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Vulnerability management is broken. Legacy scanners like Qualys & Tenable were built 20 years ago and it shows. ProjectDiscovery’s cloud platform, powered by Nuclei, delivers fast, accurate, and exploitable-focused results. Open source powered. Enterprise ready. #AppSec #CyberSecurity #VulnerabilityManagement #ProjectDiscovery #Nuclei

ProjectDiscovery

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11,930 просмотров • 1 год назад •via X (Twitter)

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Raiinmaker x Google marks a breakthrough in AI video model training. Over 450,000 contributors. 190+ countries. Real people. Real data. Real results. You can now train AI and generate beautiful, cinematic video all from your phone. It’s fast, it’s easy, it’s next-level. This is what the future looks like: Raiinmaker’s decentralized Video Intelligence platform Traiin Video, together with Google’s cutting-edge Veo technology = a new standard for ethical AI video training. Ethical AI. Human-powered. Built to win. Make AI Yours. Make it $RAIIN. Google Cloud Google Cloud Partners Google AI #GoogleCloudPartner #GoogleCloud

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Raiinmaker

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ICPX Protocol

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Immutable

13,079,429 просмотров • 2 лет назад

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Circle

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Everything you love about generative models — now powered by real physics! Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications. Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: The Genesis physics engine and simulation platform is fully open source at We'll gradually roll out access to our generative framework in the near future. Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism. We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications. Open Source Code: Project webpage: Documentation: 1/n

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Zhou Xian

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Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

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Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

28,745 просмотров • 4 месяцев назад

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

I just built an open NotebookLM clone! Here's what it can do for you: - Process multi-modal data - Scrape websites and YouTube videos - Create a unified knowledge base - Lets you do RAG over it - Remember every conversation - Generate a podcast 🎙️ The idea here is not to reinvent the wheel but to understand how one of the most powerful tools for learning and research actually works, by building it step-by-step! So by the end of this video, you'll learn how to: ↳ Process multimodal data (text, audio, video, URLs, and YouTube videos) into a format ready for LLMs ↳ Store everything in a vector database for fast retrieval ↳ Add a memory layer that remembers conversations and preferences for a personalized experience ↳ Chat with your knowledge base or generate podcasts using a fully open-source, locally running text-to-speech model The podcast generation feature is my favorite part! There's something powerful about turning written content into conversational audio that you can listen to while doing something else. The entire code is 100% open-source. I've shared a link in the replies! ____ Don't forget to drop a like if you enjoy my videos. It shows me I should be making more content like this. Cheers! :)

Akshay 🚀

83,187 просмотров • 10 месяцев назад

I just closed a $70,000 deal with help from 10 Hermes agents Here is how it happened: A couple of months ago, one of my clients asked me to review their IT spending. The biggest surprise was Atlassian. They were using Jira, Confluence, Jira Service Management, and several paid plugins because the built in reporting and time tracking were not meeting their needs. With more than 300 users, they were spending around $140,000 every year. They had been paying that for six years. That is roughly $840,000. So I asked them: “If I build something that replaces most of these tools, improves the workflows your team struggles with, and cuts your bill in half, would you pay me $70,000 per year?” They said yes. So I put 10 Hermes agents to work For the next month, they worked alongside me for almost 8 hours a day. About half of the code was built using Fable 5 and the other half using Opus 4.8. I spent approximately $11,000 on tokens :P That sounds expensive until you compare it with a $70,000 yearly contract and a product I can now offer to other companies. One month later, the product was ready. The client loved it and gave me the green light to open it to the public and it's my code :) I called it Kapvel Kapvel includes: • Project management and Jira ticket imports • A full service desk with a customer portal • Built in time tracking and reporting • Documents with real time collaboration • Spreadsheets, presentations, and whiteboards • Electronic signatures and approvals • QA test case management • A dedicated UAT portal • GitHub, Bitbucket, and GitLab integrations • Pull request workflows inside the same platform • Custom themes and branded portals The part I am most excited about is what happens when someone submits a support ticket to me: My Hermes agents can immediately review the request, investigate the issue, and start working on a solution. Kapvel is basically: Jira + Confluence + DocuSign + Everhour + QA + UAT All inside one platform, with simpler pricing and fewer third party plugins. This is not a mockup The client is already using it Now I am preparing to open Kapvel to other teams Comment KAPVEL for early access.

