Latest news with #softwareDevelopment
Yahoo
12 hours ago
- Automotive
- Yahoo
United Nations Forum Elevates InnerSource as Essential Tool for Open Source Scaling
Mercedes-Benz Tech Innovation, Bosch Digital, CURIOSS, BBC, and Dutch Tax Administration Join Forces to Discuss InnerSource as Key Driver for Open Collaboration NEW YORK, June 20, 2025 /PRNewswire/ -- The United Nations hosted its first-ever InnerSource panel, marking a significant acknowledgment of this methodology as a cornerstone of organizational transformation and the adoption of open source principles. The distinguished panel featured global leaders from Mercedes-Benz Tech Innovation, Bosch Digital, CURIOSS, BBC, and Dutch Tax Administration, highlighting how InnerSource practices have been critical in driving unprecedented levels of cooperation across private sector enterprises, academic and research institutions, and government agencies worldwide. UN Open Source Week, hosted by the UN Office of Digital and Emerging Technologies (UN-ODET) and the UN Office of Information and Communications Technology (UN-OICT), engaged with the InnerSource Commons Foundation to organize this panel. The Chair of the Foundation, Dr. Daniel Izquierdo Cortázar led the esteemed guests of the panel. The event demonstrated the growing momentum behind InnerSource adoption as entities increasingly recognize the need for transparent, cooperative approaches to software development that eliminate traditional silos while maintaining security and governance standards. Georg Grütter, Chief Expert of InnerSource at Bosch Digital, kicked off the panel with an engaging skit addressing the common struggles that businesses face—challenges that InnerSource solves. Representatives from private companies, public services, and NGOs acted out the all-too-familiar difficulty of working cooperatively across departments and agencies. It was well received by the packed room, as the audience laughed and nodded in recognition as the story unfolded. Karel Rietveld, Specialist Open Source Software within the CTO Office for the Dutch Tax Administration, shared their open source journey and how InnerSource has played an essential role. "It has helped build connections throughout our organization," he stated. "We have seen growing demand across our engineering teams, as it has eased knowledge building and transfer." Panelists highlighted the emerging trend of government agencies worldwide investing in and practicing these approaches to drive cooperation between interdependent agencies. BBC Principal Software Engineer Tom Sadler expressed that their practice has been a driving factor in capturing the value of open source development as a company. This shift represents a fundamental change as the public sector moves towards shared platforms that maximize public investment and improve service delivery. Both private companies shared how their implementations have not only transformed their internal development practices but have also advanced their external partnerships with contributors. Dr. Wolfgang Gehring, FOSS Ambassador/OSPO Lead at Mercedes-Benz Tech Innovation, spoke to the vital role InnerSource practices played in open source adoption, citing their inclusion in the company's highly referenced FOSS Manifesto. The importance of purpose, culture, and communication was emphasized throughout the discussion of scaling collaborative development. These practices have proven instrumental in building and growing trust across companies, institutions, and NGOs. Clare Dillon, Community Lead at CURIOSS, noted, "The difference between open source and InnerSource is context. When you practice InnerSource, individuals and teams have the capacity to shape policies and processes to build a culture of trust." Such trust-based environments are increasingly sought after for their ability to accelerate innovation and transformation. These insights encourage leaders worldwide to consider InnerSource as a strategic approach to digital transformation, emphasizing that the benefits extend far beyond software development to include improved transparency, enhanced teamwork, and more effective use of public resources. About the InnerSource Commons Foundation The InnerSource Commons Foundation is a 501(c)(3) public charity that supports and connects over 3,000 individuals from more than 800 companies, academic institutions, and government agencies worldwide. Founded in 2015, the InnerSource Commons empowers organisations and people worldwide to apply and gain the benefits of open collaboration in their internal work. The Foundation facilitates the creation and sharing of knowledge about InnerSource, providing organizations with resources, community support, and best practices for implementing collaborative development methodologies within their internal operations. CONTACT: Addie Girouard, Director of Communications, InnerSource Commons Foundation, Email: addie@ Website: View original content to download multimedia: SOURCE InnerSource Commons Foundation
Finextra
