Latest news with #AIapplications
Yahoo
15 hours ago
- Business
- Yahoo
C3.ai (AI) Opens Agentic AI Platform to Partners for Enterprise AI Innovation
Inc. (NYSE:AI) is one of the On August 12, Enterprise AI application software company C3 AI launched the C3 AI Strategic Integrator Program. Through the program, partners can license the C3 Agentic AI Platform to build complex Enterprise AI applications up to 100 times faster than traditional methods and then license those applications to their customers. The program allows partners to retain all intellectual property rights to product extensions, application capabilities, machine learning models, and other customizations they independently develop on C3 AI applications or with the C3 Agentic AI Platform. A professional executive in a modern office setting talking on the phone surrounded by their digital media software delivery platform. They will also be provided access partner resources including the C3 AI developer community, C3 AI Academy training, agentic coding assistance, expert support, and go-to-market collaboration. 'As we expand in 2025, C3 AI is licensing its most valuable asset — the C3 Agentic AI Platform — to trusted partners through the OEM program. Participating in this program will be a force multiplier for organizations looking to lead AI-driven digital transformation of their industries. We are enabling these partners to design, develop, and bring to market derivative works in the form of Enterprise AI applications, expanding the C3 AI ecosystem and footprint. This represents a significant growth opportunity for C3 AI.' -Thomas M. Siebel, Chairman and CEO, C3 AI. Inc. (NYSE:AI) is an enterprise artificial intelligence (AI) software company involved in building and operating enterprise-scale AI applications and accelerating digital transformation. While we acknowledge the potential of AI as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock. READ NEXT: and Disclosure: None. Sign in to access your portfolio
Geeky Gadgets
31-07-2025
- Business
- Geeky Gadgets
Introducing Align Evals : The Ultimate Tool for AI Precision and Efficiency
What if evaluating the performance of large language models (LLMs) could be as precise and seamless as setting a GPS to your destination? With the rapid rise of LLM applications in everything from creative writing to technical problem-solving, making sure these models meet user expectations has become a critical challenge. Yet, traditional evaluation methods often feel like navigating uncharted terrain—time-consuming, inconsistent, and prone to misalignment between machine outputs and human judgment. Enter Align Evals, a new feature introduced by Langsmith, designed to bring clarity and structure to the evaluation process. By aligning machine-generated assessments with human-labeled benchmarks, Align Evals promises not only greater accuracy but also a streamlined workflow that enables users to refine their applications with confidence. LangChain explain how Align Evals transforms the way developers and researchers evaluate LLM-generated outputs. From its ability to detect and resolve misalignments to its iterative prompt refinement tools, Align Evals offers a comprehensive framework for achieving consistency and reliability in LLM applications. Whether you're perfecting recipe titles or tackling complex technical content, Align Evals adapts to your unique scoring criteria, making sure your outputs align with human expectations. By the end, you'll discover how this tool not only saves time but also enhances the quality of your applications, bridging the gap between innovation and precision. The question is: how will you harness its potential? Streamlining LLM Evaluations The Purpose and Role of Align Evals Align Evals is built to make the evaluation of LLM outputs both accessible and precise. Its primary objective is to determine whether machine-generated content meets specific scoring criteria by comparing it to human-labeled benchmarks. This alignment process minimizes discrepancies, ensures evaluations reflect human judgment, and ultimately enhances the overall quality of LLM outputs. By bridging the gap between human expectations and machine-generated results, Align Evals enables users to create more reliable and consistent applications. How the Align Evals Workflow Operates The workflow of Align Evals is designed to simplify the evaluation process while maintaining flexibility and adaptability. It follows a structured, step-by-step approach that includes: Gathering representative sample runs: Collect outputs from your LLM application that represent the range of its performance. Collect outputs from your LLM application that represent the range of its performance. Labeling samples with human expertise: Use human input to create a reliable benchmark for evaluation. Use human input to create a reliable benchmark for evaluation. Iterative refinement of prompts: Continuously adjust and refine prompts to ensure the LLM's evaluations align with