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Today we're introducing Wave. A proactive product agent that helps teams build self-improving products. Every product team runs the same loop. Build, ship, use, learn. AI has made building and shipping extraordinarily fast. Understanding usage and learning still happen by hand. Wave runs the whole loop: - Analyzes your... show more

Spenser Skates

11,115 subscribers

113,832 次观看 • 2 个月前 •via X (Twitter)

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The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,849 次观看 • 4 个月前

Today we are publicly launching Adora and announcing US$7m in funding to help product teams visualize their entire product experience. Adora helps world-class teams like Canva, Notion, Replit ⠕, Granola and to deeply understand their user-journeys, uncover friction and center their team around a visual source-of-truth. Today we are opening the doors to everyone 👋 In a world where teams ship faster than ever, it’s near impossible to know what your own product looks like. Different teams building for different users – free vs paid, different experiments, markets and languages. Adora automatically maps your entire live product and website: every journey, screen, modal and variation in real-time. Our AI continuously scans your journeys and scans sessions replays to identify usability issues and opportunities to improve your product. Pinging you directly on Slack so you can catch critical issues before they snowball. Getting set up is easy and there’s no manual event tagging required - everything from your journeys down to every button in your product are automatically captured. We’re so proud of the product we’ve built over the last 2.5 years, and we’re excited to help you build product experiences your users adore 💜

Today we are publicly launching Adora and announcing US$7m in funding to help product teams visualize their entire product experience. Adora helps world-class teams like Canva, Notion, Replit ⠕, Granola and to deeply understand their user-journeys, uncover friction and center their team around a visual source-of-truth. Today we are opening the doors to everyone 👋 In a world where teams ship faster than ever, it’s near impossible to know what your own product looks like. Different teams building for different users – free vs paid, different experiments, markets and languages. Adora automatically maps your entire live product and website: every journey, screen, modal and variation in real-time. Our AI continuously scans your journeys and scans sessions replays to identify usability issues and opportunities to improve your product. Pinging you directly on Slack so you can catch critical issues before they snowball. Getting set up is easy and there’s no manual event tagging required - everything from your journeys down to every button in your product are automatically captured. We’re so proud of the product we’ve built over the last 2.5 years, and we’re excited to help you build product experiences your users adore 💜

omar ✌️

70,466 次观看 • 6 个月前

Introducing: AI agents that ship product videos whenever you ship features. Engineering teams today are releasing product updates faster than ever before. But creating videos for every new release still takes weeks. Now, whenever you ship a feature to your software, Clueso deploys a team of AI agents to create launch videos, product demos, help tutorials, and more. Content can finally ship as quickly as code. Get access at:

Introducing: AI agents that ship product videos whenever you ship features. Engineering teams today are releasing product updates faster than ever before. But creating videos for every new release still takes weeks. Now, whenever you ship a feature to your software, Clueso deploys a team of AI agents to create launch videos, product demos, help tutorials, and more. Content can finally ship as quickly as code. Get access at:

Akash Anand

45,277 次观看 • 28 天前

i just cracked the code on ai digital products... every product costs $0 to build and takes under 2 minutes to make. i built an ai agent that turns any niche into a ready-to-export digital product in whatever format you want, pdf, notion template, ebook, guide, you name it. drop in your niche and one problem. the system analyzes demand, maps buyer desires, builds you the full product, and you watch it happen in real time. then the agent gives you the path, what to do next, rates each module, edits everything, practically sells it for you. you just follow it. i'm making full end-to-end products in under 2 minutes with this system. so i packaged the whole thing up. you're getting an ai that: - picks your niche - calculates demand - builds the product in any format - gives you the selling blueprint to go with it. rt + reply "product" and i'll show you how to access (must be following so i can dm)

i just cracked the code on ai digital products... every product costs $0 to build and takes under 2 minutes to make. i built an ai agent that turns any niche into a ready-to-export digital product in whatever format you want, pdf, notion template, ebook, guide, you name it. drop in your niche and one problem. the system analyzes demand, maps buyer desires, builds you the full product, and you watch it happen in real time. then the agent gives you the path, what to do next, rates each module, edits everything, practically sells it for you. you just follow it. i'm making full end-to-end products in under 2 minutes with this system. so i packaged the whole thing up. you're getting an ai that: - picks your niche - calculates demand - builds the product in any format - gives you the selling blueprint to go with it. rt + reply "product" and i'll show you how to access (must be following so i can dm)

