Shopify has revamped its Admin interface, achieving a 30% speed increase and laying the groundwork for enhanced AI capabilities. The challenge stemmed from a massive scale—67 million daily page views and 101 teams contributing to the codebase created issues like inconsistent loading patterns and duplicate data requests. To address this, Shopify adopted Remix, a framework emphasizing web standards and predictable data flow. Key developments included creating static route manifests, which consolidated routes into a single source of truth, and utilizing Remix loaders to streamline data fetching. This not only improved load times but also standardized the loading experience across the Admin. Merchants now experience smoother transitions and reduced cognitive load. Additionally, new tools enable intelligent prefetching and AI integrations, enhancing the Sidekick assistant’s functionality. Ultimately, these changes empower merchants with a more responsive, consistent platform, supporting faster future development and refined features.
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Revamping Shopify’s Admin: Boosting Speed by 30% and Preparing for AI (2025)
Transform Your LinkedIn Presence with Profile AI for Professional Headshots
Profile AI Pro uniquely specializes in creating high-quality, AI-generated LinkedIn headshots. Unlike generic photo tools, it specifically tailors headshot styles to various professions, optimizing for LinkedIn’s requirements. The advanced AI technology focuses on realistic facial features, lighting, and professional styling, ensuring natural-looking results. Users can choose from multiple styles, including business, corporate, academic, and modern urban looks, without needing any photography experience—simply upload a photo and select a style to receive LinkedIn-ready headshots. Users retain full rights to their images, which can be used across various professional platforms. New users benefit from trial credits and can create unlimited headshots with affordable credit packs. Profile AI Pro also offers the option to enhance existing photos while ensuring complete privacy of user data. For optimal results, users are encouraged to upload clear, well-lit images and select styles appropriate for their industry.
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Revolutionary AI Tool Surpasses Human Expertise in Lung Tumor Segmentation – Health Imaging
A groundbreaking AI tool has demonstrated superior performance in lung tumor segmentation compared to human experts, marking a significant advancement in medical imaging technology. This innovative solution utilizes advanced algorithms to analyze CT scans with high precision, enhancing diagnostic accuracy. The AI’s ability to detect and delineate tumors can expedite treatment decisions, ultimately improving patient outcomes. As healthcare continues to evolve through digital transformation, integrating AI tools into radiology promises to streamline workflows and reduce the risk of human error. The success of this AI model paves the way for future applications in oncology and other medical fields, where timely and accurate imaging is critical. This breakthrough not only showcases the potential of AI in transforming healthcare but also emphasizes the importance of collaboration between technology and medical professionals to enhance patient care. By embracing these advancements, healthcare providers can better leverage data to deliver targeted treatment strategies for lung cancer patients.
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Your Ultimate Guide to AI Evals: Top FAQs Answered – Hamel’s Blog
This post compiles frequently asked questions from instructors Shreya and the author regarding AI evaluation principles, based on their experience teaching over 700 engineers and product managers. They offer opinions rather than universal truths, urging careful judgment in application.
Key questions addressed include:
- RAG’s Relevance: Despite claims of "RAG is dead," the core principle—using retrieval to enhance LLM outputs—remains vital; the focus should shift from abandoning retrieval systems to optimizing them.
- Model Selection and Evaluation: It’s often more effective to analyze errors than to hastily switch models; the same model can serve both task and evaluation efficiently.
- Custom Tools: Creating tailored annotation tools significantly enhances workflow usability, while binary evaluations often yield clearer insights than Likert scales.
Overall, the authors advocate for a structured, iterative approach to AI evaluation, emphasizing error analysis and tailored evaluation strategies. Readers are encouraged to join their final AI Evals course, offering a discount for attendees.
Raoul Pal Praises Robinhood’s Blockchain Trading of OpenAI and SpaceX as a Revolutionary Step in Finance—Ushering in the Era of Public and Private Market Integration
Macro strategist Raoul Pal praised Robinhood Markets (HOOD) for their decision to launch tokenized stock offerings, viewing it as a significant step in the “democratization of finance.” In a recent post on X, Pal shared a video featuring Robinhood CEO Vlad Tenev, who announced that European users can invest in U.S. private companies like SpaceX and OpenAI through new “stock tokens.” Pal argued this marks the beginning of the convergence of public and private markets, highlighting increased capital efficiency in crypto markets. Robinhood’s event in France also introduced cryptocurrency perpetual futures for EU customers. The firm is advocating for tokenizing private stocks and has proposed a federal framework to the SEC for integrating real-world assets on-chain. Following the announcement, Robinhood shares rose 3.12% in after-hours trading, reflecting strong momentum and growth, with the stock gaining over 150% year-to-date.
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Unlocking the GenAI Paradox: Investigating Real-World AI Applications
The rise of generative AI (gen AI) has led many companies to adopt it, yet most report no significant impact on their bottom lines, creating what is termed the “gen AI paradox.” The root of this paradox lies in the imbalance between widely used horizontal tools (like chatbots) and transformative vertical applications, which often remain in pilot stages. AI agents have the potential to transform this landscape by automating complex workflows and acting as proactive collaborators rather than passive tools. To fully leverage agents, organizations must reimagine their workflows, fostering collaboration between humans and AI. This requires adopting a new architectural framework—an “agentic AI mesh”—that allows for effective integration of custom and off-the-shelf AI systems while managing risks. CEOs must lead the shift away from experimental pilots to strategic, impactful AI governance and implementation to realize the full potential of AI agents, thus redefining operational efficiency and competitive advantage in their organizations.
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Apple’s Siri Overhaul Could Integrate OpenAI and Anthropic Technologies Instead of In-House AI Solutions—Report
Apple is revamping its Siri voice assistant, shifting its focus to collaborate with AI giants OpenAI and Anthropic, as reported by Bloomberg’s Mark Gurman. This new direction involves utilizing these companies’ large language models (LLMs) to enhance Siri’s capabilities. Despite initial plans for internal AI development, Apple may sideline its own generative AI models, creating internal discord among employees, particularly within the Foundation Models team. Delays in Siri’s upgrade, initially anticipated with iOS 18, have pushed the potential launch date to 2027, sparking consumer frustration and a class action lawsuit over allegations of false advertising related to Apple’s ongoing improvements. The anticipated next-generation Siri aims to deliver a “contextually aware” AI experience, allowing for comprehensive control of devices via voice commands. By leveraging third-party technology, Apple hopes to enhance Siri significantly, although this decision is still under deliberation.
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Open Source AI Editor: Achieving Our First Milestone
On June 30, 2025, the VS Code Team announced a significant milestone in their plan to transform VS Code into an open source AI editor: the GitHub Copilot Chat extension is now available on GitHub under the MIT license. This move is aimed at fostering community-driven innovation and enhancing data transparency in AI development. The team encourages users to explore the open-source codebase, which includes details on agent mode, context sent to LLMs, and system prompts. Contributions and feedback from the community are welcomed, with plans to integrate this code into the core VS Code. While the original GitHub Copilot extension for inline completions remains closed source, the team intends to incorporate that functionality into the open-sourced Chat extension soon. Their ongoing priorities include delivering strong performance, extensibility, and a user-friendly interface as they embark on this open-source journey.
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