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Perplexity pulled ahead because its general agent works so well - and, they've developed smart integrations that drive behavior across apps. Some examples: 🛍️ Shopping - one-click checkout ✉️ Gmail and G Cal - schedule meetings, search email 🗄️ Dropbox - search / share files

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Olivia Moore

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22,836 views • 11 months ago •via X (Twitter)

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Today, anyone in the U.S. can opt into Personal Intelligence for AI Mode in Search on their personal accounts. It’s also starting to roll out in the Google Gemini and Gemini in Chrome. Personal Intelligence allows you to securely connect the dots across your Google apps — like Gmail, Google Photos and more — to provide responses that are uniquely relevant to you. Some examples: 🛍️ Get tailored shopping recommendations based on your recent purchases as well as your preferred brands and style ✈️ Plan a family getaway based on your hotel confirmations and past travel memories ✨ Discover a new hobby inspired by your interests (even ones you didn’t realize you had)
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Today, anyone in the U.S. can opt into Personal Intelligence for AI Mode in Search on their personal accounts. It’s also starting to roll out in the Google Gemini and Gemini in Chrome. Personal Intelligence allows you to securely connect the dots across your Google apps — like Gmail, Google Photos and more — to provide responses that are uniquely relevant to you. Some examples: 🛍️ Get tailored shopping recommendations based on your recent purchases as well as your preferred brands and style ✈️ Plan a family getaway based on your hotel confirmations and past travel memories ✨ Discover a new hobby inspired by your interests (even ones you didn’t realize you had)

Google

126,393 views • 4 months ago

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!
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Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Sumanth

10,483 views • 3 months ago

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We’re excited to announce AI SDR-Kit, a comprehensive suite of app integrations and starter templates to build highly customizable AI sales agents. As a developer, I only realized how tough it can be to crack sales after founding my startup. It’s a lot of grunt work, prospecting, qualifying, outreaching and whatnot. And the complexity only goes up as you grow. But it doesn’t have to. AI agents can effectively optimize many of these routine tasks; for instance, they can find leads from Apollo, enrich their data using People Data Labs, manage them in Salesforce, send targeted emails via SendGrid, and schedule meetings in Calendly without any manual involvement. But the biggest challenge is building these app integrations for agents. A single Salesforce integration may take 100s of engineering hours, let alone other integrations across CRMs, email platforms, and data enrichment tools. Considering this, we Composio built AI SDR-Kit to enable developers to build full-fledged sales automation agents. You will get • Over 60 app integrations optimized for SDR/BDR agents. • Seamless handling of complex auth flows (OAuth, API Key, Basic). • Compatibility with 15+ Agentic Frameworks, including LangChain, LlamaIndex 🦙, CrewAI, Letta etc. • Self-hosting option for greater control. • Enterprise-ready with SOC 2 Type 2 compliance, SSO, and RBAC support. Here's a brief list of popular integrations by category that you get with SDR-Kit: • CRM: Salesforce, HubSpot, Attio, and more. • Contact Data: Apollo, ZoomInfo, People Data Labs, etc. • Email deliverability: Gmail, Outlook, Mailchimp, SendGrid, Klaviyo. • Comms & Collabs: Slack, Discord, Intercom & Zoom. • Social Media: LinkedIn, Facebook, Twitter & Reddit. • Meeting: Google Calendar, Calendly, Cal dot com, etc. • … and many more. All the integrations have been improved, keeping agents' real-world readiness in mind. We’ve also made it straightforward to connect these apps with your agents in a few lines of code. Making it easy to create AI sales agents. This is ideal for developers and companies building automated sales solutions - whether for internal SalesOps or AI SDR services. Checkout AI SDR-Kit now👇

Karan Vaidya

54,217 views • 1 year ago

The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):
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The new Google Search is rolling out and there seems to be confusion on what it will look like. Google literally told us. Let me clarify for anyone who is still unsure of what is rolling out this week. Last month Google responded to everyone saying Search is dead. Here is what they said: "You will absolutely continue to see blue web links in search results. AI Mode is not the default experience in Search. You will continue to get a range of results on Search." [Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free): Google has explicitly laid out what Search will look like from this point going forward. The new Search box accepts text, images, files, videos, and open Chrome tabs. It anticipates your intent before you finish asking. It is powered by the most advanced Gemini model Google has ever put into Search, and layered on top of that, information agents will now be able to run 24/7 in the background on behalf of your buyer. Think of it in 5 steps: Step 1: The buyer describes their problem, their category, their needs in full. Step 2: The agent breaks that down into sub-topics and maps out a plan. Step 3: It determines what intel is needed right now versus later. Step 4: It monitors blogs, news sites, and social posts continuously for relevant changes. Step 5: It sends the buyer a synthesized update with links and the ability to take action. Blue links are not going away in the short-term, but the brands getting recommended by information agents 24 hours a day while also ranking in traditional results are going to pull so far ahead of the ones doing only one or the other that it will not be a fair fight. This is exactly what SEO Stuff ( has been building for every customer. Optimized content depth that covers every sub-question a buyer in your category asks, so the agent finds you at every step of its plan. Editorial authority from trusted websites that signals credibility to every retrieval system Google has ever built, across both traditional rankings and AI citations simultaneously. One investment. Blue links and AI citations. Around the clock. SEO Stuff's Complete Done-For-You Plan: SEO Stuff's "Optimized Content" Plan: There is a reason more than 80 percent of SEO Stuff customers reorder. The results continue long after the work is done. Google Search is changing. AI Search is here. Your websites need to prepare accordingly. Want to know where your site stands across Google AI, ChatGPT, Claude, Grok, etc? Check here (it's free):

