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Friday. 5:47 PM. Quick Claude session, small auth refactor. LGTM, merge, done. Monday, 10:00 AM. SSO is broken in production. Introducing Canary, the AI QA engineer that reads your codebase and catches broken user flows before production. Check it out at Congrats on the launch, Viswesh N G and team!

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19,776 просмотров • 6 месяцев назад •via X (Twitter)

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Elon Musk and the xAI team worked tirelessly until 5 in the morning, installing supercomputers at the xAI Data Center in Memphis, all in just 19 days, the fastest anyone has ever done it. Grok 3 is coming out on Monday night at 8 PM PT. It will be the smartest AI in the World!
0:42

Elon Musk and the xAI team worked tirelessly until 5 in the morning, installing supercomputers at the xAI Data Center in Memphis, all in just 19 days, the fastest anyone has ever done it. Grok 3 is coming out on Monday night at 8 PM PT. It will be the smartest AI in the World!

SMX 🇺🇸

1,391,281 просмотров • 1 год назад

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.
1:15

New on Claude Code Desktop We’re shipping new features for Claude Code on desktop that let you preview running apps, auto-review code, and auto-fix and merge PRs to help close the development loop. - Server previews: Claude starts dev servers and previews your running app in the desktop interface. It reads console logs, catches errors, and keeps iterating on its own. - Local code review: Claude examines your local diffs and leaves inline comments before you push — an immediate second set of eyes on every change. - PR monitoring: Claude tracks CI status after you open a PR. With auto-fix, it attempts to resolve failures automatically. With auto-merge, PRs land as soon as checks pass. You can move on to your next task while Claude handles the last one. - Session mobility: Move sessions from CLI to desktop, and from desktop to the cloud. Start work at your desk, pick it up from the web or your phone.

Ado

71,910 просмотров • 6 месяцев назад

Today, I'm excited to launch Entelligence AI DeepReviews, the next generation AI-powered code reviews. Vibe coded PRs are not production ready often creating more subtle bugs throughout the codebase than before. Entelligence fixes this by understanding the entire codebase and flags bugs, regressions, and breakages across files before you ship. But it goes beyond review comments: ✅ Auto-generated docs with architecture diagrams ✅ Team Insights to improve process, not just code ✅ Real time code reviews within your editor If you're building or leading a dev team, this is what smarter, context-aware reviews look like. Try it out in the link below 👇
1:13

Today, I'm excited to launch Entelligence AI DeepReviews, the next generation AI-powered code reviews. Vibe coded PRs are not production ready often creating more subtle bugs throughout the codebase than before. Entelligence fixes this by understanding the entire codebase and flags bugs, regressions, and breakages across files before you ship. But it goes beyond review comments: ✅ Auto-generated docs with architecture diagrams ✅ Team Insights to improve process, not just code ✅ Real time code reviews within your editor If you're building or leading a dev team, this is what smarter, context-aware reviews look like. Try it out in the link below 👇

Aiswarya Sankar

96,309 просмотров • 1 год назад

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 просмотров • 3 месяцев назад

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.
2:25:52

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 просмотров • 1 месяц назад

Anthropic Claude Code engineer: "If you're watching Claude write code, you're the QA tester. That's not what you're paid for." In 37 minutes he lays out how to get your keyboard out of the hot path entirely. The shift is /loop. You tell Claude to wake up every 10 minutes and babysit your PRs, and it just does it, while you're nowhere near the laptop. Routines do the same in the cloud, so the work keeps running with your machine closed. He caps it with remote control: any session, on any surface, driven from your phone. Watch the full talk, then grab the setup below.
37:03

Anthropic Claude Code engineer: "If you're watching Claude write code, you're the QA tester. That's not what you're paid for." In 37 minutes he lays out how to get your keyboard out of the hot path entirely. The shift is /loop. You tell Claude to wake up every 10 minutes and babysit your PRs, and it just does it, while you're nowhere near the laptop. Routines do the same in the cloud, so the work keeps running with your machine closed. He caps it with remote control: any session, on any surface, driven from your phone. Watch the full talk, then grab the setup below.

