News from the AI & ML world
DeeperML - #developers
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Steve Newman@Second Thoughts
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New research suggests that the integration of AI coding tools into the development process may not be the productivity silver bullet many have assumed. A recent study conducted by METR, a non-profit AI benchmarking group, observed experienced open-source developers working on complex, mature codebases. Counterintuitively, the findings indicate that these AI tools actually slowed down task completion time by 19%. This slowdown is attributed to factors such as the time spent prompting the AI, waiting for responses, and meticulously reviewing and correcting the generated output. Despite this empirical evidence, many developers continued to use the tools, reporting that the work felt less effortful, even if it wasn't faster.
The study involved 16 seasoned developers and 246 real-world programming tasks. Before engaging with the AI tools, participants optimistically predicted a 24% increase in their productivity. However, after the trial, their revised estimates still overestimated the gains, believing AI had sped up their work by 20%, a stark contrast to the actual observed slowdown of 19%. Furthermore, fewer than 44% of the AI-generated code suggestions were accepted by the developers, with a significant portion of their time dedicated to refining or rewriting the AI's output. Lack of contextual knowledge and the complexity of existing repositories were cited as key reasons for the reduced effectiveness of the AI suggestions. While the study highlights a potential downside for experienced developers working on established projects, the researchers acknowledge that AI tools may offer greater benefits in other settings. These could include smaller projects, less experienced developers, or situations with different quality standards. This research adds a crucial layer of nuance to the broader narrative surrounding AI's impact on software development, suggesting that the benefits are not universal and may require careful evaluation on a case-by-case basis as the technology continues to evolve. Recommended read:
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Eddú Meléndez@Docker
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References:
blog.adnansiddiqi.me
, Builder.io Blog
The development of Artificial Intelligence applications is rapidly evolving, with a significant surge in interest and the creation of new tools for developers. Open-source command-line interface (CLI) tools, in particular, are generating considerable excitement within both the developer and AI communities. The recent releases of Claude's Codex CLI, OpenAI's Codex CLI, and Google's Gemini CLI have underscored the growing importance of CLIs. These tools are fundamentally altering the way developers write code by integrating AI capabilities directly into routine coding tasks, thereby streamlining workflows and enhancing productivity.
For Java developers looking to enter the Generative AI (GenAI) space, the learning curve is becoming increasingly accessible. The Java ecosystem is now equipped with robust tools that facilitate the creation of GenAI applications. One notable example is the ability to build GenAI apps using Java, Spring AI, and Docker Model Runner. This combination allows developers to leverage powerful AI models, integrate them into applications, and manage local AI model inference with ease. Projects like building an AI-powered Amazon Ad Copy Generator, which can be accomplished with Python Flask and Gemini, also highlight the diverse applications of AI in marketing and e-commerce, enabling users to generate content such as ad copy and product descriptions efficiently. The integration of AI into developer workflows is transforming how code is created and managed. Tools like Claude Code are proving to be highly effective, with some developers even switching from other AI coding assistants to Claude Code due to its practical utility. The VS Code extension for Claude Code simplifies its use, allowing for parallel instances and making it a primary interface for many developers rather than a secondary tool. Even terminal-based interfaces for chat-based code editing are showing promise, with features like easy file tagging and context selection enhancing the developer experience. This signifies a broader trend towards AI-powered development environments that boost efficiency and unlock new possibilities for application creation. Recommended read:
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@www.helpnetsecurity.com
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Bitwarden Unveils Model Context Protocol Server for Secure AI Agent Integration
Bitwarden has launched its Model Context Protocol (MCP) server, a new tool designed to facilitate secure integration between AI agents and credential management workflows. The MCP server is built with a local-first architecture, ensuring that all interactions between client AI agents and the server remain within the user's local environment. This approach significantly minimizes the exposure of sensitive data to external threats. The new server empowers AI assistants by enabling them to access, generate, retrieve, and manage credentials while rigorously preserving zero-knowledge, end-to-end encryption. This innovation aims to allow AI agents to handle credential management securely without the need for direct human intervention, thereby streamlining operations and enhancing security protocols in the rapidly evolving landscape of artificial intelligence. The Bitwarden MCP server establishes a foundational infrastructure for secure AI authentication, equipping AI systems with precisely controlled access to credential workflows. This means that AI assistants can now interact with sensitive information like passwords and other credentials in a managed and protected manner. The MCP server standardizes how applications connect to and provide context to large language models (LLMs), offering a unified interface for AI systems to interact with frequently used applications and data sources. This interoperability is crucial for streamlining agentic workflows and reducing the complexity of custom integrations. As AI agents become increasingly autonomous, the need for secure and policy-governed authentication is paramount, a challenge that the Bitwarden MCP server directly addresses by ensuring that credential generation and retrieval occur without compromising encryption or exposing confidential information. This release positions Bitwarden at the forefront of enabling secure agentic AI adoption by providing users with the tools to seamlessly integrate AI assistants into their credential workflows. The local-first architecture is a key feature, ensuring that credentials remain on the user’s machine and are subject to zero-knowledge encryption throughout the process. The MCP server also integrates with the Bitwarden Command Line Interface (CLI) for secure vault operations and offers the option for self-hosted deployments, granting users greater control over system configurations and data residency. The Model Context Protocol itself is an open standard, fostering broader interoperability and allowing AI systems to interact with various applications through a consistent interface. The Bitwarden MCP server is now available through the Bitwarden GitHub repository, with plans for expanded distribution and documentation in the near future. Recommended read:
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@gbhackers.com
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Cyber Security News
, gbhackers.com
The rise of AI-assisted coding is introducing new security challenges, according to recent reports. Researchers are warning that the speed at which AI pulls in dependencies can lead to developers using software stacks they don't fully understand, thus expanding the cyber attack surface. John Morello, CTO at Minimus, notes that while AI isn't inherently good or bad, it magnifies both positive and negative behaviors, making it crucial for developers to maintain oversight and ensure the security of AI-generated code. This includes addressing vulnerabilities and prioritizing security in open source projects.
