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jev is INSANE for email. sorted 1,000 gmail messages in 76 seconds. cateogies: needs reply, updates, promos, sales, spam. parsed every single one. total cost $0.03. might just make this my daily mail client now. open source, link below TypeSafe AI

Fazle Rahman

7,214 subscribers

74,695 次观看 • 10 天前 •via X (Twitter)

健康养生科学技术

Anya Rossi• Live Now

Private livecam show

8 条评论

Fazle Rahman 的头像

Fazle Rahman10 天前

repo:

Ahmet Saridag 的头像

Ahmet Saridag9 天前

@typesafeai original 🔥👀

Cartwright 的头像

Cartwright9 天前

@typesafeai You Can sort up to 232 mails sek so 1000 would Only Take 4-5 sek with jev

Johnny Nel | AI for Founders 的头像

Johnny Nel | AI for Founders9 天前

@typesafeai Email does that already, what am I missing?

Asaf Mazuz 的头像

Asaf Mazuz9 天前

@typesafeai Already in my directory 😄 Thank you so much for sharing this! 🙏

Chen 的头像

Chen9 天前

@typesafeai brutally accurate. lived a version of this last quarter

Pranav 的头像

Pranav9 天前

@typesafeai what if Outlook and Gmail implement this for all users 🤷🏻‍♂️

Ryan Huellen 的头像

Ryan Huellen9 天前

@typesafeai Waiting for this animation would drive me nuts. Needs to parallelized - assuming this is for the tweet.

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If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

Ruben Hassid

171,725 次观看 • 8 天前

I just replaced $200/month in SaaS with one internal tool. New lead hits Gmail. It auto-tags, updates our pipeline in Sheets, drafts the proposal with AI, and fires an SMS follow-up via Twilio. Built the whole thing in one afternoon in Lovable. People still think Lovable is just for pretty UI. It's not. You can wire up Gmail, Sheets, Twilio, anything. Build tools that actually replace your stack. I use connectors almost daily now. On client work, agency automation, side projects. I finally wrote down the entire system. Every shortcut, every mistake I made, the stuff I wish I knew on day one.

I just replaced $200/month in SaaS with one internal tool. New lead hits Gmail. It auto-tags, updates our pipeline in Sheets, drafts the proposal with AI, and fires an SMS follow-up via Twilio. Built the whole thing in one afternoon in Lovable. People still think Lovable is just for pretty UI. It's not. You can wire up Gmail, Sheets, Twilio, anything. Build tools that actually replace your stack. I use connectors almost daily now. On client work, agency automation, side projects. I finally wrote down the entire system. Every shortcut, every mistake I made, the stuff I wish I knew on day one.

Prajwal Tomar

11,815 次观看 • 2 个月前

10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.

10 Jev use cases a marketer can see: 1. Lead scoring - You get 100 new leads overnight. Jev can score which ones are most likely to buy. Sales gets the best ones first. 2. Email prioritization - Drop hundreds of emails into it. Jev can score priority, spam, category, and reply probability. 3. Support routing - A client reports a billing issue, another one has an API problem. Jev can instantly decide where each ticket should go. 4. Customer intent - A user visits pricing three times this week. Jev can score how likely they are to upgrade. 5. Marketplace matching - Someone explains exactly what service they need. Jev can score which provider is the closest match. 6. Lead qualification - A prospect fills out your website form. Jev can decide between sales, self-serve, or nurture. 7. Content clipping- Give it a 60-minute podcast transcript. Jev can score the moments most worth clipping. 8. Browser tasks - Tell an agent to find the right hotel. Jev can help choose the best one in 8 seconds. 9. Human vs AI routing - Jev can decide whether it goes to AI or a human. 10. Workflow decisions - Most workflows eventually hit the same question: What should happen next? That's probably the best place to use Jev.

