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Medium

108

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Gardens and Pots: My Journey Learning Linux, Embracing Privacy and Relearning Technology

At the age of 15, the author learned to grow wheatgrass from scratch, experiencing the joy and challenge of hands-on gardening.
Transitioning to consumer technology like iPhones and laptops with Windows, the author lacked awareness about internet dangers and cybersecurity best practices.
School integration of Google services simplified tasks but raised concerns about data privacy and surveillance by large tech corporations.
Frustrations with centralized technology ecosystems led the author to explore Linux, ultimately choosing EndeavourOS for its minimal yet customizable features.
Through learning Linux, the author delved into ricing, customization, command line operations, encryption, package management, and other advanced computing skills.
Discovering the principles of FOSS and influential figures like Richard Stallman and Linus Torvalds revolutionized the author's perspective on technology.
Embracing the autonomy and liberating experience of customizing software in Linux, the author felt empowered and in control of their technology.
Encouraging others to explore diverse tech ecosystems and avoid dependency on monopolistic companies, the author advocates for tech autonomy and user empowerment.
In conclusion, the author likens the process of learning Linux and customization to gardening a laptop, emphasizing the importance of self-mastery over reliance on established tech giants.
Experimenting with alternative technologies and supporting independent creators are promoted as ways to nurture personal autonomy in the digital age.

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Medium

409

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Qwen 3 is Here and It’s Mind-Blowing: A Technical Deep Dive

Qwen 3 offers a range of models from 0.6B to 235B parameters, catering to diverse needs from small labs to global enterprises.
Specializing in chat, coding, and mathematics, Qwen 3 delivers top-tier results in each domain.
Alibaba Cloud open-sources Qwen 3 models under permissive licenses, encouraging global AI innovation.
Qwen 3's architecture leverages the MoE framework, activating subsets of parameters for efficiency.
With innovations like GQA and global-batch load balancing, Qwen 3 ensures efficient processing and scalability.
Qwen 3's unified chat/reasoner model streamlines deployment by eliminating the need for multiple models.
In coding, mathematics, and general language tasks, Qwen 3 competes with and often outperforms top models like GPT-4o.
Qwen 3's coding models match industry leaders, offering accuracy and flexibility with models ranging from 0.5B to 32B parameters.
In mathematics, Qwen 3 excels in Chain-of-Thought and Tool-integrated Reasoning, outperforming competitors in multi-step tasks.
Qwen 3's versatility extends to 119 languages and a 128k-token context window, ideal for diverse AI solutions.

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VentureBeat

369

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Beyond A2A and MCP: How LOKA’s Universal Agent Identity Layer changes the game

Carnegie Mellon University researchers propose LOKA, an open-source interoperability protocol for autonomous AI agents' identity and ethics.
LOKA aims to establish a common framework for communication, ethical reasoning, and compliance among AI agents.
It introduces a Universal Agent Identity Layer to assign a unique and verifiable identity to agents and enable accountability and ethical governance.
LOKA competes with other agentic protocols like A2A and MCP, but has received positive feedback for its potential to create trusted, accountable, and interoperable agent ecosystems.

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Tindie

187

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SCSIknife SCA Emulator

SCSIknife SCA is an emulator that helps bridge the gap for vintage machines that depend on SCSI devices.
It is based on the ZuluSCSI Pico firmware and features a neatly laid-out board to run the firmware.
The design of SCSIknife allows easy connection with SCSI cable and has been tested with various vintage machines.
The SCSIknife is created by Antoine Bercovici, ensuring reliability and ease of use for rescuing and restoring computers from the 1980s onward.

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Hackernoon

117

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Meet The Open Hardware Startup Backed by $50 Million from Vitalik Buterin

OpenWater, founded by Mary Lou Jepsen, utilizes near-infrared light for brain-computer interfaces and early disease detection.
The company announced open-sourcing its patents and knowledge, breaking new ground in the hardware industry.
Open-source hardware projects like OpenWater's are rare in medical fields, offering a unique approach to innovation and profit.
Vitalik Buterin supported OpenWater with $50 million, emphasizing the importance of open-source collaboration.
OpenWater's technology includes an acousto-optic platform using near-infrared light and focused ultrasound for various applications.
The platform can serve as a wearable 'MRI' for continuous health monitoring and early disease detection, potentially saving lives and costs.
Focused ultrasound from OpenWater's devices could replace drug therapies for cancer treatment and other conditions.
Moreover, the technology enables non-invasive brain-machine interfaces, a field with immense potential for enhancing human capabilities.
OpenWater's revolutionary technology and open business model have the potential to transform healthcare and attract more attention and support.
With visionary leaders like Mary Lou Jepsen and Vitalik Buterin backing the project, OpenWater's impact could be monumental.

