Proxify’s Post

In an era where AI is becoming increasingly accessible, this article offers a timely and insightful look into why professional web services remain invaluable. As AI tools evolve, it's easy to assume that automated solutions can replace human expertise, but this piece emphasizes the enduring need for skilled web professionals who bring creativity, strategy, and personalization to every project. At ImageWorks Creative, we believe this article is a must-read for businesses navigating the balance between AI and the personalized service that makes a true difference in digital success.

How CloudApper Helps Developers Balance Speed and Quality in Application Development https://ift.tt/EDuWnf1 Balancing speed and quality is a top challenge for developers. Learn how CloudApper’s AI Platform streamlines workflows, ensuring faster and high-quality application development. The post How CloudApper Helps Developers Balance Speed and Quality in Application Development appeared first on CloudApper AI - Enable Enterprises To Build & Integrate AI/LLM Painlessly. via CloudApper AI - Enable Enterprises To Build & Integrate AI/LLM Painlessly https://ift.tt/DIE7tMK December 18, 2024 at 04:44PM

𝗧𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝘄𝗮𝘆 𝘁𝗼 𝗲𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄: throw your current softwares in the trash and trade them for AI-native ones. 𝗧𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆 𝘁𝗼 𝗲𝗺𝗯𝗿𝗮𝗰𝗲 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄: get $20/month AI accounts for your people and let them start messing around and integrating AI with the softwares they already use. AI is awesome, but it doesn’t need to be expensive. There are a lot of ways to get involved that are affordable… A lot of softwares will catch up eventually, but there are ways we can get our teams plugged in immediately that are easy and cheap.

The lack of transparency into #AI’s decision-making processes can lead to diminishing citizen trust.🤖 Software company SoftServe is currently working with partners to develop and test tools that can explain AI or train users to do the right thing when it comes to AI disasters - One of which are #extendedreality tools to simulate various scenarios. Read more to find out what #responsibleAI means and translates to product development from a tech provider's perspective.

Title: “AI Automation with GitHub: What’s Next for Business Deployment?” Discussion Prompt: "Now that we’ve explored setting up automation in GitHub, let’s look ahead. As AI continues to evolve, how do you see AI automation impacting deployment in the next 5 years? Are there new AI tools or trends you’re excited about incorporating into your workflows? Feel free to share your thoughts, upcoming trends, or even challenges you’re facing with AI and automation!"

Great discussion prompt! AI automation, particularly when integrated with platforms like GitHub, is already transforming the way we approach deployment, but the next 5 years will likely push those boundaries even further. One of the most significant impacts I foresee is increased deployment speed and precision. As AI tools continue to evolve, they will become better at predicting potential bugs, optimizing code, and managing complex dependencies. This will allow teams to automate not only the deployment process but also anticipate and resolve issues before they affect the end-user experience. Trends and Tools to Watch: AI-Driven Continuous Deployment (CD): We're moving towards a scenario where AI will be able to monitor the entire pipeline—from code commits to production—automatically deciding the best time to deploy based on real-time data (such as traffic, performance, and user behavior). AI-Powered Code Reviews and Quality Assurance: Tools like DeepCode and Codacy are just the beginning. In the next few years, I expect AI to play a more central role in analyzing code quality, suggesting optimizations, and even automating patches for security vulnerabilities. Predictive Analytics and Resource Management: With AI becoming more adept at resource allocation, we’ll likely see better cost optimization in cloud deployments. AI could forecast the resource needs of an application based on historical data and automatically scale servers, reducing operational costs while ensuring performance. Challenges: While these advancements are exciting, they also pose challenges, such as the need for robust AI governance and the risk of over-reliance on automation. Ensuring that AI models making deployment decisions are transparent, secure, and free from biases will be critical to maintain trust and control in business environments. I’d love to hear others’ thoughts on how they’re preparing for these shifts and any AI tools they’ve found particularly useful for automation within GitHub workflows!

AI Tool Of The Day: Typeform - Creating client surveys Today we're taking a 5-day software journey of the latest AI Tools to help you create a Tailored Client Insight Document. Let's start with Typeform. Typeform is an AI driven platform that allows you to quickly and easily create and distribute client surveys. With downloadable insights and so much more. www.typeform.com 1. Sign up to Typeform 2. Hit '+ Create new form' 3. Start from Scratch, Import questions, or Create with AI .... And away you go. That simple and easy. Typeform can revolutionise how you gather client specific insights, give it a try.

Learn how to use #LangChain to develop end-to-end #AI apps! In the realm of AI application development, LangChain serves as one of the solid foundational frameworks, enabling developers to create intelligent applications by seamlessly connecting language models with various data sources and computational tools. Developers begin by using LangChain's core components: creating prompts that guide AI interactions, selecting appropriate language models, implementing memory mechanisms for context retention, and designing agents capable of complex reasoning and task completion. As the application complexity grows, LangGraph steps in to provide advanced workflow orchestration, allowing developers to build sophisticated AI systems with state management, conditional logic, and intricate process flows. LangGraph enables the creation of multi-step, dynamic AI workflows that can make decisions, loop through processes, and handle complex interaction patterns. Throughout the development and deployment process, LangSmith acts as a critical observability layer, providing comprehensive logging, monitoring, and evaluation tools. Developers can trace each step of their AI application, conduct performance analysis, run A/B tests, and gain deep insights into the application's behavior. This integrated ecosystem—LangChain for building, LangGraph for orchestrating, and LangSmith for observing—creates a powerful, end-to-end solution for developing intelligent, reliable, and sophisticated AI applications. Here is my complete guide on LangChain for beginners: https://lnkd.in/grWUZXDR Here is my step-by-step guide on using LangGraph: https://lnkd.in/gKaEjEvk Here is my another guide on building RAG applications using LangChain: https://lnkd.in/gsz_cXfv You can learn about LangSmith here: https://lnkd.in/gakewC-E

Learn how to use #LangChain to develop end-to-end #AI apps!

✨ Building an agent to support customer service Agents is the next holy grail 🍹 of AI since they represent the ability to plan, reason and execute actions pretty much like a human does. As such, there is plenty of debate as to what constitutes an agent versus a simple combination of multiple LLM calls. This debate is to a large extent irrelevant for the industry that cares about building system that are agent like, or as Andrew Ng puts it “agentic”, and automate some of employee’s tasks while speeding up others 🚀 Building agent like systems is hard as LLM are unreliable and become more so as you increase the number of calls until the end result. One way to resolve this is to build modular components that represent the specific skills or tasks you want your agent to accomplish. This allows you to define, build and debug those components as well as audit the steps the agent is taking to reach its final state 👌 It also allow you to expand the toolkit and skillset of your agent as you observe failure cases ⛔ 🔗 Gradient Labs wrote a really nice post on this topic which you should read if you want to dive deeper into the topic https://lnkd.in/eSKfNH93