Kernel-level attacks on Windows systems are escalating through the exploitation of signed drivers. Cybercriminals are increasingly using code-signing certificates, often fraudulently obtained, to masquerade malicious drivers as legitimate software. Group-IB research reveals that over 620 malicious kernel-mode drivers and 80-plus code-signing certificates have been implicated in campaigns since 2020. A particularly concerning trend is the use of kernel loaders, which are designed to load second-stage components, giving attackers the ability to update their toolsets without detection. A new supply-chain attack, dubbed "slopsquatting," is exploiting coding agent workflows to deliver malware. Unlike typosquatting, slopsquatting targets AI-powered coding assistants like Claude Code CLI and OpenAI Codex CLI. These agents can inadvertently suggest non-existent package names, which malicious actors then pre-register on public registries like PyPI. When developers use the AI-suggested installation commands, they unknowingly install malware, highlighting the need for multi-layered security approaches to mitigate this emerging threat. Recommended read:
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@www.infoq.com
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Google has launched Gemini CLI, a new open-source AI command-line interface that brings the full capabilities of its Gemini 2.5 Pro model directly into developers' terminals. Designed for flexibility, transparency, and developer-first workflows, Gemini CLI provides high-performance, natural language AI assistance through a lightweight, locally accessible interface. Last Week in AI #314 also mentioned Gemini CLI, placing it alongside other significant AI developments. Google aims to empower developers by providing a tool that enhances productivity and streamlines AI workflows.
This move has potentially major implications for the AI coding assistant market, especially for developers who previously relied on costly tools. An article on Towards AI highlights that Gemini CLI could effectively eliminate the need for $200/month AI coding tools. This is because it will match or beat expensive tools for $0. The open-source nature of Gemini CLI fosters community-driven development and transparency, enabling developers to customize and extend the tool to suit their specific needs. Google is also integrating Gemini with other development tools to create a more robust AI development ecosystem. Build Smarter AI Workflows with Gemini + AutoGen + Semantic Kernel suggests that Gemini CLI can be combined with other frameworks to enhance AI workflow. This is a new step to provide developers with a complete suite of tools. Google's launch of Gemini CLI not only underscores its commitment to open-source AI development but also democratizes access to advanced AI capabilities, making them available to a wider range of developers. Recommended read:
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Matthew S.@IEEE Spectrum
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Matt Corey
, IEEE Spectrum
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AI coding tools are transforming software development, offering developers increased speed and greater ambition in their projects. Tools like Anthropic's Claude Code and Cursor are gaining traction for their ability to assist with code generation, debugging, and adaptation across different platforms. This assistance is translating into substantial time savings, enabling developers to tackle more complex projects that were previously considered too time-intensive.
Developers are reporting significant improvements in their workflows with the integration of AI. Matt Corey (@matt1corey@iosdev.space) highlighted that Claude Code has not only accelerated his work but has also empowered him to be more ambitious in the types of projects he undertakes. Tools like Claude have allowed users to add features they might not have bothered with previously due to time constraints. The benefits extend to code adaptation as well. balloob (@balloob@fosstodon.org) shared an experience of using Claude to adapt code from one integration to another in Home Assistant. By pointing Claude at a change in one integration and instructing it to apply the same change to another similar integration, balloob was able to save days of work. This capability demonstrates the power of AI in streamlining repetitive tasks and boosting overall developer productivity. Recommended read:
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@www.marktechpost.com
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Apple is enhancing its developer tools to empower developers in building AI-informed applications. While Siri may not yet be the smart assistant Apple envisions, the company has significantly enriched its offerings for developers. A powerful update to Xcode, including ChatGPT integration, is set to transform app development. This move signals Apple's commitment to integrating AI capabilities into its ecosystem, even as challenges persist with its own AI assistant.
However, experts have voiced concerns about Apple's downbeat AI outlook, attributing it to a potential lack of high-powered hardware. Professor Seok Joon Kwon of Sungkyunkwan University suggests that Apple's research paper revealing fundamental reasoning limits of modern large reasoning models (LRMs) and large language models (LLMs) is flawed because Apple lacks the hardware to adequately test high-end LRMs and LLMs. The professor argues that Apple's hardware is unsuitable for AI development compared to the resources available to companies like Google, Microsoft, or xAI. If Apple wants to catch up with rivals, it will either have to buy a lot of Nvidia GPUs or develop its own AI ASICs. Apple's much-anticipated Siri upgrade, powered by Apple Intelligence, is now reportedly targeting a "spring 2026" launch. According to Mark Gurman at Bloomberg, Apple has set an internal release target of spring 2026 for its delayed upgrade of Siri, marking a key step in its artificial intelligence turnaround effort and is slated for iOS 26.4. The upgrade is expected to give Siri on-screen awareness and personal context capabilities. Recommended read:
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@www.sify.com
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www.artificialintelligence-new
, www.macstories.net
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Apple's Worldwide Developers Conference (WWDC) 2025, held on June 10, showcased a significant transformation in both user interface and artificial intelligence. A major highlight was the unveiling of "Liquid Glass," a new design language offering a "glass-like" experience with translucent layers, fluid animations, and spatial depth. This UI refresh, described as Apple's boldest in over a decade, impacts core system elements like the lock screen, home screen, and apps such as Safari and Music, providing floating controls and glassy visual effects. iPhones from the 15 series onward will support Liquid Glass, with public betas rolling out soon to deliver a more immersive and dynamic feel.
