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This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. Of course, it feels obvious after you see it: • You write a prompt • The system evaluates it across different scenarios • Based on the results, it... refines it to improve results I recorded a quick video to show you how it works. It's pretty cool stuff! Here are some of the problems and best practices for teams building AI applications: 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. Here is the link: Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!show more

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Shipped the next version of ✨ 1. You can finally sign up and create projects. 2. Projects are to group prompts you want to monitor for your brand or product visibility across LLMs. 3. You can upgrade to get access to more prompts and models. I know it is not perfect. I know there will be a lot of issues. And it lacks a lot of features. Working on it! LLMConsole will become the best marketing platform for LLMs 😘

Dmytro Krasun

14,405 просмотров • 1 год назад

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

39,101 просмотров • 2 лет назад

Season 1 of Prompt 2 Earn is NOW LIVE! 🤖 You can now start using the Dongo AI platform to earn $DONGO rewards at the end of the season. Here’s how it works: 🔹 Do daily research 🔹 Use unique prompts 🔹 Get rewarded with $DONGO tokens at the end of the season Remember, only unique prompts will be recognized by our thorough AI point system. This system is designed to detect and reward creativity, ensuring fairness and encouraging innovative use of our platform. Why participate? It's simple: we want you to explore the platform, demonstrate the power of research + AI, and help us gather valuable data to improve our models. Get started now and make the most of your creativity. The more unique and creative your prompts, the more you earn! 🚀 🔗

Season 1 of Prompt 2 Earn is NOW LIVE! 🤖 You can now start using the Dongo AI platform to earn $DONGO rewards at the end of the season. Here’s how it works: 🔹 Do daily research 🔹 Use unique prompts 🔹 Get rewarded with $DONGO tokens at the end of the season Remember, only unique prompts will be recognized by our thorough AI point system. This system is designed to detect and reward creativity, ensuring fairness and encouraging innovative use of our platform. Why participate? It's simple: we want you to explore the platform, demonstrate the power of research + AI, and help us gather valuable data to improve our models. Get started now and make the most of your creativity. The more unique and creative your prompts, the more you earn! 🚀 🔗

Dongo AI

17,359 просмотров • 2 лет назад

Building with AI gets easier every day. Here is an open-source library that makes integrating AI into an application extremely easy: Star the repository! This library alone can make React the best front-end framework out there! There are a bunch of cool things I like about CopilotKit. Here are 3 of them: 1. It allows you to take any -powered agent and bring it into your application. (This is a brand-new feature!) 2. You can build an AI-powered chatbot in your application. The chatbot will have access to your context and can act on the application. 3. You can build a RAG workflow to process and answer questions from a real-time knowledge base. I recorded a video to show you how simple it is to make some of this happen. A few lines of code, and you are in business. Here is a link to the sample application: CopilotKit is open-source. You can self-host it. You can use it with any LLM. Thanks to the team for showing me their tool and collaborating with me on this post!

Santiago

108,824 просмотров • 2 лет назад

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:

New short course: DSPy: Build and Optimize Agentic Apps DSPy is a powerful open-source framework for automatically tuning prompts for GenAI applications. In this course, you'll learn to use DSPy, together with MLflow. This is built in partnership with Databricks and taught by Chen Qian, co-lead of the DSPy framework. Many AI builders spend hours hand-tuning prompts. When given a set of evals, DSPy automates this process. It’s especially useful for optimizing prompts, including few-shot prompts, in complex agentic AI workflows. Further, if you switch an application to a newer LLM, performance can degrade if your prompts were optimized to the previous model. DSPy automatically optimizes the entire system for the new LLM as well, using just a few evaluation examples. This course teaches DSPy works, and best practices for using it. You’ll write programs using DSPy’s signature-based programming model, debug them with MLflow tracing -- to gain visibility into how different parts of a pipeline, as well as how the overall system, are performing -- and automatically improve their accuracy with DSPy Optimizer. Please sign up here:

Andrew Ng

181,457 просмотров • 1 год назад

This is how you can get programmatic access to any website. You need two things: 1. The URL of the website 2. A prompt specifying what you want to do on the site Mino is a neat platform that uses browser automation and AI-powered navigation to understand your prompt, open the website, and extract the information you want. This opens the doors to unlimited potential automations you could build using data from everyday websites that don't give you an API. Mino uses AI to go way further than you could get by using a regular web scraping tool: • It can handle dynamic JavaScript content • It can handle login walls • It can navigate through interactive booking flows • It adapts to different interfaces and layout changes • It can fill out forms automatically • It supports stealth browser mode In my experience, running Mino on relatively straightforward sites takes about 30-60 seconds to complete. The more times you run it, the faster it gets. I recorded a quick video to show you the platform. You can check their site here: Thanks to the team for the support, onboarding me on their platform, and the collaboration on this post.

This is how you can get programmatic access to any website. You need two things: 1. The URL of the website 2. A prompt specifying what you want to do on the site Mino is a neat platform that uses browser automation and AI-powered navigation to understand your prompt, open the website, and extract the information you want. This opens the doors to unlimited potential automations you could build using data from everyday websites that don't give you an API. Mino uses AI to go way further than you could get by using a regular web scraping tool: • It can handle dynamic JavaScript content • It can handle login walls • It can navigate through interactive booking flows • It adapts to different interfaces and layout changes • It can fill out forms automatically • It supports stealth browser mode In my experience, running Mino on relatively straightforward sites takes about 30-60 seconds to complete. The more times you run it, the faster it gets. I recorded a quick video to show you the platform. You can check their site here: Thanks to the team for the support, onboarding me on their platform, and the collaboration on this post.

Santiago

31,742 просмотров • 6 месяцев назад

Introducing Candy Machine 🍭🤖 An AI-powered vault that doesn't open with keys, it opens with prompts. To open it? You’ve got to prompt your way in. Craft the right prompt, convince the AI, and the reward is yours. Built on Aptos by Cluster Protocol Think you can outsmart AI ->

Introducing Candy Machine 🍭🤖 An AI-powered vault that doesn't open with keys, it opens with prompts. To open it? You’ve got to prompt your way in. Craft the right prompt, convince the AI, and the reward is yours. Built on Aptos by Cluster Protocol Think you can outsmart AI ->

Cluster Protocol

100,589 просмотров • 1 год назад

Great AI starts with great prompts, and Salesforce Einstein 1 Platform makes it easy to create prompts grounded in your customer data. The Einstein Trust Layer ensures your data is never stored outside of Salesforce, so these prompts are trusted. What kind of prompts will you build?

Great AI starts with great prompts, and Salesforce Einstein 1 Platform makes it easy to create prompts grounded in your customer data. The Einstein Trust Layer ensures your data is never stored outside of Salesforce, so these prompts are trusted. What kind of prompts will you build?

Marc Benioff

192,823 просмотров • 2 лет назад

90% of AI products fail in the first month. It's not bad models but bad prompts. Adaline AI is a platform to create prompts, test across models and modalities, integrate tools, evaluate performance, and deploy with real-time monitoring. Find the best prompt for your LLMs...👇

90% of AI products fail in the first month. It's not bad models but bad prompts. Adaline AI is a platform to create prompts, test across models and modalities, integrate tools, evaluate performance, and deploy with real-time monitoring. Find the best prompt for your LLMs...👇

Akshay 🚀

46,821 просмотров • 1 год назад

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Web scraping is a critical skill, and yet nobody talks about it. How do you think companies are training their Large Language Models? Where do you think the data comes from? But web scraping goes beyond all of that. Imagine giving an AI agent access to any public online data in real time! I like to call this "web-scrapping on demand", and I'm pretty sure it's going to unlock unlimited power for AI applications. I recorded a quick video to show you how you can do this using Apify. I've talked about them before, and they are collaborating with me on this post. They have one of the best open-source web scraping and browser automation libraries out there: But it gets much better than this! You can use MCP to connect your AI Agents and applications to the Apify platform and use any specialized actor on demand to scrape and process online data. In the video, I used Cursor to scrape LinkedIn posts with the words "Machine Learning" in real time. Worked like a charm with no code needed! Here is a link to the platform: Think about this: You can now feed your AI applications with any public data on demand! We aren't ready for what's coming.