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Medium

253

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Week 17 Update: Building Kolmogorov-Machine for Neural Network Analysis

This week's focus was on developing the Kolmogorov-Machine module for analyzing neural network distributions.
Transformew2 was successfully integrated as a git submodule to improve repository organization.
Extensive educational content was added on layer normalization, neural network fundamentals, and specialized neurons.
Challenges include managing repository complexity, cross-framework compatibility, and incomplete code examples.

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Siliconangle

617

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Meta, Booz Allen develop ‘Space Llama’ AI system for the International Space Station

Meta Platforms Inc. and Booz Allen Holding Corp. are developing an AI system for the International Space Station (ISS).
The AI system, called Space Llama, is based on Meta's Llama 3.2 language model series.
Space Llama is designed to support science projects in the ISS National Laboratory and facilitate research on material combinations and various other areas.
The system runs on the Spaceborne Computer-2 appliance and utilizes Nvidia libraries for boosting AI performance.

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Github

412

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How the GitHub CLI can now enable triangular workflows

The GitHub CLI has introduced improvements to support triangular workflows, allowing developers to pull changes from different branches directly into their feature branches.
This feature is particularly useful for keeping branches updated without constant merging or rebasing, with the recent release (v2.71.2) ensuring smoother operations with triangular workflows.
A lesson in Git fundamentals introduces concepts like Refs, pushing and pulling, and the @{push} revision syntax, essential for understanding Git workflows.
Differentiating between centralized and triangular workflows, the article highlights how triangular workflows involve pushing to and pulling from different refs, streamlining collaboration and maintenance.
GitHub CLI has been enhanced to handle triangular workflows seamlessly, resembling Git behavior for pull requests, respecting configurations set up in Git config files.
The process of setting up triangular branch and fork workflows using Git configurations is detailed, providing insights into configuring pushremotes and remote.pushDefault for efficient workflow management.
The updated GitHub CLI's gh pr command set now aligns with Git configurations, automatically resolving pullRefs and pushRefs according to the established triangular workflow configurations.
The CLI native support for triangular workflows, a significant milestone after 4.5 years of development, aims to provide a more efficient and streamlined experience for developers using the GitHub CLI.
Acknowledgments are given to contributors who played crucial roles in providing feedback, reporting bugs, and supporting the enhancement of the GitHub CLI for triangular workflows.
The GitHub CLI Team expresses pride in delivering these updates, emphasizing ongoing efforts to improve the tool's functionality and user experience.

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Medium

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How to Use Python UV for Lightning-Fast Dependency Management

UV is a next-generation Python package manager that aims to streamline dependency management.
It is written in Rust and is significantly faster than traditional tools like pip.
UV reduces conflicts and errors with its modern resolver and supports PEP 508 and PEP 621.
UV focuses on managing dependencies and environments, making it a go-to solution for faster Python setup.

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VentureBeat

224

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Ethically trained AI startup Pleias releases new small reasoning models optimized for RAG with built-in citations

French AI startup Pleias has released two small reasoning models optimized for retrieval-augmented generation, citation synthesis, and structured multilingual output.
The models, Pleias-RAG-350M and Pleias-RAG-1B, are based on the Pleias 1.0 family of language models and available in CPU-optimized GGUF format.
These models aim to provide cost-effective alternatives to large-scale language models without compromising traceability, multilingual capabilities, or structured reasoning workflows.
Pleias positions their design choice of built-in source citation as an ethical imperative that aligns with regulatory demands for explainable AI.
The Pleias-RAG models can autonomously assess queries, determine complexity, and decide on responses based on source adequacy, offering structured and reasoned answers.
Despite their small size, the Pleias-RAG models exhibit behavior associated with larger systems, showcasing efficient performance on standard CPUs.
In benchmark evaluations, these models outperform larger models on tasks like HotPotQA and show strength in multilingual scenarios with minimal performance degradation.
The models' multilingual support is achieved through careful tokenizer design and adversarial training exercises for language-switching.
Pleias envisions their models being used to augment the performance of existing AI models in orchestration settings, highlighting their cost-effectiveness and complementarity.
The models are released under the Apache 2.0 license, emphasizing commercial reuse and integration into various systems and applications.