19 hours ago
- Business
- Finextra
Inside AI Assisted Software Development and why tools are not enough (Part 1): By John Adam
The recent squeeze on funding and margins is by no means only being felt in the financial services and fintech sectors. But it's fair to say the pinch is particularly hard and the necessity to quickly and effectively innovate is simultaneously more pressing than ever. The good news is, new AI tools can speed up delivery and improve the quality of software projects without adding to headcount. But even if that general statement is true, just using tools is not enough. Especially in a regulated industry like financial services. If there is no pre-approved list of tools and how and where they are applied in an SDLC (software development lifecycle), organisations have governance, observability, measurability and consistency issues. If 'real' gains are not measured by benchmarking against 'before', do they really exist? Tree falling in a forest metaphor. Certainly not in a way that can be scaled across or up an organisation. There is no clear business case, just intuition. Are tools and where and how they are being used compliant with organisational policy and regulatory frameworks? Has anyone read the privacy policies? I'm personally convinced that a big AI company having its Facebook/Cambridge Analytica moment falls under 'when, not if'. And when the first big AI privacy scandal does break, you don't want your organisation published in a list in a newspaper. To benefit from and scale the gains of an AI-assisted SDLC, organisations need a framework for structured, consistent integration + governance, observability and measurability. Just tools isn't enough. Realistic gains from an AI-assisted SDLC It's important to note that at the time of writing, we are in a period of rapid change in AI tooling. A good framework operates at a level or two higher than specific tools and allows for them to be interchangeable with upgrades. The market most of us operate in is at a point in its cycle where resources are at a premium. Most of the organisations I work with are expected to deliver more with less compared with pre-2023. In that context, banking the productivity gains achievable with AI tooling is non-negotiable. Organisations are demanding it in the demand for greater, better output despite fewer resources. Getting it right is also non-negotiable and that means marrying increased productivity with measurability, observability and governance, which I cover in-depth in Part 2 of this article. As an introduction to building a proper framework, I'll start by explaining the realistic improvements AI can provide to each stage of the SDLC: Product prototyping Developers use prototypes to test idea viability and functionality, and to gather user and investor feedback. Historically, the average prototype required 2 to 6 weeks of teamwork to complete. But by amplifying developers' work via low-code/no-code prototyping and AI-generated code and other AI tools, a clickable prototype can now be completed in days or even hours. UX/UI design UX (user experience) and UI (user interface) designers collaborate closely with developers to design website and app interfaces. Using AI tools that can quickly generate multiple design mock-ups and UI components based on foundational style guides and example concepts, designers can visualise ideas and user flows in various contexts to improve design clarity and direction long before designs touch a developer's desktop. Clarity improves the quality of initial designs and reduces designer-developer back-and-forth, meaning larger projects that took 4 to 6 months to complete now require far less effort and time. Even UXR (User Experience Research) is accelerated and refined. User interviews are, by necessity, long and complex, and result in large, qualitative datasets. AI tools can highlight patterns and repetition in datasets and transcripts in seconds—shining a spotlight on insights, false positives or even biased questions that human researchers may have overlooked. Architecture Software architects plan higher-level design, bridging technical and business requirements. Their diagrams include the sum of a products' components and their respective interactions; until recently, the initial design phase alone took 1 to 2 weeks. Using AI, architects can quickly draw up diagrams to easily visualise these relationships and standardise dependency versions across services. AI can also be trained to use PR comments to report architectural violations, and libraries can be unified to encourage stability across features. Better consistency and immediate feedback mean architects can work faster and create fewer iterations of a product before diagrams meet stakeholder expectations. Coding AI-powered tools for coding have a variety of use cases. My team uses a mix of tools and GenAI to: ensure comprehensive project documentation, automate code documentation and README generation, scan for duplicate code and suggest improvements, improve