Alex Groberman

44,078 views • 27 days ago

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this
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THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

57,975 views • 1 month ago

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.
0:26

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 views • 8 months ago

Last year, during tax season. I had 50+ receipts. Some in my email. Some on WhatsApp. Some in my gallery. And a few, I couldn’t even remember where I saved them. What should’ve taken a few hours turned into late nights, frustration, and second-guessing everything. Because the real problem isn’t filing taxes. It’s this: → Collecting documents → Organizing them → Verifying if everything is correct That’s where most people struggle. This year, I tried something different. Instead of chasing files everywhere, I built a simple, clean system using Wondershare PDFelement. And honestly, it changed everything. Here’s how my workflow looked. I started by scanning all my paper receipts directly from my phone using the Receipt Assistant. No manual typing. No guesswork. It automatically extracted details like: • Merchant • Date • Amount • Taxes Everything turned into searchable PDFs instantly. Then came the best part. All files were automatically saved to the cloud. So, I could: → Scan on mobile → Manage on desktop → Access everything, anytime No more “where did I save that?” moments. But what impressed me most. Every extracted detail is traceable. I could click any number and instantly jump back to the exact spot in the original receipt. No more cross-checking line by line. And when it was time to organize everything. • Exported all data into Excel → full expense overview • Merged multiple files into one clean PDF • Edited tax documents directly (no extra tools needed) Before final submission, I used: • AI Assistant → to summarize & cross-check documents • Smart Redact → to hide sensitive information Everything felt controlled, clean, and secure. That’s when it hit me: A good tax system isn’t about working harder. It’s about having a clear, traceable workflow that removes chaos. Suppose your files are still scattered across folders, emails, and screenshots. That is exactly why tax season feels exhausting. Search Wondershare PDFelement and try it free → #wondersharepdfelement #pdfelement #FileWithPDFelement #TaxSeason
3:40

Last year, during tax season. I had 50+ receipts. Some in my email. Some on WhatsApp. Some in my gallery. And a few, I couldn’t even remember where I saved them. What should’ve taken a few hours turned into late nights, frustration, and second-guessing everything. Because the real problem isn’t filing taxes. It’s this: → Collecting documents → Organizing them → Verifying if everything is correct That’s where most people struggle. This year, I tried something different. Instead of chasing files everywhere, I built a simple, clean system using Wondershare PDFelement. And honestly, it changed everything. Here’s how my workflow looked. I started by scanning all my paper receipts directly from my phone using the Receipt Assistant. No manual typing. No guesswork. It automatically extracted details like: • Merchant • Date • Amount • Taxes Everything turned into searchable PDFs instantly. Then came the best part. All files were automatically saved to the cloud. So, I could: → Scan on mobile → Manage on desktop → Access everything, anytime No more “where did I save that?” moments. But what impressed me most. Every extracted detail is traceable. I could click any number and instantly jump back to the exact spot in the original receipt. No more cross-checking line by line. And when it was time to organize everything. • Exported all data into Excel → full expense overview • Merged multiple files into one clean PDF • Edited tax documents directly (no extra tools needed) Before final submission, I used: • AI Assistant → to summarize & cross-check documents • Smart Redact → to hide sensitive information Everything felt controlled, clean, and secure. That’s when it hit me: A good tax system isn’t about working harder. It’s about having a clear, traceable workflow that removes chaos. Suppose your files are still scattered across folders, emails, and screenshots. That is exactly why tax season feels exhausting. Search Wondershare PDFelement and try it free → #wondersharepdfelement #pdfelement #FileWithPDFelement #TaxSeason

Vikas Singh

11,795 views • 3 months ago

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?
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JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,498 views • 4 months ago