I just closed a $70,000 deal with help from 10 Hermes agents Here is how it happened: A couple of months ago, one of my clients asked me to review their IT spending. The biggest surprise was Atlassian. They were using Jira, Confluence, Jira Service Management, and several paid plugins because the built in reporting and time tracking were not meeting their needs. With more than 300 users, they were spending around $140,000 every year. They had been paying that for six years. That is roughly $840,000. So I asked them: “If I build something that replaces most of these tools, improves the workflows your team struggles with, and cuts your bill in half, would you pay me $70,000 per year?” They said yes. So I put 10 Hermes agents to work For the next month, they worked alongside me for almost 8 hours a day. About half of the code was built using Fable 5 and the other half using Opus 4.8. I spent approximately $11,000 on tokens :P That sounds expensive until you compare it with a $70,000 yearly contract and a product I can now offer to other companies. One month later, the product was ready. The client loved it and gave me the green light to open it to the public and it's my code :) I called it Kapvel Kapvel includes: • Project management and Jira ticket imports • A full service desk with a customer portal • Built in time tracking and reporting • Documents with real time collaboration • Spreadsheets, presentations, and whiteboards • Electronic signatures and approvals • QA test case management • A dedicated UAT portal • GitHub, Bitbucket, and GitLab integrations • Pull request workflows inside the same platform • Custom themes and branded portals The part I am most excited about is what happens when someone submits a support ticket to me: My Hermes agents can immediately review the request, investigate the issue, and start working on a solution. Kapvel is basically: Jira + Confluence + DocuSign + Everhour + QA + UAT All inside one platform, with simpler pricing and fewer third party plugins. This is not a mockup The client is already using it Now I am preparing to open Kapvel to other teams Comment KAPVEL for early access.

Luke The Dev

23,021 просмотров • 20 дней назад

Today we're launching Accomplish FREE - powered by our new hybrid model router. Since we launched Accomplish a few weeks ago, we've been blown away by what users are building with it. Hundreds of thousands of you downloaded the app and took it for a spin, but one thing kept coming up: not everyone wants to bring their own API key just to get started. So today we're fixing that with free, built-in models - made possible by a massive shift that's happened in just the past few weeks: the rise of fully hosted open-weight models - locally on your Windows machine with NVIDIA NVIDIA GeForce, on your Mac with Apple MLX, or on Accomplish cloud, for FREE. Our new hybrid routing algorithm dynamically routes between cloud models and models running locally on your machine - optimizing for local execution by automatically detecting your hardware capabilities and each sub-task's complexity: coding, visuals, simple classifications - every LLM call is routed to the best model. We also brought some of our favorite enterprise features to the free tier: scheduled task dispatch, Google Workspace integration (Google Drive Docs, Sheets, Slides) via the new Google Workspace CLI, native Slack MCP connectivity, and more. Accomplish FREE is available for macOS, Windows, and Linux. Download, send a task - and boom, it just works with ZERO configuration! Download link in bio / first comment >>

Today we're launching Accomplish FREE - powered by our new hybrid model router. Since we launched Accomplish a few weeks ago, we've been blown away by what users are building with it. Hundreds of thousands of you downloaded the app and took it for a spin, but one thing kept coming up: not everyone wants to bring their own API key just to get started. So today we're fixing that with free, built-in models - made possible by a massive shift that's happened in just the past few weeks: the rise of fully hosted open-weight models - locally on your Windows machine with NVIDIA NVIDIA GeForce, on your Mac with Apple MLX, or on Accomplish cloud, for FREE. Our new hybrid routing algorithm dynamically routes between cloud models and models running locally on your machine - optimizing for local execution by automatically detecting your hardware capabilities and each sub-task's complexity: coding, visuals, simple classifications - every LLM call is routed to the best model. We also brought some of our favorite enterprise features to the free tier: scheduled task dispatch, Google Workspace integration (Google Drive Docs, Sheets, Slides) via the new Google Workspace CLI, native Slack MCP connectivity, and more. Accomplish FREE is available for macOS, Windows, and Linux. Download, send a task - and boom, it just works with ZERO configuration! Download link in bio / first comment >>

