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OpenCode says it has reached 13M monthly users, 4.6M weekly users and roughly $40M in annualized revenue. Anthropic restricted competing coding tools. OpenCode works with any model, which gives teams a way around building their coding stack on one lab.

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21,226 просмотров • 9 дней назад •via X (Twitter)

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Since the start of the year, OpenCode (YC W21) — an open source alternative to Claude Code and Codex that works with any model — exploded to 4.6 million weekly active users, 13 million monthly actives, and roughly $40M in annualized revenue. In this episode of The Lightcone, Harj Taggar, Jared Friedman, and Diana talk with Jay V (Jay), OpenCode’s CEO, about what’s driving this wild growth, the Anthropic clampdown that inadvertently fueled it, and the almost 20 year founder journey that led him here. 00:44 — OpenCode’s Explosive Growth 01:16 — 20x Growth, 13M Users, and 7 Trillion Tokens 03:39 — The Anthropic Controversy That Changed Everything 05:43 — Bringing AI Coding Agents to the World 06:39 — When Open Source Models Became Good Enough 08:56 — What Millions of Developers Are Actually Using 13:31 — Why OpenCode Is Huge Outside the US 15:27 — Why Fortune 500 Companies Choose OpenCode 16:36 — The Economics of AI Tokens 20:02 — How Enterprises Are Using Coding Agents 22:58 — AI’s New Unit Economics 24:56 — Why Model Choice Matters 29:55 — The Product Decisions Behind OpenCode 34:21 — A 16-Year Overnight Success 41:16 — Why Jay Never Gave Up

Since the start of the year, OpenCode (YC W21) — an open source alternative to Claude Code and Codex that works with any model — exploded to 4.6 million weekly active users, 13 million monthly actives, and roughly $40M in annualized revenue. In this episode of The Lightcone, Harj Taggar, Jared Friedman, and Diana talk with Jay V (Jay), OpenCode’s CEO, about what’s driving this wild growth, the Anthropic clampdown that inadvertently fueled it, and the almost 20 year founder journey that led him here. 00:44 — OpenCode’s Explosive Growth 01:16 — 20x Growth, 13M Users, and 7 Trillion Tokens 03:39 — The Anthropic Controversy That Changed Everything 05:43 — Bringing AI Coding Agents to the World 06:39 — When Open Source Models Became Good Enough 08:56 — What Millions of Developers Are Actually Using 13:31 — Why OpenCode Is Huge Outside the US 15:27 — Why Fortune 500 Companies Choose OpenCode 16:36 — The Economics of AI Tokens 20:02 — How Enterprises Are Using Coding Agents 22:58 — AI’s New Unit Economics 24:56 — Why Model Choice Matters 29:55 — The Product Decisions Behind OpenCode 34:21 — A 16-Year Overnight Success 41:16 — Why Jay Never Gave Up

Y Combinator

728,565 просмотров • 9 дней назад

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

FREE 2M tokens. Opus 4.8, GPT-5.5, and every major coding model in one API key. Plugs directly into Claude Code. Inferall gives you access to the full model stack - Opus 4.8, GPT-5.5, and every other serious coding model - through one unified API. Here's the exact setup: → go to inferall(.)ai with VPN or proxy → sign in and register with any email (Google accounts work best if you hit rate limits) → create an API key - copy the key in format ifu_xxxxxxxxxxxxxxxx → go to Billing → Add a card → Activate free tier → add your card - a card with zero balance works fine(!!) → set country to United States during checkout Done. 2,000,000 free tokens loaded. Plug into Claude Code or any agent stack: BASE_URL: API_KEY: ifu_XXXXXXXXXXXXXX Works with Claude Code, OpenCode, Hermes Agent, any OpenAI-compatible setup. Opus 4.8 available immediately. 2M tokens is enough for weeks of serious coding sessions.

Atenov int.