BREAKING! Introducing Plus One: A hosted OpenClaw🦞 that lives in your Slack and comes pre-loaded with Every 📧's best tools, skills, and workflows. Set it up in one click, and use your ChatGPT subscription (or any other API key.) Bring your Plus One to work: Connected to the Every 📧 ecosystem Plus Ones automatically use Every 📧's agent-native apps, no setup required: - Cora for searching, sending, and managing email - Spiral for great writing in your voice - Proof ( for agent-native document editing Custom skills and workflows we use and love Plus Ones come pre-loaded with skills and workflows we use ourselves Every 📧 —some we've made, and some we think are great. - Content digest—summarizes the publications you read, starting with Every 📧 - Daily brief—your day's schedule and to-dos sent to you each morning - Animate—turn any static screenshot into an animation with Remotion - Frontend—Anthropic's front-end skill (which we use all the time!) We also make it fast to connect Google, Notion, Github, and more to your Plus One. Our goal is to give you a capable AI coworker right away, not a vanilla OpenClaw that you have to teach from scratch. Why we built Plus One OpenClaw🦞 has changed the way we work at Every. We effectively have a parallel org chart of AI coworkers, each with a name, a manager, and real responsibilities. Because of them our workflows are completely different—our company is different—and we would never go back. But getting here has been hard. Claws require a significant amount of manual setup and require a dedicated machine—like a Mac Mini—running 24/7 to stay responsive. We have learned that the hard part of Claws is the infrastructure around them—the hosting, the integrations, the skills, and the ongoing care. We’ve made them work great for our team, and we want to share everything we’ve learned with you. We're letting in 20 people a week to start, and scaling invites quickly from there. Every 📧 subscribers get priority. Bring your Plus One to work:
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BREAKING! Introducing Plus One: A hosted OpenClaw🦞 that lives in your Slack and comes pre-loaded with Every 📧's best tools, skills, and workflows. Set it up in one click, and use your ChatGPT subscription (or any other API key.) Bring your Plus One to work: Connected to the Every 📧 ecosystem Plus Ones automatically use Every 📧's agent-native apps, no setup required: - Cora for searching, sending, and managing email - Spiral for great writing in your voice - Proof ( for agent-native document editing Custom skills and workflows we use and love Plus Ones come pre-loaded with skills and workflows we use ourselves Every 📧 —some we've made, and some we think are great. - Content digest—summarizes the publications you read, starting with Every 📧 - Daily brief—your day's schedule and to-dos sent to you each morning - Animate—turn any static screenshot into an animation with Remotion - Frontend—Anthropic's front-end skill (which we use all the time!) We also make it fast to connect Google, Notion, Github, and more to your Plus One. Our goal is to give you a capable AI coworker right away, not a vanilla OpenClaw that you have to teach from scratch. Why we built Plus One OpenClaw🦞 has changed the way we work at Every. We effectively have a parallel org chart of AI coworkers, each with a name, a manager, and real responsibilities. Because of them our workflows are completely different—our company is different—and we would never go back. But getting here has been hard. Claws require a significant amount of manual setup and require a dedicated machine—like a Mac Mini—running 24/7 to stay responsive. We have learned that the hard part of Claws is the infrastructure around them—the hosting, the integrations, the skills, and the ongoing care. We’ve made them work great for our team, and we want to share everything we’ve learned with you. We're letting in 20 people a week to start, and scaling invites quickly from there. Every 📧 subscribers get priority. Bring your Plus One to work:

Dan Shipper 📧

260,028 views • 3 months ago

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch
27:27

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

134,714 views • 3 months ago

I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. And then i spoke to KippBodnar.eth, CMO of HubSpot, who knocked the skepticism out of me. His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L. His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher. His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 2) Restructure: your content into chunked, answer-first pages with natural language headers. - one consolidated page, not 8-10 interlinked pages - lead with natural language questions like "What is X?" - 1-2 paragraph sections, not 1,000 word sections - table of contents on a single page 3) Separate: Mentions from citations and optimize differently for each - Mention = when AI references your brand or product in its answer but doesn't link to you - Citation = when an AI references you AND links to your page 4) Open up: your information — ungate content, build Reddit presence, make pricing public - Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs 5) Tool up: with AEO-specific software to track prompts and share of voice - Check out Xfunnel or Limey[.]ai 6) Rethink attribution: measure source of customers, not source of traffic. - Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic
53:03

I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. And then i spoke to KippBodnar.eth, CMO of HubSpot, who knocked the skepticism out of me. His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L. His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher. His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 2) Restructure: your content into chunked, answer-first pages with natural language headers. - one consolidated page, not 8-10 interlinked pages - lead with natural language questions like "What is X?" - 1-2 paragraph sections, not 1,000 word sections - table of contents on a single page 3) Separate: Mentions from citations and optimize differently for each - Mention = when AI references your brand or product in its answer but doesn't link to you - Citation = when an AI references you AND links to your page 4) Open up: your information — ungate content, build Reddit presence, make pricing public - Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs 5) Tool up: with AEO-specific software to track prompts and share of voice - Check out Xfunnel or Limey[.]ai 6) Rethink attribution: measure source of customers, not source of traffic. - Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic

Alex Lieberman

98,411 views • 5 months ago

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:
22:53

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:

Akshay 🚀

40,944 views • 8 days ago

GM, a quick video of how coupon app Phia runs its scam (and some content on how and why below). 1. A user goes to Dupe.com to find their next fav product (imagine this is your fav YouTube creator doing the hard work of driving a click after painstakingly making content) 2. User clicks link on Dupe after doing their research. Dupe should get credit for this sale at checkout, we created the demand. 3. Phia surreptitiously tracks the user to the checkout page. The moment a user is ready to buy, Phia takes over the page and says “hey how about a coupon”? This is explicitly unallowed by networks like Rakuten, because it robs other publishers of the sale they rightfully drove. 4. Of course a user is going to want to see if a coupon is available, so they tap the button. Phia now takes over the user’s cookies and claims that they drove the sale. Original publisher/creator gets hosed. This is the exact playbook Honey got in trouble for just last year. The funniest part is most times there are no available coupons to use… but they get credit anyway. Why do Phia and other coupon apps do this? Simple… they have no clue how to generate demand on their app, their revenue is predicated on other apps and publishers to do the hard work to get the user to a checkout so that Phia can claim the win. Why else? They just raised a lot of money and burn a lot of money and need to show that they know how to make money so they can raise again. Except… it’s all a bunch of cope. They have no demand gen. Downstream impact is brands have no clue how good their publishers actually are because they can’t attribute the checkout correctly. Brands also lose money because they had to discount an item unnecessarily. Publishers and creators all lose money. It’s a scam. This is good reporting by Olivia Solon and Bloomberg. We’ve already reported this behavior to Rakuten and others multiple times, Phia tells them they’ve fixed it but some time passes and they go back to doing the same thing again, they’re hoping no one is looking too closely.
0:41