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TechCrunch

332

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This tool estimates how much electricity your chatbot messages consume

Hugging Face engineer Julien Delavande has built a tool to estimate the electricity consumption of AI models.
AI models consume energy when run on GPUs and specialized chips, driving the need for more power.
Delavande's tool provides real-time energy consumption estimates for messages sent to and from AI models.
The tool compares model energy usage to common household appliances and aims to promote transparency in AI energy consumption.

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Silicon

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DeepSeek Transferred Data Without Consent, Says South Korea

South Korea's data protection authority has accused AI start-up DeepSeek of transferring user information and prompts without consent.
The Personal Information Protection Commission stated that user consent was not obtained by DeepSeek when transferring personal information to several companies in China and the United States.
DeepSeek was temporarily suspended from South Korea's app stores in February due to privacy concerns.
Several countries, including the US, Italy, Taiwan, and Australia have banned DeepSeek from government devices over national security concerns.

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Medium

329

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Fine-Tuning Virtuoso LLM on Lambda Cloud for Remaining Useful Life Prediction

Adapting large language models (LLMs) to specialized domains, such as predicting the remaining useful life (RUL) of engines, requires efficient fine-tuning techniques and powerful computational resources.
The fine-tuning process involves leveraging the Virtuoso-Small-v2 model, a pre-trained 14-billion-parameter language model, and preparing the CMAPSS dataset for the specialized RUL prediction task.
To ensure computational efficiency, parameter-efficient fine-tuning (PEFT) with LoRA (Low-Rank Adaptation) is employed, updating only a small fraction of Virtuoso's parameters.
The fine-tuning process is accelerated using an NVIDIA H100 GPU with 80GB of HBM3 memory on Lambda Cloud, demonstrating the efficient use of high-performance computing resources.

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Marktechpost

246

AWS Introduces SWE-PolyBench: A New Open-Source Multilingual Benchmark for Evaluating AI Coding Agents

AWS AI Labs has introduced SWE-PolyBench, a multilingual, repository-level benchmark for evaluating AI coding agents.
SWE-PolyBench consists of 2,110 tasks across four programming languages - Java, JavaScript, TypeScript, and Python.
The benchmark incorporates real pull requests (PRs) and introduces Concrete Syntax Tree (CST)-based metrics for assessment.
The evaluation of agents on SWE-PolyBench demonstrates varying performance across languages and task types.

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Hackernoon

379

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My Journey Down the Rabbit Hole of Vibe Coding

Vaani is a minimal, private, universal speech-to-text desktop application built as a response to Vibe Coding, aiming to shift from AI as an assistant to a coding partner.
The goal was to create a speech-to-text app that operates locally, exclusively for Windows users, and can be invoked with a hotkey or hot word.
Named Vaani meaning 'speech' in Sanskrit, the application ensures privacy, versatility across Windows apps, and cross-platform functionality.
The development journey involved using AI code assistants like Claude Sonnet 3.7 and Google Gemini 2.5 Pro for AI collaboration and code reviewing.
Vibe coding offered a rapid application structure generation, streamlined by an iterative loop involving AI code generation, integration, testing, feedback, and refinement.
Challenges in the development process included handling callback communication, concurrency issues, UI state persistence, and continuous speech processing complexity.
The article emphasizes the need for a human-AI dialogue, active validation, intuitive design changes, and structure implementation for maintainable software development.
Vibe coding accelerates prototyping, but risks include subtle bugs, poor architecture, difficult debugging cycles, maintainability concerns, skill erosion potential, and neglect of non-functional requirements.
Recommendations for effective collaboration in vibe coding include validating AI output, acting as a complexity filter, planning for structure, focusing on understanding, and leveraging established tooling and practices.
Vibe coding, while powerful, requires active engagement, critical thinking, and oversight to complement and maximize the capabilities of AI in software development.
Embracing vibe coding as a partner, not a replacement, offers a glimpse into a future where human creativity and AI collaborate guided by sound engineering judgment.

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