25,085 просмотров • 1 месяц назад

🇺🇸 xAI launched Grok Build, a coding agent that runs directly from your terminal, now in early beta for SuperGrok and X Premium Plus subscribers. It lets you plan and approve changes before anything runs, works with tools you already use like MCP servers and plugins, and can spin up multiple subagents working in parallel on larger tasks. Some users say it's already competing with Claude Code and GitHub Copilot Workspace. Source: xAI

🇺🇸 xAI launched Grok Build, a coding agent that runs directly from your terminal, now in early beta for SuperGrok and X Premium Plus subscribers. It lets you plan and approve changes before anything runs, works with tools you already use like MCP servers and plugins, and can spin up multiple subagents working in parallel on larger tasks. Some users say it's already competing with Claude Code and GitHub Copilot Workspace. Source: xAI

Mario Nawfal

99,324 просмотров • 2 месяцев назад

Kimi's CEO🇨🇳 says every AI lab has the wrong obsession Zhilin Yang, on why K3 beat the frontier: "Every lab, like Claude, thinks the model matters most. That's wrong. It's how you organize the people building it that wins." Then Moonshot proved the philosophy. K3 sold out every plan on purpose, cutting their own revenue instead of throttling existing users. When Anthropic hit that wall in April, they cut users' usage 50% at peak. His one big idea: long context is the AI era's RAM. The 128K-to-gigabytes jump, compressed into 2 years instead of 40. The real moat: "your biggest advantage, perhaps your only advantage, is your organization." Model, or the team behind it: which actually wins?

Kimi's CEO🇨🇳 says every AI lab has the wrong obsession Zhilin Yang, on why K3 beat the frontier: "Every lab, like Claude, thinks the model matters most. That's wrong. It's how you organize the people building it that wins." Then Moonshot proved the philosophy. K3 sold out every plan on purpose, cutting their own revenue instead of throttling existing users. When Anthropic hit that wall in April, they cut users' usage 50% at peak. His one big idea: long context is the AI era's RAM. The 128K-to-gigabytes jump, compressed into 2 years instead of 40. The real moat: "your biggest advantage, perhaps your only advantage, is your organization." Model, or the team behind it: which actually wins?

Ruben

1,775,125 просмотров • 13 дней назад

🚨BREAKING: • Viktor hit $20M annualized revenue run rate before a single Microsoft Teams user could access it. As of today, they can. Teams users, welcome. Not another chatbot in the sidebar. Not Copilot waiting for a prompt. An actual employee you can @ mention in any channel — one that remembers your team’s history, pulls data from your tools, builds reports, and ships the work while you’re in the same conversation. This changes everything for the 320 million people who live in Teams every day. The future of work is here. 🧵👇

🚨BREAKING: • Viktor hit $20M annualized revenue run rate before a single Microsoft Teams user could access it. As of today, they can. Teams users, welcome. Not another chatbot in the sidebar. Not Copilot waiting for a prompt. An actual employee you can @ mention in any channel — one that remembers your team’s history, pulls data from your tools, builds reports, and ships the work while you’re in the same conversation. This changes everything for the 320 million people who live in Teams every day. The future of work is here. 🧵👇

Wesley

142,928 просмотров • 1 месяц назад

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

Coding agents shouldn't run on a laptop. That's why I've been building Cave. It's a self-hosted platform for running OpenCode agents in isolated sandboxes on your own server. You give it a GitHub repo, it creates an isolated sandbox, clones the code, sets everything up, and gives you a coding agent. Check in from your phone, run multiple agents side by side. It's not open source yet (it will be), but I'm opening a private beta so you can install it on your own server already. I've been dogfooding it since day one. If you're running a software factory: multiple agents working in parallel across repos, Cave gives you one place to monitor and manage all of it. Now, I'm looking for people who want to give it a spin. DM me, happy to set you up 😊 🚀

André König

36,102 просмотров • 4 месяцев назад

$SNOW's biggest threat is not Amazon. It's Anthropic. Sridhar Ramaswamy told Nicolai Tangen Nicolai Tangen that AI coding agents scare him more than any cloud hyperscaler. The conventional CEO answer names AWS or Azure. Ramaswamy named Anthropic. His logic: software has always been protected by scarcity - engineers as concert pianists, he called them. Coding agents are industrializing that craft at a scale that breaks the moat around purpose-built platforms like Snowflake. If you model $SNOW against Redshift and Fabric, you are watching the wrong fight. The competitive battleground is now the coding agent interface layer. Full competitive threat map - which layer Anthropic is actually attacking and what Snowflake is doing about it: Source: In Good Company with Nicolai Tangen -

$SNOW's biggest threat is not Amazon. It's Anthropic. Sridhar Ramaswamy told Nicolai Tangen Nicolai Tangen that AI coding agents scare him more than any cloud hyperscaler. The conventional CEO answer names AWS or Azure. Ramaswamy named Anthropic. His logic: software has always been protected by scarcity - engineers as concert pianists, he called them. Coding agents are industrializing that craft at a scale that breaks the moat around purpose-built platforms like Snowflake. If you model $SNOW against Redshift and Fabric, you are watching the wrong fight. The competitive battleground is now the coding agent interface layer. Full competitive threat map - which layer Anthropic is actually attacking and what Snowflake is doing about it: Source: In Good Company with Nicolai Tangen -

Podcast Alpha

61,446 просмотров • 1 месяц назад

Claude Code is now included in the Claude Pro plan which means Pro plan subscribers can use their rate limits for Claude apps and Claude Code! To celebrate, here's a conversation with Claude Code's founding engineer, Boris Cherny, on: - how we use Claude Code at Anthropic - underrated tips for using it effectively - the future of coding tools - and more!