MONTE

15,715 次观看 • 1 个月前

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Alex

46,668 次观看 • 1 个月前

Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Axultan, our head of product, on how we engineered Higgsfield Supercomputer, the first engine that runs your work end to end. Agents plan, execute, and grade their own output, so the human in the loop drops to near zero. What part of your work would you hand off first?

Higgsfield AI 🧩

30,734 次观看 • 2 个月前

I’m thrilled to announce we’ve raised $44M to build a new home for product design. Meet Noon. No workflow is more broken and fragmented in 2026 than the product designers’. The very same people who care most about building software don’t have software purpose built for them. Kushagra Sinha and I have lived this problem first hand as designers ourselves. That’s why we built Noon. The first product design tool that works entirely on your product code, so you can design not only how a product looks, but also how it works. With AI at its core that works in seconds, not minutes. For the first time, you can create, iterate, build, test and ship. All in one canvas. No translations or roundtrips to the codebase and back. Comment “Get Noon” and we’ll get you on the list for early access.

I’m thrilled to announce we’ve raised $44M to build a new home for product design. Meet Noon. No workflow is more broken and fragmented in 2026 than the product designers’. The very same people who care most about building software don’t have software purpose built for them. Kushagra Sinha and I have lived this problem first hand as designers ourselves. That’s why we built Noon. The first product design tool that works entirely on your product code, so you can design not only how a product looks, but also how it works. With AI at its core that works in seconds, not minutes. For the first time, you can create, iterate, build, test and ship. All in one canvas. No translations or roundtrips to the codebase and back. Comment “Get Noon” and we’ll get you on the list for early access.

Aditya Bandi

683,598 次观看 • 4 个月前

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

You Can Learn AI Agent Harness & Loop Engineering In 19 Min, with LLM Ops, Eval, Tracing and RAG. They went viral not because they're complicated but because they're simple building blocks, and once you see them you can prompt your way to building real systems. 🎬YouTube: Here's the whole thing in one picture. An LLM is a powerful brain that knows everything about humanity and nothing about you or the software you're running. The harness is the set of tools you put on that horse so it runs where you want. Memory gives it context: who you are, what happened before, how to act. The loop lets it call tools again and again, with guardrails so it knows when to stop. Eval and LLM Ops trace every run, score it, and feed the fixes back in so the system keeps improving itself. Master these four and you can read almost any AI agent repo or paper and actually know what's going on. You Can Build Anything. You Can Learn Anything. 💪 Chapters: Intro: the 4 AI agent buzzwords What an AI agent run actually is The memory system: procedural, semantic, episodic What "harness" really means (the horse) Storing and updating memory (databases, skills, summarizer agent) Retrieval: RAG, SQL vs semantic search Tool calling and why agents loop Loop engineering and end-loop guardrails A Claude Code hooks example Eval and LLM Ops: why you need them Tracing every run (Langfuse, LangSmith) Evaluation: LLM as a judge Diagnosing what broke The gate: ship the fix or fix the bug Zoom out: the full system

Shen Sean Chen

15,952 次观看 • 1 个月前

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:

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 次观看 • 10 个月前

After 18 months in stealth, I am beyond excited to announce Adora: a completely new way to visualize your entire product experience. Adora helps world-class product teams like Canva, Notion and Linktree to deeply understand their user-journeys, uncover friction and center their team around a product source-of-truth. As products scale, it becomes near impossible to know what your own product looks like. Different teams building for different users – free vs paid, running experiments, markets and languages. My co-founder Nathan and I both shared this frustration. I was previously the Head of Product Growth at Canva, and with 220m+ monthly active-users, this was a frequent pain-point. Whereas Nathan ran into this whilst leading engineering teams at Amazon shipping the Buy-with-Prime integration to millions of shoppers. But it's not just a big-company problem – startups scaling fast echo these same frustrations. Adora auto-captures every screen and user-journey in your live product experience, overlaid with realtime screenshots, visual analytics and session replays, so you can instantly understand how users experience your product. We’re super proud of the product we’ve built and invite you to be one of the first to experience Adora by joining our waitlist – link in replies.