Or Hiltch

103,543 просмотров • 4 месяцев назад

I built my own charting platform with Claude Fable, and it does a few things Tradingview straight up can't.. I call it EchoCharts.. so what it basically does is 1) Echoes: this is the big one. it takes the exact shape price action is forming right now them scans thousands of past candles, and finds every time the market looked just like this before. then it shows you what happened next. it'll tell you something like "20 matches, 30% closed higher 24 bars later, median -0.05%", and it draws those past paths forward on the chart so you see the full spread. 2) Sketch search: draw any shape with your mouse, and it finds where price actually did that in real history, then jumps you straight to it. great for the patterns you can feel but can't name. 3) Market clock: A 24-hour dial showing when this market actually moves. volatility, direction and volume, split by hour of the day and day of the week. so you stop trading dead hours and start trading when it counts. plus the basics done clean, candles, volume, a moving average, and RSI. So how it basically works is, it all runs on real binance data, 6,000 live candles.. Echoes matches the shape of the move using correlation, not the price level, so a setup today of bitcoin:native at $63K can match one from years ago at $10K and it only ever looks at fully finished history, it never peeks at the future, so the "what happened next" numbers stay honest. Now, Here’s how i built it: i described what i wanted and claude fable built it. plain javascript, the lightweight-charts library for the chart, around 700 lines, no framework. static site, opens in any browser. one thing i'll be straight about, echoes shows you what happened after similar setups in the past. that's history, not a prediction. it shows you the lay of the land, it doesn't call the future. might open-source the whole thing soon.

I built my own charting platform with Claude Fable, and it does a few things Tradingview straight up can't.. I call it EchoCharts.. so what it basically does is 1) Echoes: this is the big one. it takes the exact shape price action is forming right now them scans thousands of past candles, and finds every time the market looked just like this before. then it shows you what happened next. it'll tell you something like "20 matches, 30% closed higher 24 bars later, median -0.05%", and it draws those past paths forward on the chart so you see the full spread. 2) Sketch search: draw any shape with your mouse, and it finds where price actually did that in real history, then jumps you straight to it. great for the patterns you can feel but can't name. 3) Market clock: A 24-hour dial showing when this market actually moves. volatility, direction and volume, split by hour of the day and day of the week. so you stop trading dead hours and start trading when it counts. plus the basics done clean, candles, volume, a moving average, and RSI. So how it basically works is, it all runs on real binance data, 6,000 live candles.. Echoes matches the shape of the move using correlation, not the price level, so a setup today of bitcoin:native at $63K can match one from years ago at $10K and it only ever looks at fully finished history, it never peeks at the future, so the "what happened next" numbers stay honest. Now, Here’s how i built it: i described what i wanted and claude fable built it. plain javascript, the lightweight-charts library for the chart, around 700 lines, no framework. static site, opens in any browser. one thing i'll be straight about, echoes shows you what happened after similar setups in the past. that's history, not a prediction. it shows you the lay of the land, it doesn't call the future. might open-source the whole thing soon.

Axel Bitblaze 🪓

57,362 просмотров • 2 месяцев назад

SUNDAR PICHAI: AI IS REWRITING EARTH’S INFRASTRUCTURE IN TWO YEARS Sundar laid it out with quiet certainty: the AI boom isn’t coming, it’s already rewriting the laws of scale: "You know, maybe four years ago Google was spending less than $30 billion per year. This year, that number is gonna be over $90 billion. And if you collectively add what all the companies are doing, we have well over a trillion dollars of investment going in, in building the infrastructure for this moment. And one way I think about it is in the next couple of years, we'll end up building what we probably built in the past 10 to 20 years. So hopefully just in a couple of years. So that gives you the scale at which this is ramping up." It’s the kind of scale that makes empires blink. Alphabet’s AI capex shot from $24 billion in 2021 to over $90 billion in 2025, powered by Gemini, Vertex AI, and an arms race for compute that’s now burning through a trillion dollars across Google, Microsoft, Amazon, and Meta. Source: thewizeai / Sundar Pichai

SUNDAR PICHAI: AI IS REWRITING EARTH’S INFRASTRUCTURE IN TWO YEARS Sundar laid it out with quiet certainty: the AI boom isn’t coming, it’s already rewriting the laws of scale: "You know, maybe four years ago Google was spending less than $30 billion per year. This year, that number is gonna be over $90 billion. And if you collectively add what all the companies are doing, we have well over a trillion dollars of investment going in, in building the infrastructure for this moment. And one way I think about it is in the next couple of years, we'll end up building what we probably built in the past 10 to 20 years. So hopefully just in a couple of years. So that gives you the scale at which this is ramping up." It’s the kind of scale that makes empires blink. Alphabet’s AI capex shot from $24 billion in 2021 to over $90 billion in 2025, powered by Gemini, Vertex AI, and an arms race for compute that’s now burning through a trillion dollars across Google, Microsoft, Amazon, and Meta. Source: thewizeai / Sundar Pichai