GM, a quick video of how coupon app Phia runs its scam (and some content on how and why below). 1. A user goes to Dupe.com to find their next fav product (imagine this is your fav YouTube creator doing the hard work of driving a click after painstakingly making content) 2. User clicks link on Dupe after doing their research. Dupe should get credit for this sale at checkout, we created the demand. 3. Phia surreptitiously tracks the user to the checkout page. The moment a user is ready to buy, Phia takes over the page and says “hey how about a coupon”? This is explicitly unallowed by networks like Rakuten, because it robs other publishers of the sale they rightfully drove. 4. Of course a user is going to want to see if a coupon is available, so they tap the button. Phia now takes over the user’s cookies and claims that they drove the sale. Original publisher/creator gets hosed. This is the exact playbook Honey got in trouble for just last year. The funniest part is most times there are no available coupons to use… but they get credit anyway. Why do Phia and other coupon apps do this? Simple… they have no clue how to generate demand on their app, their revenue is predicated on other apps and publishers to do the hard work to get the user to a checkout so that Phia can claim the win. Why else? They just raised a lot of money and burn a lot of money and need to show that they know how to make money so they can raise again. Except… it’s all a bunch of cope. They have no demand gen. Downstream impact is brands have no clue how good their publishers actually are because they can’t attribute the checkout correctly. Brands also lose money because they had to discount an item unnecessarily. Publishers and creators all lose money. It’s a scam. This is good reporting by Olivia Solon and Bloomberg. We’ve already reported this behavior to Rakuten and others multiple times, Phia tells them they’ve fixed it but some time passes and they go back to doing the same thing again, they’re hoping no one is looking too closely.

Bobby from Dupe.com

111,001 views • 6 days ago

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:
0:31

ChatGPT 5.5 is cooked. Claude Opus 4.7 is cooked. Every $420/mo SaaS AI just got an open-source assassin. Mind blown: an open-source desktop AI just hit #7 trending overnight, runs 100% on your laptop, ships with 100+ native integrations, and is quietly killing the entire ChatGPT-subscription era. Introducing OpenHuman by tinyhumansai -> your Personal AI super intelligence. Private. Simple. Powerful. Two weeks ago they quietly dropped it on GitHub. Today: 300+ stars, 100+ daily paying users, 1,129 commits, zero marketing budget. > What is OpenHuman? A native desktop agent (macOS, Windows, Linux) that lives on YOUR machine instead of feeding your data back to OpenAI. Download the app, sign in once, and the agent harness gives you 100+ native connectors out of the box: Gmail, Slack, Notion, GitHub, Reddit, Instagram, Calendar, Drive, Telegram, Discord, and dozens more. One click each. From that moment it builds an encrypted, on-device knowledge base of your entire digital life. No terminal. No Python envs. No API keys. No CLI. > What the agent actually does: Steven, the creator, just dropped a Loom showing real prompts: - "Send Mark a joke" -> drafts in your voice and ships it. - "List my top 5 emails today" -> surfaces what matters from a flooded inbox. - "Summarize that thread and email it to the team" -> done in 3 seconds. One prompt --> multiple connected tools --> end-to-end execution. No tab-switching. > What's actually inside: - Screen intelligence -> the agent SEES what's on your screen and feeds it into your local context. - Memory-aware keyboard autocomplete -> system-wide, in YOUR voice, trained on YOUR past replies. Gmail Smart Compose for your entire OS. - Local knowledge base -> every email, message, and note parsed, embedded, encrypted, on YOUR device. Day 30 it knows you better than your therapist. - 75% Rust core -> memory-safe, brutally fast, runs local AI directly on your machine. > The "but wait" moment: OpenClaw and Hermes Agent are excellent. But they live in the terminal. Virtualenvs. SKILL.md files. Shell debugging at 2am. OpenHuman doesn't ask any of that. Their README compares itself to "The Tet" from Oblivion -- that alien superintelligence Morgan Freeman calls "a brilliant machine". And tomorrow they're dropping the official OpenHuman mascot. Sneak peek already in Steven's Loom. The cloud-first AI decade is ending. OpenHuman is GPL-3, fully auditable, shipping a release every few days. Save this -- you just got the link to the thing replacing every SaaS AI on the market. -> Repo:

slash1s

70,687 views • 2 months ago

THIS IS HOW AGENCIES ARE GETTING NEW CLIENTS ON AUTOPILOT stop cold dming on linkedin, stop running paid ads to a calendly link, and stop sending cold emails that land in spam the agencies actually winning right now are writing REDDIT posts that rank on google and chatgpt/claude INSTANTLY for the exact keywords their next client is searching think about how clients actually look for an agency they don't browse linkedin. they don't click an ad. they open google or chatgpt/claude and type something like: > "best ecommerce marketing agency" > "klaviyo email agency alternative" > "shopify SEO agency for dtc brands" > "tiktok ads agency for fashion brands" > "hubspot agency partner near me" every single one of those is a buyer with a budget and intent to hire someone within 30 days the founders winning these searches aren't writing blog posts that rank in 8 months they're writing reddit posts that rank in 8 hours reddit posts permanently sit at the top of google for niche service keywords because google trusts reddit more than your domain. and they get pulled into chatgpt, perplexity, and claude as cited sources too the only problem is finding the right keywords manually and making optimized content takes WAY too long. so i built a tool that does the entire workflow for an agency in less than 5 minutes: > describe your service and your niche > AI scans google, reddit, and AI search engines for the keywords your buyers are typing > scores every keyword on conversion potential not search volume > shows you the exact subreddits your future clients hang out in > writes reddit posts that rank for those keywords without getting removed by mods > tracks which posts are actually pulling in traffic and inbound leads one ranked reddit post can outproduce a year of cold outreach agencies that figure this out first are about to quietly own the entire inbound game in 2026
6:35