understanding of complex, inconsistent or unfamiliar code bases, unify code styles and standards across different microservices, and perform code completion and check for bugs and inconsistencies based on defined standards. Paired with manual oversight to catch any mistakes, we've accelerated writing and testing code by a minimum of 20% across projects. GenAI makes complex codebases easily understandable—meaning team members can flexibly move to work on unfamiliar projects and diminish time spent on internal comms by about 25%. One tool we use is SonarQube, which reviews code without executing it. It runs automatically in GitLab CI/CD (Continuous Integration/Continuous Delivery and Deployment) pipeline to find bugs, report security vulnerabilities, and enforce code standards to unify style and mitigate potential misunderstandings down the line with better code readability. Testing and QA (Quality Assurance) As they write code, developers write and run unit tests to detect initial bugs and security issues that eat up between 10% and 20% of their time. The SDLC is slowed further by code reviews and PRs, or feedback from experienced colleagues. Tests are postponed by days, sometimes weeks, if various code reviews are required and dependent on busy colleagues. GenAI can augment developers' efforts by writing unit tests, conducting code reviews and PRs in real time, and automatically generating and solving for edge cases to overcome bottlenecks like a lack of expertise or teammates' availability. AI augmented QA can reduce redundancy, unify access to code, and consolidate fragmented knowledge across a project to make a QA team more efficient. And AI-driven tools like Selenium, for example, can automate web app test writing and execution, accelerating product releases and improving product reliability. Automated testing is especially compelling in the context of projects with tight deadlines and few resources. For example, my team's AI toolkit for QA testing includes Llama 3.3 LLM to generate test cases and analyse code and Excel-based legacy documents, IntelliJ AI Assistant to automatically standardise test case formatting, and GitLab to run and test scripts automatically in the CI/CD pipeline. QA is one of the most impactful applications of AI tools in the SDLC and can commonly slash the resources required by up to 60%, while increasing test coverage. Deployment When a product is deployed to end users, AI can be added to the CI/CD to forecast use patterns and improve caching strategies, as well as automatically prioritise and schedule tasks for parallel execution. With AI oversight, the number of repetitive tasks is automatically reduced and resource allocation anticipated, improving latency and product release cycles without added manual effort. And AI-driven caching accelerates and simplifies rollbacks (reverting a newly deployed system to a more stable version of itself) by analysing previous deployments and predicting the necessary steps, reducing further manual effort by DevOps teams, for instance. My team uses Dytrance during deployment, which monitors and analyses system status, and sends self-healing recommendations in real time. Maintenance and Monitoring At this stage, teams work to fix bugs, keep the system secure and functioning well, and make improvements based on user feedback, performance data and unmet user needs. AI can automatically perform root cause analysis for error monitoring, and suggest solutions for maintenance and debugging. Tools my team uses include AWS Cloud Watch and Azure Monitor with AIOps, which automatically collect, analyse, and suggest responses based on monitoring data, accelerating issue response and system updates by 10x. The big picture The acceleration of the individual stages of software development is incentive enough for some teams to add tools and GenAI models to their workflows; especially at stages like QA and coding, where use cases are various and results potent. But by taking a step back and considering AI's impacts on the SDLC holistically, the argument in favour of AI implementation can be turned into a real business case. A business case that can be used to accelerate AI transformation across an organisation: Backed by a strong framework, organisations implementing AI across their SDLC see a 30%+ acceleration across projects in the first 6 months. The keyword being 'strong.' Organisations need a framework that guides leadership to select tools and govern their use, measures outcomes to understand the amount of value different tools offer, and encourages adoption in teams' workflows. Without it, teams are unable to measurably extract the full potential from new tools and efforts, and risk breaching internal and third-party governance in areas such as data privacy. Keeping my word count and your patience in mind, I split my deep dive into a framework for AI governance, measurement and adoption into a separate article: Here is Inside an AI-assisted software development framework: using tools is not enough Part 2.