Yum⋆₊˚

130,108 次观看 • 10 天前

🚨Tracey Martin issues a statement to Candace Owens regarding her Daily Mail claims🚨 “For the record: Candace Owens publicly identified me on X as the "source" and "family friend" behind a Daily Mail article. That statement is unequivocally false. After the article was published, revised, and republished by the Daily Mail, my name has been publicly included to a story I had no involvement in sourcing. I did not provide information to the Daily Mail, nor was I the confidential source referenced in the article. To resolve this matter, I have respectfully requested that the Daily Mail issue a public statement confirming that I was not their source. They can make that clarification without disclosing the identity of the actual source. When public figures make accusations, those words carry weight. Accuracy matters. Evidence matters. Integrity matters. Every person deserves to have their name protected from false allegations. I have no interest in fueling online outrage or participating in misleading people. I am interested in one thing: the truth. Truth doesn't need embellishment. It only needs to be told.”

🚨Tracey Martin issues a statement to Candace Owens regarding her Daily Mail claims🚨 “For the record: Candace Owens publicly identified me on X as the "source" and "family friend" behind a Daily Mail article. That statement is unequivocally false. After the article was published, revised, and republished by the Daily Mail, my name has been publicly included to a story I had no involvement in sourcing. I did not provide information to the Daily Mail, nor was I the confidential source referenced in the article. To resolve this matter, I have respectfully requested that the Daily Mail issue a public statement confirming that I was not their source. They can make that clarification without disclosing the identity of the actual source. When public figures make accusations, those words carry weight. Accuracy matters. Evidence matters. Integrity matters. Every person deserves to have their name protected from false allegations. I have no interest in fueling online outrage or participating in misleading people. I am interested in one thing: the truth. Truth doesn't need embellishment. It only needs to be told.”

Sarcastic Liberty 🇺🇸

162,256 次观看 • 2 个月前

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:

this is f**king dangerous. someone just open sourced an entire library of ready-made JEV skills for building insane AI agent workflows. instead of building common agent patterns from scratch, you can grab skills for things like: → jev-route for routing tasks → jev-guardrail for adding checks and verification → jev-fanout for running multiple tasks in parallel → reusable skills you can plug into your own workflows the interesting part is that these solve problems you'll run into once your agents start doing more than simple tasks. you don't have to reinvent routing, verification, or parallel execution every time you build a new workflow. save this now. you'll probably need one of these skills the next time you're building an agent and don't want to start from scratch. repo below:

MIKE

99,316 次观看 • 1 天前

Forget Muse. Forget Instinct. The real move is OpenMuse + Jev 🤯 OpenMuse is the open-source personal agent that dropped last week. Its own browser, your apps, actually does things for you. You host it yourself. But the routing is the real unlock. Jev picks the best model for every single step. Opus when it needs to think, cheap models when it just needs to click. That's what makes running your own agent all day actually affordable. Your agent. Your server. Every frontier model on tap. Watch: > "find me a cheap nonstop flight SF to Tokyo, Dec 8 to 13" > Opus 5.5 makes the plan > DeepSeek V4.1 Flash opens Google Flights and reads every option > Opus comes back: $801 nonstop > "now text my professor I'll miss class, and my wife the flight details" > Opus writes the careful one to the professor > DeepSeek writes the quick one to the wife Total cost: $0.046. It even caught that the flight lands Dec 9, so you'd miss class anyway. And it didn't book a thing without asking.

Forget Muse. Forget Instinct. The real move is OpenMuse + Jev 🤯 OpenMuse is the open-source personal agent that dropped last week. Its own browser, your apps, actually does things for you. You host it yourself. But the routing is the real unlock. Jev picks the best model for every single step. Opus when it needs to think, cheap models when it just needs to click. That's what makes running your own agent all day actually affordable. Your agent. Your server. Every frontier model on tap. Watch: > "find me a cheap nonstop flight SF to Tokyo, Dec 8 to 13" > Opus 5.5 makes the plan > DeepSeek V4.1 Flash opens Google Flights and reads every option > Opus comes back: $801 nonstop > "now text my professor I'll miss class, and my wife the flight details" > Opus writes the careful one to the professor > DeepSeek writes the quick one to the wife Total cost: $0.046. It even caught that the flight lands Dec 9, so you'd miss class anyway. And it didn't book a thing without asking.