human-labeled data. This iterative process ensures that the evaluation remains dynamic, allowing you to adapt as your application evolves. By following this workflow, you can identify and address inconsistencies, making sure that your LLM application meets the desired standards. How Align Evals Improves Large Language Model Performance Watch this video on YouTube. Here are more detailed guides and articles that you may find helpful on LLM evaluation. Handling Evaluations and Scoring Criteria Align Evals enables you to use the LLM itself as a judge to score outputs against predefined criteria. For example, if you are evaluating recipe titles, you might establish a rule to avoid unnecessary adjectives or overly complex phrasing. By iteratively refining prompts and evaluators, Align Evals ensures the scoring process aligns with your specific standards. This approach not only enhances the accuracy of evaluations but also helps identify and resolve misalignments effectively. The tool's ability to adapt to different scoring criteria makes it suitable for a wide range of applications. Whether you are evaluating creative content, technical outputs, or user-facing text, Align Evals provides the flexibility needed to meet your unique requirements. Key Features of Align Evals Align Evals is equipped with a comprehensive set of tools designed to support and streamline the evaluation process. These features include: Evaluator creation and modification: Build, test, and refine evaluators to assess LLM outputs effectively. Build, test, and refine evaluators to assess LLM outputs effectively. Iterative prompt refinement: Continuously improve prompts to align machine evaluations with human-labeled benchmarks. Continuously improve prompts to align machine evaluations with human-labeled benchmarks. Misalignment detection and resolution: Identify discrepancies between machine and human evaluations and address them systematically. Identify discrepancies between machine and human evaluations and address them systematically. Progress tracking tools: Monitor alignment improvements over time to ensure consistent evaluation quality. These features work together to provide a robust framework for evaluating LLM applications. By using these tools, users can achieve greater consistency, accuracy, and efficiency in their evaluation processes. A Practical Example: Evaluating Recipe Titles To illustrate the functionality of Align Evals, consider a scenario where you are tasked with evaluating recipe titles. Your goal might be to ensure that the titles are concise, clear, and free from unnecessary adjectives. Using Align Evals, you can follow these steps: Define the evaluation criteria: Establish clear rules, such as avoiding overly descriptive language or making sure brevity. Establish clear rules, such as avoiding overly descriptive language or making sure brevity. Label sample titles with human input: Create a benchmark by labeling a set of sample titles according to the defined criteria. Create a benchmark by labeling a set of sample titles according to the defined criteria. Refine the LLM's evaluation prompts: Adjust prompts iteratively until the LLM's scoring aligns with your expectations. This process not only saves time but also ensures that the evaluation outcomes are consistent and aligned with your goals. By automating parts of the evaluation while maintaining human oversight, Align Evals strikes a balance between efficiency and accuracy. Inspiration and Availability Align Evals draws inspiration from Eugene Yan's research on 'Align Eval,' which emphasizes the importance of aligning LLM evaluations with human preferences. Now widely available, Align Evals offers a user-friendly interface and a suite of powerful tools to enhance the evaluation process. Its design prioritizes accessibility and precision, making it an invaluable resource for developers and researchers working with LLM applications. By incorporating insights from research and practical use cases, Align Evals provides a reliable and adaptable solution for evaluating machine-generated outputs. Its availability ensures that users across various industries can benefit from its capabilities, improving the quality and reliability of their LLM applications. Enhancing LLM Applications with Align Evals Align Evals represents a significant advancement in the evaluation of LLM-generated outputs. By aligning machine evaluations with human-labeled data, it ensures greater accuracy, reliability, and consistency. Whether you are refining prompts, addressing misalignments, or defining specific scoring criteria, Align Evals offers a structured and efficient solution to meet your needs. With its robust features and intuitive design, this tool enables users to align LLM-generated content with human preferences, streamlining the evaluation process and enhancing the quality of applications. Media Credit: LangChain Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
30-07-2025
- Business
- Yahoo
Stock-Split Watch: Is CoreWeave Next?