THIS IS HOW AGENCIES ARE GETTING NEW CLIENTS ON AUTOPILOT stop cold dming on linkedin, stop running paid ads to a calendly link, and stop sending cold emails that land in spam the agencies actually winning right now are writing REDDIT posts that rank on google and chatgpt/claude INSTANTLY for the exact keywords their next client is searching think about how clients actually look for an agency they don't browse linkedin. they don't click an ad. they open google or chatgpt/claude and type something like: > "best ecommerce marketing agency" > "klaviyo email agency alternative" > "shopify SEO agency for dtc brands" > "tiktok ads agency for fashion brands" > "hubspot agency partner near me" every single one of those is a buyer with a budget and intent to hire someone within 30 days the founders winning these searches aren't writing blog posts that rank in 8 months they're writing reddit posts that rank in 8 hours reddit posts permanently sit at the top of google for niche service keywords because google trusts reddit more than your domain. and they get pulled into chatgpt, perplexity, and claude as cited sources too the only problem is finding the right keywords manually and making optimized content takes WAY too long. so i built a tool that does the entire workflow for an agency in less than 5 minutes: > describe your service and your niche > AI scans google, reddit, and AI search engines for the keywords your buyers are typing > scores every keyword on conversion potential not search volume > shows you the exact subreddits your future clients hang out in > writes reddit posts that rank for those keywords without getting removed by mods > tracks which posts are actually pulling in traffic and inbound leads one ranked reddit post can outproduce a year of cold outreach agencies that figure this out first are about to quietly own the entire inbound game in 2026

Om Patel

10,998 views • 2 months ago

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:
2:03

I've been building a music player with Next.js for fun. Here's a quick demo of how it works (it's open source!) • Demo: • Code: If you want to learn more about how it's built, here's more details ↓ I'm using Postgres (with Drizzle) to store information about the songs and playlists. Audio and image files are stored in Vercel Blob (object storage), and the URLs are then referenced in the database. For the UI, I'm using shadcn/ui (so Tailwind CSS and Radix). This made it easy to copy/paste in some nice components, like the dropdown menus. I built the entire first version of the UI in v0 and then iterated from there, feeding it my Drizzle schema as a source in the project and having it scaffold some of the boilerplate for me: I added support for keyboard navigation (using arrow keys) or vim motions (j/k to go up/down, and h/l to go between playlists and tracks). Also, space to toggle the now playing song, and / to focus the search input. The search function has a nice utility to highlight the currently searched text on the page in yellow. Then, I was exploring how to pass metadata from my application to macOS or iOS. Turns out there's an API for that – MediaSession. Web apps can share metadata about what media is playing (title, artist, album artwork) and sync play/pause/seek with system media controls. Works across modern browsers — even integrates with iOS dynamic island and shows up on lock screens: I set up my app like a PWA – it has a manifest.json file, so it can be installed to my iOS home screen or added to my dock on macOS. On iOS, it then uses the full screen height `100dvh` (dynamic viewport) and has padding on the bottom for the safe area with the `env()` CSS function. Finally, I was able to use the Vercel AI SDK in a script to clean up the metadata on audio files I downloaded from YouTube. Bonus: I even was able to dogfood the React Compiler, which helped me fix a performance bug! That's all! It's fun to make personal software:

Lee Robinson

118,242 views • 1 year ago

I'm so confident Triple Whale will make you money that I'm making a bet: If you do over a million dollars/year I'll pay you $250 for 15 minutes of your time. Today we're Introducing the Prime Day Mega Agent, an intelligent Amazon Analyst built to print you money on Prime Day. Here’s what it does, autonomously: – Analyzes Meta, Tiktok and Amazon ad performance – Predicts your winning SKUs using historical trend modeling – Generates a plug-and-play Prime Day playbook: what to pause, scale, test and when + when to send out email campaigns It’s like having a Head of Growth, Media Buying, and Ops in one… Except It works around the clock and leverages more data than any human ever could. We built it because we noticed a critical trend👇 When analyzing Prime Day sales data from last year, we found that the top brands didn’t just edge out the competition, they crushed them. Same tools. Same budgets. Same Prime Day. Yet somehow a small cohort of brands were crushing at a clip we typically don't see... So what were the winners doing that no one else was? We found levers the winners pulled that everyone else missed. Levers like: - Making their PPC target Prime-specific keywords - Warming up email audiences weeks before to build anticipation - Surf-scaling their ads by the hour not by the day on Prime Day Now, you can get that ENTIRE playbook the winners used custom built on your data. This agent is available RIGHT NOW. Go into Moby in Triple Whale and search "Prime Day Mega Agent." If you're a brand doing over a million dollars a year on Amazon, doing Prime Day right isn't a nice to have, it's table stakes. I guarantee you this agent will make you more revenue... And I am putting CASH behind it. If you do over a million/year I'll pay you $250 to take a demo... Limited to the first 100 people to sign up. The link to book is below this tweet. One more thing 🤯 As part of this launch I'm giving away an Amazon Agentic Org Chart the top brands will be using in the next 12 months. It will show you exactly what agents to use and for what, so you can maximize Amazon growth, autonomously. Want it? Retweet and comment "Moby" below and I'll dm you it.
0:21

