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Kaspersky

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The true cost of open-source support in companies | Kaspersky official blog

96% of surveyed companies use open-source applications due to wide selection, customization options, and zero licensing costs.
Over half of firms surveyed face challenges with ongoing maintenance, with 63% struggling to keep solutions updated and apply patches.
Key considerations when selecting open-source software include frequency and scope of updates, security-related updates, and complexity of installing updates.
Assessing engineering culture and cybersecurity hygiene, examining vulnerabilities, and monitoring dependencies are crucial to predicting cybersecurity issues.
Dependencies on third-party open-source components require assessment to determine the speed of patched component updates integration.
Compliance with internal and regulatory requirements, license compliance audits, and developing plans for compliance audits are essential.
Support costs for in-house teams, outsourced technical support, and organizational readiness for open source adoption impact the cost and complexity of technical support.
Considerations like vulnerability scanners, risk management tools, IT asset tracking support, and CI/CD pipeline inclusion are important for open-source adoption readiness.
Addressing risks such as project discontinuation, minor dependencies proliferation, and other supply-chain risks is crucial.

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Marktechpost

341

StepFun Introduces Step-Audio-AQAA: A Fully End-to-End Audio Language Model for Natural Voice Interaction

StepFun introduces Step-Audio-AQAA, a fully end-to-end audio language model for natural voice interaction.
Audio-language modeling aims to enable machines to understand and respond using voice alone for more human-like interactions.
Current systems rely on cascaded speech pipelines with separate modules, leading to performance degradation and lack of expressive control.
Efforts have been made to move from token-based models to fully unified large audio-language models (LALMs) for better performance.
Step-Audio-AQAA directly transforms audio input to expressive output without relying on text intermediaries.
It combines a dual-codebook tokenizer, a large backbone LLM named Step-Omni, and a flow-matching vocoder for natural speech synthesis.
The method involves linguistic and semantic tokenizers, a multimodal decoder trained on various data types, and a vocoder for voice generation.
Step-Audio-AQAA excelled in benchmark evaluations, achieving high Mean Opinion Scores compared to other state-of-the-art models.
The model offers fine-grained voice control, including emotional tone and speech rate, for context-aware audio responses.
Step-Audio-AQAA's design enables it to generate highly accurate, emotionally rich, and contextually aware audio responses.
By combining expressive audio tokenization and a powerful multimodal LLM, Step-Audio-AQAA marks progress towards enabling machines to communicate expressively via speech.

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Medium

381

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The Open Source Beginner Contributor Guide

Open source is more than just software distribution or coding; it's a philosophy focused on principles like responsibility, sharing, communication, open-mindedness, and community.
Many essential software tools are developed by open source projects, thanks to the collaborative efforts of numerous contributors.
To learn about open source, users can engage with open source software, join communities, and contribute to projects.
Contributing to open source entails investing time in tasks like reading documentation, troubleshooting, discussing solutions, and coding, so it's important to choose projects you are genuinely interested in.
For those interested in contributing to open source, the guide aims to assist in navigating this fascinating domain.

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Medium

1.9k

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14 Open-Source Tools That Will Sharpen You Into an Ultimate Developer

Developers often seek tools to enhance their workflows and solve problems efficiently.
Becoming a top-tier developer involves leveraging the right tools along with writing clean code.
The author has explored various open-source tools that can revolutionize development processes.
The guide highlights 14 battle-tested open-source tools that cater to building web apps, APIs, infrastructure, and games.
These tools are valuable for developers working individually, within startups, or in large enterprise systems.

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Hackernoon

227

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Denmark's Digital Liberation - A Historic Victory for Open Source Advocacy

Denmark's Minister of Digitalization announces the government's move from Microsoft Office to LibreOffice by fall 2025, signaling a victory for open-source advocacy.
The shift is part of a broader digital sovereignty strategy, focusing on control, security, and independence.
Denmark's decision is driven by the significant cost savings and geopolitical security concerns associated with reliance on foreign digital infrastructure.
The country's phased approach includes transitioning employees to Linux-based computers and adopting LibreOffice as the standard office software.
The move is seen as a model for other nations, with Denmark's example potentially paving the way for systemic change towards open-source adoption.
The decision is particularly relevant for Pacific Island States due to economic constraints, digital sovereignty issues, and cultural alignment with open-source principles.
Denmark's choice underscores the maturity of open-source technologies and their capability to meet enterprise-grade requirements effectively.
The article urges Pacific Island leaders to audit costs, conduct pilot programs, invest in capacity building, collaborate regionally, and develop policies favoring open source solutions.
The call to action emphasizes the immediate need for transition and highlights the economic and strategic benefits of embracing open-source alternatives.
Denmark's decision is celebrated as a vindication of years of advocacy for digital independence, economic sustainability, and technological self-determination.

