bottlenecked by review, security, verification, testing, and deployment which make up the other 80%. The enterprises that win are the ones that identify which part of their system is the new constraint after AI accelerates the old one and fix that next. This is why Anthropic's Claude Code focuses on the full development loop, not just generation, and why the verification and security layer of the AI stack is where the next wave of enterprise value gets created. This is also why Anthropic as a company is positioned differently than most people realize. Anthropic's 2026 Agentic Coding Trends Report found that organizations using full-loop agentic coding workflows where AI handles not just generation but testing, review, and deployment validation reduced their software defect rates by 43% while increasing velocity by 2.8x. Claude Code now authors 4% of all GitHub commits and is on track to hit 20%+ by year-end, with the full-loop use case growing 3x faster than pure code generation. Dario has been building Anthropic around the exact insight he's describing publicly ,the constraint isn't writing code but rather everything that has to happen after.

Milk Road AI

52,190 Aufrufe • vor 3 Monaten