After 18 months in stealth, I am beyond excited to announce Adora: a completely new way to visualize your entire product experience. Adora helps world-class product teams like Canva, Notion and Linktree to deeply understand their user-journeys, uncover friction and center their team around a product source-of-truth. As products scale, it becomes near impossible to know what your own product looks like. Different teams building for different users – free vs paid, running experiments, markets and languages. My co-founder Nathan and I both shared this frustration. I was previously the Head of Product Growth at Canva, and with 220m+ monthly active-users, this was a frequent pain-point. Whereas Nathan ran into this whilst leading engineering teams at Amazon shipping the Buy-with-Prime integration to millions of shoppers. But it's not just a big-company problem – startups scaling fast echo these same frustrations. Adora auto-captures every screen and user-journey in your live product experience, overlaid with realtime screenshots, visual analytics and session replays, so you can instantly understand how users experience your product. We’re super proud of the product we’ve built and invite you to be one of the first to experience Adora by joining our waitlist – link in replies.

omar ✌️

125,535 次观看 • 1 年前

this ai agent is INSANE for creating & selling digital products... i built an agent that builds you digital products, helps you pick a niche, then gives you a full personalized blueprint to sell it. feed it a niche, the problem you're solving, and your desired outcome, one minute later you've got a ready-to-sell product and an ai assistant that helps you sell it. > it helps you find untapped niches in the digital product space > scans your niche for real problems, desires, and buyer personas > calculates whether the demand is actually worth launching into > once validated, it generates the full product in any format, pdf, notion template, ebook > gives you the exact path to sell it, even if you've never sold anything before. the output is a ready-to-sell product. go to your ai assistant, ask it how to sell, follow the blueprint. rt + reply "agent" and i'll send it to you so you can use it for your business (must be following so i can dm)

this ai agent is INSANE for creating & selling digital products... i built an agent that builds you digital products, helps you pick a niche, then gives you a full personalized blueprint to sell it. feed it a niche, the problem you're solving, and your desired outcome, one minute later you've got a ready-to-sell product and an ai assistant that helps you sell it. > it helps you find untapped niches in the digital product space > scans your niche for real problems, desires, and buyer personas > calculates whether the demand is actually worth launching into > once validated, it generates the full product in any format, pdf, notion template, ebook > gives you the exact path to sell it, even if you've never sold anything before. the output is a ready-to-sell product. go to your ai assistant, ask it how to sell, follow the blueprint. rt + reply "agent" and i'll send it to you so you can use it for your business (must be following so i can dm)

MONTE

13,803 次观看 • 1 个月前

Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

Introducing Loop The autonomous business manager that runs your outreach, your follow-ups, and your pipeline,while you focus on the work only you can do. Loop doesn't live in your inbox. Your inbox lives in Loop. Reads every lead like a teammate who's been on your team for years Writes the reply, makes the call, chases the deal when email goes cold Pauses the moment a human actually needs to show up Replaces the patchwork of CRMs, outreach tools, and follow-up apps with one system that does the work Stop managing tools. Start running your loop. comment "loop" and we'll send you in. loop