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Siliconangle

227

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How Databricks’ Agent Bricks uses AI to judge AI

Databricks Inc. introduces Agent Bricks, a tool on the Mosaic AI platform to evaluate AI performance using large language model judges.
Agent Bricks aims to reflect best practices and techniques for building reliable AI agents.
AI scientist Jonathan Frankle discusses the development and evolution of Agent Bricks.
The tool helps users evaluate the reliability of their agents by defining criteria and comparing performance.
Agent Bricks employs large language models (LLMs) as judges, ensuring alignment with human judgment.
Human involvement is crucial in the agent development process to train agents effectively.
Databricks focuses on scaled reinforcement learning to customize LLMs using available data.
Tools like Agent Bricks encourage users to think like software engineers and prioritize reliability over quick demonstrations.
AI engineering involves careful calibration to solve specific problems effectively.
The aim is to continuously test and evaluate AI models to enhance reliability and performance.
Databricks updates enable vibe coding, but tools like Agent Bricks aim to improve users' engineering mindset.
Feedback and support to SiliconANGLE is appreciated to continue offering free and relevant content.
TheCUBE's coverage of Databricks' Data + AI Summit includes an interview with Jonathan Frankle.
Join the community on YouTube with over 15,000 #CubeAlumni experts, including industry luminaries like Andy Jassy and Michael Dell.

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Siliconangle

302

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AI at a crossroads: Databricks’ take on secure adoption

AI adoption in enterprises is met with varying responses - some are eager to accelerate innovation while others proceed cautiously due to compliance, security, and cost concerns.
Omar Khawaja, CISO of Databricks, discusses secure AI adoption with a focus on the 'gas and brakes' approach to balancing innovation and oversight.
The metaphor of 'gas and brakes' highlights the importance of aligning acceleration and caution in AI adoption to create situational awareness and define safe experimentation zones.
Databricks' AI security strategy involves a comprehensive framework with subsystems, components, risks, and controls to enable targeted governance based on specific use cases.
The goal is to avoid enforcing all controls and instead implement tailored governance measures to ensure safety in AI applications.

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Siliconangle

298

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AI agents take flight at Virgin Atlantic Airways

Virgin Atlantic Airways is utilizing Databricks' data and AI platform for managing customer data and flight operations.
The collaboration involves the use of vibe coding for prototype design and automation.
Richard Masters, VP of data and AI at Virgin Atlantic Airways, mentions using vibe coding to facilitate fast design changes.
Databricks' platform has enabled a wider range of employees at Virgin Atlantic, such as business analysts, to benefit from AI.
Democratization of access and improved communications have resulted in a faster production cycle at Virgin Atlantic.
Databricks' serverless Lakebase helps Virgin Atlantic provide real-time responses to numerous queries simultaneously.
The collaboration aims to simplify calculations and measure ROI for Virgin Atlantic.

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Siliconangle

133

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From lakehouse to live code: Databricks bets on integrated AI experiences — theCUBE analysis

Databricks Inc. is focusing on an enterprise world driven by artificial intelligence with a focus on vibe coding, recently introduced at its annual conference.
During the conference, Databricks unveiled products such as the serverless Lakebase database, Mosaic Agent Bricks for automating agent building, and tools for coding with AI through natural language prompts.
The company aims to enhance applications, particularly in terms of governance and support for transactions, by leveraging the underlying Lakebase database.
Databricks' data lakehouse platform involves the integration of Lakebase, a transactional database based on PostgreSQL obtained through the acquisition of Neon Inc., making AI-driven app development easier.
Lakebase simplifies data and analytics management by allowing customers to use a single set of controls.
Agent Bricks, another major announcement, is a tool for optimizing AI agents automatically during development, letting users request specific agent tasks through natural language prompts.
With each update, Databricks is moving towards integrating AI deeply into every layer of a customer's ecosystem, focusing on vibe coding and live coding with AI tools to unify data environments.
Databricks emphasizes building tools for AI engineers and software developers to interact efficiently with Databricks' data and tools through vibe coding.

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Marktechpost

271

Meta AI Releases V-JEPA 2: Open-Source Self-Supervised World Models for Understanding, Prediction, and Planning

Meta AI has introduced V-JEPA 2, an open-source self-supervised world model for visual understanding, prediction, and planning.
V-JEPA 2 is pretrained on 1 million hours of internet-scale video and 1 million images using a visual mask denoising objective.
The model uses data scaling, model scaling, training schedule, and spatial-temporal augmentation to achieve an 88.2% average accuracy on benchmark tasks.
It demonstrates strong motion and appearance understanding capabilities and transferable visual features.
V-JEPA 2 encoder shows competence in temporal reasoning tasks without language supervision during pretraining.
V-JEPA 2-AC is an action-conditioned variant fine-tuned on robot video data, enabling zero-shot planning through model-predictive control.
The model outperforms baselines in planning efficiency, achieving a 100% success rate on reach tasks and excelling in grasp and manipulation tasks.
Operating with a monocular RGB camera, V-JEPA 2-AC showcases generalization capabilities for real-world applications.
Meta's V-JEPA 2 signifies progress in self-supervised learning for physical intelligence, showcasing the potential of visual representations for perception and control.
The research paper, models on Hugging Face, and GitHub page are available for further exploration.
Meta AI encourages engagement through Twitter, Reddit, and their Newsletter.