Forbes
09-06-2025
- Business
- Forbes
What Every Creator Should Know Before Launching A Digital Product
Man podcaster influencer blogger smiling while broadcasting his live audio podcast in studio using ... More headphones, laptop and headphones. Male radio host making podcast or interview The creator economy is entering its next major phase, not one defined by followers or viral content, but by real product ownership and long-term equity. From digital memberships to full-fledged software ventures, creators are unlocking serious revenue streams and reshaping what it means to build an audience online. Miles Sellyn, VP of Creator Partnerships at Rare Days, has helped some of the biggest names in the industry, from Colin and Samir to Ryan Trahan, launch products that now generate millions. I spoke to him to unpack what's working, what's not and how creators can move from content to commerce with intention. 'What the market is telling me is the biggest opportunity right now are memberships and subscriptions,' Sellyn explained. 'It's a bit of an evolution from courses or communities. You might have a course within a membership. You might have a community within a membership. You might have AI chatbots. It's a flexible container for delivering content.' Still, Sellyn's eyes are on a more ambitious horizon: creators building software. 'The cost of developing software is dropping dramatically. That opens the door for creators to own the product rather than just being the face of it,' he said. 'It creates long-term enterprise equity value.' 'The eye-popping ones: we work with a creator who is making more than $15 million a year through their digital products,' Sellyn shared. 'Another did $300,000 within 30 days of launching. We have creators who have sold $15 million plus of courses.' But big numbers aren't the only metric. 'Even $60,000–$70,000 a month in digital product sales can change the game for creators relying on brand deals. It's great for mental health and strategic freedom.' So what makes a creator product-ready? 'A ruthless standard for quality,' Sellyn emphasized. 'That stems from a deep respect for the audience. The top creators care about the person on the other end of the screen. They're not just selling—they're delivering outsized value.' He also urges creators to go beyond intuition and mine their DMs and comments. 'That's where the product ideas are hiding.' Rare Days uses 'feature vignettes' to validate product ideas. These are low-fidelity mockups that gauge audience interest. 'We'll create 10 to 20 of these and test them directly with the creator's audience. That feedback is gold.' When creators don't have a product idea, that's not a deal-breaker. 'They're filmmakers, creatives, educators, not necessarily product strategists. But if they know their audience, we can find the opportunity together.' The timeline depends on complexity. A course or membership might take three to four months. Custom software can take up to a year. One warning: 'Creators almost always underestimate the content burden. They're already making a YouTube video every week. Creating a product is another layer entirely.' Pricing is both art and science. Sellyn recommends Jay Clouse's four-question pricing framework, based on the Van Westendorp Model: Then, price toward the lower end. 'You want customers to feel like they're getting 10 times the value for every dollar they spend.' 'One of the biggest mistakes? The creator launches and goes dark,' Sellyn warned. 'You need to talk about it constantly. Algorithms don't guarantee reach, so act like you're inviting your audience to a party. They need to know it exists.' Sellyn recommends a two-week pre-launch window, followed by strategic post-launch engagement. 'It's not 'Field of Dreams.' You can't just build it and expect people to come. You have to market it.' Entertainment creators can still sell, but it's tougher. 'If your content is a vitamin, not a painkiller, the product needs a lot more thought. But if your content solves problems, the audience is already primed.' One standout example is Hannah Williams of Salary Transparent Street. Instead of launching a course, she built a salary database and a job board. 'It fits her mission and audience. Not everything has to be an educational product.' Sellyn sees a wave of creator-led SaaS products on the horizon. 'Creators used to partner with software tools. Now they're building their own. We're seeing creators in 3D modeling, for example, realize they can build plugins for $20,000 and keep the upside, rather than just taking affiliate fees.' It's not just about products. It's about ownership. If you're a creator thinking about launching a product? 'Spend time in your DMs,' Sellyn said. 'Read every comment. Look for pain points. Then build solutions around those. That's your roadmap.' And when you're ready to scale? 'Your audience is your edge. But your product is your future.' This article is based on an interview from my podcast, The Business of Creators.
Associated Press
30-05-2025
- Business
- Associated Press
Cleveroad Recognized as a Clutch Global Leader for Software Development Services 2025
