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⚡ hunk 0.16.0 is out: - supports all 65 diffs[dot]com/shiki themes (!) - smoother scrolling - split layout in lazygit - more agent-driven cleanup tools

Ben Vinegar

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43,215 просмотров • 3 месяцев назад •via X (Twitter)

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IN NEWS: Framer raises a $100M Series D round. We spoke with Koen Bok (CEO) and Jorn van Dijk (Founder) on the future of the web: “The dot com is still one of the most durable assets out there. The web as a way to present your brand and build equity isn’t going anywhere.” What changes, they argue, is the experience: “Websites have only gotten more boring over the last 10 years. Browsers can already do so much more: 3D, video, richer interaction. With better tools, we can make the web cool again.”

TBPN

56,426 просмотров • 1 год назад

🚨Mark Cuban on the AI Bubble: Who Actually Gets Wiped Out? Mark Cuban is BACK on All-In, joining @jason LIVE! (0:00) Bubble talk: Comparing today's AI market to the dot-com bubble (7:50) AI's real-world limits: why enterprise AI is harder than expected (16:38) Transitioning to an AI-First Workspace (21:26) Health & AI: self-directed healthcare and biometric data (24:30) Politics, wealth tax, and AI as a truth-seeking force (35:12) Knicks & Sports Talk ----------------------------- Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Starting a business? Northwest Registered Agent gives you everything you need to build a complete Business Identity including free tools and built-in privacy. Get more at Northwest Registered Agent LLC

🚨Mark Cuban on the AI Bubble: Who Actually Gets Wiped Out? Mark Cuban is BACK on All-In, joining @jason LIVE! (0:00) Bubble talk: Comparing today's AI market to the dot-com bubble (7:50) AI's real-world limits: why enterprise AI is harder than expected (16:38) Transitioning to an AI-First Workspace (21:26) Health & AI: self-directed healthcare and biometric data (24:30) Politics, wealth tax, and AI as a truth-seeking force (35:12) Knicks & Sports Talk ----------------------------- Thanks to our partners for making this possible! Most advertisers have never heard of the platform with an $11B annual run rate in ad spend. AppLovin Ads — 1B+ daily active users, full-screen video ads watched for a median of 35 seconds, and businesses are profitably spending hundreds of thousands of dollars a day on it. Advertiser access is in closed beta. The window is open at Starting a business? Northwest Registered Agent gives you everything you need to build a complete Business Identity including free tools and built-in privacy. Get more at Northwest Registered Agent LLC

The All-In Podcast

164,499 просмотров • 2 месяцев назад

🚨 NOW: Scott Presler is going DOOR TO DOOR in the POURING RAIN in Pennsylvania His goal is to kick out liberal Supreme Court justices who are OPPOSED to election integrity and encourage all residents to vote RED. Scott is a WORKHORSE! 🇺🇸 "We would love some more help. This election on Tuesday November 4th is so important, I wouldn't be out here getting soaked for you unless I cared." "Help us. Scott Presler's Early Vote Action dot com. Tuesday November 4th. New Jersey, Virginia, Pennsylvania. Come on my friends. I need your help!" - ThePersistence

🚨 NOW: Scott Presler is going DOOR TO DOOR in the POURING RAIN in Pennsylvania His goal is to kick out liberal Supreme Court justices who are OPPOSED to election integrity and encourage all residents to vote RED. Scott is a WORKHORSE! 🇺🇸 "We would love some more help. This election on Tuesday November 4th is so important, I wouldn't be out here getting soaked for you unless I cared." "Help us. Scott Presler's Early Vote Action dot com. Tuesday November 4th. New Jersey, Virginia, Pennsylvania. Come on my friends. I need your help!" - ThePersistence

Eric Daugherty

126,770 просмотров • 11 месяцев назад

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Adobe.. has released a tool that combines Generative Gaussian Splats with a Diffusion layer and it's not all over the internet? WHAT IS GOING ON :D I had to test this out ofc! Substance 3D viewer, the new 3d viewer just released by Adobe, not only supports viewing of a large, large list of 3d formats, but it also supports generating of 3d models as Gaussian Splats. Combine that with the built-in 3d to image functionality and you basically have at least a few of the steps I've been doing for the last year or so using multiple tools. Exciting stuff! The new Photoshop Beta also supports the 3D viewer and you can import 3d files directly in photoshop as smart objects linked to the viewer. The elements you prompt in based on your models, can be easily exported to your clipboard without backgrounds making it very easy to use this in your normal image editing workflows. I have a suspicion that we'll see much more of this in other Adobe tools and I'm very curious to see this used for things like this in more 3d tools than "just" Adobe Neo (which is fantastic btw). #adobe #art #gaussiansplatting