Alex Prompter

13,590 次观看 • 23 小时前

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

Here is a step-by-step introduction to building a workflow with a custom AI agent that uses MCP. I explain every component in the video: 1. Building the MCP server 2. Building the agent and an MCP client 3. Building a workflow that uses the agent The goal is simple: Generate a dialogue between two people and make one yell and the other answer with sarcasm. Kestra is sponsoring this video. They are an open-source orchestration platform (repo link below), and I used them to build the workflow that connects every component.

Santiago

51,527 次观看 • 1 年前

🚨 THIS IS INSANE A developer has released a free AI voice studio capable of cloning almost any voice in just a few seconds. It's called Voicebox. And it has already surpassed 50,500 stars on GitHub. Here's how it works: 1.- Upload a short audio sample and clone a voice in seconds. 2.- Generate speech in 23 different languages using multiple TTS engines. 3.- Run everything locally for greater privacy, and you can integrate it into your own applications. It also includes voice dictation, support for AI agents, and MCP integration. All in a single open-source tool. Save this tool. I've left the link in the next comment 👇

🚨 THIS IS INSANE A developer has released a free AI voice studio capable of cloning almost any voice in just a few seconds. It's called Voicebox. And it has already surpassed 50,500 stars on GitHub. Here's how it works: 1.- Upload a short audio sample and clone a voice in seconds. 2.- Generate speech in 23 different languages using multiple TTS engines. 3.- Run everything locally for greater privacy, and you can integrate it into your own applications. It also includes voice dictation, support for AI agents, and MCP integration. All in a single open-source tool. Save this tool. I've left the link in the next comment 👇

Nova

26,530 次观看 • 1 个月前

Claude tokens can be saved by using this simple hack. Every reply, Claude rereads your entire chat from the top before responding. Silently burning your limit before a single word comes back. Someone on Reddit figured out a one-line fix that cuts usage by 75%. Same answers. Same AI. Just stops letting it talk more than it needs to. Exact prompt is pinned in the first comment. Free. I share updates like these in my free AI community on WhatsApp. Join here 👇

Claude tokens can be saved by using this simple hack. Every reply, Claude rereads your entire chat from the top before responding. Silently burning your limit before a single word comes back. Someone on Reddit figured out a one-line fix that cuts usage by 75%. Same answers. Same AI. Just stops letting it talk more than it needs to. Exact prompt is pinned in the first comment. Free. I share updates like these in my free AI community on WhatsApp. Join here 👇

Vaibhav Sisinty

22,408 次观看 • 5 个月前

i genuinely don't understand why everyone isn't using this yet diogo almeida, one of the people behind chatgpt, spent two years in stealth and shipped this on monday. his own words about it: "jev is off the charts", the same frontier intelligence you're already paying for, "two orders of magnitude faster and more efficient" in normal words: jev is an ai that never writes anything. it only decides. yes or no, which pile does this go in, how good is it. one answer per item, under half a second each, with a number for how sure it is. it isn't writing anything, so it can't make anything up so you stop feeding chatgpt one thing at a time and hand jev the whole pile. 400 unread emails into reply today, later, never. 300 comments into questions, complaints, praise. 200 job posts into worth it and not. a competitor's entire ad library into what's actually working. the whole pile, in seconds, for cents full guide: > join the waitlist on people are approved the same day > open the dashboard and create an api key > tell claude or your agent: install the typesafe skill > paste your pile and name the buckets you want it sorted into > it hands everything back labelled, with a confidence number on each one no code, nothing to learn. someone ran 1,891 ads through it in 19 seconds for 12 cents, and it's free on vercel's gateway right now five minutes to set up, and the sorting job that used to eat your evening is done before you stand up full step-by-step guide, link below bookmark this