Claude Code is now included in the Claude Pro plan which means Pro plan subscribers can use their rate limits for Claude apps and Claude Code! To celebrate, here's a conversation with Claude Code's founding engineer, Boris Cherny, on: - how we use Claude Code at Anthropic - underrated tips for using it effectively - the future of coding tools - and more!

Alex Albert

123,756 просмотров • 1 год назад

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Andrew Ng, on the biggest shift in AI right now: “The most important trend is agentic AI workflows.” In one slide, he showed the part most people still miss: GPT-3.5 scored 48% on a coding benchmark, GPT-4 reached 67%, but GPT-3.5 inside an agentic workflow jumped to around 95%. That is not a prompt trick. It is the model planning, using tools, checking its own work, fixing mistakes and running the loop again. Everyone is still arguing about which model is smarter, while the real edge is moving to the system wrapped around the model. Watch the clip, then build the workflow around your own process below.

Gipp 🦅

17,267 просмотров • 1 месяц назад

This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO

This is why Ridges AI | SN62 is an absolute monster 🔥 🔵 "If you were to take the top coding systems from these major companies with billions of dollars in funding at multi hundred billion dollar valuations and take their primary product which is coding agents and you stack it up against ours.. Ours is better" 🔵 "It solves more problems faster, for orders of magnitude cheaper, like 70x cheaper to use us than the flagship #Anthropic model to solve these problems.. and we're beating them now" ⚪️ "For way less capital, we've created a system that is way more efficient and actually better at solving problems for multiples cheaper, with a system that is now profitable to run" ⚪️ "With probably less capital than it costs our competitors to pay their engineers to do research in this time" Shakeel $TAO

ＤＲＥＡＤＢＯＮＧＯ

18,356 просмотров • 10 месяцев назад

Robinhood Chain could become the next billion-dollar meme ecosystem. The reason is simple Every successful ecosystem in crypto was never built around a single meme coin pumping. It was built around the users, attention, and ecosystem effects that memes created. $BRETT once reached a $2B market cap and became one of the most iconic memes in the Base ecosystem. It helped Base attract massive attention, users, and liquidity. Solana followed a similar path. Over the past few years, meme coins became one of Solana’s strongest user acquisition channels. further proved that speculation can be transformed into massive on-chain user growth. Almost every successful ecosystem has gone through a meme-driven growth phase. And Robinhood Chain happens to have several unique advantages: First, it has Robinhood’s distribution power with tens of millions of traditional finance users. Second, Robinhood is building around RWA and tokenized stocks, which requires bringing more users on-chain. Third, memes could become the lowest-friction entry point connecting traditional finance users with the crypto world. Robinhood’s CEO said: “While we’re building Robinhood Chain to be the best chain for RWA… it works great for memes too.” Many people see this as just a casual comment. But I think it reveals Robinhood’s deeper product strategy. Because RWA is unlikely to be the reason users enter crypto for the first time. Very few people will download a wallet and learn how bridges work just to buy a tokenized stock. But a viral meme can become the first interaction that brings users on-chain. Once users enter the ecosystem, they can gradually explore stablecoins, RWA, tokenized stocks, and other on-chain products. If Robinhood can replicate the Base and Solana playbook, Robinhood Chain could become the catalyst for the next major meme wave in crypto.