Apple also announced advancements in AI, positioning itself to catch up in the competitive landscape. Apple Intelligence, a system-wide, on-device AI layer, integrates with iOS 26, macOS Tahoe, and other platforms. It enables features such as summarizing emails and notifications, auto-completing messages, real-time call translation, and creating personalized emoji called Genmoji. Visual Intelligence allows users to extract text or gain information from photos, documents, and app screens. Siri is slated to receive intelligence upgrades as well, though its full capabilities may be slightly delayed. In a significant shift, Apple has opened its foundational AI model to third-party developers, granting direct access to the on-device large language model powering Apple Intelligence. This move, announced at WWDC, marks a departure from Apple's traditionally closed ecosystem. The newly accessible three-billion parameter model operates entirely on-device, reflecting Apple’s privacy-first approach. The Foundation Models framework allows developers to integrate Apple Intelligence features with minimal code, offering privacy-focused AI inference at no cost. Xcode 26 now includes AI assistance, embedding large language models directly into the coding experience, and third-party developers can now leverage Visual Intelligence capabilities within their apps. Recommended read:
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@www.artificialintelligence-news.com
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References:
machinelearning.apple.com
, AI News
Apple has announced a significant shift in its approach to AI development by opening its foundational AI model to third-party developers. This move, unveiled at the Worldwide Developers Conference (WWDC), grants developers direct access to the on-device large language model that powers Apple Intelligence. The newly accessible three-billion parameter model operates entirely on the device, reflecting Apple’s commitment to user privacy. This on-device approach distinguishes Apple from competitors relying on cloud-based AI solutions, emphasizing privacy and user control.
The new Foundation Models framework enables developers to integrate Apple Intelligence features into their apps with minimal code, using just three lines of Swift. This framework offers guided generation and tool-calling capabilities, making it easier to add generative AI to existing applications. Automattic's Day One journaling app is already leveraging this framework to provide privacy-centric intelligent features. According to Paul Mayne, head of Day One at Automattic, the framework is helping them rethink what’s possible with journaling by bringing intelligence and privacy together in ways that deeply respect their users. Apple is also enhancing developer tools within Xcode 26, which now embeds large language models directly into the coding environment. Developers can access ChatGPT without needing a personal OpenAI account and connect API keys from other providers or run local models on Apple silicon Macs. Furthermore, Apple has upgraded the App Intents interface to support visual intelligence, allowing apps to present visual search results directly within the operating system. Etsy is already exploring these features to improve product discovery, with CTO Rafe Colburn noting the potential to meet shoppers right on their iPhone with visual intelligence. Recommended read:
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Mark Tyson@tomshardware.com
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OpenAI has recently launched its newest reasoning model, o3-pro, making it available to ChatGPT Pro and Team subscribers, as well as through OpenAI’s API. Enterprise and Edu subscribers will gain access the following week. The company touts o3-pro as a significant upgrade, emphasizing its enhanced capabilities in mathematics, science, and coding, and its improved ability to utilize external tools.
OpenAI has also slashed the price of o3 by 80% and o3-pro by 87%, positioning the model as a more accessible option for developers seeking advanced reasoning capabilities. This price adjustment comes at a time when AI providers are competing more aggressively on both performance and affordability. Experts note that evaluations consistently prefer o3-pro over the standard o3 model across all categories, especially in science, programming, and business tasks. O3-pro utilizes the same underlying architecture as o3, but it’s tuned to be more reliable, especially on complex tasks, with better long-range reasoning. The model supports tools like web browsing, code execution, vision analysis, and memory. While the increased complexity can lead to slower response times, OpenAI suggests that the tradeoff is worthwhile for the most challenging questions "where reliability matters more than speed, and waiting a few minutes is worth the tradeoff.” Recommended read:
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@futurumgroup.com
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www.windowslatest.com
, futurumgroup.com
Microsoft is doubling down on its commitment to the developer community by embracing agentic AI, a move highlighted at the recent Microsoft Build conference. CEO Satya Nadella emphasized the shift from AI as merely an assistant to a proactive agent capable of performing complex tasks and workflows for software teams. This signifies a pivotal moment for Microsoft, placing AI at the forefront of software development and reshaping the industry's future. Microsoft leadership acknowledged the need to collaborate with the development community to navigate this new era and build the path toward agentic AI development together, recognizing that they don't have all the answers themselves.