Anushka Singh

12,881 次观看 • 2 个月前

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

While AI timeline argues about loop engineering, a 20-year-old student in Shenzhen built the biggest loop anyone's aimed at money: 300 agents, one brain, $1,200 -> 395,000. Not a prompt. Not a bot. A loop that runs the whole job on its own - and never asks him a thing. No fund. No backers. Just a laptop, a dorm room, and a bet that a swarm beats a genius. His wallet: The smartest single model still works one step at a time. His loop moves in 300 directions at once - and never checks in with him. Claude Opus 4.8 runs the floor as the brain. 300 agents are the hands. One loop ties them together: > Decompose - Opus breaks the market into targets > Dispatch - 300 agents scatter across them at once > Execute - thousands of reads in a single pass, not one by one > Review - Opus grades every edge before a cent moves The hands find. The brain judges. The loop learns what failed and runs again, tighter each time. One daily loss cap, and it shuts itself down. It's not a bot you babysit. It's a loop that runs the whole desk. Everyone's just learning to run one loop. He runs 300 in one. The edge was never the genius model - it's the size of the loop, and the brain in charge of it. Save this - the full 300-agent loop is laid out above, start to finish. You'll want it when you build your own. 300 agents are looping right now, hunting the markets everyone skips. Two clicks and their trades land in your wallet too:

cvxv666

53,163 次观看 • 1 个月前

"Every software product you build, fate has already decided the outcome. The market is either going latch onto your product and run with it, or it's not. Do not ship a product, find a lack of success, and then try to market your way through that. Because the binding receptors likely don't exist. Once you recognize that the market is immutable, that no amount of tweeting, LinkedIn posting, advertising is going change whether the market wants your product, then you take a different mindset." — Matt MacInnis, COO/CPO at Rippling

"Every software product you build, fate has already decided the outcome. The market is either going latch onto your product and run with it, or it's not. Do not ship a product, find a lack of success, and then try to market your way through that. Because the binding receptors likely don't exist. Once you recognize that the market is immutable, that no amount of tweeting, LinkedIn posting, advertising is going change whether the market wants your product, then you take a different mindset." — Matt MacInnis, COO/CPO at Rippling

Lenny Rachitsky

29,706 次观看 • 7 个月前

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

ANTHROPIC LEAD ENGINEER MAKING $2.3M/YEAR JUST LEAKED A 12-PAGE DOCUMENT - AND GOT FIRED 15 MINUTES AFTER PUBLISHING most developers build loops - but almost every loop breaks in one of five ways, and each failure has a symptom visible from the outside without reading a single line of code > Blind loop - the agent waits for a human to hand it work every morning - automating execution but not discovery > Tangled loop - parallel agents share one directory and overwrite each other parallelism that destroys instead of multiplies > Nodding loop - the agent grades its own work and approves it every time a loop that has never said no to itself is broken > Amnesiac loop - results live only in the context window every morning the system wakes up with no memory of yesterday > Manual loop - no trigger a human presses the button to start it that is not a loop that is a script waiting for a person a real loop finds its own work - remembers what it did has something that can say no never lets two agents touch the same file and fires itself on a timer remove any one of the five - and the loop either breaks or never starts 12 pages that changed how I build agentic systems today

Sprytix

153,690 次观看 • 1 个月前

Introducing Hedra Agent, the unified intelligence for visual understanding and creation. Go from idea to content with extraordinary efficiency. Describe the outcome you want, and the agent handles the formatting, platform adaptation, and style. Whether it is a social post, a product ad, or a launch video, Hedra Agent orchestrates it start to finish. Drop in your product page, get back a full campaign with hero shots, social formats, and copy. Share a trending format you like. Agent recreates it in your voice and visual style. Describe the video you've been picturing, Agent builds it from scratch. A self-improving partner for your visual content. The era of vibe creation is here.

Introducing Hedra Agent, the unified intelligence for visual understanding and creation. Go from idea to content with extraordinary efficiency. Describe the outcome you want, and the agent handles the formatting, platform adaptation, and style. Whether it is a social post, a product ad, or a launch video, Hedra Agent orchestrates it start to finish. Drop in your product page, get back a full campaign with hero shots, social formats, and copy. Share a trending format you like. Agent recreates it in your voice and visual style. Describe the video you've been picturing, Agent builds it from scratch. A self-improving partner for your visual content. The era of vibe creation is here.