i genuinely don't understand why everyone isn't using this yet diogo almeida, one of the people behind chatgpt, spent two years in stealth and shipped this on monday. his own words about it: "jev is off the charts", the same frontier intelligence you're already paying for, "two orders of magnitude faster and more efficient" in normal words: jev is an ai that never writes anything. it only decides. yes or no, which pile does this go in, how good is it. one answer per item, under half a second each, with a number for how sure it is. it isn't writing anything, so it can't make anything up so you stop feeding chatgpt one thing at a time and hand jev the whole pile. 400 unread emails into reply today, later, never. 300 comments into questions, complaints, praise. 200 job posts into worth it and not. a competitor's entire ad library into what's actually working. the whole pile, in seconds, for cents full guide: > join the waitlist on people are approved the same day > open the dashboard and create an api key > tell claude or your agent: install the typesafe skill > paste your pile and name the buckets you want it sorted into > it hands everything back labelled, with a confidence number on each one no code, nothing to learn. someone ran 1,891 ads through it in 19 seconds for 12 cents, and it's free on vercel's gateway right now five minutes to set up, and the sorting job that used to eat your evening is done before you stand up full step-by-step guide, link below bookmark this

Argona

32,058 次观看 • 10 天前

🌟 Builder Spotlight: MagicMinute by Adarsh Singh Chauhan Email overload is universal. Every inbox fills faster than we can clear it. Adarsh built an agent that takes that weight off your shoulders. 📥 The pain You open Gmail. 43 unread messages. Half need a quick reply. The rest can wait, but they’ll slip through the cracks if you don’t set reminders. That’s where MagicMinute comes in. 🗣️ From intent to draft in seconds No need to write long messages. Just say what you need. “Email Sarah about the project delay. Mention the client pushed back the deadline and ask if she needs help with the presentation.” MagicMinute turns that into a draft right away. Then you say: – “Send it” – Or “Schedule at 9:45am” 🧠 Contextual smart replies Toggle on a simple away message when you’re off. Or let the agent read and reply for you. You can switch static or smart replies in the sidebar. Example: Mail: “Can you share the prime number code in Python?” Reply: “Here’s a short snippet that does it…” The agent remembers your conversation context. It responds in the same tone as you would, but faster. 🗂️ Memory and follow-ups Scheduling, follow-ups, and memory make this more than just a ChatGPT wrapper tapped into your Gmail. MagicMinute remembers what's scheduled and what needs attention. That’s the superior agent loop in practice: Read → Decide → Act → Remember. 📌 Use cases Students set reminders for assignments and track professor response times. Assistants handle routine email while flagging anything that needs personal attention. Founders can do more outreach while keeping their personal voice. Anyone buried in email can use it. 🧭 What’s next MagicMinute roadmap includes: – Voice & SMS input – Contact memory – Digital signatures – Metadata export as ERC-3525 profiles – Multi-agent inbox delegation Adarsh built this MVP in four weeks during the Summer Residency, using our open-source framework. His demo had that mix of polish and curiosity we look for, so we offered him an internship on the spot. Today he’s co-authoring a paper with us on the learnability of prime number sequences by ML models. This is a great reminder: good agents don’t always need to be flashy. They just need to solve problems people face every day.