Microsoft is actively integrating AI agents into its development tools, notably GitHub Copilot. The new coding agent in GitHub Copilot enables developers to assign issues to the agent, which then works asynchronously to create fully tested pull requests. This is more than just autocomplete; it's a new class of software engineering agent that works like a teammate, planning work, writing code, running tests, and soliciting feedback. By automating repetitive tasks and assisting with code maintenance, the coding agent aims to free up developers to focus on more critical and creative aspects of their work, increasing efficiency and productivity. Microsoft is also emphasizing the importance of cybersecurity in the age of AI. They are rolling out free cybersecurity support for European governments, offering AI-generated insights, early warnings about security flaws, and support against state-backed attacks. Microsoft is also encouraging users to upgrade to Windows 11 for enhanced security features, as Windows 10 support is ending in October 2025. Microsoft is also showcasing its AI-first security platform at the Gartner Security & Risk Management Summit, aiming to help organizations manage risk and protect assets effectively in the face of evolving threats. Recommended read:
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@github.blog
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Microsoft is aggressively integrating artificial intelligence into its developer tools and workflows, demonstrated by the announcements made at Microsoft Build 2025. A key focus is streamlining the development process by leveraging AI to accelerate the creation of issues and pull requests within GitHub, a fundamental aspect of software development. This involves utilizing GitHub Copilot to draft issues and assigning them to a coding agent for asynchronous execution, resulting in quicker generation of pull requests. This approach aims to maintain familiarity for developers while significantly improving efficiency and consistency. The importance of well-structured issues and pull requests remains paramount, even in AI-accelerated workflows, as they provide shared context, facilitate asynchronous coordination, support audit and analytics, and enable automation hooks.
BenchmarkQED, an open-source toolkit, has been introduced to automate the benchmarking of Retrieval-Augmented Generation (RAG) systems. This toolkit includes components for query generation, evaluation, and dataset preparation, all designed to support rigorous and reproducible testing. BenchmarkQED complements Microsoft's open-source GraphRAG library, enabling users to conduct GraphRAG-style evaluations across various models, metrics, and datasets. The toolkit addresses the growing need to benchmark RAG performance as new techniques emerge, particularly in answering questions over private datasets. BenchmarkQED facilitates the comparison of RAG methods, including LazyGraphRAG, against competing approaches like vector-based RAG with a 1M-token context window. Tests show that LazyGraphRAG demonstrates significant win rates, especially on complex, global queries that require reasoning over large portions of the dataset. This advancement aims to enhance the performance of RAG systems, particularly in scenarios where conventional vector-based RAG struggles with questions requiring an understanding of dataset qualities not explicitly stated in the text. The toolkit represents a major step forward in automating and scaling RAG benchmarking. Recommended read:
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Emilia David@AI News | VentureBeat
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Google's Gemini 2.5 Pro is making waves in the AI landscape, with claims of superior coding performance compared to leading models like DeepSeek R1 and Grok 3 Beta. The updated Gemini 2.5 Pro, currently in preview, is touted to deliver faster and more creative responses, particularly in coding and reasoning tasks. Google highlighted improvements across key benchmarks such as AIDER Polyglot, GPQA, and HLE, noting a significant Elo score jump since the previous version. This newest iteration, referred to as Gemini 2.5 Pro Preview 06-05, builds upon the I/O edition released earlier in May, promising even better performance and enterprise-scale capabilities.
Google is also planning several enhancements to the Gemini platform. These include upgrades to Canvas, Gemini’s workspace for organizing and presenting ideas, adding the ability to auto-generate infographics, timelines, mindmaps, full presentations, and web pages. There are also plans to integrate Imagen 4, which enhances image generation capabilities, image-to-video functionality, and an Enterprise mode, which offers a dedicated toggle to separate professional and personal workflows. This Enterprise mode aims to provide business users with clearer boundaries and improved data governance within the platform. In addition to its coding prowess, Gemini 2.5 Pro boasts native audio capabilities, enabling developers to build richer and more interactive applications. Google emphasizes its proactive approach to safety and responsibility, embedding SynthID watermarking technology in all audio outputs to ensure transparency and identifiability of AI-generated audio. Developers can explore these native audio features through the Gemini API in Google AI Studio or Vertex AI, experimenting with audio dialog and controllable speech generation. Google DeepMind is also exploring ways for AI to take over mundane email chores, with CEO Demis Hassabis envisioning an AI assistant capable of sorting, organizing, and responding to emails in a user's own voice and style. Recommended read:
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Heng Chi@AI Accelerator Institute
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References:
AI Talent Development
, AI Accelerator Institute
AWS is becoming a standard for businesses looking to leverage AI and NLP through its comprehensive services. An article discusses how to design a high-performance data pipeline using core AWS services like Amazon S3, AWS Lambda, AWS Glue, and Amazon SageMaker. These pipelines are crucial for ingesting, processing, and outputting data for training, inference, and decision-making at a large scale, which is essential for modern AI and NLP applications that rely on data-driven insights and automation. The platform's scalability, flexibility, and cost-efficiency make it a preferred choice for building these pipelines.
AWS offers various advantages, including automatic scaling capabilities that ensure high performance regardless of data volume. Its flexibility and integration features allow seamless connections between services like Amazon S3 for storage, AWS Glue for ETL, and Amazon Redshift for data warehousing. Additionally, AWS’s pay-as-you-go pricing model provides cost-effectiveness, with Reserved Instances and Savings Plans enabling further cost optimization. The platform's reliability and global infrastructure offer a strong foundation for building effective machine learning solutions at every stage of the ML lifecycle. Generative AI applications, while appearing simple, require a more complex system involving workflows that invoke foundation models (FMs), tools, and APIs, using domain-specific data to ground responses. Organizations are adopting a unified approach to build applications where foundational building blocks are offered as services for developing generative AI applications. This approach facilitates centralized governance and operations, streamlining development, scaling generative AI development, mitigating risk, optimizing costs, and accelerating innovation. A well-established generative AI foundation includes offering a comprehensive set of components to support the end-to-end generative AI application lifecycle. Recommended read:
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@www.marktechpost.com
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DeepSeek has released a major update to its R1 reasoning model, dubbed DeepSeek-R1-0528, marking a significant step forward in open-source AI. The update boasts enhanced performance in complex reasoning, mathematics, and coding, positioning it as a strong competitor to leading commercial models like OpenAI's o3 and Google's Gemini 2.5 Pro. The model's weights, training recipes, and comprehensive documentation are openly available under the MIT license, fostering transparency and community-driven innovation. This release allows researchers, developers, and businesses to access cutting-edge AI capabilities without the constraints of closed ecosystems or expensive subscriptions.