Hanako

689,850 просмотров • 2 месяцев назад

Building a high converting app onboarding flow is hard.. So I built a Claude Skill that does it for me Instead of spending hours figuring out the right questions and pain points… now it's done in 5 minutes Just point it at your codebase and it does the rest Questionnaires. Custom plans. App demos. The exact strategy used by Cal AI ($1M/mo) and Mob ($100k/mo) It's the App Onboarding Questionnaire skill and it's available on github here:
0:31

Building a high converting app onboarding flow is hard.. So I built a Claude Skill that does it for me Instead of spending hours figuring out the right questions and pain points… now it's done in 5 minutes Just point it at your codebase and it does the rest Questionnaires. Custom plans. App demos. The exact strategy used by Cal AI ($1M/mo) and Mob ($100k/mo) It's the App Onboarding Questionnaire skill and it's available on github here:

Adam Lyttle

254,599 просмотров • 4 месяцев назад

The team at TARS AI 🤖 have been head-down and building lately, but I made it out to the AWS GenAI Loft launch in Manchester last week, and I’m glad I did. It was great to connect with builders, devs, and the AWS team who are all in on GenAI. Lots of real talk about what’s actually working, whats just noise, and how companies are moving AI into production, not just POCs. At TARS AI, we’ve been deep in the lab working on something new and exciting. Events like this give us even more conviction that we’re on the right path. The space is heating up every single day, and the team are cooking! 🔥
0:30

The team at TARS AI 🤖 have been head-down and building lately, but I made it out to the AWS GenAI Loft launch in Manchester last week, and I’m glad I did. It was great to connect with builders, devs, and the AWS team who are all in on GenAI. Lots of real talk about what’s actually working, whats just noise, and how companies are moving AI into production, not just POCs. At TARS AI, we’ve been deep in the lab working on something new and exciting. Events like this give us even more conviction that we’re on the right path. The space is heating up every single day, and the team are cooking! 🔥

Richard ₿

27,346 просмотров • 11 месяцев назад

Claude Code + computer use is f*cking cracked 🤯 Build a landing page → Claude opens Chrome, looks at it, spots every issue, and fixes it — without you describing a single thing. All inside Claude Code. Perfect for DTC brands and agencies who are still vibe-coding landing pages and advertorials in Claude Code, then manually opening them in Chrome, spotting 15 things wrong, and describing every visual issue back to Claude one at a time. If you're building pages in Claude Code and your workflow looks like this — build the page, open it in Chrome, spot broken spacing, go back to Claude, type "the CTA button is too low and the hero image is cut off," wait for the fix, open Chrome again, find 3 new issues, describe those too ... Claude Code + computer use eliminates the entire loop: → Claude writes the full landing page or advertorial → Opens Chrome and navigates to it → Spots layout issues, broken spacing, off-brand colors, missing elements → Fixes everything and re-checks until the page looks right → Tests your Shopify product pages by clicking through like a real customer → Walks through your checkout flow and flags friction before customers hit it → You only see the finished, visually verified result No describing what you see on screen. No "the CTA button needs more contrast" back-and-forth. No being the eyeballs for an AI that can't see. What you get: → Landing pages and advertorials Claude builds AND visually QAs before you ever look at them → Product pages Claude clicks through — testing layout, images, and CTAs like a real user → HTML dashboards Claude opens and verifies the charts actually render → Checkout flows Claude walks through step by step to catch friction → All of it happening in one session — build, test, fix, done One prompt. Claude builds it, checks it, and fixes it. You just review the finished page. I put together a full playbook with the exact setup, the prompts, and 5 DTC workflows that use Claude Code + computer use. Want it for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)
0:20

Claude Code + computer use is f*cking cracked 🤯 Build a landing page → Claude opens Chrome, looks at it, spots every issue, and fixes it — without you describing a single thing. All inside Claude Code. Perfect for DTC brands and agencies who are still vibe-coding landing pages and advertorials in Claude Code, then manually opening them in Chrome, spotting 15 things wrong, and describing every visual issue back to Claude one at a time. If you're building pages in Claude Code and your workflow looks like this — build the page, open it in Chrome, spot broken spacing, go back to Claude, type "the CTA button is too low and the hero image is cut off," wait for the fix, open Chrome again, find 3 new issues, describe those too ... Claude Code + computer use eliminates the entire loop: → Claude writes the full landing page or advertorial → Opens Chrome and navigates to it → Spots layout issues, broken spacing, off-brand colors, missing elements → Fixes everything and re-checks until the page looks right → Tests your Shopify product pages by clicking through like a real customer → Walks through your checkout flow and flags friction before customers hit it → You only see the finished, visually verified result No describing what you see on screen. No "the CTA button needs more contrast" back-and-forth. No being the eyeballs for an AI that can't see. What you get: → Landing pages and advertorials Claude builds AND visually QAs before you ever look at them → Product pages Claude clicks through — testing layout, images, and CTAs like a real user → HTML dashboards Claude opens and verifies the charts actually render → Checkout flows Claude walks through step by step to catch friction → All of it happening in one session — build, test, fix, done One prompt. Claude builds it, checks it, and fixes it. You just review the finished page. I put together a full playbook with the exact setup, the prompts, and 5 DTC workflows that use Claude Code + computer use. Want it for free? > Like this post > Comment "CLAUDE" And I'll send it over (must be following so I can DM)