Santiago

101,473 просмотров • 11 месяцев назад

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

108 workflow templates you can use to build AI applications without writing any code. You can use these templates with n8n. I recorded the attached video to show you how it works. n8n is the workhorse behind an open-source, self-hosted AI starter kit you can install on your computer. They are sponsoring this post. Here is the link to the starter kit repository: And here is the spreadsheet with the 108 templates: Whatever idea you have, search for something similar in the list of templates, and you'll save a ton of time. Lately, I've talked to many non-coders who want to start using AI more seriously to build things. n8n is perfect for that.

Santiago

78,133 просмотров • 1 год назад

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

Santiago

84,445 просмотров • 1 год назад

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Imagine an AI application that can type anywhere you can and use the full context of what's on your screen. This is the application we all deserve (at least if you have macOS.) Check out Omnipilot. It's an app that works with every other macOS application and uses Claude Sonet 3.5 in the background—it also supports Gemini and GPT-4o. Here is the idea: You can use the tool to ask questions about anything on your screen. Or you can use it to autocomplete the text you are typing. You don't need to copy and paste anymore or waste your time providing context to a model. It sees what you see. It works right where you are. That's pretty cool. Here are a couple of cool examples: • Use it to reply to an email • Use it in the terminal to autocomplete a command • Use it to finish a document • Use to send a message on Slack AI at the system level is bonkers. You can read a ton more on their Product Hunt launch page: Thanks to the Omnipilot team for collaborating with me on this post!

Santiago

72,306 просмотров • 1 год назад

In this week's Stability Seconds, we’re getting you up to speed on how to use simple prompting techniques to create more precise images faster by not having to rewrite your prompt. Here’s how: 1️⃣ Use positive and negative prompts: Combine positive prompts to describe what should appear in the image, like “an editorial close-up,” and negative prompts to guide the model away from unwanted elements, such as “low resolution.” 2️⃣ Create prompt journals: When using Stable Image Ultra, try the prompt journal node to store a list of prompts directly into your workflow. This keeps your best prompts in one place, so you can easily reuse them or build on them later. You can find more prompting techniques and start creating with Stable Image Ultra here 👉

In this week's Stability Seconds, we’re getting you up to speed on how to use simple prompting techniques to create more precise images faster by not having to rewrite your prompt. Here’s how: 1️⃣ Use positive and negative prompts: Combine positive prompts to describe what should appear in the image, like “an editorial close-up,” and negative prompts to guide the model away from unwanted elements, such as “low resolution.” 2️⃣ Create prompt journals: When using Stable Image Ultra, try the prompt journal node to store a list of prompts directly into your workflow. This keeps your best prompts in one place, so you can easily reuse them or build on them later. You can find more prompting techniques and start creating with Stable Image Ultra here 👉

Stability AI

14,109 просмотров • 1 год назад

I've made $4.7M with AI. Today, I recorded myself building an entire OpenClaw🦞 business in an hour. You'll learn how to: • Find a winning niche using AI • Build an offer people actually want to pay for • To get your first clients • To use AI to productize and fulfill the entire service • The exact prompts I use to orchestrate all of it After building the #1 Lovable agency. This is the entire playbook behind AI native businesses. The full video is live on YouTube. Comment "Playbook" and I'll DM you the link.

I've made $4.7M with AI. Today, I recorded myself building an entire OpenClaw🦞 business in an hour. You'll learn how to: • Find a winning niche using AI • Build an offer people actually want to pay for • To get your first clients • To use AI to productize and fulfill the entire service • The exact prompts I use to orchestrate all of it After building the #1 Lovable agency. This is the entire playbook behind AI native businesses. The full video is live on YouTube. Comment "Playbook" and I'll DM you the link.