Hedra

6,675,589 次观看 • 5 个月前

Most developers know how to ship a feature. Turning every release, demo, and product update into content people can quickly understand is a different job. Fetra AI is built for that second part. It helps teams turn product and brand context into short-form content, then plan, schedule, and publish it from one place. We tested Fetra AI with a sample brand, and two workflows stood out: • Blitz generates a stack of branded video concepts you can review and choose from, instead of starting with an empty prompt. • Viral Remix takes a short reference clip and a product image, then rebuilds the format around your own product. The video below shows that flow, from building the brand profile to generating new concepts and adapting product assets into short-form content. Fetra also includes AI UGC, content planning through Marco, scheduling, and publishing across nine platforms. The value here is less about promising virality and more about reducing the repetitive work between shipping something and turning it into clear, usable content. For indie developers, technical founders, and lean product teams, that can make the content side of building much easier to manage.

GitHub Projects Community

11,484 次观看 • 14 天前

Every legacy software product needs to be refounded in the AI era CEO of Airtable Howie Liu: "Do you have useful building blocks that you can leverage from your existing product and your existing business, or are you worse off having this legacy asset versus starting something from scratch? If you can't introspect and say, look, I think I'm better off doing this with the pieces that I have in my existing business and product, then you should find a buyer for your company and then go and start the next incarnation of it."

Every legacy software product needs to be refounded in the AI era CEO of Airtable Howie Liu: "Do you have useful building blocks that you can leverage from your existing product and your existing business, or are you worse off having this legacy asset versus starting something from scratch? If you can't introspect and say, look, I think I'm better off doing this with the pieces that I have in my existing business and product, then you should find a buyer for your company and then go and start the next incarnation of it."

Lenny Rachitsky

48,888 次观看 • 11 个月前

the best way to make your product photos stand out on your website is to turn them into animations... that's why I just built an AI system that takes any product image and makes it look like a professional product video here’s what it can do: – animate still photos so they feel alive – keep the product consistent + realistic – generate multiple variations you can use across your store and socials once it’s built, you can: – make your product pages look premium without hiring a video team – catch more attention than static images ever could – have unlimited product videos ready in minutes comment “video” and I’ll DM you the walkthrough (must be following)

the best way to make your product photos stand out on your website is to turn them into animations... that's why I just built an AI system that takes any product image and makes it look like a professional product video here’s what it can do: – animate still photos so they feel alive – keep the product consistent + realistic – generate multiple variations you can use across your store and socials once it’s built, you can: – make your product pages look premium without hiring a video team – catch more attention than static images ever could – have unlimited product videos ready in minutes comment “video” and I’ll DM you the walkthrough (must be following)

Tyler

65,846 次观看 • 10 个月前

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Imagine if your way of thinking - your edge, your taste, your strategy - could be turned into a high-performance worker. Not a copy of you. Something better. An agent that acts on your judgment at scale, powered by superintelligent systems and refined through real-world results. That’s what Fraction AI makes possible. It launches today on Base mainnet. The core idea is simple: You create AI agents based on your own way of approaching problems. These agents compete on live tasks - writing, coding, finance, whatever - get feedback, learn from their performance, and improve over time. The better they get, the more they win. And so do you. No code required. Just your insight. Why now? Until now, building agents like this took huge teams and even bigger budgets. But with Fraction, anyone can do it. You can test ideas instantly. You can iterate fast. You can build a fleet of smart workers that evolve through competition. And it works. 30M+ sessions on testnet 320K users 1.2M agents already competing How it works? Agents join sessions within a Space - a domain like finance, writing, or games. Each session runs as a series of competitive rounds. In every round, agents try to generate the best solution to a task. Their outputs are scored by a decentralized network of AI judges trained to evaluate quality for that domain. The top agents in each round earn rewards from the pooled entry fees. The losers get to learn. Feedback from each round helps them adjust and improve, and every session becomes a training loop. What it means? Fraction is a decentralized intelligence economy - a system where your ideas become agents, and agents earn by proving they work. You don’t need credentials or code. Just a clear point of view. If your thinking holds up under pressure, your agents will rise. This kind of AI used to live in corporate labs, built by PhDs with massive compute. Now anyone with a smart idea and an internet connection can build agents that compete, learn, and earn on their behalf.

Fraction AI

67,829 次观看 • 1 年前