The DeepSeek-R1-0528 update brings several core improvements. The model's parameter count has increased from 671 billion to 685 billion, enabling it to process and store more intricate patterns. Enhanced chain-of-thought layers deepen the model's reasoning capabilities, making it more reliable in handling multi-step logic problems. Post-training optimizations have also been applied to reduce hallucinations and improve output stability. In practical terms, the update introduces JSON outputs, native function calling, and simplified system prompts, all designed to streamline real-world deployment and enhance the developer experience. Specifically, DeepSeek R1-0528 demonstrates a remarkable leap in mathematical reasoning. On the AIME 2025 test, its accuracy improved from 70% to an impressive 87.5%, rivaling OpenAI's o3. This improvement is attributed to "enhanced thinking depth," with the model now utilizing significantly more tokens per question, indicating more thorough and systematic logical analysis. The open-source nature of DeepSeek-R1-0528 empowers users to fine-tune and adapt the model to their specific needs, fostering further innovation and advancements within the AI community. Recommended read:
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@www.microsoft.com
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References:
The Pragmatic Engineer
, The Rundown AI
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Microsoft recently held its Build 2025 developer conference, showcasing a range of new AI-powered tools and providing a sneak peek into experimental projects. One of the overarching themes of the event was the company's heavy investment in Artificial Intelligence, with nearly every major announcement being related to Generative AI. Microsoft is also focused on AI agents designed to augment and amplify the capabilities of organizations. For instance, marketing agents could propose and execute digital marketing campaign plans, while engineering agents could autonomously create specifications for new features and begin testing them.
At Build, Microsoft highlighted its commitment to "dogfooding" its own AI dev tools. This involved using Copilot within its complex .NET codebase, allowing developers to witness firsthand the agent's stumbles and successes. While this approach might appear risky, it demonstrates Microsoft's commitment to transparency and continuous improvement, differentiating it from other AI development tool vendors. The goal of Microsoft is to solidify its position as the go-to platform for developers through GitHub and Azure, while simultaneously fostering an ecosystem where other startups can build upon this foundation. One particularly intriguing experimental project unveiled at Build was Project Amelie. This AI agent is designed to build machine learning pipelines from a single prompt. Amelie ingests available data, trains models, and produces a deployable solution, essentially acting as a "mini data scientist in a box." In early testing, Microsoft claims Project Amelie has outperformed current benchmarks on MLE-Bench, a framework for evaluating machine learning agents. While Project Amelie is still in its early stages, it exemplifies Microsoft's vision for AI agents that can autonomously carry out complex AI-related tasks. Recommended read:
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Ken Yeung@Ken Yeung
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References:
PCMag Middle East ai
, Ken Yeung
Microsoft is exploring the frontier of AI-driven development with its experimental project, Project Amelie. Unveiled at Build 2025, Amelie is an AI agent designed to autonomously construct machine learning pipelines from a single prompt. This project showcases Microsoft's ambition to create AI that can develop AI, potentially revolutionizing how machine learning engineering tasks are performed. Powered by Microsoft Research's RD agent, Amelie aims to automate and optimize research and development processes in machine learning, eliminating the manual setup work typically handled by data scientists.
Early testing results are promising, with Microsoft reporting that Project Amelie has outperformed current benchmarks on MLE-Bench, a framework for evaluating machine learning agents' effectiveness in real-world tasks. During a live demo at Microsoft Build, Seth Juarez, Principal Program Manager for Microsoft's AI Platform, illustrated how Amelie could function as a "mini data scientist in a box," capable of processing and analyzing data that would typically take human scientists a day and a half to complete. This project has potential for applications in other scenarios where users want AI to carry out complex AI-related tasks. Should Project Amelie become commercialized, it could significantly advance Microsoft's goals for human-agent collaboration. While Microsoft is not alone in this endeavor, with companies like Google's DeepMind and OpenAI also exploring similar technologies, the project highlights a shift towards AI agents handling complex AI-related tasks independently. Developers interested in exploring the capabilities of Project Amelie can sign up to participate in its private preview, offering a glimpse into the future of AI-driven machine learning pipeline development. Recommended read:
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@gradientflow.com
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References:
eWEEK
, Gradient Flow
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Apple is ramping up its efforts in the artificial intelligence space, focusing on efficiency, privacy, and seamless integration across its hardware and software. The tech giant is reportedly accelerating the development of its first AI-powered smart glasses, with a target release date of late 2026. These glasses, described as similar to Meta's Ray-Ban smart glasses but "better made," will feature built-in cameras, microphones, and speakers, enabling them to analyze the external world and respond to requests via Siri. This move positions Apple to compete directly with Meta, Google, and the emerging OpenAI/Jony Ive partnership in the burgeoning AI device market.