Martin Nebelong

280,121 просмотров • 1 год назад

Just six months ago, DHH (creator of Ruby on Rails and Omarchy) said how he doesn’t really use AI tools to write code, because they are not good enough. Things have changed, a lot. Timestamps: 00:00 Intro 02:11 Omarchy and Ruby on Rails 08:25 37signals overview 10:12 Launching HEY 18:38 Building HEY 22:47 Designers at 37signals 28:08 The craft of design 31:52 Why DHH now embraces AI workflows 39:45 The AI inflection point 44:23 DHH’s agent-first workflow 55:09 AI’s impact on junior developers 1:03:08 Developer experience with AI 1:16:43 What does AI mean for developers? 1:23:33 37signals teams and hiring 1:38:20 Work-life balance with AI 1:41:41 Why DHH keeps building 1:45:24 Closing Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. Stop switching between different tools, and have them all in one place. • WorkOS – Everything you need to make your app enterprise ready. WorkOS gives you APIs to ship enterprise features in days. Check out • Sonar – The makers of SonarQube, the industry standard for automated code review. See how SonarQube Advanced Security is empowering the Agent Centric Development Cycle (AC/DC) with new capabilities. Three interesting observations from this conversation: #1 DHH's philosophy on AI has not changed, but the available tools very much have. Autocomplete-style coding assistants were genuinely annoying for experienced developers six months ago. Things changed with the shift from tab-completion to agent harnesses, plus the emergence of powerful models like Opus 4.5 – when agents started producing code which DHH does want to merge with little to no alteration. #2 Beautiful code and products aren’t matters of vanity; they’re signals of correctness. Dipping into philosophy, DHH says: “When something is beautiful, it’s likely to be correct.” He argues that Steve Jobs wanted the inside of a computer to be beautiful because people who care about circuit board layout are also those who sweat on the details of the UI. #3 DHH’s development workflow, today: He runs tmux to have two models running, and neovim in the center. Specifics: - One fast LLM running (typically Gemini 2.5) in one split terminal - A slow but more powerful model in another terminal (usually Opus) - NeoVim for reviewing diffs via Lazygit

Just six months ago, DHH (creator of Ruby on Rails and Omarchy) said how he doesn’t really use AI tools to write code, because they are not good enough. Things have changed, a lot. Timestamps: 00:00 Intro 02:11 Omarchy and Ruby on Rails 08:25 37signals overview 10:12 Launching HEY 18:38 Building HEY 22:47 Designers at 37signals 28:08 The craft of design 31:52 Why DHH now embraces AI workflows 39:45 The AI inflection point 44:23 DHH’s agent-first workflow 55:09 AI’s impact on junior developers 1:03:08 Developer experience with AI 1:16:43 What does AI mean for developers? 1:23:33 37signals teams and hiring 1:38:20 Work-life balance with AI 1:41:41 Why DHH keeps building 1:45:24 Closing Brought to you by: • Statsig – ⁠ The unified platform for flags, analytics, experiments, and more. Stop switching between different tools, and have them all in one place. • WorkOS – Everything you need to make your app enterprise ready. WorkOS gives you APIs to ship enterprise features in days. Check out • Sonar – The makers of SonarQube, the industry standard for automated code review. See how SonarQube Advanced Security is empowering the Agent Centric Development Cycle (AC/DC) with new capabilities. Three interesting observations from this conversation: #1 DHH's philosophy on AI has not changed, but the available tools very much have. Autocomplete-style coding assistants were genuinely annoying for experienced developers six months ago. Things changed with the shift from tab-completion to agent harnesses, plus the emergence of powerful models like Opus 4.5 – when agents started producing code which DHH does want to merge with little to no alteration. #2 Beautiful code and products aren’t matters of vanity; they’re signals of correctness. Dipping into philosophy, DHH says: “When something is beautiful, it’s likely to be correct.” He argues that Steve Jobs wanted the inside of a computer to be beautiful because people who care about circuit board layout are also those who sweat on the details of the UI. #3 DHH’s development workflow, today: He runs tmux to have two models running, and neovim in the center. Specifics: - One fast LLM running (typically Gemini 2.5) in one split terminal - A slow but more powerful model in another terminal (usually Opus) - NeoVim for reviewing diffs via Lazygit