Key Points Stock splits aren't always easy to predict, but there are clues investors can watch for. The big clues are around the share price and whether it's made a big move, either up or down. CoreWeave went public at the end of March, and the stock has done quite well since. 10 stocks we like better than CoreWeave › Large initial public offerings have done well this year. One of them is CoreWeave (NASDAQ: CRWV), a company that fashions data centers with advanced graphics processing units (GPUs) specifically for running large language models and artificial intelligence (AI) applications. Instead of having to build their own infrastructure, a costly and complex undertaking, companies looking to create and run AI applications can essentially rent the infrastructure from CoreWeave. Since going public in March, CoreWeave has gone on a parabolic run and is already up 200% to a $59 billion market cap (as of July 25). Could a stock split soon be in the cards? Understanding stock splits Stock splits and reverse stock splits are tools used by companies to change the share price of a stock and a company's outstanding shares without changing the market cap. That's crucial for investors to understand. If you own a stock before it undergoes some kind of stock split, you will see the stock price and number of shares you own change, but your equity position will remain the same. Stock splits decrease the share price and increase the shares outstanding. They can be a useful way for a company to make the stock feel more attainable for investors if it just went on a big run and now trades for hundreds or thousands of dollars per share. Stock splits can also boost liquidity. An example of a stock split would be a 2-for-1 stock split. Let's say an investor owned 10 shares of a stock trading at $200 per share, meaning their total equity position amounted to $2,000. In this scenario, the company would exchange two shares for each one the investor owned, so the number of shares the investor owned would double from 10 to 20. But remember, the equity position of $2,000 remains the same, so the new share price would be $100 ($2,000/20 shares). A reverse stock split does the opposite and increases the share price while lowering the total share count. A common use of a reverse stock split would be if a company is struggling to get into compliance with rules set by the New York Stock Exchange or Nasdaq. Both exchanges require companies to trade for $1 for 30 consecutive trading days. A reverse stock split could help a company get its stock price above $1 and back into compliance if it thinks it will be able to turn things around and wants to stay on a major exchange. Companies may also use a reverse stock split to increase its stock price up to a level more in line with peers. Is CoreWeave Next? CoreWeave has been on a big run, but it's not abnormal to see large AI stocks trading for hundreds of dollars per share, as they've been popular. I suppose the company could conduct a stock split to get its share price down to make the stock more attainable, but I don't see a real need. CoreWeave is a fast-growing company in the AI space, so I suppose if the AI rally continues, the stock could go on another big run, making management once again think about a stock split. However, this seems unlikely to happen in the near term. According to MarketWatch, only about 74% of CoreWeave's outstanding shares are public right now. That's because several large shareholders are still under lock-up agreements, which is common to see after an IPO and means they aren't allowed to sell their shares for a certain amount of time. Most of the lock-up periods for insiders at CoreWeave reportedly expire in late September, when most insiders will be able to sell shares. Not only will this add liquidity, but it could induce selling pressure as more supply floods the market. Additionally, CoreWeave does not appear to be at any risk of breaching compliance rules with the Nasdaq, with its huge market cap and a share price over $120, as of this writing. Do the experts think CoreWeave is a buy right now? The Motley Fool's expert analyst team, drawing on years of investing experience and deep analysis of thousands of stocks, leverages our proprietary Moneyball AI investing database to uncover top opportunities. They've just revealed their to buy now — did CoreWeave make the list? When our Stock Advisor analyst team has a stock recommendation, it can pay to listen. After all, Stock Advisor's total average return is up 1,041% vs. just 183% for the S&P — that is beating the market by 858.71%!* Imagine if you were a Stock Advisor member when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $636,628!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,063,471!* The 10 stocks that made the cut could produce monster returns in the coming years. Don't miss out on the latest top 10 list, available when you join Stock Advisor. See the 10 stocks » *Stock Advisor returns as of July 28, 2025 Bram Berkowitz has no position in any of the stocks mentioned. The Motley Fool has no position in any of the stocks mentioned. The Motley Fool has a disclosure policy. Stock-Split Watch: Is CoreWeave Next? was originally published by The Motley Fool Sign in to access your portfolio