Cleveroad earned a top global ranking for its custom software development services in the 2025 Spring Clutch Global Awards, highlighting its proven ability to deliver innovative, high-quality digital solutions backed by verified client success, enterprise-grade expertise, and a growing international presence. Recognized among the top 15 custom software firms worldwide, Cleveroad's inclusion reaffirmed its position as a trusted technology partner for businesses pursuing digital transformation. CLAYMONT, DE / ACCESS Newswire / May 30, 2025 / Cleveroad, a trusted provider of custom software development services, proudly announces its recognition as a 2025 Spring Clutch Global Award winner for Software Development. This accolade from Clutch, the leading global marketplace of B2B service providers, marks a significant milestone in Cleveroad's journey of delivering innovation-driven software Recognized as a Clutch Global Leader for Software Development Services 2025 Cleveroad, a custom software development company, proudly announces its recognition as a 2025 Spring Clutch Global Award winner for Software Development services. Winners of the Clutch Global Awards are determined using a rigorous criteria of evaluation based on the Clutch's proprietary Ability to Deliver framework. This model ranks service providers using verified client feedback, successful outcomes of projects, market presence, and proven industry expertise. Cleveroad has proven an outstanding ability to deliver impactful results for their clients with top rankings consistently based on verified reviews and successful project outcomes. 'Being named a 2025 Clutch Global Award winner is a reflection of our clients' faith in us and the effort our team puts into every project,' said Yevgen Altynpara, Founder and CEO at Cleveroad. 'It is a confirmation of the rigorous work and technical excellence we put in every day. We're honored to be recognized among the world's elite software development firms.' The Clutch Global Awards only recognizes the world's top 15 Custom Software Development firms. Being included in this category underscores Cleveroad's established role as a trusted provider of advanced digital transformation solutions. 'The companies named Clutch Global Award winners this spring have demonstrated an exceptional ability to deliver for their clients,' said Mike Beares, Clutch Founder and CEO. 'Their dedication to quality, innovation, and service excellence puts them at the very top of their industries and sets a global standard for what buyers should expect from a top-tier partner.' Over the past year, Cleveroad has expanded its enterprise client portfolio, launched a suite of AI-based services in its custom software development offerings, and strengthened its presence in global markets across North America and Europe. These efforts have not only expanded the company's size but also solidified its reputation as a trusted digital partner. About Cleveroad Cleveroad is a trusted custom software development company headquartered in the U.S., with R&D centers in Eastern Europe. Specializing in web and mobile solutions, Cleveroad empowers startups, SMBs, and enterprises through agile software engineering, UX/UI design, and cloud-native technology. The company has served over 170 clients globally across industries such as Healthcare, Fintech, Logistics, and Media. Contact Information: Ivan Stepan'kov Head of Marketing [email protected] SOURCE: Cleveroad press release
Geeky Gadgets
27-05-2025
- Business
- Geeky Gadgets
Claude 4 Sonnet & Opus AI Models Coding Performance Tested
What if the future of coding wasn't just faster, but smarter—capable of reasoning through complex problems, retaining context over hours, and even adapting to your unique workflow? Enter Claude 4 Sonnet and Opus, two new AI models from Anthropic that promise to redefine how we approach software development. With benchmark scores that rival or surpass industry leaders like GPT-4.1, these models aren't just tools—they're collaborators. Whether you're debugging intricate systems or generating creative code for a game, the precision and adaptability of these models could fundamentally transform your process. But with innovation comes complexity: How do you choose between Opus's high-end, long-term capabilities and Sonnet's affordable, rapid-fire efficiency? World of AI explores the technological innovations behind Claude 4 Sonnet and Opus, unpacking their unique strengths, limitations, and use cases. From Opus's unparalleled memory retention and advanced reasoning to Sonnet's hybrid thinking mode and cost-effective performance, each model offers distinct advantages depending on your goals. You'll discover how these models integrate seamlessly with tools like VS Code and GitHub Actions, and why they're being hailed as a new standard in AI-driven development. By the end, you might just find yourself rethinking what's possible with coding—and what it means to collaborate with AI. Claude 4 AI Coding Models Claude 4 Opus: Built for Complex, Long-Term Workflows Claude 4 Opus is specifically designed to handle high-performance, long-duration tasks. It excels in advanced reasoning, memory retention, and multifile code comprehension, making it a robust choice for tackling intricate software engineering challenges. With benchmark scores of 72.5% on Sway Bench and 43.2% on Terminal Bench, Opus demonstrates its ability to manage demanding workflows with precision. Its standout features include: Long-Term Memory: Retains context over extended interactions, making sure seamless task continuity. Retains context over extended interactions, making sure seamless task continuity. Reliable Reasoning: Excels in logical problem-solving, debugging, and complex decision-making. Excels in logical problem-solving, debugging, and complex decision-making. Enhanced Debugging: Identifies and resolves code issues efficiently, reducing development time. Opus is particularly effective for tasks such as autonomous agent development, app generation, and prompt engineering. Its ability to integrate with external tools, execute parallel tasks, and manage context effectively makes it a powerful asset for developers working on large-scale or intricate projects. However, this advanced performance comes at a premium. Priced at $15 per 1 million input tokens and $75 per 1 million output tokens, Opus is a costly solution. Additionally, its 200k context length limit may pose challenges for tasks requiring larger context