Gergely Orosz

329,135 просмотров • 5 месяцев назад

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

The same kinds of productivity gains we've seen in coding with AI agents are heading to the rest of knowledge work. This is the jump when you go from having a chatbot to being able to actually have an agent go off and do work for minutes or even hours and come back with a complete work output that you then review. Here's an example of the new Box Agent filling out an RFP response from an existing knowledge base. This process would normally take hours to fill out, and requires the full attention of the user doing the work. Now, you provide the Box Agent with the RFP questions, and it will go off, make a plan, extract all the relevant questions, read through existing source material to come up with an answer, and then generate a new word document as the final output. All while you're doing something else. The key to this architecture is that the agent is able to use all of the same tools in the background that a user uses to get work done. The agent can search for documents, read entire files, run scripts and tools in the background, and even be able to write code on the fly to automate tasks it hasn't seen before. And best of all, the Box Agent will (soon) work from the Box MCP and CLI so you can invoke it in any agentic system as a step in a process. This kind of agent complexity would have been impossible even 6 months ago. Models consistently failed at tracking long running tasks or using the right tools at the right moment for the task. But this is all now possible because of models like GPT-5.4, Opus 4.6, and Gemini 3, and is only getting better by the month. Just as we moved from engineers writing code and using AI as an assistant to answer questions, in many areas of knowledge work -like legal, finance, consulting, sales, marketing, and more- when we have a problem we'll just kick off the AI agent to just go work on it for us in the background.

Aaron Levie

24,728 просмотров • 5 месяцев назад

I've been building a "front page" for Toad (my terminal UI for agentic coding). This will the first page the user will see if they run "toad" from the command line without additional arguments. For here they can browse / search for agents and install directly from the command line. There are 10 agent supported so far, with more being added all the time. I also see this is a place to install other more specialized AI tools in addition to agentic coding. Check out the video! #toad #AI

I've been building a "front page" for Toad (my terminal UI for agentic coding). This will the first page the user will see if they run "toad" from the command line without additional arguments. For here they can browse / search for agents and install directly from the command line. There are 10 agent supported so far, with more being added all the time. I also see this is a place to install other more specialized AI tools in addition to agentic coding. Check out the video! #toad #AI

Will McGugan

35,626 просмотров • 10 месяцев назад

The latest RAG trend for the current agent harnesses (Codex, Cowork) is to do two passes of document processing to solve a knowledge work task over a data room of documents: 1️⃣ A fast and light pass, oftentimes using a free/OSS doc parsing tool. This can be cheaply run across 10-100-1k’s of files, and enables the agent to then do retrieval (e.g. grep, semantic) to find relevant subsets of context. 2️⃣ A “just-in-time” VLM-based pass. Once the agent finds the relevant pages of context, it will screenshot the documents can call its own VLM (or write code) to dissect the pages. The issue with only using VLM-based OCR tools over massive ad-hoc customer file dumps is that it’s slow and expensive. Doing JIT VLM OCR allows the agent to filter through the data cheaply, but still preserve accuracy for the context that’s needed for the task. The agent harnesses do two-pass document processing by default using off the shelf-tools: pdf2text as the first pass, and using itself (Opus 5) as the second pass. See the below video where Cowork runs over a bunch of PDFs to answer a question about a benchmark graph in the Kimi k3 paper. The main issues here with the “out of the box” doc processing these agents offer are: * Opus 5 is not the best VLM for OCR. It is also way too expensive at scale and lacks grounding * The OSS tools like pypdf, pdf2text, may not be versatile enough as the first pass. * The agent will write a lot of throwaway code to rewrite things an OCR tool would’ve provided out of the box, like chart processing, bounding boxes, confidence scores, leading to increased cost and speed. We have all the tools within LlamaIndex 🦙 to help any agent do two-pass document processing with higher accuracy and lower cost. 1️⃣ We have liteparse for the first pass - a free/OSS parser written in Rust that’s faster/more accurate than other OSS parsers, and supports 50+ document types 2️⃣ We have LlamaParse for the second pass - an agentic document engine that uses VLMs+harnesses to achieve SOTA in accuracy and cost across various doc parsing and extraction tasks. It can be called from any agent harness as an MCP or skill. It takes in page numbers as input, so that the agent can choose to run LlamaParse over a subset of the doc instead of the full doc as a “zoom-in” pass. Come check it out! LiteParse: LlamaParse: All the relevant docs, including MCP, are here:

The latest RAG trend for the current agent harnesses (Codex, Cowork) is to do two passes of document processing to solve a knowledge work task over a data room of documents: 1️⃣ A fast and light pass, oftentimes using a free/OSS doc parsing tool. This can be cheaply run across 10-100-1k’s of files, and enables the agent to then do retrieval (e.g. grep, semantic) to find relevant subsets of context. 2️⃣ A “just-in-time” VLM-based pass. Once the agent finds the relevant pages of context, it will screenshot the documents can call its own VLM (or write code) to dissect the pages. The issue with only using VLM-based OCR tools over massive ad-hoc customer file dumps is that it’s slow and expensive. Doing JIT VLM OCR allows the agent to filter through the data cheaply, but still preserve accuracy for the context that’s needed for the task. The agent harnesses do two-pass document processing by default using off the shelf-tools: pdf2text as the first pass, and using itself (Opus 5) as the second pass. See the below video where Cowork runs over a bunch of PDFs to answer a question about a benchmark graph in the Kimi k3 paper. The main issues here with the “out of the box” doc processing these agents offer are: * Opus 5 is not the best VLM for OCR. It is also way too expensive at scale and lacks grounding * The OSS tools like pypdf, pdf2text, may not be versatile enough as the first pass. * The agent will write a lot of throwaway code to rewrite things an OCR tool would’ve provided out of the box, like chart processing, bounding boxes, confidence scores, leading to increased cost and speed. We have all the tools within LlamaIndex 🦙 to help any agent do two-pass document processing with higher accuracy and lower cost. 1️⃣ We have liteparse for the first pass - a free/OSS parser written in Rust that’s faster/more accurate than other OSS parsers, and supports 50+ document types 2️⃣ We have LlamaParse for the second pass - an agentic document engine that uses VLMs+harnesses to achieve SOTA in accuracy and cost across various doc parsing and extraction tasks. It can be called from any agent harness as an MCP or skill. It takes in page numbers as input, so that the agent can choose to run LlamaParse over a subset of the doc instead of the full doc as a “zoom-in” pass. Come check it out! LiteParse: LlamaParse: All the relevant docs, including MCP, are here:

Jerry Liu

22,763 просмотров • 1 месяц назад

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Wanna become a top-tier investor? 🤑 Look no further - our most recent project Kvants has got all the tools to make you one. Check out more about this one-of-a-kind AI-powered asset management platform: ◽️ AI-Driven Quantitative Trading Strategies developed by leading hedge fund managers ◻️ Data aggregation platform powered by Sentiment AI Agent ◻️ Kvants DEX Trading Terminal: the fastest on-chain trading bot on Solana & EVM Chains ◻️ Integrated by Binance, OKX, ByBit, KuCoin, HTX The average APY for users of Kvants platform is 64% 🤯 You don't have just to dream big... now you can win big! IDO loading...

Seedify

83,626 просмотров • 1 год назад

Beta Blocker just got a big update on Windows and Android. Buckle in 🐾 🎨 New Censor Styles Windows gets a full new lineup of effects: Static, Glitch, RGB Shift, Terminal and lot's more. Android gets also gets most of them too! 🎭 Preset Styles Windows now has themed preset styles, letting you swap between complete censor looks instantly instead of tuning every setting by hand. 🔄 Reverse Censor Reverse Censor is now available on Android, with a new Reverse Strength slider to choose your strength. There’s also a setup popup when enabling it, plus stronger One App coverage through Android Accessibility. On Windows, Reverse Censor is now more consistent across live mode, exports, recording, and virtual cam. 🎯 Per-Detection Overrides Windows now supports per-detection customization, so different detections can have their own style, text, and image instead of all sharing one global setup. 🧹 Fixes & Polish Fixed heavy cursor flickering during active blocking, improved performance for animated effects and larger boxes, and cleaned up several parts of the UI. This update also brings better language access, smoother scrolling, cleaner style controls, updated Android device targeting, support for the new Android settings in packs, cleaner overlay behaviour, and a smoother install/update experience. Oh, and the pack creator program was also updated to support all of the new features, and got a major cleanup, so this is essentially 3 programs getting major updates at the same time 🤗 Enjoy 💕