Geeky Gadgets
20-07-2025
- Business
- Geeky Gadgets
Why Most AI Apps Fail Before Launch and How to Beat the Odds
Have you ever wondered why so many promising AI applications never make it past the prototype stage or fail to deliver on their potential? Despite the buzz around artificial intelligence, shipping a functional, scalable AI app is far from straightforward. Unlike traditional software, AI applications come with a unique set of challenges: skyrocketing operational costs, safeguarding against misuse, and the constant pressure to meet ever-evolving user expectations. It's a high-stakes balancing act where even small missteps can lead to spiraling expenses or a poor user experience. For developers and businesses alike, the road to deploying AI isn't just bumpy—it's a minefield. App developer Chris Raroque explains the hidden complexities of bringing AI-powered applications to life and uncover strategies to navigate them effectively. From optimizing operational expenses to designing user-centric platforms, you'll gain insights into the real-world challenges that go beyond the hype. Whether it's using multiple AI models to balance performance and cost or carving out a competitive edge with niche-specific solutions, this guide will show you how to overcome the hurdles that make shipping AI apps so hard. Because in a world where innovation often outpaces practicality, success lies in mastering the details that others overlook. AI App Development Challenges Cost Management: Optimizing Operational Expenses AI applications, particularly those powered by large language models, demand significant computational resources, which can lead to high operational costs. For instance, processing lengthy conversation histories or prompts for every interaction can quickly inflate expenses. To manage these costs effectively, consider adopting the following strategies: Shorten prompts to include only the most relevant information, reducing unnecessary data processing. Implement a 'window' technique to limit the conversation history processed by the model, focusing only on recent and pertinent interactions. These methods help minimize resource consumption while maintaining a seamless user experience. By optimizing operational expenses, you can ensure your application remains cost-effective without compromising its functionality or quality. Abuse Prevention: Protecting Your System AI systems are inherently vulnerable to misuse, which can result in excessive costs, degraded performance, or even system failures. To safeguard your application and maintain its reliability, you should implement robust protective measures, such as: Setting limits on message size and user activity, such as daily or monthly usage caps, to prevent overuse. Incorporating a remote kill switch to disable abusive accounts in real time, making sure immediate action against misuse. Using analytics tools to monitor usage patterns and detect anomalies that may indicate abuse. These safeguards not only protect your system from potential threats but also ensure a consistent and reliable experience for all users, fostering trust and satisfaction. The Real Challenges of Deploying AI Apps Watch this video on YouTube. Browse through more resources below from our in-depth content covering more areas on AI application development. Using Multiple AI Models: Balancing Efficiency and Performance Relying on a single AI model for all tasks may seem straightforward, but it is often inefficient and costly. Instead, deploying multiple models optimized for specific tasks can significantly enhance both performance and cost efficiency. For example: A lightweight model can handle basic queries quickly and efficiently. A more advanced model can address complex or nuanced requests that require deeper analysis. By incorporating a decision layer, your system can dynamically select the most appropriate model based on the user's input. This approach ensures that resources are allocated efficiently, reducing costs while maintaining high levels of performance and responsiveness. Platform Optimization: Designing for the Right Environment The success of your AI application depends heavily on how well it aligns with its intended platform. For example, if your application is primarily used on mobile devices, adopting a mobile-first design approach is essential. Features such as voice dictation, quick commands, and streamlined interfaces can significantly enhance usability for on-the-go users. By tailoring your design to the platform, you can create a seamless and intuitive user experience that meets the specific needs of your audience. Framework Utilization: Accelerating Development Building an AI application from scratch can be a