Robinhood Chain could become the next billion-dollar meme ecosystem. The reason is simple Every successful ecosystem in crypto was never built around a single meme coin pumping. It was built around the users, attention, and ecosystem effects that memes created. $BRETT once reached a $2B market cap and became one of the most iconic memes in the Base ecosystem. It helped Base attract massive attention, users, and liquidity. Solana followed a similar path. Over the past few years, meme coins became one of Solana’s strongest user acquisition channels. further proved that speculation can be transformed into massive on-chain user growth. Almost every successful ecosystem has gone through a meme-driven growth phase. And Robinhood Chain happens to have several unique advantages: First, it has Robinhood’s distribution power with tens of millions of traditional finance users. Second, Robinhood is building around RWA and tokenized stocks, which requires bringing more users on-chain. Third, memes could become the lowest-friction entry point connecting traditional finance users with the crypto world. Robinhood’s CEO said: “While we’re building Robinhood Chain to be the best chain for RWA… it works great for memes too.” Many people see this as just a casual comment. But I think it reveals Robinhood’s deeper product strategy. Because RWA is unlikely to be the reason users enter crypto for the first time. Very few people will download a wallet and learn how bridges work just to buy a tokenized stock. But a viral meme can become the first interaction that brings users on-chain. Once users enter the ecosystem, they can gradually explore stablecoins, RWA, tokenized stocks, and other on-chain products. If Robinhood can replicate the Base and Solana playbook, Robinhood Chain could become the catalyst for the next major meme wave in crypto.

Shawn

61,518 просмотров • 25 дней назад

I respectfully disagree for several reasons. Calling a customer, whether free or paying, an idiot is simply wrong. OpenCode, like any other coding agent, clearly tries to preserve the prompt cache as much as possible. Otherwise, it would be painfully slow. The “Stop Using OpenCode” article, which I believe Dax is referring to, focused on specific cases that can invalidate the cached prefix. These issues become much more visible and painful when using local models. Modifying AGENTS.md is one example, as shown in this video. During this coding session, cache efficiency is extremely high. But the moment I modify AGENTS.md, boom: 62K tokens need to be processed again as new input. The date change is another valid point raised in the article. I understand that both issues may sound trivial, and personally I've never been impacted by them, but their impact on the OpenCode end-user experience can be significant when using local models. I agree that both the feedback and the article were too direct and were probably written by the author out of frustration. However, they also contained constructive points that could help improve the product. I use OpenCode alongside several other tools, and I like it overall. But I really dislike this kind of reaction to user feedback, regardless of how that feedback is expressed. I would have preferred a response focused more on listening, understanding the problem, and improving the product.

I respectfully disagree for several reasons. Calling a customer, whether free or paying, an idiot is simply wrong. OpenCode, like any other coding agent, clearly tries to preserve the prompt cache as much as possible. Otherwise, it would be painfully slow. The “Stop Using OpenCode” article, which I believe Dax is referring to, focused on specific cases that can invalidate the cached prefix. These issues become much more visible and painful when using local models. Modifying AGENTS.md is one example, as shown in this video. During this coding session, cache efficiency is extremely high. But the moment I modify AGENTS.md, boom: 62K tokens need to be processed again as new input. The date change is another valid point raised in the article. I understand that both issues may sound trivial, and personally I've never been impacted by them, but their impact on the OpenCode end-user experience can be significant when using local models. I agree that both the feedback and the article were too direct and were probably written by the author out of frustration. However, they also contained constructive points that could help improve the product. I use OpenCode alongside several other tools, and I like it overall. But I really dislike this kind of reaction to user feedback, regardless of how that feedback is expressed. I would have preferred a response focused more on listening, understanding the problem, and improving the product.

Ivan Fioravanti ᯅ

36,983 просмотров • 11 дней назад

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Nat Eliason’s (Nat Eliason) career arc is borderline absurd—but it works. He’ll spot a new tool or trend, master it, build a business around it, and move on. Nat’s pulled it off with the note-taking wave ($600k in sales from a Roam Research course), real estate (6x return flipping property in Austin), and crypto (published his insider story with Random House). Now it’s AI: he’s running a viral course on building apps with AI—$200k in pre-sales in just a week, 800 students and counting. I’ve known Nat for a long time and I think he has a great sense for where the puck is headed. He was one of the first guests I had on the podcast and I was delighted to have him on again. Here are a few takeaways from our conversation: - Coding with AI has become orders of magnitude easier for non-technical people over the last 2 years—Nat rarely has to help students fix bugs; they troubleshoot in Cursor on their own. - AI coding assistants are creating new behaviours in programming, like using a speech-to-text model to talk to an agent and having it write code for you. - The traditional learning curve of coding is flattening because AI tools let beginners build and iterate in faster feedback loops. - AI has given Nat leverage in spades—it increases his ability to be a creator while also building a robust business with as few people to manage as possible. He demos an AI book editor he coded for his sci-fi novel. - In the age of AI, software is becoming content and the barriers to create are lower than ever—but custom software for everything isn’t the answer. Nat’s model is that personalized tools make sense for that one thing you care the most about. - Nat believes that the future of writing with AI is a Cursor-style interface with a model that’s trained on your style and voice. This episode is a must-watch for writers, creators, and anyone interested in the future of product building. Watch below! Timestamps: Introduction: 00:01:45 The origins of Nat’s viral course on building apps with AI: 00:11:45 How coding with AI has evolved over the last two years: 00:18:46 Nat creates an app using Composer, Cursor’s AI assistant: 00:22:22 Tactical tips for coding with Cursor: 00:26:06 How coding with AI is creating new behaviours in programming: 00:29:06 What excites Nat the most about the future of AI: 00:32:41 A demo of Hubbard, the AI editor Nat built for his science fiction writing: 00:38:58 When does it makes sense to build custom software: 00:44:52 Nat’s take on the future of writing with AI: 00:49:18

