News from the AI & ML world
DeeperML - #developers
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@google.github.io
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Google Cloud has announced the public preview of Vertex AI Agent Engine Memory Bank, a significant advancement for developers building conversational AI agents. This new managed service is designed to empower agents with long-term memory, enabling them to maintain context, personalize interactions, and remember user preferences across multiple sessions. This addresses a critical limitation in current AI agent development, where agents often "forget" previous interactions, leading to repetitive conversations and a less engaging user experience. Memory Bank aims to eliminate this by providing a persistent and up-to-date information store for agents.
The integration of Memory Bank with the Google Agent Development Kit (ADK) and support for popular frameworks like LangGraph and CrewAI are key features of this announcement. Developers can now leverage Memory Bank to create more sophisticated and stateful agents that can recall past conversations and user details, leading to more natural and efficient interactions. The service utilizes Google's powerful Gemini models to extract and manage these memories, ensuring that agents have access to relevant and accurate information. This move by Google Cloud is set to streamline the development of truly personalized and context-aware AI assistants. This release marks a crucial step forward in making AI agents more helpful and human-like. By moving beyond the limitations of solely relying on an LLM's context window, which can be expensive and inefficient, Memory Bank offers a robust solution for managing an agent's knowledge. This capability is essential for building production-ready AI agents that can handle complex user needs and provide consistent, high-quality assistance over time. The public preview availability signifies Google Cloud's commitment to providing developers with the tools needed to innovate in the rapidly evolving field of generative AI. Recommended read:
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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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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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References:
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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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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