Beta Blocker just got a big update on Windows and Android. Buckle in 🐾 🎨 New Censor Styles Windows gets a full new lineup of effects: Static, Glitch, RGB Shift, Terminal and lot's more. Android gets also gets most of them too! 🎭 Preset Styles Windows now has themed preset styles, letting you swap between complete censor looks instantly instead of tuning every setting by hand. 🔄 Reverse Censor Reverse Censor is now available on Android, with a new Reverse Strength slider to choose your strength. There’s also a setup popup when enabling it, plus stronger One App coverage through Android Accessibility. On Windows, Reverse Censor is now more consistent across live mode, exports, recording, and virtual cam. 🎯 Per-Detection Overrides Windows now supports per-detection customization, so different detections can have their own style, text, and image instead of all sharing one global setup. 🧹 Fixes & Polish Fixed heavy cursor flickering during active blocking, improved performance for animated effects and larger boxes, and cleaned up several parts of the UI. This update also brings better language access, smoother scrolling, cleaner style controls, updated Android device targeting, support for the new Android settings in packs, cleaner overlay behaviour, and a smoother install/update experience. Oh, and the pack creator program was also updated to support all of the new features, and got a major cleanup, so this is essentially 3 programs getting major updates at the same time 🤗 Enjoy 💕

Isla

80,532 просмотров • 3 месяцев назад

So I’ve been using grok Grok Bot for a few days. It’s starting to click Bro they leapfrogged codex app and Claude code entirely. Super app 2.0 Grok bot’s marketing is completely wrong and misleading. It’s not an easy normie friendly chat. Don’t pay attention to the bot or chat UX, that’s not the point. Total distraction. Logo is flat out wrong. They legit did the Shoggoth meme with a smiley face mask in front of an AI demon. Grok bots is actually the highest difficulty, most powerful, and highest skillcap agent harness by far. Super rough around the edges. Dev tool, power user quality, nowhere remotely close to consumer level of approachability. Which is fine, they chose a lane, VSCode dev heavy design sensibilities bleed through, and to be fair it’s in beta. But importantly they nailed a new agent stepping stone: Persistent agent driven cloud computers Progression: - Chat - Chat + Tools, Calculate & Code Interpreter, MCPs - Claude Code & Coding Agent Harnesses: Chat + Tools + Read/Write local Files + Local CLI - Codex/Conductor Super App: Agent Harness + vertical tabs + embedded browser + computer use - Grok Bots: Agent Harness + Computer use + Browser Use + Entire cloud computer with all your files and apps, saved Grok bots absolutely mogs ChatGPT work, and the codex super app + ChatGPT remote. UX of having the cloud computer instance be persistent and not ephemeral per thread is 10000x better. Also you can actually open the computer from your phone and click and type and look at things. It’s not some black box only the agent sees. These were the 2 missing pieces This pattern is about to get copied and adopted by everyone.

Nick Dobos

460,678 просмотров • 1 месяц назад

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

The founder of LangChain says both models and harnesses have gotten really good between December and now. According to Harrison Chase, the core idea of an agent before Christmas was a model running in a loop and calling tools. This had been the north star for 3 years. - langchain had this when it launched - autogpt was the same idea - openclaw is kind of a future version of it Then about a year ago, they started getting really good. Claude Code, Manus, and Deep Research were all launched around the same time. All of them use the same pattern: running in a loop with harnesses (planning tools, file systems, code execution, etc) Harness engineering became a thing. Then Opus came out in November and really unlocked it. - the harness let the model do more and more - less hardcoded logic - way more control Then everyone went on vacation, played around, and realized that the model and the harness finally worked reliably.