time-intensive and error-prone process. To streamline development and improve reliability, you can use existing frameworks like the Versel AI SDK or similar tools. These frameworks offer pre-built functionalities that simplify the development process, including: Streaming capabilities for real-time interactions, enhancing responsiveness. Error handling mechanisms to improve system stability and reliability. Tool integration options to expand the functionality of your application. By using proven frameworks, you can focus on developing core features while reducing development time and making sure a stable, high-quality product. Personalization: Delivering Tailored User Experiences Personalization plays a crucial role in creating engaging and user-friendly AI applications. Allowing users to specify preferences in natural language can significantly enhance their experience. For instance, users might request a specific tone, style, or level of detail in responses, and your application can adapt accordingly. This level of customization not only improves user satisfaction but also helps differentiate your product from generic AI tools, making it more appealing and valuable to your target audience. Niche-Specific Solutions: Carving Out a Competitive Edge In a market dominated by general-purpose AI tools like ChatGPT or Claude, focusing on niche-specific solutions can give your application a distinct competitive advantage. By addressing the unique needs of a specific audience, you can provide a more tailored and efficient experience. For example, an AI tool designed for legal professionals might include features such as legal document summarization, case law analysis, or contract drafting assistance. These specialized capabilities make your product more valuable and relevant to its target users, helping it stand out in a crowded marketplace. Key Considerations for Successful AI Application Development To navigate the complexities of AI application development effectively, keep the following considerations in mind: Monitor costs and usage from the outset to avoid unexpected expenses and ensure long-term sustainability. Use multiple models to balance efficiency and performance, optimizing resource allocation. Design with the intended platform in mind to create a seamless and intuitive user experience. Use existing frameworks to accelerate development and enhance system reliability. Focus on niche-specific solutions to differentiate your product and meet the unique needs of your target audience. By addressing these factors with careful planning and strategic execution, you can create AI applications that are not only functional but also scalable, cost-effective, and user-friendly. In an increasingly competitive landscape, these considerations will help ensure the success and longevity of your AI product. Media Credit: Chris Raroque Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
25-06-2025
- Business
- Yahoo
Nebius Group N.V. (NBIS) Is Bought After NVIDIA Is Sold, Says Jim Cramer
Nebius Group N.V. (NASDAQ:NBIS) is one of the . Nebius Group N.V. (NASDAQ:NBIS) is an artificial intelligence company that provides businesses with hardware and software to develop and run AI applications. It was previously the holding company for the Russian search engine platform Nebius Group N.V. (NASDAQ:NBIS) but sold Yandex after US sanctions on Russia after the Ukraine invasion. Nebius Group N.V. (NASDAQ:NBIS)'s shares are up 57% year-to-date and have gained 35% in June so far. The stock has gained on the back of several tailwinds such as a $1 billion capital raise which allows Nebius Group N.V. (NASDAQ:NBIS) to expand its presence in the AI infrastructure market. Cramer discussed the firm in the context of its popularity among younger investors: 'Carl when I talk to younger people, after they mention NVIDIA, they say took the NVIDIA money and they're buying Nebius. . .' Later during the day, he discussed Nebius Group N.V. (NASDAQ:NBIS) in Mad Money: 'Okay, I went to their booth when I was out at the conference, the Nvidia GTC conference. I was very impressed. I think they do good things. I didn't, wasn't prepared to be impressed frankly, because I like CoreWeave. But let me just tell you how I feel about this Nebius, this stock has… it has an allure. People like it so much. It doesn't have a lot of people writing about it. It's very hard for it to disappoint. I'm actually going to say that I think Nebius is going higher. There we go.' While we acknowledge the potential of NBIS as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and have limited downside risk. If you are looking for an extremely cheap AI stock that is also a major beneficiary of Trump tariffs and onshoring, see our free report on the best short-term AI stock. READ NEXT: 20 Best AI Stocks To Buy Now and 30 Best Stocks to Buy Now According to Billionaires. Disclosure: None. This article is originally published at Insider Monkey. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