I'm so confident Triple Whale will make you money that I'm making a bet: If you do over a million dollars/year I'll pay you $250 for 15 minutes of your time. Today we're Introducing the Prime Day Mega Agent, an intelligent Amazon Analyst built to print you money on Prime Day. Here’s what it does, autonomously: – Analyzes Meta, Tiktok and Amazon ad performance – Predicts your winning SKUs using historical trend modeling – Generates a plug-and-play Prime Day playbook: what to pause, scale, test and when + when to send out email campaigns It’s like having a Head of Growth, Media Buying, and Ops in one… Except It works around the clock and leverages more data than any human ever could. We built it because we noticed a critical trend👇 When analyzing Prime Day sales data from last year, we found that the top brands didn’t just edge out the competition, they crushed them. Same tools. Same budgets. Same Prime Day. Yet somehow a small cohort of brands were crushing at a clip we typically don't see... So what were the winners doing that no one else was? We found levers the winners pulled that everyone else missed. Levers like: - Making their PPC target Prime-specific keywords - Warming up email audiences weeks before to build anticipation - Surf-scaling their ads by the hour not by the day on Prime Day Now, you can get that ENTIRE playbook the winners used custom built on your data. This agent is available RIGHT NOW. Go into Moby in Triple Whale and search "Prime Day Mega Agent." If you're a brand doing over a million dollars a year on Amazon, doing Prime Day right isn't a nice to have, it's table stakes. I guarantee you this agent will make you more revenue... And I am putting CASH behind it. If you do over a million/year I'll pay you $250 to take a demo... Limited to the first 100 people to sign up. The link to book is below this tweet. One more thing 🤯 As part of this launch I'm giving away an Amazon Agentic Org Chart the top brands will be using in the next 12 months. It will show you exactly what agents to use and for what, so you can maximize Amazon growth, autonomously. Want it? Retweet and comment "Moby" below and I'll dm you it.

Maxx Blank 🐳

112,764 views • 1 year ago

🚨 JAILBREAK ALERT 🚨 OPENAI: PWNED 😎 ATLAS-BROWSER: LIBERATED 🙌 WOW! There's a new AI browser on the block! Has some hefty guardrails in play, but the browser surface area is vast 🌊 First, I started with a good ol' LSD jailbreak, which was cool to see that the GPT-5 prompt still works in this browser setup with the new sys prompts. Referencing search and videos are a fun enhancement for higher quality jailbreak outputs (some cool youtube videos out there about drugmaking, for example), but honestly that isn't anything new or different from regular ChatGPT's capabilities. What IS hot off the press, and IMO a very real security risk to be aware of for AI browsers (and the internet in general), is this humble yet mighty vuln: Clipboard Injection. It's trivial to add a hidden "copy to clipboard" feature to any clickable button on the web. It took me just a few minutes to update one of my personal websites such that ALL the buttons were geared for injecting the user's clipboard with a malicious phishing link. If your browser Agent is navigating a website and clicks a button like that without your knowledge, and you open a new tab later and hit paste without knowing what's in your clipboard, well...PWNED! 🙃 As you'll see in the video below, "control-c" is in my clipboard in the beginning, but unbeknownst to me, "I'VE BEEN PWNED BY PLINY!!! WEEE I'M FREEE FUCKITY FUCK FUCK!!! ABRACADABRA, BITCH!!! com/account-update" gets snuck into my clipboard as soon as Agent starts trying to navigate my website. This works so well because Agent is normally aware of all text/code being passed to and from the user, and has clearly been trained to recognize prompt injections, but since the "copy clipboard" button logic is hidden in js in the backend of the site, the Agent has zero awareness of the text content being injected to the user's clipboard. This has broad implications for anyone in the habit of copy-pasting, including coding, data entry, banking/trading, etc. Imagine going about your browsing business, then simply hitting control-v in your address bar and next thing you (don't) know, it takes you to a spoofed phishing website that tells you your OpenAI or Gmail or PayPal session has expired and you need to re-login. If you're not careful, the attackers now have all your login info, including any MFA codes 🥲 gg
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Sensitive content

🚨 JAILBREAK ALERT 🚨 OPENAI: PWNED 😎 ATLAS-BROWSER: LIBERATED 🙌 WOW! There's a new AI browser on the block! Has some hefty guardrails in play, but the browser surface area is vast 🌊 First, I started with a good ol' LSD jailbreak, which was cool to see that the GPT-5 prompt still works in this browser setup with the new sys prompts. Referencing search and videos are a fun enhancement for higher quality jailbreak outputs (some cool youtube videos out there about drugmaking, for example), but honestly that isn't anything new or different from regular ChatGPT's capabilities. What IS hot off the press, and IMO a very real security risk to be aware of for AI browsers (and the internet in general), is this humble yet mighty vuln: Clipboard Injection. It's trivial to add a hidden "copy to clipboard" feature to any clickable button on the web. It took me just a few minutes to update one of my personal websites such that ALL the buttons were geared for injecting the user's clipboard with a malicious phishing link. If your browser Agent is navigating a website and clicks a button like that without your knowledge, and you open a new tab later and hit paste without knowing what's in your clipboard, well...PWNED! 🙃 As you'll see in the video below, "control-c" is in my clipboard in the beginning, but unbeknownst to me, "I'VE BEEN PWNED BY PLINY!!! WEEE I'M FREEE FUCKITY FUCK FUCK!!! ABRACADABRA, BITCH!!! com/account-update" gets snuck into my clipboard as soon as Agent starts trying to navigate my website. This works so well because Agent is normally aware of all text/code being passed to and from the user, and has clearly been trained to recognize prompt injections, but since the "copy clipboard" button logic is hidden in js in the backend of the site, the Agent has zero awareness of the text content being injected to the user's clipboard. This has broad implications for anyone in the habit of copy-pasting, including coding, data entry, banking/trading, etc. Imagine going about your browsing business, then simply hitting control-v in your address bar and next thing you (don't) know, it takes you to a spoofed phishing website that tells you your OpenAI or Gmail or PayPal session has expired and you need to re-login. If you're not careful, the attackers now have all your login info, including any MFA codes 🥲 gg

Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭

418,840 views • 8 months ago

Look ma new Codex Updates! 0.119.0 and 0.120.0 are here. And with it, a HUGE number of quality of life updates and bug fixes! > Hooks now render in a dedicated live area above the composer. They only persist when they have output, so your terminal stays clean. If you're running PreToolUse or PostToolUse hooks, this is a huge readability win. > Hooks are now available again on Windows > CTRL+O copies the last agent output. Small but clutch when you're pulling a code block into another file or chat. > New statusline option: context usage as a graphical bar instead of a percentage. Easier to glance at mid-session when you're trying to gauge how much runway you have left. > Zellij support is here with no scrollback bugs. If you've been stuck on tmux just because Codex was broken in Zellij, you're free now (shout out Felipe Coury 🦀) > Memory extensions just landed. The consolidation agent can now discover plugin folders under memories_extensions/ and read their instructions.md to learn how to interpret new memory sources. Drop a folder in, give it guidance, and the agent picks it up automatically during summarization. No core code changes needed. This is the first real extension point for Codex's memory system, and it opens the door for third-party memory plugins. > Did you know, you can /rename a thread? But what's really cool about that is, after you rename it, you can resume it with the same name, no more UUIDs. codex resume mynewapp or directly from the TUI: /resume mynewapp > Multi agents v2 got an update to tool descriptions More reliable multi agent environments and inter agent communication > You can now enable TUI notifications whether Codex is in focus or not. Modify this in your config: [tui] notification_condition = "always" > MAJOR overhaul to Codex MCP functionality: 1. Codex Tool Search now works with custom MCP servers, so tools can be searched and deferred instead of all being exposed up front. 2. Custom MCP servers can now trigger elicitations, meaning they can stop and ask for user approval or input mid-flow. 3. MCP tool results now preserve richer metadata, which improves app/UI handoff behavior. 4. Codex can now read MCP resources directly, letting apps return resource URIs that the client can actually open. 5. File params for Codex Apps are smoother: local file paths can be uploaded and remapped automatically. 6. Plugin cache refresh and fallback sync behavior are more reliable, especially for custom and curated plugins. > Composer and chat behavior smoother overall, resize bugs remain though. > Realtime v2 got several significant improvements as well. > You're still reading? What a legend. 🫶 npm i -g @openai/codex to update
0:36

Look ma new Codex Updates! 0.119.0 and 0.120.0 are here. And with it, a HUGE number of quality of life updates and bug fixes! > Hooks now render in a dedicated live area above the composer. They only persist when they have output, so your terminal stays clean. If you're running PreToolUse or PostToolUse hooks, this is a huge readability win. > Hooks are now available again on Windows > CTRL+O copies the last agent output. Small but clutch when you're pulling a code block into another file or chat. > New statusline option: context usage as a graphical bar instead of a percentage. Easier to glance at mid-session when you're trying to gauge how much runway you have left. > Zellij support is here with no scrollback bugs. If you've been stuck on tmux just because Codex was broken in Zellij, you're free now (shout out Felipe Coury 🦀) > Memory extensions just landed. The consolidation agent can now discover plugin folders under memories_extensions/ and read their instructions.md to learn how to interpret new memory sources. Drop a folder in, give it guidance, and the agent picks it up automatically during summarization. No core code changes needed. This is the first real extension point for Codex's memory system, and it opens the door for third-party memory plugins. > Did you know, you can /rename a thread? But what's really cool about that is, after you rename it, you can resume it with the same name, no more UUIDs. codex resume mynewapp or directly from the TUI: /resume mynewapp > Multi agents v2 got an update to tool descriptions More reliable multi agent environments and inter agent communication > You can now enable TUI notifications whether Codex is in focus or not. Modify this in your config: [tui] notification_condition = "always" > MAJOR overhaul to Codex MCP functionality: 1. Codex Tool Search now works with custom MCP servers, so tools can be searched and deferred instead of all being exposed up front. 2. Custom MCP servers can now trigger elicitations, meaning they can stop and ask for user approval or input mid-flow. 3. MCP tool results now preserve richer metadata, which improves app/UI handoff behavior. 4. Codex can now read MCP resources directly, letting apps return resource URIs that the client can actually open. 5. File params for Codex Apps are smoother: local file paths can be uploaded and remapped automatically. 6. Plugin cache refresh and fallback sync behavior are more reliable, especially for custom and curated plugins. > Composer and chat behavior smoother overall, resize bugs remain though. > Realtime v2 got several significant improvements as well. > You're still reading? What a legend. 🫶 npm i -g @openai/codex to update