🌟 Builder Spotlight: MagicMinute by Adarsh Singh Chauhan Email overload is universal. Every inbox fills faster than we can clear it. Adarsh built an agent that takes that weight off your shoulders. 📥 The pain You open Gmail. 43 unread messages. Half need a quick reply. The rest can wait, but they’ll slip through the cracks if you don’t set reminders. That’s where MagicMinute comes in. 🗣️ From intent to draft in seconds No need to write long messages. Just say what you need. “Email Sarah about the project delay. Mention the client pushed back the deadline and ask if she needs help with the presentation.” MagicMinute turns that into a draft right away. Then you say: – “Send it” – Or “Schedule at 9:45am” 🧠 Contextual smart replies Toggle on a simple away message when you’re off. Or let the agent read and reply for you. You can switch static or smart replies in the sidebar. Example: Mail: “Can you share the prime number code in Python?” Reply: “Here’s a short snippet that does it…” The agent remembers your conversation context. It responds in the same tone as you would, but faster. 🗂️ Memory and follow-ups Scheduling, follow-ups, and memory make this more than just a ChatGPT wrapper tapped into your Gmail. MagicMinute remembers what's scheduled and what needs attention. That’s the superior agent loop in practice: Read → Decide → Act → Remember. 📌 Use cases Students set reminders for assignments and track professor response times. Assistants handle routine email while flagging anything that needs personal attention. Founders can do more outreach while keeping their personal voice. Anyone buried in email can use it. 🧭 What’s next MagicMinute roadmap includes: – Voice & SMS input – Contact memory – Digital signatures – Metadata export as ERC-3525 profiles – Multi-agent inbox delegation Adarsh built this MVP in four weeks during the Summer Residency, using our open-source framework. His demo had that mix of polish and curiosity we look for, so we offered him an internship on the spot. Today he’s co-authoring a paper with us on the learnability of prime number sequences by ML models. This is a great reminder: good agents don’t always need to be flashy. They just need to solve problems people face every day.

Superior Agents

12,203 次观看 • 1 年前

.Dwarkesh Patel: By 2030, it will be less expensive to monitor every single nook and cranny in America than it is to remodel the White House. “Mass surveillance is, at least in certain forms, already legal. It has just been impractical to enforce so far. Under current law, you have no Fourth Amendment protection against any data you share with a third party. That includes your bank, your ISP, your phone carrier, and your email provider. The government reserves the right to purchase and read this data in bulk without a warrant. What’s been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every single camera, read every single message, and cross-reference every single transaction. However, that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, then for 30 billion dollars, you can process every single camera in America. And remember that a given level of AI ability gets 10x cheaper every single year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it’ll be less expensive to monitor every single nook and cranny in this country than it is to remodel the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian state is the political expectation that this is not something we do here.”

.Dwarkesh Patel: By 2030, it will be less expensive to monitor every single nook and cranny in America than it is to remodel the White House. “Mass surveillance is, at least in certain forms, already legal. It has just been impractical to enforce so far. Under current law, you have no Fourth Amendment protection against any data you share with a third party. That includes your bank, your ISP, your phone carrier, and your email provider. The government reserves the right to purchase and read this data in bulk without a warrant. What’s been missing is the ability to actually do anything with all of this data — no agency has the manpower to monitor every single camera, read every single message, and cross-reference every single transaction. However, that bottleneck goes away with AI. There are 100 million CCTV cameras in America. You can get pretty good open source multimodal models for 10 cents per million input tokens. So if you process a frame every ten seconds, and each frame is 1,000 tokens, then for 30 billion dollars, you can process every single camera in America. And remember that a given level of AI ability gets 10x cheaper every single year - so a year from now it’ll cost 3 billion, and then a year after 300 million, and by 2030, it’ll be less expensive to monitor every single nook and cranny in this country than it is to remodel the White House. Once the technical capacity for mass surveillance and political suppression exists, the only thing standing between us and an authoritarian state is the political expectation that this is not something we do here.”