Dan Shipper 📧

27,207 просмотров • 1 год назад

Possibly the biggest change in Microsoft Teams history? Teams without the Teams? Public preview in November to desktop, mobile, iOS, and Android. Here is what you need to know: 🟣 Microsoft is introducing a new combined chat and channels experience. Meaning chat and teams/channels will show up under the "Chat" sidebar icon. No more dedicated Teams sidebar button. 🟣 Teams and Channels will have their own section in chat. 🟣 Group your chats and/or channels however best works for you with favourites and custom sections (up to 50 sections and 50 items per section) 🟣 “Unread,” “Chat,” “Channels,” “Meetings,” and “Muted” Filters 🟣 New message can initiate a new chat or new channel post 🟣 New Mention view gathers all your mentions into a single interactive list. 🟣 At launch, a self-service, guided onboarding will help users discover the new experience and configure it to their preferences (on or off) 🟣 Users who prefer to keep chat and channels separate can easily do so during the onboarding process or at any time later. So far, it looks like there are no IT/tenant-level override controls. This experience change is targeted at getting more users using teams/channels. Most Teams users live in "chats", not teams/channels. There will undoubtedly be some pushback from users who use teams/channels well and like having the mental separation of teams/channels vs chats. I've been fortunate to be using it for a while, and I'm personally a big fan; my use of the correct channels has gone way up, and now I can find them all in one place in the app, particularly on mobile. I'm now much more likely to see channel conversations I need to see. What do you think? #MicrosoftTeams #Microsoft365

Possibly the biggest change in Microsoft Teams history? Teams without the Teams? Public preview in November to desktop, mobile, iOS, and Android. Here is what you need to know: 🟣 Microsoft is introducing a new combined chat and channels experience. Meaning chat and teams/channels will show up under the "Chat" sidebar icon. No more dedicated Teams sidebar button. 🟣 Teams and Channels will have their own section in chat. 🟣 Group your chats and/or channels however best works for you with favourites and custom sections (up to 50 sections and 50 items per section) 🟣 “Unread,” “Chat,” “Channels,” “Meetings,” and “Muted” Filters 🟣 New message can initiate a new chat or new channel post 🟣 New Mention view gathers all your mentions into a single interactive list. 🟣 At launch, a self-service, guided onboarding will help users discover the new experience and configure it to their preferences (on or off) 🟣 Users who prefer to keep chat and channels separate can easily do so during the onboarding process or at any time later. So far, it looks like there are no IT/tenant-level override controls. This experience change is targeted at getting more users using teams/channels. Most Teams users live in "chats", not teams/channels. There will undoubtedly be some pushback from users who use teams/channels well and like having the mental separation of teams/channels vs chats. I've been fortunate to be using it for a while, and I'm personally a big fan; my use of the correct channels has gone way up, and now I can find them all in one place in the app, particularly on mobile. I'm now much more likely to see channel conversations I need to see. What do you think? #MicrosoftTeams #Microsoft365

Tom Arbuthnot

64,526 просмотров • 1 год назад

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Kanika

20,148 просмотров • 9 дней назад

Making an A+ website shouldn't be hard. But current tools are bulky, over-templatized, and fragile. So we started building something better for our users. Introducing: Gamma sites. Now, anyone can spin up a website and maintain it completely on their own. It’s like having your own AI design partner - no design or coding skills required. You can host it on our gamma(dot)site subdomain, or on the custom domain of your choice. And yes, all sites are: → Mobile friendly → SEO optimized → Easy to edit Come launch your portfolio, side hustle, project, course, and anything else you've been dreaming of. Publishing a site has never been easier with

