Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

Perplexity Computer in 60 seconds: 1. It's a cloud-based AI employee that runs tasks in the background. 2. 19 models working together. Claude for reasoning, GPT-5.2 for research, Grok for speed tasks. You don't pick. It routes automatically. 3. 400+ connectors. Gmail, Slack, Notion, Salesforce, HubSpot. One click to... enable each. 4. Credits, not tokens. Simple tasks cost ~30. Complex builds cost 1,000+. Vague prompts waste them. Specific prompts save them. 5. Spaces = persistent project folders. Upload context once, every task inherits it. 6. Scheduled tasks run on autopilot. "Every Monday, prep my calendar." Set it and forget it. The PRD hack alone (in the article) will save you hundreds in credits. Full breakdown in the article below.show more

Corey Ganim

45,329 subscribers

106,105 просмотров • 3 месяцев назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Claude Code Scheduled Tasks is now available... here's a solid idea to connect it with Telegram Save this so you don't forget to set it up! First, ask Claude to add a simple Telegram messaging module to your repo. You can use the Telegram Bot Builder Skill from Link: Install command: npx claude-code-templates@latest --skill enterprise-communication/telegram-bot-builder Once the module is in your project, grab your bot credentials from BotFather and add the bot ID to your .env file That's it! ✅ Now every Scheduled Task you create should end with an instruction for Claude to send the task result to Telegram using that module. Claude will handle the delivery automatically on every task it runs

Daniel San

91,123 просмотров • 4 месяцев назад

most people open Claude every morning and re-explain their entire life. every. single. time. then I built 7 layers that remember everything: Layer 1: tell Claude who I am, once Layer 2: build separate brains for separate work Layer 3: turn on memory so it learns me Layer 4: upload 5 writing samples so it sounds like me Layer 5: dump my world into project files Layer 6: connect Gmail, Calendar, Drive, Slack Layer 7: schedule tasks that run while I sleep 60 minutes to set up. spread across one week. now Claude finishes my sentences. knows which client I mean from one word. catches mistakes I'd miss. it's not a chatbot anymore. it's a personal AI that knows me better than most coworkers do. your AI doesn't know you yet. this article fixes that.

most people open Claude every morning and re-explain their entire life. every. single. time. then I built 7 layers that remember everything: Layer 1: tell Claude who I am, once Layer 2: build separate brains for separate work Layer 3: turn on memory so it learns me Layer 4: upload 5 writing samples so it sounds like me Layer 5: dump my world into project files Layer 6: connect Gmail, Calendar, Drive, Slack Layer 7: schedule tasks that run while I sleep 60 minutes to set up. spread across one week. now Claude finishes my sentences. knows which client I mean from one word. catches mistakes I'd miss. it's not a chatbot anymore. it's a personal AI that knows me better than most coworkers do. your AI doesn't know you yet. this article fixes that.

Nav Toor

62,350 просмотров • 2 месяцев назад

GPT-5.5 is MUCH more reliable on longer running tasks - for the first time with any model. As we speak I have a migration running for over 7+ hours - this literally never happened before, the models would maybe run for 30 mins or of you really shout at them for 2-3 hours. Last night I went to sleep, set a long running task, then queued up 10 prompts to 'keep it going'. It did not stop after the first prompt and kept going for 8+ hours and I woke up to all the same prompts still queued up. The ability to run for a long time, in combination with ability to validate with computer use & other tools, makes it much more useful for building real applications.

GPT-5.5 is MUCH more reliable on longer running tasks - for the first time with any model. As we speak I have a migration running for over 7+ hours - this literally never happened before, the models would maybe run for 30 mins or of you really shout at them for 2-3 hours. Last night I went to sleep, set a long running task, then queued up 10 prompts to 'keep it going'. It did not stop after the first prompt and kept going for 8+ hours and I woke up to all the same prompts still queued up. The ability to run for a long time, in combination with ability to validate with computer use & other tools, makes it much more useful for building real applications.