Arjun Khemani

145,893 次观看 • 6 个月前

this is 100% AI and it only cost me a few dollars one seedance 2.5 generation. 30 seconds. zero editing, zero cuts, no capcut. she doesn't exist voice synced, skin real, camera shake real. no creator, no brief, no reshoots. this is what ai ugc looks like now the catch nobody tells you: seedance 2.5 is not cheap every trash prompt is money down the drain so claude writes my prompts now. i built it a skill, perfect structure every single time drop "skill" and ill dm you the exact claude skill for free (must RT + follow so i can dm) if you're a brand and want ai ugc like this for your products launch a campaign on Affiliate Network below

Sam

22,921 次观看 • 1 个月前

SOMEONE BUILT AN OBSIDIAN VAULT THAT RUNS THEIR ENTIRE BUSINESS WHILE THEY SLEEP Every morning at 6AM four things are waiting before they open a single app - project pulse across every active client, content brief with ranked angles for the day, intelligence summary of what matters in their industry and a pre-call brief generated 30 minutes before every meeting with full relationship history and suggested agenda. They log a payment in their daily note and the revenue tracker, client folder and invoice records all update automatically. They drop a research request before bed and the brief is ready when they wake up. The system reads a single file - business identity, active clients, revenue targets, voice - before every operation so every output is business-specific rather than generic AI noise. Total cost: $5/month for a server and $20/month for Claude. Month one the system learns the business. Month six it runs it.

SOMEONE BUILT AN OBSIDIAN VAULT THAT RUNS THEIR ENTIRE BUSINESS WHILE THEY SLEEP Every morning at 6AM four things are waiting before they open a single app - project pulse across every active client, content brief with ranked angles for the day, intelligence summary of what matters in their industry and a pre-call brief generated 30 minutes before every meeting with full relationship history and suggested agenda. They log a payment in their daily note and the revenue tracker, client folder and invoice records all update automatically. They drop a research request before bed and the brief is ready when they wake up. The system reads a single file - business identity, active clients, revenue targets, voice - before every operation so every output is business-specific rather than generic AI noise. Total cost: $5/month for a server and $20/month for Claude. Month one the system learns the business. Month six it runs it.

Noisy

49,080 次观看 • 4 个月前

Ready to have your mind blown It costs over $100,000 on average to build a single family home in California just in fees This isn’t building, labor, or materials, this is just bureaucracy - Pure building permit + plan review fees for a new single-family home are often in the $20,000 - $40,000 range - Total “soft costs,” meaning permits + impact fees, school fees, utility connections, environmental reviews, traffic mitigation, parks, etc often reach $100,000+ in major cities and counties - One home in Fremont cost $160,000 just in government fees before any building started Contrast this with Texas Texas base building permits for a new single-family home are typically $1,000–$3,000. Impact and development fees average under $1,000 per unit San Francisco planned on spending $1.7 million to build a public toilet, after extreme backlash online they were able to build it for around $200,000 and it’s now open

Ready to have your mind blown It costs over $100,000 on average to build a single family home in California just in fees This isn’t building, labor, or materials, this is just bureaucracy - Pure building permit + plan review fees for a new single-family home are often in the $20,000 - $40,000 range - Total “soft costs,” meaning permits + impact fees, school fees, utility connections, environmental reviews, traffic mitigation, parks, etc often reach $100,000+ in major cities and counties - One home in Fremont cost $160,000 just in government fees before any building started Contrast this with Texas Texas base building permits for a new single-family home are typically $1,000–$3,000. Impact and development fees average under $1,000 per unit San Francisco planned on spending $1.7 million to build a public toilet, after extreme backlash online they were able to build it for around $200,000 and it’s now open

Wall Street Apes

99,975 次观看 • 4 个月前

this is f*cking insane.. Higgsfield just spent $500,000 making a 95 minute AI Film called Hell Grind and they just LEAKED everything.. they open sourced their whole entire system.. the brand briefs, the creative research, the prompting system & every asset they used the system they put together to make this film is genuinely insane. this is not some random AI video or AI slop reel. this is a full professional grade AI film with every single piece of the production released publicly no one would ever just casually release $500,000 worth of professional AI filmmaking material for free. but that's exactly what happened if you're doing anything serious in the AI creative space then this is for you. whether you're making AI UGC, faceless content, animations, short films, whatever. the workflows and prompting systems in here apply to everything. this is the most detailed framework at how a professional AI video gets made . this has ever been released publicly, you're not going to find anything close to this anywhere else. i put together everything you need in one place.. here's what you're getting: > the full project brief breaking down their production system from A-Z with every single prompt they used across 95 minutes > all the video, image and audio assets > the full creative research process > how they maintained consistency across a movie length film RT + reply "FILM" and i'll send it over (must follow so i can dm)