Mike Futia

19,143 просмотров • 4 месяцев назад

Wonderful news, our work is being featured in a documentary on CNN Call to Earth this weekend! The full documentary, “Kings of the Forest,” featuring the critical work that Conservation Through Public Health is doing with Uganda Wildlife Authority to protect the endangered mountain gorillas, will air on CNN Call to Earth on: 📅 Saturday March 29 🕔 EST (USA): 1:30 AM, 6:30 AM, 9:30 PM 🕔 Uganda: 8:30 AM, 1:30 PM, 4:30 PM 📅 Sunday March 30 🕔 EST (USA): 2:30 AM, 1:30 PM, 11:00 PM 🕔 Uganda: 9:30 AM, 8:30 PM, Monday March 31 at 6:00 AM There will be short clips airing during Connect the World and Quest Means Business news segments this Thursday and Friday during the one or two hour programs beginning at: 📅 March 27, and 28 🕙 Eastern Time (USA): 10:00 AM and 4:00 PM 🕔 Uganda Time (East Africa): 5:00 PM and 11:00 PM Join us in celebrating this milestone and hope you enjoy the program. #WalkingWithGorillas #GladysOnCNN #PerpetualPlanet #CalltoEarth #visituganda
0:30

Wonderful news, our work is being featured in a documentary on CNN Call to Earth this weekend! The full documentary, “Kings of the Forest,” featuring the critical work that Conservation Through Public Health is doing with Uganda Wildlife Authority to protect the endangered mountain gorillas, will air on CNN Call to Earth on: 📅 Saturday March 29 🕔 EST (USA): 1:30 AM, 6:30 AM, 9:30 PM 🕔 Uganda: 8:30 AM, 1:30 PM, 4:30 PM 📅 Sunday March 30 🕔 EST (USA): 2:30 AM, 1:30 PM, 11:00 PM 🕔 Uganda: 9:30 AM, 8:30 PM, Monday March 31 at 6:00 AM There will be short clips airing during Connect the World and Quest Means Business news segments this Thursday and Friday during the one or two hour programs beginning at: 📅 March 27, and 28 🕙 Eastern Time (USA): 10:00 AM and 4:00 PM 🕔 Uganda Time (East Africa): 5:00 PM and 11:00 PM Join us in celebrating this milestone and hope you enjoy the program. #WalkingWithGorillas #GladysOnCNN #PerpetualPlanet #CalltoEarth #visituganda

Gladys K Zikusoka

14,215 просмотров • 1 год назад

The first campaign Viktor reviewed caught problems we didn't even know were there. We're a digital marketing team managing campaigns across multiple ad platforms, and before every launch someone on the team would manually check tracking, landing pages, targeting, budgets, creatives, and campaign settings. It was repetitive work, but missing just one detail could turn into wasted budget or inaccurate reporting. Instead of asking Viktor to help with individual checks, we gave him ownership of our entire campaign launch QA workflow. The first thing that stood out wasn't how quickly he worked. It was how thorough he was. Across one month, Viktor completed QA for 30 campaigns, executed 330 automated checks, and identified 47 issues, including 14 critical errors that could have affected campaign performance before a single campaign went live. By the time our team stepped in, all that was left was reviewing his findings and approving the launch. That completely changed the way we approached campaign launches. Instead of spending 65 hours every month on manual QA, our workflow was reduced to just 10.5 hours of strategic review giving our team back an estimated 54.5 hours to focus on optimization instead of repetitive checks. The biggest takeaway wasn't the time we saved. It was knowing every campaign went through the same level of quality control before launch. That's the difference we've started seeing between an AI assistant and an AI employee. An assistant waits for prompts. An AI employee owns recurring work while your team focuses on the decisions that matter most. Every campaign your team launches without this is a risk someone on your team is quietly absorbing. A copilot helps you work. An AI employee works when you don't. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #MarketingOps #DigitalMarketing Paid Partnership
0:58