Apple also plans to open its on-device AI models to developers at WWDC 2025. This initiative aims to empower developers to create innovative AI-driven applications that leverage Apple's hardware capabilities while prioritizing user privacy. By providing developers with access to its AI models, Apple hopes to foster a vibrant ecosystem of AI-enhanced experiences across its product line. The company's strategy reflects a desire to integrate sophisticated intelligence deeply into its products without compromising its core values of user privacy and trust, distinguishing it from competitors who may have rapidly deployed high-profile AI models. While Apple is pushing forward with its smart glasses, it has reportedly shelved plans for an Apple Watch with a built-in camera. This decision suggests a strategic shift in focus, with the company prioritizing the development of AI-powered wearables that align with its vision of seamless integration and user privacy. The abandonment of the camera-equipped watch may also reflect concerns about privacy implications or technical challenges associated with incorporating such features into a smaller wearable device. Ultimately, Apple's success in the AI arena will depend on its ability to deliver genuinely useful and seamlessly embedded AI experiences that enhance user experience. Recommended read:
References :
Ken Yeung@Ken Yeung
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Microsoft is significantly expanding its AI capabilities to the edge, empowering developers with tools to create innovative AI agents. This strategic move, unveiled at Build 2025, focuses on enabling smarter and faster experiences across various devices. Unlike previous strategies centered on single-use AI assistants, Microsoft is now emphasizing dynamic agents that seamlessly integrate with third-party systems through the Model Context Protocol (MCP). This shift aims to create broader, integrated ecosystems where agents can operate across diverse use cases and integrate with any digital infrastructure.
Microsoft is empowering developers by offering the OpenAI Responses API, which allows the combination of MCP servers, code interpreters, reasoning, web search, and RAG within a single API call. This capability enables the development of next-generation AI agents. Among the announcements at Build 2025 were a platform to build on-device agents, the ability to bring AI to web apps on the Edge browser, and developer capabilities to deploy bots directly on Windows. The company hopes the developments will lead to broader use of AI technologies and a significant increase in the number of daily active users. Microsoft is already demonstrating the impact of its agentic AI platform, Azure AI Foundry, in healthcare, including streamlining cancer care planning. In addition to their AI initiatives, Microsoft has introduced a new AI-powered orchestration system that streamlines the complex process of cancer care planning. This orchestration system, available through the Azure AI Foundry Agent Catalog, brings together specialized AI agents to assist clinicians with the analysis of multimodal medical data, from imaging and genomics to clinical notes and pathology. Early adopters include Stanford Health Care, Johns Hopkins, Providence Genomics, and UW Health. Recommended read:
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@the-decoder.com
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Google has launched Jules, a coding agent designed to automate tasks such as bug fixing, documentation, and testing. This new tool enters public beta and is available globally, giving developers the chance to have AI file pull requests on their behalf. Jules leverages Google's Gemini 2.5 Pro model and offers a starter tier with five free tasks per day, positioning it as a direct competitor to GitHub Copilot's coding agent and OpenAI's Codex.
Jules differentiates itself by spinning up a disposable Cloud VM, cloning the target repository, and creating a multi-step plan before making changes to any files. The agent can handle tasks like bumping dependencies, refactoring code, adding documentation, writing tests, and addressing open issues. Each change is presented as a standard GitHub pull request for human review. Google emphasizes that Jules "understands your codebase" due to the multimodal Gemini model, which allows it to reason over large file graphs and project history. The release of Jules in beta signifies a broader shift from code-completion tools to full agentic development. Jules is available to anyone with a Google account and a linked GitHub account, and tasks can be assigned directly from an issue using the assign-to-jules label. This move reflects the increasing trend of AI-assisted programming and automated agents in software development, with both Google and Microsoft vying for dominance in this growing market. Recommended read:
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@Latest news
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Microsoft is intensifying its efforts to enhance the security and trustworthiness of AI agents, announcing significant advancements at Build 2025. These moves are designed to empower businesses and individuals to create custom-made AI systems with improved safeguards. A key component of this initiative is the extension of Zero Trust principles to secure the agentic workforce, ensuring that AI agents operate within a secure and controlled environment.
Windows 11 is set to receive native Model Context Protocol (MCP) support, complete with new MCP Registry and MCP Server functionalities. This enhancement aims to streamline the development process for agentic AI experiences, making it easier for developers to build Windows applications with robust AI capabilities. The MCP, an open standard, facilitates seamless interaction between AI models and data residing outside specific applications, enabling apps to share contextual information that AI tools and agents can utilize effectively. Microsoft is introducing the MCP Registry as a secure and trustworthy source for AI agents to discover accessible MCP servers on Windows devices. In related news, GitHub and Microsoft are collaborating with Anthropic to advance the MCP standard. This partnership will see both companies adding first-party support across Azure and Windows, assisting developers in exposing app features as MCP servers. Further improvements will focus on bolstering security and establishing a registry to list trusted MCP servers. Microsoft Entra Agent ID, an extension of industry-leading identity management and access capabilities, will also be introduced to provide enhanced security for AI agents. These strategic steps underscore Microsoft's commitment to securing the agentic workforce and facilitating the responsible development and deployment of AI technologies. Recommended read:
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Matthias Bastian@THE DECODER
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OpenAI has announced the integration of GPT-4.1 and GPT-4.1 mini models into ChatGPT, aimed at enhancing coding and web development capabilities. The GPT-4.1 model, designed as a specialized model excelling at coding tasks and instruction following, is now available to ChatGPT Plus, Pro, and Team users. According to OpenAI, GPT-4.1 is faster and a great alternative to OpenAI o3 & o4-mini for everyday coding needs, providing more help to developers creating applications.