Miko

13,747 次观看 • 1 个月前

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

169,974 次观看 • 9 天前

Open source is dead. That’s not a statement we ever thought we’d make. Cal.com was built on open source. It shaped our product, our community, and our growth. But the world has changed faster than our principles could keep up. AI has fundamentally altered the security landscape. What once required time, expertise, and intent can now be automated at scale. Code is no longer just read. It is scanned, mapped, and exploited. Near zero cost. In that world, transparency becomes exposure. Especially at scale. After a lot of deliberation, we’ve made the decision to close the core Cal.com codebase. This is not a rejection of what open source gave us. It’s a response to what risks AI is making possible. We’re still supporting builders, releasing the core code under a new MIT-licensed open source project called cal. diy for hobbyists and tinkerers, but our priority now is simple: Protecting our customers and community at all costs. This may not be the most popular call. But we believe many companies will come to the same conclusion. My full explanation below ↓

Open source is dead. That’s not a statement we ever thought we’d make. Cal.com was built on open source. It shaped our product, our community, and our growth. But the world has changed faster than our principles could keep up. AI has fundamentally altered the security landscape. What once required time, expertise, and intent can now be automated at scale. Code is no longer just read. It is scanned, mapped, and exploited. Near zero cost. In that world, transparency becomes exposure. Especially at scale. After a lot of deliberation, we’ve made the decision to close the core Cal.com codebase. This is not a rejection of what open source gave us. It’s a response to what risks AI is making possible. We’re still supporting builders, releasing the core code under a new MIT-licensed open source project called cal. diy for hobbyists and tinkerers, but our priority now is simple: Protecting our customers and community at all costs. This may not be the most popular call. But we believe many companies will come to the same conclusion. My full explanation below ↓

Bailey Pumfleet

1,566,770 次观看 • 5 个月前

this is pure f*cking treasure Engineer at TypeSafeAI just mapped 389 public Jev builds in one list: skills, MCP servers, SDKs, agents, benchmarks and guides ROUTING > jev-router sends every Claude Code task to the cheapest model that can do it > Switchboard picks the model and reasoning effort per task, then keeps it stable so the prompt cache survives > jev-oncall triages alerts: 418 ms p50, $0.04 per 1,000 alerts GUARDRAILS > jev-axi gates shell commands for Claude Code and Codex, 44/44 on its labeled set > hermes-jev-approvals: 8.7x faster approvals, 4.4x fewer prompts to the user > Sniff Test lints AI slop out of your writing at 182 ms a paragraph AGENTS > Jev Ultrafast: Jev picks every click, a small LLM only types > fast-jev-compaction scores your context instead of summarizing it > jev-browser-use reports 5 to 10x faster browser runs inside Codex SEARCH & RAG > jev-retrieval placed 2nd of 90 models on a reranking leaderboard > jevsearch: 83% Hit@1 against 41% for keyword search alone SDKs & MCP > clients for Swift, Go, Rust, Kotlin, Ruby, Elixir, .NET, Laravel and Spring > MCP servers for Claude Code, Cursor and Codex > Jev inside SQLite, DuckDB and Postgres, straight from SQL EVALS > pytest-jev checks LLM replies in 5.3 s where Claude took 27.1 s > a medical hallucination check at 92.9% accuracy, 204 ms, $0.03 per 1,000 OPEN MODELS > Laya answers in one ~35 ms forward pass > kev trains and runs on a MacBook GAMES, ROBOTS, TRADING > Jev plays Mario, Pokemon and chess > it drives a robot arm and a drone > it trades on Monad with 81 ms decisions 389 builds. one decision layer. go steal the ones you need