Making an A+ website shouldn't be hard. But current tools are bulky, over-templatized, and fragile. So we started building something better for our users. Introducing: Gamma sites. Now, anyone can spin up a website and maintain it completely on their own. It’s like having your own AI design partner - no design or coding skills required. You can host it on our gamma(dot)site subdomain, or on the custom domain of your choice. And yes, all sites are: → Mobile friendly → SEO optimized → Easy to edit Come launch your portfolio, side hustle, project, course, and anything else you've been dreaming of. Publishing a site has never been easier with

Grant Lee

45,314 просмотров • 2 лет назад

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

The most overlooked part of the SpaceX IPO thesis is the model and most people are completely missing it (Save this) Everyone has been focused on the Anthropic compute deal and the Colossus revenue because those are numbers you can put in a spreadsheet. Six months ago, xAI was competing reasonably well on model performance but was not clearly on the frontier. Then SpaceX exercised its option to acquire Cursor for $60 billion, the largest startup acquisition in history just days after completing the largest IPO in history at $75 billion. Cursor is a team of 700 to 800 people, was on track to exit 2026 at up to $10 billion in revenue, had millions of professional developers using it daily, and had already built a team with the genuine potential to compete at the frontier, the one thing holding them back was compute. SpaceX just gave them the largest GPU cluster in the world to work with. Grok 4.3, a 1.5 trillion parameter model, is currently training with Cursor's proprietary coding data being injected directly into pre-training, not just fine tuning which is a fundamentally more powerful integration than anything the market is currently modeling. The prior version, Grok 4, was already on the Pareto frontier as of 10 to 12 days ago, the most intelligent 500 billion parameter model in the world, sitting alongside Google Gemini, Anthropic, and OpenAI as one of only four systems at the true frontier. Composer 2.5, the previous Cursor model was Pareto dominant in coding tasks just before the acquisition closed, meaning SpaceX inherited a model that was already best-in-class in the highest-value AI use case in the market. The AWS parallel is the one everyone keeps missing. Bezos built data center capacity for Black Friday, sat on idle infrastructure the rest of the year, and monetized it into what was at the time the most profitable technology business in history and investors hated it in 2009 and 2010 because he was burning free cash flow on capacity that had no obvious revenue yet. SpaceX is in exactly that position, it built Colossus for xAI's own training needs, is monetizing excess capacity to Anthropic at $1.25 billion per month across 220,000 Nvidia GPUs, and has reportedly secured up to 20% of Nvidia's early Vera Rubin allocation, giving it the most powerful and scarcest GPU infrastructure in the world during the critical window when those chips are hardest to get. The $60 billion Cursor acquisition closed at a moment when SpaceX had essentially unlimited compute, a team already at the frontier, and a product with deep enterprise distribution, three things no other model lab had simultaneously when it was at this stage. The market is pricing the compute business conservatively and ignoring the model call option entirely, and coding is the fastest path to AGI, once you are on the Pareto frontier with that compute, revenue scales fast. Anthropic went from negligible revenue to $30 billion annualized in under 18 months and that is the existence proof. Bullish on SpaceXAI and Elon Musk

Milk Road AI

69,446 просмотров • 1 месяц назад

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Brad Menezes

4,640,561 просмотров • 3 месяцев назад

The most powerful AI model on earth was alive for 3 days then the US government turned it off. for everyone. on the planet. Anthropic launched Claude Fable 5 on june 9th. their best model ever. june 12th: the Commerce Department issued an export control order blocking foreign nationals Anthropic couldn’t filter foreign users in real time so they shut it down. worldwide. in hours. including for their own employees the irony: Anthropic begged for regulation. sat with the Pope. donated millions for AI rules… the government regulated them.. by killing their best product overnight the most powerful AI in the world has an off switch?? and the company doesn’t hold it first time in history a government force-killed a PUBLIC AI model it won’t be the last.

The most powerful AI model on earth was alive for 3 days then the US government turned it off. for everyone. on the planet. Anthropic launched Claude Fable 5 on june 9th. their best model ever. june 12th: the Commerce Department issued an export control order blocking foreign nationals Anthropic couldn’t filter foreign users in real time so they shut it down. worldwide. in hours. including for their own employees the irony: Anthropic begged for regulation. sat with the Pope. donated millions for AI rules… the government regulated them.. by killing their best product overnight the most powerful AI in the world has an off switch?? and the company doesn’t hold it first time in history a government force-killed a PUBLIC AI model it won’t be the last.

Mika

49,171 просмотров • 1 месяц назад

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,161 просмотров • 1 год назад