Peter Gostev

105,642 просмотров • 3 месяцев назад

subagents are just recursive agents where you can apply different prompts + models depending on the task. since they’re just a primitive, Cursor cli can actually spawn subagents by calling cursor-agent in headless mode via shell commands. that’s what makes the cli so nice. you can extend it, experiment, and have a lot of fun exploring orchestration patterns. here’s one way to do it w. dynamic model selection: 1. create a subagents.mdc rule 2. drop in: ``` --- alwaysApply: true --- ALWAYS spawn subagents by running `cursor-agent -p [task] --output-format=text --force --model [model]` in the terminal. Each subagent should return a summary of the changes it made. Subagents should be used for ALL tasks You can adopt a fan-out pattern where you spawn subagents to perform parallel isolated tasks, and then fan-in the results. Use the following models: - `--model gpt-5` for reasoning, researching, and planning - `--model sonnet-4` for implementation ``` 3. start cursor cli and try it out you can also adjust the rule to be more explicit when it should use subagents, when not to, which models when etc.

subagents are just recursive agents where you can apply different prompts + models depending on the task. since they’re just a primitive, Cursor cli can actually spawn subagents by calling cursor-agent in headless mode via shell commands. that’s what makes the cli so nice. you can extend it, experiment, and have a lot of fun exploring orchestration patterns. here’s one way to do it w. dynamic model selection: 1. create a subagents.mdc rule 2. drop in: ``` --- alwaysApply: true --- ALWAYS spawn subagents by running `cursor-agent -p [task] --output-format=text --force --model [model]` in the terminal. Each subagent should return a summary of the changes it made. Subagents should be used for ALL tasks You can adopt a fan-out pattern where you spawn subagents to perform parallel isolated tasks, and then fan-in the results. Use the following models: - `--model gpt-5` for reasoning, researching, and planning - `--model sonnet-4` for implementation ``` 3. start cursor cli and try it out you can also adjust the rule to be more explicit when it should use subagents, when not to, which models when etc.

eric zakariasson

57,554 просмотров • 11 месяцев назад

this is f*cking gold. I told Fable 5 it's a retiring engineer on its last week, and its final task is to leave everything behind for the team replacing it. It read my entire git history. Every mistake, every dead end, every fix that finally worked. Then it started writing them into skill files so the cheaper models replacing it never repeat what I got wrong. Fable leaves July 12. What it leaves behind is up to you. (full breakdown in the article below)

this is f*cking gold. I told Fable 5 it's a retiring engineer on its last week, and its final task is to leave everything behind for the team replacing it. It read my entire git history. Every mistake, every dead end, every fix that finally worked. Then it started writing them into skill files so the cheaper models replacing it never repeat what I got wrong. Fable leaves July 12. What it leaves behind is up to you. (full breakdown in the article below)

Prajwal Tomar

125,537 просмотров • 21 дней назад

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 просмотров • 6 месяцев назад

Claude + GPT Image 2 + seedance + Meta ads MCP Replaced my 10k/month performance marketing agency Here's the exact stack (and how it works): Step 1: Research Feed Claude your product URL, your competitors' URLs, and your top-performing ad angles. It builds your full brand brief + competitor intelligence in minutes. No agency strategist needed. Step 2: Static ads in seconds Claude writes image generation prompts based on your brief. Those prompts go straight into GPT Image 2. Out comes scroll-stopping creative. Batched. On-brand. No designer. Step 3: Video ads that convert Claude writes video prompts. Those go into Seedance 2.0. UGC-style videos. AI actor formats. Product showcases. All generated, not filmed. (Both GPT Image 2 + Seedance are live on HeyOz right now.) Step 4: Publish + optimize on autopilot Connect Claude to Meta Ads MCP. It publishes your creatives, monitors performance, and keeps iterating. Your agency was charging you for this. This entire workflow is documented in a guide I put together, covering prompts, setup, and the exact MCP config. Why this matters: Most brands are still paying for slow, expensive creative production. The ones who figure this out in the next 90 days will have an unfair advantage. Don't be the last one to know. Comment "REPLACE" and I'll send it to you directly.

HeyOz

25,027 просмотров • 2 месяцев назад

🚨 Do you understand what Claude just quietly dropped while everyone was distracted? 1 million tokens. Let me explain what that actually means because the number alone doesn't hit right. > A senior engineer joins a company and spends 3 to 6 months just reading code.. Understanding how things connect. Learning where the bugs hide. Why that one file nobody touches exists. It takes months because a codebase is massive and human memory is small. > Claude just loaded the entire thing in one prompt. 30 seconds. Every file, Every function, Every line. All of it. Sitting in memory like it's been working there for years. And it scored highest among every single frontier model. Not GPT.. Not Gemini, Nobody. > Yesterday Amazon's AI nuked production because it couldn't see the full picture - it made a decision with partial context and deleted everything. Today an AI can hold 1 million tokens of context at once. That's the fix. That's the "before and after" moment for AI coding. > 600 images in one request. Entire PDFs. Full repos. And they dropped it on a Friday on all plans like it was a patch note. The scariest AI updates aren't the ones with press conferences. They're the ones that drop in a tweet at 6pm and change everything by Monday morning.