this is pure f*cking treasure Engineer at TypeSafeAI just mapped 389 public Jev builds in one list: skills, MCP servers, SDKs, agents, benchmarks and guides ROUTING > jev-router sends every Claude Code task to the cheapest model that can do it > Switchboard picks the model and reasoning effort per task, then keeps it stable so the prompt cache survives > jev-oncall triages alerts: 418 ms p50, $0.04 per 1,000 alerts GUARDRAILS > jev-axi gates shell commands for Claude Code and Codex, 44/44 on its labeled set > hermes-jev-approvals: 8.7x faster approvals, 4.4x fewer prompts to the user > Sniff Test lints AI slop out of your writing at 182 ms a paragraph AGENTS > Jev Ultrafast: Jev picks every click, a small LLM only types > fast-jev-compaction scores your context instead of summarizing it > jev-browser-use reports 5 to 10x faster browser runs inside Codex SEARCH & RAG > jev-retrieval placed 2nd of 90 models on a reranking leaderboard > jevsearch: 83% Hit@1 against 41% for keyword search alone SDKs & MCP > clients for Swift, Go, Rust, Kotlin, Ruby, Elixir, .NET, Laravel and Spring > MCP servers for Claude Code, Cursor and Codex > Jev inside SQLite, DuckDB and Postgres, straight from SQL EVALS > pytest-jev checks LLM replies in 5.3 s where Claude took 27.1 s > a medical hallucination check at 92.9% accuracy, 204 ms, $0.03 per 1,000 OPEN MODELS > Laya answers in one ~35 ms forward pass > kev trains and runs on a MacBook GAMES, ROBOTS, TRADING > Jev plays Mario, Pokemon and chess > it drives a robot arm and a drone > it trades on Monad with 81 ms decisions 389 builds. one decision layer. go steal the ones you need

NO1ennn

24,281 次观看 • 1 天前

A GUY GAVE A HERMES AI AGENT ONE GOAL AND IT GENERATED $22,000 IN LEADS FOR A STRANGER'S BUSINESS IN 24 HOURS On stage at a conference, he picked a random small business from a LinkedIn post in the audience. A web design studio in Carlisle, Cumbria. He gave Hermes Agent – open source, runs on any LLM – one goal: "Find companies in the area. Take screenshots of their current websites. Redesign every single one. If they don't have a website, build one. Email each business owner offering the redesign for £2,000." The agent went off and did it autonomously. Hundreds of full website redesigns. Real ones. Not AI slop – actual professional-looking sites. It could only send 50 emails a day to avoid looking like spam. 100 emails sent. 11 responses. Roughly $22,000 in leads sitting in one inbox. From one prompt. For a business that didn't even know this was happening. The agent didn't need a team, a sales department, or weeks of outreach. It needed one goal and permission to keep going. Bookmark this post. Full demo in the video below.

A GUY GAVE A HERMES AI AGENT ONE GOAL AND IT GENERATED $22,000 IN LEADS FOR A STRANGER'S BUSINESS IN 24 HOURS On stage at a conference, he picked a random small business from a LinkedIn post in the audience. A web design studio in Carlisle, Cumbria. He gave Hermes Agent – open source, runs on any LLM – one goal: "Find companies in the area. Take screenshots of their current websites. Redesign every single one. If they don't have a website, build one. Email each business owner offering the redesign for £2,000." The agent went off and did it autonomously. Hundreds of full website redesigns. Real ones. Not AI slop – actual professional-looking sites. It could only send 50 emails a day to avoid looking like spam. 100 emails sent. 11 responses. Roughly $22,000 in leads sitting in one inbox. From one prompt. For a business that didn't even know this was happening. The agent didn't need a team, a sales department, or weeks of outreach. It needed one goal and permission to keep going. Bookmark this post. Full demo in the video below.

SCOTTY BEAM

18,877 次观看 • 3 个月前