Ivan Burazin

33,948 просмотров • 5 месяцев назад

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

Nihan

19,296 просмотров • 4 месяцев назад

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

229,172 просмотров • 5 месяцев назад

Just about 10 days left to file your taxes. While figuring out taxation for trading and investments is usually a nightmare, we've made it super easy. I think our tax statements are easily the best in the industry by a mile. On Console, you’ll find all the statements you need to file taxes for your trades and investments: Tax P&L statement with trade-wise charges, turnover across all segments, equity and non-equity segregation, capital gains split for pre- and post-July 23, 2024, and more. Ability to download one year of contract notes at a time Full tradebooks. Various filters to make it easy to figure out STCG (short-term capital gains) and LTCG (long-term capital gains). And more... If you need help filing your taxes, we've also partnered with Quicko to make the process even smoother.

Just about 10 days left to file your taxes. While figuring out taxation for trading and investments is usually a nightmare, we've made it super easy. I think our tax statements are easily the best in the industry by a mile. On Console, you’ll find all the statements you need to file taxes for your trades and investments: Tax P&L statement with trade-wise charges, turnover across all segments, equity and non-equity segregation, capital gains split for pre- and post-July 23, 2024, and more. Ability to download one year of contract notes at a time Full tradebooks. Various filters to make it easy to figure out STCG (short-term capital gains) and LTCG (long-term capital gains). And more... If you need help filing your taxes, we've also partnered with Quicko to make the process even smoother.

Nithin Kamath

24,008 просмотров • 1 год назад

NVIDIA just released a repo that scans AI agent skills for security risks BEFORE you run them, which matters more than people realize now that everyone's installing random tools, skills, and MCPs from GitHub. I break down that one AND 4 other GitHub repos trending over the last 30 days in this episode of The Startup Ideas Podcast (SIP) 🧃: 1. A skill that kills AI slop finally (better writing) 2. A CRM thats built for AI agents (so you can have agents follow up, reach out etc) 3. An agent that edits your videos for you (so you can focus on creative) 4. An opensource CapCut alternative built for agents (because editing is a headache) All free, all open source. Watch my breakdown of the 5 GitHub repos trending over the last 30 days below The tools people will be talking about in six months are on GitHub today. That's the fun part of digging through GitHub right now.

NVIDIA just released a repo that scans AI agent skills for security risks BEFORE you run them, which matters more than people realize now that everyone's installing random tools, skills, and MCPs from GitHub. I break down that one AND 4 other GitHub repos trending over the last 30 days in this episode of The Startup Ideas Podcast (SIP) 🧃: 1. A skill that kills AI slop finally (better writing) 2. A CRM thats built for AI agents (so you can have agents follow up, reach out etc) 3. An agent that edits your videos for you (so you can focus on creative) 4. An opensource CapCut alternative built for agents (because editing is a headache) All free, all open source. Watch my breakdown of the 5 GitHub repos trending over the last 30 days below The tools people will be talking about in six months are on GitHub today. That's the fun part of digging through GitHub right now.

GREG ISENBERG

54,582 просмотров • 22 дней назад

One of the hardest problems with using AI agents to automate meaningful document work (contracts, KYC, diligence, claims, and more) is not actually building the agent, but building the UI/UX audit trail so the human can understand decisions linking back to the source documents - in PDFs, Powerpoint, Word, etc. This requires really good layout detection and segmentation, that you need to surface as *metadata context* to the AI agent, beyond any document -> markdown conversion. We've built really-nice VLM-capabilities to identify and segment all the elements within your document: from tables to charts to form boxes. You can use this metadata to directly provide experiences where humans can analyze the source document even as decisions are being made. Come check out LlamaParse!

One of the hardest problems with using AI agents to automate meaningful document work (contracts, KYC, diligence, claims, and more) is not actually building the agent, but building the UI/UX audit trail so the human can understand decisions linking back to the source documents - in PDFs, Powerpoint, Word, etc. This requires really good layout detection and segmentation, that you need to surface as metadata context to the AI agent, beyond any document -> markdown conversion. We've built really-nice VLM-capabilities to identify and segment all the elements within your document: from tables to charts to form boxes. You can use this metadata to directly provide experiences where humans can analyze the source document even as decisions are being made. Come check out LlamaParse!

Jerry Liu

19,283 просмотров • 6 месяцев назад