🚨 Do you understand what Claude just quietly dropped while everyone was distracted? 1 million tokens. Let me explain what that actually means because the number alone doesn't hit right. > A senior engineer joins a company and spends 3 to 6 months just reading code.. Understanding how things connect. Learning where the bugs hide. Why that one file nobody touches exists. It takes months because a codebase is massive and human memory is small. > Claude just loaded the entire thing in one prompt. 30 seconds. Every file, Every function, Every line. All of it. Sitting in memory like it's been working there for years. And it scored highest among every single frontier model. Not GPT.. Not Gemini, Nobody. > Yesterday Amazon's AI nuked production because it couldn't see the full picture - it made a decision with partial context and deleted everything. Today an AI can hold 1 million tokens of context at once. That's the fix. That's the "before and after" moment for AI coding. > 600 images in one request. Entire PDFs. Full repos. And they dropped it on a Friday on all plans like it was a patch note. The scariest AI updates aren't the ones with press conferences. They're the ones that drop in a tweet at 6pm and change everything by Monday morning.

Tuki

206,260 просмотров • 4 месяцев назад

Claude + GPT Image 2 + Seedance + Meta Ads MCP Just fired my $10k/month performance marketing agency. Here's the exact stack (and how it runs): Step 1: Research Hand Claude your product page, your competitors' pages, and your best ad angles. It spits out a full brand brief + competitor breakdown in minutes. No strategist. No $200/hour consultant. Step 2: Static ads in seconds Claude crafts image prompts from your brief. Drop those into GPT Image 2. You get scroll-stopping creative. Batched. On-brand. No Figma needed. Step 3: Video ads that convert Claude writes video prompts. Feed them into Seedance 2.0. UGC-style clips. AI actor formats. Product showcases. All generated. Zero filming. (Both GPT Image 2 + Seedance are live on HeyOz right now.) Step 4: Publish + optimize on autopilot Hook Claude into Meta Ads MCP. It pushes your creatives live, tracks performance, and keeps iterating. Your agency was billing you hourly for this exact thing. I documented the full workflow in a guide: prompts, setup, and the exact MCP config. Why this matters: Most brands are still bleeding money on slow, overpriced creative production. The ones who crack this in the next 90 days will own an unfair advantage. Don't be the brand that figures it out too late. Comment "REPLACE" and I'll DM it to you.

Ahad Shams

18,270 просмотров • 2 месяцев назад

A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.

A 17-year-old student spent $4,200 on 7 Mac minis. Small silver boxes. Stacked on a desk. Connected in one room. From the outside, it looked like a stupid purchase. But inside, it wasn't just 7 computers. It was Skills. Hooks. Memory. Worktrees. One machine handled repeatable tasks. One ran checks automatically. One kept context between sessions. Others ran parallel jobs without touching each other's work. While most people were still typing the same instructions again and again, his setup was already moving. A lot of people pay $200 a month for Claude and still use maybe 20% of it. He built a system around it. Skills turned repeated work into reusable workflows. Hooks made actions fire automatically. Memory stopped every session from starting at zero. Worktrees let multiple tasks run at the same time without collisions. That changed everything. Setup time: under 1 hour once. Time returned: 3 to 5 hours every day. He spent $4,200 once. He made $16,000 in the first week. Not because he found a secret tool. Not because he wrote magical prompts. Because he stopped using it like a chatbot and started using it like infrastructure. 7 Mac minis. 1 student. $4,200 in. $16,000 out. And most people would still call it just a stack of computers.

Gipp 🦅

21,269 просмотров • 3 месяцев назад

Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Most AI agent setups treat every message the same. Simple question? top-tier model. complex task? top-tier model. Your token bill just keeps climbing. I tested OpenSquilla this week on a real document drafting workflow, and the routing caught me off guard. It judges each message's complexity locally, then picks the model tier that fits. Simple tasks go to cheaper models. Complex ones still get the heavy lifting done. You're not paying reasoning tokens for a "hello." I ran a longer workflow, and the context didn't collapse the way it usually does. It distills important information before compression, so you're not starting from scratch mid-session. If you run agents regularly, the bill adds up faster than you think. This is built specifically for that problem. They're running the 10M Token Bill Challenge right now. worth joining if you want to see what smart routing actually saves you in practice. #10MTokenChallenge OpenSquilla