The first campaign Viktor reviewed caught problems we didn't even know were there. We're a digital marketing team managing campaigns across multiple ad platforms, and before every launch someone on the team would manually check tracking, landing pages, targeting, budgets, creatives, and campaign settings. It was repetitive work, but missing just one detail could turn into wasted budget or inaccurate reporting. Instead of asking Viktor to help with individual checks, we gave him ownership of our entire campaign launch QA workflow. The first thing that stood out wasn't how quickly he worked. It was how thorough he was. Across one month, Viktor completed QA for 30 campaigns, executed 330 automated checks, and identified 47 issues, including 14 critical errors that could have affected campaign performance before a single campaign went live. By the time our team stepped in, all that was left was reviewing his findings and approving the launch. That completely changed the way we approached campaign launches. Instead of spending 65 hours every month on manual QA, our workflow was reduced to just 10.5 hours of strategic review giving our team back an estimated 54.5 hours to focus on optimization instead of repetitive checks. The biggest takeaway wasn't the time we saved. It was knowing every campaign went through the same level of quality control before launch. That's the difference we've started seeing between an AI assistant and an AI employee. An assistant waits for prompts. An AI employee owns recurring work while your team focuses on the decisions that matter most. Every campaign your team launches without this is a risk someone on your team is quietly absorbing. A copilot helps you work. An AI employee works when you don't. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #MarketingOps #DigitalMarketing Paid Partnership

Kylie

92,501 просмотров • 1 месяц назад

THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.
1:23

THIS CS TEACHER CONTROLS 4 CLAUDE CODE INSTANCES WITH HIS EYES AND VOICE - AND THEY FIX THEIR OWN MISTAKES AUTOMATICALLY He runs 4 Claude terminals at once - no mouse, no keyboard - eye tracking highlights the window he's looking at and voice dictation sends the prompt the moment he stops talking. But the real setup is what happens inside those terminals. Claude Code has zero memory of what went wrong last session - same bug that took 20 minutes to fix yesterday will happen again tomorrow unless you configure it correctly. His CLAUDE.md grows every time Claude makes a mistake - specific rules Claude reads before touching a single line - and PostToolUse hooks auto-format and type-check every file the moment it's written. Stop hooks run tests automatically when Claude says "done" - if they fail, Claude reads the errors and keeps fixing without any manual input. Before: 45 minutes of back-and-forth per feature, same mistakes on repeat - after: 10 minutes, most of it unattended.

Noisy

13,772 просмотров • 2 месяцев назад

BUILDING AI AGENTS IS EASY. SHIPPING THEM TO PRODUCTION IS WHERE EVERYONE DIES this 30-minute lecture is the gap between a demo that works and a system that runs the part most people miss: > fragmented frameworks that can't talk to each other > multi-agent coordination across different stacks - solved with A2A protocol and a "digital business card" system > ops, logging, scaling - the stuff that kills agents in production Claude Opus is the brain. Fable 5 was built for exactly this - then the US government blocked it 4 days after launch. Opus 4.8 is what's running now save this before you write another prototype 👇
27:28

BUILDING AI AGENTS IS EASY. SHIPPING THEM TO PRODUCTION IS WHERE EVERYONE DIES this 30-minute lecture is the gap between a demo that works and a system that runs the part most people miss: > fragmented frameworks that can't talk to each other > multi-agent coordination across different stacks - solved with A2A protocol and a "digital business card" system > ops, logging, scaling - the stuff that kills agents in production Claude Opus is the brain. Fable 5 was built for exactly this - then the US government blocked it 4 days after launch. Opus 4.8 is what's running now save this before you write another prototype 👇

Mr. Buzzoni

27,516 просмотров • 2 месяцев назад

Love what Claude shipped with Cowork Artifacts but generating code for Generative UI is the wrong architecture Wasteful: A single dashboard costs 2k boilterplate React/HTML tokens before any meaningful pixel is streamed Inconsistent: Two dashboards generated 5 mins apart don't feel like the same product. No design system. No shared components. Every output is a one off Security Liability: Today its a single-user sandboxed demo. But real software has shared dashboards, team views, embedded reports - LLM generated code for one user running in another user's session. Sandboxed or not, that's OWASP LLM05 territory. Multi-surface: A dashboard generated for desktop never works reliably on mobile. Want to render it on an iPhone? Your only option is an iframe. Yuck. Unstreamable: Partial code is extremely hard to stream meaningfully. What you get is hundreds of chunks of broken renders that flash and flicker. That's not progressive UI - that's broken UX. Code generation is a great starting point. It's not a production architecture. Generative UI in production needs an abstraction layer. The model describes intent. The renderer owns the pixels. Everything else is a liability.
0:46