Mario Nawfal

163,412 просмотров • 8 месяцев назад

Perplexity declared war on the biggest open source AI movement of 2026. This changes how millions of people will interact with AI agents forever. Here is what happened and why almost nobody is talking about the real implications. OpenClaw exploded in January and it became one of the fastest growing open source projects in GitHub history. The premise was radical. An AI agent that runs on your own machine, connects to your messaging apps and actually does things while you sleep. Developers went wild and over 700 community built skills appeared on ClawHub. People were negotiating car deals, filing legal rebuttals, and building entire social networks run by AI agents. Then Perplexity showed up with something different. A cloud powered system that coordinates 20 frontier AI models at once. They call it Perplexity Computer and this week they went even further. They announced Personal Computer. An always on AI agent that lives on a Mac mini in your home, connected to your local files and Perplexity's secure servers around the clock. It never sleeps or stops working and you control it from any device, anywhere. But the real story is what CEO Aravind Srinivas said during the Q&A session at their inaugural developer conference in San Francisco. He called Perplexity Computer a product "meant for serious people." He talked about Uber drivers asking him when they could stop driving and let AI make them passive income. That, he said, is the actual vision and then he went directly after OpenClaw. He said even a former Perplexity engineer struggled to get OpenClaw running on their own machine. He warned about unvetted malware being imported through OpenClaw's community skill hub, with no control over what people are contributing. He called the hobbyist approach of managing 700 API keys and sub agent configuration files a dead end for mainstream adoption. And four years of building world class orchestration gives Perplexity something an open source project cannot match. Enterprise grade security for solopreneurs and businesses alike. On one side, OpenClaw represents radical openness. Your data stays local, you choose your own models, you own everything and the community builds the tools. On the other hand, Perplexity is betting that most people don’t want to be system administrators. They want results, security guarantees, and something that just works out of the box. The Personal Computer runs on Perplexity's SOC 2 certified infrastructure. Every sensitive action requires user approval, every action is logged, and there is a kill switch. The enterprise version connects natively to Snowflake, Salesforce, HubSpot, and hundreds of other platforms. Teams can query data warehouses and build financial models without waiting on an analytics team. The real question is not which product is technically better. The real question is whether the future of AI agents looks like Linux or looks like the iPhone. Because the Uber driver Srinivas described is not going to configure sub agent routing tables. That person needs something that works the moment they open it. And if Perplexity captures that market, the open source movement becomes a niche for developers instead of a revolution for everyone. That is the billion dollar bet being made right now.

Perplexity declared war on the biggest open source AI movement of 2026. This changes how millions of people will interact with AI agents forever. Here is what happened and why almost nobody is talking about the real implications. OpenClaw exploded in January and it became one of the fastest growing open source projects in GitHub history. The premise was radical. An AI agent that runs on your own machine, connects to your messaging apps and actually does things while you sleep. Developers went wild and over 700 community built skills appeared on ClawHub. People were negotiating car deals, filing legal rebuttals, and building entire social networks run by AI agents. Then Perplexity showed up with something different. A cloud powered system that coordinates 20 frontier AI models at once. They call it Perplexity Computer and this week they went even further. They announced Personal Computer. An always on AI agent that lives on a Mac mini in your home, connected to your local files and Perplexity's secure servers around the clock. It never sleeps or stops working and you control it from any device, anywhere. But the real story is what CEO Aravind Srinivas said during the Q&A session at their inaugural developer conference in San Francisco. He called Perplexity Computer a product "meant for serious people." He talked about Uber drivers asking him when they could stop driving and let AI make them passive income. That, he said, is the actual vision and then he went directly after OpenClaw. He said even a former Perplexity engineer struggled to get OpenClaw running on their own machine. He warned about unvetted malware being imported through OpenClaw's community skill hub, with no control over what people are contributing. He called the hobbyist approach of managing 700 API keys and sub agent configuration files a dead end for mainstream adoption. And four years of building world class orchestration gives Perplexity something an open source project cannot match. Enterprise grade security for solopreneurs and businesses alike. On one side, OpenClaw represents radical openness. Your data stays local, you choose your own models, you own everything and the community builds the tools. On the other hand, Perplexity is betting that most people don’t want to be system administrators. They want results, security guarantees, and something that just works out of the box. The Personal Computer runs on Perplexity's SOC 2 certified infrastructure. Every sensitive action requires user approval, every action is logged, and there is a kill switch. The enterprise version connects natively to Snowflake, Salesforce, HubSpot, and hundreds of other platforms. Teams can query data warehouses and build financial models without waiting on an analytics team. The real question is not which product is technically better. The real question is whether the future of AI agents looks like Linux or looks like the iPhone. Because the Uber driver Srinivas described is not going to configure sub agent routing tables. That person needs something that works the moment they open it. And if Perplexity captures that market, the open source movement becomes a niche for developers instead of a revolution for everyone. That is the billion dollar bet being made right now.

Milk Road AI

22,256 просмотров • 5 месяцев назад