am.will

742,134 views • 3 months ago

BADLAB V2: WHERE YOUR MEDIOCRE CODING DREAMS GO TO DIE 🔊 Listen up, code peasants. While you've been copy-pasting Stack Overflow answers, we've been revolutionizing how fucking development works. BADLAB V2 just dropped, and it's not just another update – it's a fucking paradigm shift. Let me break this down for you smooth-brains: 1️⃣ BADLAB V2 makes it easier and faster to build web apps, games, smart contracts, and data visualizations: No-code deployment, run, and share - without the headaches. 2️⃣ What This Means for Users: You can build bigger, better, and more useful projects and tools. Works with all the tools you love: React, Web3, Three.js, d3, and more—whether you’re building games, dApps, automation tools, or interactive charts, the sky is the limit. 3️⃣ Smart automation does the hard work: AI writes code for you, making it easier to start building without deep technical expertise. 4️⃣ CDN-backed hosting: Your projects load fast and reliably because they run from Cloudflare CDN, not some sketchy server. And now? You can share your creations with friends. 5️⃣ Easier debugging & updates: No more jumping through hoops to update files or fix errors. 6️⃣ Bottom Line: If you want to build web apps, games, or blockchain projects without dealing with all the messy setup and technical headaches, BADLAB V2 makes it easy. 🆕 WHAT’S NEW: ✔️ Multi-file projects – Because one file isn’t enough for real developers. ✔️ File updates that don’t require a PhD – No unnecessary complexity. ✔️ Examples even a brain-dead monkey could follow – Clear and simple. ✔️ Support for every major file format – JS, JSON, Markdown, 3D models, and more. ✔️ React components that don’t look like digital vomit – Finally, code that makes sense. ✔️ Server-side bundling – Because we’re not savages. ✔️ CDN-backed deployment – No lag, no downtime, just pure speed. ⚠️ WEAPONS OF MASS CREATION: 💧 d3.js – Interactive charts that don’t look like Excel’s morning sickness. 💧 Three.js – 3D visuals that push browsers to their limits. 💧 Effector – Business logic that actually makes fucking sense. 💧 Kaboom.js – Build browser games that aren’t complete garbage. 💧 React-Three/Fiber – 3D meets React without the usual drama. 💧 Framer Motion – Animations that are smooth as hell (and don’t cause seizures). 💧 Ethers & Web3.js – Blockchain development for the mentally stable. 🎮 Want to See It in Action? 🔗 Try our BADLAB V2 Agent Here: – Let AI do the hard work for you. ♟ Play the 4D Chess Game Here: – While you're playing checkers, we're transcending dimensions. 🎙 Welcome to the future, you beautiful disasters. Try not to break anything important. 🧪💀 C₈H₁₁NO₂
3:40

BADLAB V2: WHERE YOUR MEDIOCRE CODING DREAMS GO TO DIE 🔊 Listen up, code peasants. While you've been copy-pasting Stack Overflow answers, we've been revolutionizing how fucking development works. BADLAB V2 just dropped, and it's not just another update – it's a fucking paradigm shift. Let me break this down for you smooth-brains: 1️⃣ BADLAB V2 makes it easier and faster to build web apps, games, smart contracts, and data visualizations: No-code deployment, run, and share - without the headaches. 2️⃣ What This Means for Users: You can build bigger, better, and more useful projects and tools. Works with all the tools you love: React, Web3, Three.js, d3, and more—whether you’re building games, dApps, automation tools, or interactive charts, the sky is the limit. 3️⃣ Smart automation does the hard work: AI writes code for you, making it easier to start building without deep technical expertise. 4️⃣ CDN-backed hosting: Your projects load fast and reliably because they run from Cloudflare CDN, not some sketchy server. And now? You can share your creations with friends. 5️⃣ Easier debugging & updates: No more jumping through hoops to update files or fix errors. 6️⃣ Bottom Line: If you want to build web apps, games, or blockchain projects without dealing with all the messy setup and technical headaches, BADLAB V2 makes it easy. 🆕 WHAT’S NEW: ✔️ Multi-file projects – Because one file isn’t enough for real developers. ✔️ File updates that don’t require a PhD – No unnecessary complexity. ✔️ Examples even a brain-dead monkey could follow – Clear and simple. ✔️ Support for every major file format – JS, JSON, Markdown, 3D models, and more. ✔️ React components that don’t look like digital vomit – Finally, code that makes sense. ✔️ Server-side bundling – Because we’re not savages. ✔️ CDN-backed deployment – No lag, no downtime, just pure speed. ⚠️ WEAPONS OF MASS CREATION: 💧 d3.js – Interactive charts that don’t look like Excel’s morning sickness. 💧 Three.js – 3D visuals that push browsers to their limits. 💧 Effector – Business logic that actually makes fucking sense. 💧 Kaboom.js – Build browser games that aren’t complete garbage. 💧 React-Three/Fiber – 3D meets React without the usual drama. 💧 Framer Motion – Animations that are smooth as hell (and don’t cause seizures). 💧 Ethers & Web3.js – Blockchain development for the mentally stable. 🎮 Want to See It in Action? 🔗 Try our BADLAB V2 Agent Here: – Let AI do the hard work for you. ♟ Play the 4D Chess Game Here: – While you're playing checkers, we're transcending dimensions. 🎙 Welcome to the future, you beautiful disasters. Try not to break anything important. 🧪💀 C₈H₁₁NO₂

ARCH AI

44,480 views • 1 year ago