windows, potentially necessitating additional workarounds for certain use cases. Claude 4 Sonnet: Affordable and Fast For those seeking a cost-effective and responsive alternative, Claude 4 Sonnet offers a compelling option. With a benchmark score of 72.7% on Sway Bench, Sonnet delivers strong performance while maintaining lower latency and cost, making it an attractive choice for developers with budget constraints or time-sensitive projects. Key features of Sonnet include: Hybrid Thinking Mode: Adapts to task requirements, switching between instant replies and deep reasoning as needed. Adapts to task requirements, switching between instant replies and deep reasoning as needed. Improved Tool Integration: Seamlessly connects with APIs, web search, and cloud-based tools to enhance functionality. Seamlessly connects with APIs, web search, and cloud-based tools to enhance functionality. Memory Management: Optimized for shorter, dynamic interactions, making sure efficient task execution. Priced at $3 per 1 million input tokens and $15 per 1 million output tokens, Sonnet is a more accessible option for developers. Its flexibility makes it particularly well-suited for responsive web development, creative coding, and game generation. By balancing affordability with performance, Sonnet provides a practical solution for a wide range of applications. Claude 4 Sonnet & Opus Tested Watch this video on YouTube. Discover other guides from our vast content that could be of interest on AI coding models. Technological Innovations Driving Claude 4 Models Both Claude 4 Opus and Sonnet incorporate innovative features that enhance their usability and performance, setting them apart from other AI coding models. These innovations include: Hybrid Thinking Mode: Offers the flexibility to adapt to task requirements, whether instant responses or extended reasoning are needed. Offers the flexibility to adapt to task requirements, whether instant responses or extended reasoning are needed. Tool Integration: Connects seamlessly with external resources such as APIs, web search, and cloud tools, expanding their functionality. Connects seamlessly with external resources such as APIs, web search, and cloud tools, expanding their functionality. Parallel Tool Execution: Processes multiple tasks simultaneously, improving efficiency and reducing development time. Processes multiple tasks simultaneously, improving efficiency and reducing development time. Cloud Code Tool: Supports native integration with popular development environments like VS Code and JetBrains extensions. Supports native integration with popular development environments like VS Code and JetBrains extensions. API Capabilities: Includes advanced features such as code execution, MCP connector, files API, and prompt caching for streamlined workflows. These technological advancements position Claude 4 models as leaders in AI-driven software engineering. In coding benchmarks, they outperform competitors like OpenAI's Codex and GPT-4.1. For instance, Opus achieves 79.4% accuracy in parallel test time compute, while Sonnet reaches 80.2%, demonstrating their superior capabilities in handling complex coding tasks. Applications and Use Cases Claude 4 Opus and Sonnet cater to a diverse range of applications, making them valuable tools for developers, researchers, and creative professionals. Their use cases include: AI-Assisted Web Development: Streamline the creation of responsive websites with intelligent coding assistance. Streamline the creation of responsive websites with intelligent coding assistance. Creative Coding: Generate SVG designs, build interactive games like Tetris, or explore other creative projects. Generate SVG designs, build interactive games like Tetris, or explore other creative projects. Game Development: Develop and simulate games with advanced reasoning and memory capabilities. Develop and simulate games with advanced reasoning and memory capabilities. Custom App Creation: Build applications such as finance trackers or TV channel simulators tailored to specific needs. These models empower users to tackle complex projects with greater efficiency, using their advanced reasoning, memory, and integration capabilities to achieve results that would otherwise require significant time and effort. Limitations and Accessibility While both models offer impressive capabilities, they are not without limitations. Opus's high cost and 200k context length limit may restrict its use for tasks requiring larger context windows. However, for users with demanding, long-term workflows, its unparalleled performance often justifies the investment. Both Opus and Sonnet are accessible through Anthropic's chatbot, console, API, and OpenRouter. They integrate seamlessly with popular tools like Cursor and GitHub Actions, making sure compatibility with existing workflows. This accessibility makes it easier for developers to incorporate these models into their projects, regardless of their preferred tools or platforms. Claude 4: A New Standard in AI Coding Models Claude 4 Opus and Sonnet represent a significant advancement in AI-driven software engineering. Opus is ideal for high-end, long-duration tasks, offering unmatched performance and advanced features for developers tackling complex challenges. Sonnet, on the other hand, provides a cost-effective alternative with competitive capabilities and faster response times, making it a practical choice for a broader audience. Together, these models set a new benchmark in AI coding, allowing you to achieve more with less effort. Whether your priority is performance, affordability, or flexibility, Claude 4 offers tailored solutions to meet your needs, empowering you to innovate and excel in your projects. Media Credit: WorldofAI 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.