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VentureBeat

138

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Databricks open-sources declarative ETL framework powering 90% faster pipeline builds

Databricks open-sources its declarative ETL framework as Apache Spark Declarative Pipelines at the Data + AI Summit, aiming to enhance the Apache Spark community's capabilities.
The move serves to strengthen Databricks' commitment to open ecosystems and compete with Snowflake's recent Openflow service for data integration.
Spark Declarative Pipelines simplifies data pipeline authoring, automates operations, and supports both batch and streaming workloads.
Engineers define pipelines using SQL or Python, allowing Apache Spark to handle execution, dependency tracking, and operational tasks.
The framework supports various data types and sources, enabling real-time and periodic processing through a unified API with validations before execution.
By leveraging the declarative approach, Databricks aims to streamline the Apache Spark experience and simplify end-to-end pipeline development.
Numerous enterprises have benefited from the framework, reducing development time, maintenance efforts, and achieving optimized performance.
Databricks' open-source strategy makes the Spark Declarative Pipelines accessible to a broader user base beyond its existing customers.
The rollout aligns with Databricks' past contributions to the open-source community and coincides with the commercial version's availability.
Apache Spark Declarative Pipelines will be integrated into the Apache Spark codebase soon, while the commercial version offers additional enterprise features.

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Marktechpost

223

Mistral AI Releases Magistral Series: Advanced Chain-of-Thought LLMs for Enterprise and Open-Source Applications

Mistral AI introduces Magistral, a series of reasoning-optimized large language models (LLMs) targeting inference-time reasoning.
Magistral series includes Magistral Small open-source model and Magistral Medium enterprise-tier variant.
Key features include chain-of-thought supervision, multilingual reasoning support, and optimized deployment options.
Benchmark results show competitive accuracy levels for Magistral Small and Medium models.
Magistral Medium offers high throughput with speeds reaching 1,000 tokens per second and optimized for latency-sensitive environments.
Models feature a bespoke reinforcement learning (RL) fine-tuning pipeline and reasoning language alignment for consistent outputs.
Magistral is positioned for adoption in regulated industries, emphasizes model efficiency, and offers strategic differentiation through its release strategy.
Public benchmarking is awaited, and the models aim to be efficient, transparent, and aligned with European AI leadership.

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Siliconangle

151

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Mistral AI debuts new Magistral series of reasoning LLMs

Mistral AI introduces Magistral, a new lineup of reasoning-optimized large language models, including Magistral Small and Magistral Medium.
Magistral Small, with 24 billion parameters, is open source, while Magistral Medium is a proprietary model available through Mistral AI's cloud services.
Both models in the Magistral series offer multilingual capabilities and feature a chain-of-thought feature for breaking down complex tasks. Magistral Medium outperformed Magistral Small in solving math problems, scoring 73.6% and 90% with optimized settings, respectively.
Mistral AI used reinforcement learning to develop the Magistral series, training without a critic model. Magistral Small's code is released on Hugging Face, while Magistral Medium is accessible via Le Chat and the company's API for developers.

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Medium

192

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Your Private Assistant, Forever with You

VocRT is an open-source, voice-to-voice solution designed to be a forever assistant where users can provide all types of information and have it readily available.
VocRT utilizes powerful RAG functionality to turn documents and data into vector embeddings, allowing users to embed their entire digital life for easy access.
Users can upload research papers, notes, and sensitive data to VocRT for instant access without any information leaving their local machine, offering 100% local processing.
VocRT can assist students by providing answers based on uploaded materials, help with home organization by remembering details, and aid professionals in summarizing data or project documentation, representing a new way of AI interaction that respects privacy and data ownership.

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Medium

384

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How I Went from “Maybe Next Year” to “Welcome Aboard!” with Protean in C4GT’25

The writer registered for Apna by providing name, email, and project choices among several organizations.
The writer selected Mifos' Voice-based Banking project as the top choice and Protean's AI-Powered Analytics Chatbot as the second choice for project submissions.
Despite facing setbacks and rejection from Protean initially, the writer eventually received an acceptance email on June 7th, 2025, to join Code for GovTech's Dedicated Mentoring Program for the Protean project.
The writer expressed excitement and readiness to work on the Protean project, aiming to make KPI insights for businesses more accessible through the AI-Powered Analytics Chatbot.

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