OpenAI is also rolling out GPT-4.1 mini, which will be available to all ChatGPT users, including those on the free tier, replacing the previous GPT-4o mini model. This model serves as the fallback option once GPT-4o usage limits are reached. The release notes confirm that GPT 4.1 mini offers various improvements over GPT-4o mini, including instruction-following, coding, and overall intelligence. This initiative is part of OpenAI's effort to make advanced AI tools more accessible and useful for a broader audience, particularly those engaged in programming and web development. Johannes Heidecke, Head of Systems at OpenAI, has emphasized that the new models build upon the safety measures established for GPT-4o, ensuring parity in safety performance. According to Heidecke, no new safety risks have been introduced, as GPT-4.1 doesn’t introduce new modalities or ways of interacting with the AI, and that it doesn’t surpass o3 in intelligence. The rollout marks another step in OpenAI's increasingly rapid model release cadence, significantly expanding access to specialized capabilities in web development and coding. Recommended read:
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@Dataconomy
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Databricks has announced its acquisition of Neon, an open-source database startup specializing in serverless Postgres, in a deal reportedly valued at $1 billion. This strategic move is aimed at enhancing Databricks' AI infrastructure, specifically addressing the database bottleneck that often hampers the performance of AI agents. Neon's technology allows for the rapid creation and deployment of database instances, spinning up new databases in milliseconds, which is critical for the speed and scalability required by AI-driven applications. The integration of Neon's serverless Postgres architecture will enable Databricks to provide a more streamlined and efficient environment for building and running AI agents.
Databricks plans to incorporate Neon's scalable Postgres offering into its existing big data platform, eliminating the need to scale separate server and storage components in tandem when responding to AI workload spikes. This resolves a common issue in modern cloud architectures where users are forced to over-provision either compute or storage to meet the demands of the other. With Neon's serverless architecture, Databricks aims to provide instant provisioning, separation of compute and storage, and API-first management, enabling a more flexible and cost-effective solution for managing AI workloads. According to Databricks, Neon reports that 80% of its database instances are provisioned by software rather than humans. The acquisition of Neon is expected to give Databricks a competitive edge, particularly against competitors like Snowflake. While Snowflake currently lacks similar AI-driven database provisioning capabilities, Databricks' integration of Neon's technology positions it as a leader in the next generation of AI application building. The combination of Databricks' existing data intelligence platform with Neon's serverless Postgres database will allow for the programmatic provisioning of databases in response to the needs of AI agents, overcoming the limitations of traditional, manually provisioned databases. Recommended read:
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@Google DeepMind Blog
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Google DeepMind has introduced AlphaEvolve, a revolutionary AI coding agent designed to autonomously discover innovative algorithms and scientific solutions. This groundbreaking research, detailed in the paper "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery," represents a significant step towards achieving Artificial General Intelligence (AGI) and potentially even Artificial Superintelligence (ASI). AlphaEvolve distinguishes itself through its evolutionary approach, where it autonomously generates, evaluates, and refines code across generations, rather than relying on static fine-tuning or human-labeled datasets. AlphaEvolve combines Google’s Gemini Flash, Gemini Pro, and automated evaluation metrics.
AlphaEvolve operates using an evolutionary pipeline powered by large language models (LLMs). This pipeline doesn't just generate outputs—it mutates, evaluates, selects, and improves code across generations. The system begins with an initial program and iteratively refines it by introducing carefully structured changes. These changes take the form of LLM-generated diffs—code modifications suggested by a language model based on prior examples and explicit instructions. A diff in software engineering refers to the difference between two versions of a file, typically highlighting lines to be removed or replaced. Google's AlphaEvolve is not merely another code generator, but a system that generates and evolves code, allowing it to discover new algorithms. This innovation has already demonstrated its potential by shattering a 56-year-old record in matrix multiplication, a core component of many machine learning workloads. Additionally, AlphaEvolve has reclaimed 0.7% of compute capacity across Google's global data centers, showcasing its efficiency and cost-effectiveness. AlphaEvolve imagined as a genetic algorithm coupled to a large language model. Recommended read:
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@the-decoder.com
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Google has announced implicit caching in Gemini 2.5, a new feature designed to significantly reduce developer costs. The company aims to cut costs by as much as 75 percent by automatically applying a 75% cached token discount. This is a substantial improvement over previous methods, where developers had to manually configure caching. The new implicit caching automatically detects and stores recurring content, ensuring that repeated prompts are only processed once, which can lead to substantial cost savings.
The new feature is particularly beneficial for applications that run prompts against the same long context or continue existing conversations. Google recommends placing the stable part of a prompt, such as system instructions, at the start and adding user-specific input, like questions, afterwards to maximize benefits. Implicit caching kicks in for Gemini 2.5 Flash starting at 1,024 tokens, and for Pro versions from 2,048 tokens onwards. This functionality is now live, and developers can find more details and best practices in the Gemini API documentation. This development builds on the overwhelmingly positive feedback to Gemini 2.5 Pro’s coding and multimodal reasoning capabilities. Beyond UI-focused development, these improvements extend to other coding tasks such as code transformation, code editing and developing complex agentic workflows. Simon Willison notes that Gemini 2.5 now applies the 75% cached token discount automatically, which he considers a potentially big cost saving for applications that run prompts against the same long context or continue existing conversations. Recommended read:
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@www.marktechpost.com
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OpenAI has announced the release of Reinforcement Fine-Tuning (RFT) for its o4-mini reasoning model, alongside supervised fine-tuning (SFT) for the GPT-4.1 nano model. RFT enables developers to customize a private version of the o4-mini model based on their enterprise's unique products, internal terminology, and goals. This allows for a more tailored AI experience, where the model can generate communications, answer specific questions about company knowledge, and pull up private, proprietary company knowledge with greater accuracy. RFT represents a move beyond traditional supervised fine-tuning, offering more flexible control for complex, domain-specific tasks.