Parul Gautam

26,685 просмотров • 2 месяцев назад

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

Hamza Khalid

32,300 просмотров • 20 дней назад

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime

youtube is paying $8,217 a month to a channel with zero humans. no face. just 6 AI tools publishing anime on autopilot twice a week and youtube has no idea the algorithm doesn't check who made the video. it checks one number: how long people keep watching that's the entire game an 8-hour lofi anime stream plays on loop. one upload turns into hundreds of hours of watchtime every month at $3-8 RPM that's $2,400-6,400 from a single file the pipeline runs itself claude writes the script. midjourney draws the frames. runway animates. elevenlabs voices it. suno writes the soundtrack. assembles and publishes humans in the process: zero from prompt to a finished 12-minute episode: 2 hours. from episode to youtube: zero one channel. $8,217 last month article below - every prompt for every step most people ask "will AI take my job". better question - why are you still trading hours for money when a pipeline trades prompts for watchtime

Ventry

118,212 просмотров • 2 месяцев назад

after 9/11 the CIA built a team whose only job was to find holes in their own thinking. they called it the Red Cell. the playbook they used is now public. 40 pages. free. almost nobody reads it. I turned their 4 best techniques into 4 prompts you paste into Claude: → prompt 1: what hidden assumptions is my plan built on? → prompt 2: it's 18 months later and my idea failed. walk me through what went wrong. → prompt 3: a competitor with $100M wants to crush me in 90 days. what's their plan? → prompt 4: write the 1-star review from the customer who felt cheated. 30 minutes. your idea either dies here or comes out stronger. both save you 6 months. all 4 prompts are in this article. try them on your next big decision. before reality tries it for you.

after 9/11 the CIA built a team whose only job was to find holes in their own thinking. they called it the Red Cell. the playbook they used is now public. 40 pages. free. almost nobody reads it. I turned their 4 best techniques into 4 prompts you paste into Claude: → prompt 1: what hidden assumptions is my plan built on? → prompt 2: it's 18 months later and my idea failed. walk me through what went wrong. → prompt 3: a competitor with $100M wants to crush me in 90 days. what's their plan? → prompt 4: write the 1-star review from the customer who felt cheated. 30 minutes. your idea either dies here or comes out stronger. both save you 6 months. all 4 prompts are in this article. try them on your next big decision. before reality tries it for you.

Nav Toor

253,527 просмотров • 29 дней назад

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

THIS SITE COST AROUND $12 IN CREDITS TO BUILD. STUDIOS QUOTE $35,000 FOR THE SAME THING. What's on screen isn't a basic landing page. It's a fully animated, scroll-driven site, generated end to end in one agentic session with Claude Code + Higgsfield. What's actually on the page: → Cinematic motion clips pulled from 30+ generative models → Scroll animations written automatically - zero hand-coded keyframes → 6 cinematic effects baked in with no config: film grain, particles, vignette, glass cards, color tints, scroll pacing Scroll the demo and one question won't go away: did Claude really assemble all of this in a single pass? For boutique studios billing $100-149/hr, that question lands like a verdict. What it normally takes: → A designer, a motion artist, and a developer → Weeks of handoffs between them → 6 systems wired by hand - GSAP ScrollTrigger, Lenis smooth-scroll, frame extraction, asset optimization, layout, copy That pipeline was the moat. It's what justified the invoice. Here's the part studios and their clients won't enjoy hearing. The price gap: → Boutique agency build: $6,000-$35,000+ → Industry average project: ~$5,280 → Delivery cost: a Claude subscription + a few dollars of Higgsfield credits → Timeline: weeks of production → a single session One operator can now run all six systems in one pass and ship a working site - without touching a frame extractor or writing a CSS keyframe by hand. Full breakdown of how it's built in the article below. Save it & read today 👇

ZEUS⚡️

477,187 просмотров • 1 месяц назад

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,251 просмотров • 1 месяц назад

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,839,572 просмотров • 3 месяцев назад

Claude "Puzzling" while GPT 5.6 on GOD-MODE just bade a banger that's mindblowing. Here's the exact way to get a site like this, step by step: > open the desktop app, pick Sol, reasoning on High. taste work never goes to small models > drop it 3 sites with motion you love and one line: "reverse-engineer the art direction: mood, typography, pacing, and WHY each animation exists. save it as a style bible" > brief in one paragraph, goal not steps: "[your niche] site, cinematic scroll, every animation has a job. follow the bible" > house rules on top: no template hero, no stock gradients, nothing on the page moves without a reason > now the bar: "a motion designer can't tell this from an agency build." spin up a SECOND 5.6 with fresh context whose only job is to FAIL the build against that bar > /loop overnight: build, grade, close the biggest gap, again. you're asleep for all of it > when the verifier runs out of complaints: tag Sites. live URL, one click, zero hosting The deeper version of every step (the full contract, the house rules, the verifier trick, when Ultra is worth the bill) is in the article below. P.S. send the article to your GPT and tell it "we're doing this tonight".