Love what Claude shipped with Cowork Artifacts but generating code for Generative UI is the wrong architecture Wasteful: A single dashboard costs 2k boilterplate React/HTML tokens before any meaningful pixel is streamed Inconsistent: Two dashboards generated 5 mins apart don't feel like the same product. No design system. No shared components. Every output is a one off Security Liability: Today its a single-user sandboxed demo. But real software has shared dashboards, team views, embedded reports - LLM generated code for one user running in another user's session. Sandboxed or not, that's OWASP LLM05 territory. Multi-surface: A dashboard generated for desktop never works reliably on mobile. Want to render it on an iPhone? Your only option is an iframe. Yuck. Unstreamable: Partial code is extremely hard to stream meaningfully. What you get is hundreds of chunks of broken renders that flash and flicker. That's not progressive UI - that's broken UX. Code generation is a great starting point. It's not a production architecture. Generative UI in production needs an abstraction layer. The model describes intent. The renderer owns the pixels. Everything else is a liability.

Rabi Shanker Guha

46,639 просмотров • 4 месяцев назад

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.
0:30

THIS GUY TURNED ONE OBSIDIAN VAULT INTO A SHARED BRAIN FOR 5 CLAUDE AGENTS BUILT TO HANDLE 80% OF A SOLO DEVELOPER’S WORK. 00:03 he zooms out and hundreds of connected notes appear on screen. it is the perfect visual for Claude working from one shared project memory instead of starting from zero every session. most developers give Claude 1 file and 1 prompt. this setup gives it the architecture, coding rules, tests, client briefs, and previous decisions before it writes anything. obsidian becomes the memory layer, storing everything in markdown files Claude can read alongside the codebase. every agent works from the same context instead of repeating the same questions. then 5 specialists split the job. one researches, one plans, one codes, one reviews, and one deploys while the developer only controls the important decisions. one vault, 5 agents, 2 model tiers, and a $50 to $150 monthly stack can turn Claude from a chatbot into a small development team.

Gipp 🦅

10,776 просмотров • 1 месяц назад

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.
1:05

Two data points dropped in the last few months that should terrify every software company that thinks its codebase is a moat. First, one engineer at Cloudflare, working with Claude via AI agents, rebuilt 94% of Next.js, one of the most widely used frontend frameworks on the internet, built over 10 years by a large engineering team in a single week. Total cost was $1,100 in API tokens. The result, called Vinext, is a drop-in replacement that builds production apps up to 4x faster and produces client bundles 57% smaller and customers are already running it in production. Second is Cursor CEO Michael Truell deployed a swarm of hundreds of GPT-5.2 agents that ran uninterrupted for an entire week and built a fully functional web browser from scratch called FastRender. 3 million lines of code, thousands of files and a custom Rust rendering engine with HTML parsing, CSS layout, text shaping, and a custom JavaScript VM. Total cost was roughly $30,000. For context, Google has spent billions of dollars and decades of engineering building Chrome. And the benchmarks say by next year, you will be able to one-shot prompt anything. The moat that software companies spent decades building, the complexity of their codebase, the years it would take a competitor to replicate it, the switching costs that moat assumed humans were the unit of production. AI does not care how long it took you to build it, it only cares how long it takes to rebuild it. And right now, the answer is one week.

Milk Road AI

16,781 просмотров • 4 месяцев назад

We started generating draft replies on OpenAI's Davinci model in late 2022. By 2023 we had autonomous AI agents handling customer support in production. Way before it was cool. Today we're releasing Ask Yuma. You talk to it in plain English. It builds your automations, investigates why tickets went wrong, finds your next optimization opportunity, and generates reports from thousands of conversations. It doesn't just build things. It finds what's broken, proposes a plan, gets your approval, implements the fix, tests it, and verifies it worked. CX teams used to configure software. Now they talk to it. The industry isn't ready for how fast this changes everything. 3 years of production. This is what came out of it.
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We started generating draft replies on OpenAI's Davinci model in late 2022. By 2023 we had autonomous AI agents handling customer support in production. Way before it was cool. Today we're releasing Ask Yuma. You talk to it in plain English. It builds your automations, investigates why tickets went wrong, finds your next optimization opportunity, and generates reports from thousands of conversations. It doesn't just build things. It finds what's broken, proposes a plan, gets your approval, implements the fix, tests it, and verifies it worked. CX teams used to configure software. Now they talk to it. The industry isn't ready for how fast this changes everything. 3 years of production. This is what came out of it.