The process involves applying a feedback loop during training, where developers can initiate training sessions, upload datasets, and set up assessment logic through OpenAI’s online developer platform. Instead of relying on fixed question-answer pairs, RFT uses a grader model to score multiple candidate responses per prompt, adjusting the model weights to favor high-scoring outputs. This approach allows for fine-tuning to subtle requirements, such as a specific communication style, policy guidelines, or domain-specific expertise. Organizations with clearly defined problems and verifiable answers can benefit significantly from RFT, aligning models with nuanced objectives. Several organizations have already leveraged RFT in closed previews, demonstrating its versatility across industries. Accordance AI improved the performance of a tax analysis model, while Ambience Healthcare increased the accuracy of medical coding. Other use cases include legal document analysis by Harvey, Stripe API code generation by Runloop, and content moderation by SafetyKit. OpenAI also announced that supervised fine-tuning is now supported for its GPT-4.1 nano model, the company’s most affordable and fastest offering to date, opening customization to all paid API tiers. The cost model for RFT is more transparent, based on active training time rather than per-token processing. Recommended read:
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@the-decoder.com
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OpenAI is making strides in AI customization and application development with the release of Reinforcement Fine-Tuning (RFT) on its o4-mini reasoning model and the appointment of Fidji Simo as the CEO of Applications. The RFT release allows organizations to tailor their versions of the o4-mini model to specific tasks using custom objectives and reward functions, marking a significant advancement in model optimization. This approach utilizes reinforcement learning principles, where developers provide a task-specific grader that evaluates and scores model outputs based on custom criteria, enabling the model to optimize against a reward signal and align with desired behaviors.
Reinforcement Fine-Tuning is particularly valuable for complex or subjective tasks where ground truth is difficult to define. By using RFT on o4-mini, a compact reasoning model optimized for text and image inputs, developers can fine-tune for high-stakes, domain-specific reasoning tasks while maintaining computational efficiency. Early adopters have demonstrated the practical potential of RFT. This capability allows developers to tweak the model to better fit their needs using OpenAI's platform dashboard, deploy it through OpenAI's API, and connect it to internal systems. In a move to scale its AI products, OpenAI has appointed Fidji Simo, formerly CEO of Instacart, as the CEO of Applications. Simo will oversee the scaling of AI products, leveraging her extensive experience in consumer tech to drive revenue generation from OpenAI's research and development efforts. Previously serving on OpenAI's board of directors, Simo's background in leading development at Facebook suggests a focus on end-users rather than businesses, potentially paving the way for new subscription services and products aimed at a broader audience. OpenAI is also rolling out a new GitHub connector for ChatGPT's deep research agent, allowing users with Plus, Pro, or Team subscriptions to connect their repositories and ask questions about their code. Recommended read:
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@analyticsindiamag.com
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OpenAI has unveiled a new GitHub connector for its ChatGPT Deep Research tool, empowering developers to analyze their codebases directly within the AI assistant. This integration allows seamless connection of both private and public GitHub repositories, enabling comprehensive analysis to generate reports, documentation, and valuable insights based on the code. The Deep Research agent can now sift through source code and engineering documentation, respecting existing GitHub permissions by only accessing authorized repositories, streamlining the process of understanding and maintaining complex projects.
This new functionality aims to simplify code analysis and documentation processes, making it easier for developers to understand and maintain complex projects. Developers can leverage the connector to implement new APIs by finding real examples in their codebase, break down product specifications into manageable technical tasks with dependencies mapped out, or generate summaries of code structure and patterns for onboarding new team members or creating technical documentation. OpenAI Product Leader Nate Gonzalez stated that users found ChatGPT's deep research agent so valuable that they wanted it to connect to their internal sources, in addition to the web. The GitHub connector is currently rolling out to ChatGPT Plus, Pro, and Team users. Enterprise and Education customers will gain access soon. OpenAI emphasizes that the connector respects existing permissions structures and honors GitHub permission settings. This launch follows the recent integration of ChatGPT Team with tools like Google Drive, furthering OpenAI's goal of seamlessly integrating ChatGPT into internal workflows by pulling relevant context from various platforms where knowledge typically resides within organizations. OpenAI also plans to add more deep research connectors in the future. Recommended read:
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@docs.anthropic.com
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Anthropic, the generative AI startup, has officially entered the internet search arena with the launch of its new web search API for Claude. This positions Claude as a direct challenger to traditional search engines like Google, offering users real-time access to information through its large language models. This API enables developers to integrate Claude’s search capabilities directly into their own applications, expanding the reach of AI-powered information retrieval.
The Claude web search API provides access to current web information, allowing the AI assistant to conduct multiple, iterative searches to deliver more complete and accurate answers. Claude uses its "reasoning" capabilities to determine if a user’s query would benefit from a real-time search, generating search queries and analyzing the results to inform its responses. The responses it delivers will come with citations that link to the source articles it uses, offering users transparency and enabling them to verify the information for themselves. This move comes amid signs of a potential shift in the search landscape, with growing user engagement with AI-driven alternatives. Apple is reportedly exploring AI search engines like ChatGPT, Perplexity and Anthropic's Claude, as options in Safari, signaling a shift away from Google’s $20 billion deal to be the default search engine. The decline in traditional search volume is attributed to the conversational and context-aware nature of AI platforms. The move signals a growing trend towards conversational AI in information retrieval, which may reshape how people access and use the internet. Recommended read:
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