Jacob Klug

123,966 просмотров • 3 месяцев назад

Here's how to track your "AI visibility" across ChatGPT, Perplexity and Google’s AI Overviews: [Watch the 3-min video] Ahrefs' Brand Radar runs on a massive database of prompts: 🔸 957k ChatGPT prompts 🔸 953.5k Perplexity prompts 🔸 76.7M AI Overviews This gives you an instant snapshot of your brand’s presence across the three most popular “AI search engines” - and shows how you stack up against your competitors. You can then filter for LLM outputs where your competitors are mentioned but your brand is not, and use that data to inform your product, content and PR strategies. Check it out and let us know what you think! 👀

Ahrefs

209,133 просмотров • 1 год назад

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Weave is launching the number 1 prompt router in the world. It enables you to get 70% more efficient use of your tokens. We analyzed millions of prompts and found that the vast majority don't need a frontier model. Weave Router fixes that. It analyzes your prompt and routes it to the highest quality model with the lowest cost (across open and closed source models). This all happens in your current workflow on Claude, Cursor or Codex so you don't have to change a thing. Early customers have seen an ~70% reduction in costs without any slowdowns. Source code available.

Adam Cohen

19,522 просмотров • 1 месяц назад

GROK IMAGINE PROMPT TIP: There's a very simple way to improve your prompts: use the prompts from the templates offered by Grok Imagine. These templates provide a wealth of prompts available to everyone. Take a prompt and analyze it to understand the purpose of each word. For example, I took the fish template and added a color change in the prompt using the video extension.

GROK IMAGINE PROMPT TIP: There's a very simple way to improve your prompts: use the prompts from the templates offered by Grok Imagine. These templates provide a wealth of prompts available to everyone. Take a prompt and analyze it to understand the purpose of each word. For example, I took the fish template and added a color change in the prompt using the video extension.

Déborah

550,955 просмотров • 3 месяцев назад

How to prompt hyper-realistic UGC avatars for your ads I've spent 3 weeks fine-tuning the prompts that power ads like these. Here is the 80/20: 1. Be specific about imperfections "His hair is thinning slightly on top with some gray at the sides, and his face shows a few natural wrinkles from years of sun exposure." 2. Specify the camera shot "A low-resolution, authentic selfie-style video recorded with an iPhone front camera." 3. Define imperfect lighting "The lighting is natural and slightly uneven, with a bit of grain to preserve an unpolished, realistic, everyday feel" For eComm operators who want to scale ad creative fast, this is a huge unlock. No more spending hundreds on UGC creators. No more shipping out endless product samples. But you have to understand the prompting. If it looks fake, it simply won't perform. If you want me to send you the full detailed prompts that generated these characters: Like & RT this post Comment "Prompts" Follow me (so the auto-dm can send) i'll dm you

How to prompt hyper-realistic UGC avatars for your ads I've spent 3 weeks fine-tuning the prompts that power ads like these. Here is the 80/20: 1. Be specific about imperfections "His hair is thinning slightly on top with some gray at the sides, and his face shows a few natural wrinkles from years of sun exposure." 2. Specify the camera shot "A low-resolution, authentic selfie-style video recorded with an iPhone front camera." 3. Define imperfect lighting "The lighting is natural and slightly uneven, with a bit of grain to preserve an unpolished, realistic, everyday feel" For eComm operators who want to scale ad creative fast, this is a huge unlock. No more spending hundreds on UGC creators. No more shipping out endless product samples. But you have to understand the prompting. If it looks fake, it simply won't perform. If you want me to send you the full detailed prompts that generated these characters: Like & RT this post Comment "Prompts" Follow me (so the auto-dm can send) i'll dm you

David Roberts

33,250 просмотров • 7 месяцев назад

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,675 просмотров • 2 лет назад