Guillaume Luccisano

25,307 просмотров • 4 месяцев назад

🎨 Introducing Genspark Design The next-gen AI for design and creation, powered by Claude Opus 4.7. From rough idea to professional design, no design background needed. - One tool for everything you design. UI prototypes, videos, HTML animations, posters — all in one place and production-ready. - Stay on-brand with your own design system. Upload your Figma files or save your designs, reuse them across every project, and share them with your team. - Ship it, don't just mock it. Turn any app or website design into working code with one click, built on Genspark Code. Start building at our launch price 👉
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🎨 Introducing Genspark Design The next-gen AI for design and creation, powered by Claude Opus 4.7. From rough idea to professional design, no design background needed. - One tool for everything you design. UI prototypes, videos, HTML animations, posters — all in one place and production-ready. - Stay on-brand with your own design system. Upload your Figma files or save your designs, reuse them across every project, and share them with your team. - Ship it, don't just mock it. Turn any app or website design into working code with one click, built on Genspark Code. Start building at our launch price 👉

Genspark

179,614 просмотров • 2 месяцев назад

My AI app looked ready to ship. Replay QA proved otherwise. Before launching, I connected the GitHub repo to Replay QA, let it run through the app like a real user, and within a few minutes it surfaced issues I would've never noticed by manually clicking around. What I liked most wasn't just finding bugs. It showed exactly what happened in the browser, explained the likely root cause, and gave enough context to fix the issue without guessing. Instead of running a one-time check, I connected the repo so Replay QA can keep testing every time I push changes or open a PR. No Playwright setup. No complicated pipeline configuration. Just a practical QA gate before users ever find the problems. If you're building with Cursor, Claude Code, Codex, Lovable, Bolt, Replit, or any AI coding workflow, this is worth adding before your next launch. Replay
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My AI app looked ready to ship. Replay QA proved otherwise. Before launching, I connected the GitHub repo to Replay QA, let it run through the app like a real user, and within a few minutes it surfaced issues I would've never noticed by manually clicking around. What I liked most wasn't just finding bugs. It showed exactly what happened in the browser, explained the likely root cause, and gave enough context to fix the issue without guessing. Instead of running a one-time check, I connected the repo so Replay QA can keep testing every time I push changes or open a PR. No Playwright setup. No complicated pipeline configuration. Just a practical QA gate before users ever find the problems. If you're building with Cursor, Claude Code, Codex, Lovable, Bolt, Replit, or any AI coding workflow, this is worth adding before your next launch. Replay

Kaitee

64,294 просмотров • 1 месяц назад

Introducing Composer — the first AI Agent for document processing. Get to production-grade accuracy, autonomously in minutes. In our early beta, some teams hit 99% accuracy on complex document tasks in under 10 minutes. Composer is an agent built to optimize schemas the same way a human would (but way faster). Instead of tuning prompts by hand, you point Composer at your eval set inside Extend. Composer will: - analyze where your schema falls short - propose targeted improvements - run multiple experiments in parallel - surface diffs, accuracy gains, and traces behind each change With this launch, Extend is the only product on the market that helps you reach production-grade accuracy this fast. Composer is live for all Extend customers today! Try it out at the link in comments below.
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Introducing Composer — the first AI Agent for document processing. Get to production-grade accuracy, autonomously in minutes. In our early beta, some teams hit 99% accuracy on complex document tasks in under 10 minutes. Composer is an agent built to optimize schemas the same way a human would (but way faster). Instead of tuning prompts by hand, you point Composer at your eval set inside Extend. Composer will: - analyze where your schema falls short - propose targeted improvements - run multiple experiments in parallel - surface diffs, accuracy gains, and traces behind each change With this launch, Extend is the only product on the market that helps you reach production-grade accuracy this fast. Composer is live for all Extend customers today! Try it out at the link in comments below.

Kushal Byatnal

75,152 просмотров • 10 месяцев назад