Claude "Puzzling" while GPT 5.6 on GOD-MODE just bade a banger that's mindblowing. Here's the exact way to get a site like this, step by step: > open the desktop app, pick Sol, reasoning on High. taste work never goes to small models > drop it 3 sites with motion you love and one line: "reverse-engineer the art direction: mood, typography, pacing, and WHY each animation exists. save it as a style bible" > brief in one paragraph, goal not steps: "[your niche] site, cinematic scroll, every animation has a job. follow the bible" > house rules on top: no template hero, no stock gradients, nothing on the page moves without a reason > now the bar: "a motion designer can't tell this from an agency build." spin up a SECOND 5.6 with fresh context whose only job is to FAIL the build against that bar > /loop overnight: build, grade, close the biggest gap, again. you're asleep for all of it > when the verifier runs out of complaints: tag Sites. live URL, one click, zero hosting The deeper version of every step (the full contract, the house rules, the verifier trick, when Ultra is worth the bill) is in the article below. P.S. send the article to your GPT and tell it "we're doing this tonight".

Miraqle

201,492 просмотров • 15 дней назад

FABLE 5 + HIGGSFIELD JUST KILLED THE $35,000 WEB AGENCY. The same animated website that used to cost between $6,000 and $35,000 can now be built in a single session for around $12 in AI credits. Claude Code handles the website. Higgsfield creates the visuals. Together, they can build a complete scroll-driven website from a simple prompt. Claude writes the layout, GSAP ScrollTrigger animations, Lenis smooth scrolling, responsive pages, and checks everything before you ship. Higgsfield generates the hero videos, cinematic transitions, ambient loops, thumbnails, and every visual asset you need. By the end of one session, you have a fully animated website with cinematic motion, smooth scrolling, optimized assets, responsive layouts, and polished visual effects like film grain, particles, vignette, glass cards, and color tints. Getting started only takes a few minutes. Add Higgsfield as an MCP server inside Claude Code, complete the OAuth login once, and Claude can generate and pull videos directly into your project without manually exporting anything. The prompts are simple. Give Claude your project brief and ask it to script the entire scroll experience. Tell it to generate a hero video and supporting clips for every section. Ask it to add the finishing touches like film grain, particles, glass cards, and scroll pacing. Then let it review the site, improve loading speed, fix mobile layouts, and rewrite anything that doesn’t work. This replaces work that usually looks like this: A $6,000-$35,000 web agency. An $800-$2,000 motion designer. A $2,000-$10,000 front-end developer. And weeks of back-and-forth before launch. Now it’s Fable 5, Higgsfield, a subscription, a few dollars in AI credits, and one session. The pipeline used to be the advantage. Now it’s just a prompt. Full breakdown in the article below.

FABLE 5 + HIGGSFIELD JUST KILLED THE $35,000 WEB AGENCY. The same animated website that used to cost between $6,000 and $35,000 can now be built in a single session for around $12 in AI credits. Claude Code handles the website. Higgsfield creates the visuals. Together, they can build a complete scroll-driven website from a simple prompt. Claude writes the layout, GSAP ScrollTrigger animations, Lenis smooth scrolling, responsive pages, and checks everything before you ship. Higgsfield generates the hero videos, cinematic transitions, ambient loops, thumbnails, and every visual asset you need. By the end of one session, you have a fully animated website with cinematic motion, smooth scrolling, optimized assets, responsive layouts, and polished visual effects like film grain, particles, vignette, glass cards, and color tints. Getting started only takes a few minutes. Add Higgsfield as an MCP server inside Claude Code, complete the OAuth login once, and Claude can generate and pull videos directly into your project without manually exporting anything. The prompts are simple. Give Claude your project brief and ask it to script the entire scroll experience. Tell it to generate a hero video and supporting clips for every section. Ask it to add the finishing touches like film grain, particles, glass cards, and scroll pacing. Then let it review the site, improve loading speed, fix mobile layouts, and rewrite anything that doesn’t work. This replaces work that usually looks like this: A $6,000-$35,000 web agency. An $800-$2,000 motion designer. A $2,000-$10,000 front-end developer. And weeks of back-and-forth before launch. Now it’s Fable 5, Higgsfield, a subscription, a few dollars in AI credits, and one session. The pipeline used to be the advantage. Now it’s just a prompt. Full breakdown in the article below.

MIKE

138,059 просмотров • 16 дней назад

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

Cody Schneider

78,363 просмотров • 2 месяцев назад