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Stackexchange

275

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Is there a Way I Could Contribute to the Bitcoin Ecosystem in an Open Source Project?

An individual passionate about Bitcoin, working at a crypto exchange with experience as a data/machine learning engineer seeks to contribute to the Bitcoin ecosystem in an open-source project.
The individual has extensive experience in MLOps, cloud technologies, Python, Scala, C++, and Rust, with a focus on data engineering tools, not being a Blockchain/smart contract developer.
Desires to contribute to any open-source project within the Bitcoin ecosystem, such as enhancing wallet software or the Lightning Network, and is willing to upgrade knowledge if needed.
Looking for guidance on how to start contributing, whether by joining Discord servers/groups or sending the CV to relevant parties for assistance in getting involved.

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Microsoft

224

Image Credit: Microsoft

Introducing Azure DevOps ID Token Refresh and Terraform Task Version 5

The recent updates introduced improve Workload identity federation (OpenID Connect) experience with Azure DevOps and Terraform on Microsoft Azure.
ID Token Refresh allows requesting a new ID Token and exchanging it for an access token when the previous token has expired.
Errors like AADSTS700024 occur when a token times out without ID Token Refresh.
Terraform providers (azurerm, azapi, azuread) and azurerm backend were updated to support ID Token Refresh.
Microsoft DevLabs Terraform Task Version 5 now supports ID Token Refresh by default.
Configuring ID Token Refresh involves setting environment variables like ARM_OIDC_AZURE_SERVICE_CONNECTION_ID, ARM_OIDC_REQUEST_URL, and ARM_OIDC_REQUEST_TOKEN.
The article provides examples of configuring ID Token Refresh with Azure CLI task and Terraform Tasks.
Feedback is encouraged, and further improvements to reduce the number of required environment variables are being worked on.
Acknowledgments are given to the teams and individuals involved in updating the providers, backend, and tasks for ID Token Refresh support.
The updates are aimed at enhancing the Azure DevOps and Terraform experience, ensuring smoother authentication and access token management.

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Marktechpost

JetBrains Open Sources Mellum: A Developer-Centric Language Model for Code-Related Tasks

JetBrains has open-sourced Mellum, a specialized 4-billion-parameter language model tailored for software development tasks.
Mellum is optimized for programming-related tasks like autocompletion and structural understanding of source code in various languages.
The model was trained using over 4.2 trillion tokens and achieves strong performance in benchmarks, reflecting its focus on structured code understanding.
JetBrains released Mellum under the Apache 2.0 license to promote transparency, reusability, community collaboration, and pedagogical value, indicating a shift toward specialized, efficient language models for developer tooling.

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Siliconangle

Image Credit: Siliconangle

Astronomer nabs $93M for its data pipeline platform

Astronomer Inc., a startup specializing in data pipeline management, has secured a $93 million funding in a Series D round led by Bain Capital Ventures and participation from Salesforce Ventures, Insight, and others.
The company offers a paid cloud version of Apache Airflow, a popular open-source platform for creating data pipelines, enabling organizations to move data between applications efficiently.
Astronomer's commercial version, Astro, provides high-availability features, automation tools, and a 'scale to zero' feature that allows hibernating the Airflow environment when not in use, reducing unnecessary infrastructure costs.
The funding will be used to enhance Astro, expand international presence, and pursue profitability goals within two years, as the company reported a 140% growth in annual recurring revenue from Astro.

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Hackernoon

302

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How to Build a Smart Documentation - Based on OpenAI Embeddings (Chunking, Indexing, and Searching)

The article discusses building a 'smart documentation' chatbot by indexing documentation into manageable chunks, generating embeddings with OpenAI, and performing similarity search.
The purpose is to create a chatbot that can provide answers from documentation based on user queries, using Markdown files as an example.
The solution involves three main parts: reading documentation files, indexing the documentation through chunking and embedding, and searching the documentation.
Documentation files can be scanned from a folder or fetched from a database or CMS.
Indexing involves chunking documents, generating vector embeddings for each chunk, and storing embeddings locally.
Chunking is vital to prevent data exceeding model limits, while overlap ensures context continuity between chunks.
Vector embeddings from OpenAI are used for similarity searches between user queries and document chunks.
Cosine similarity is calculated to filter relevant document chunks based on user queries.
A small Express.js endpoint integrates OpenAI's Chat API to generate responses based on the most relevant document chunks.
The article provides code snippets and explains the process step by step, offering a template for building a chatbot with a chat-like interface.

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18 Likes

TechCrunch

138

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Ai2’s new small AI model outperforms similarly-sized models from Google, Meta

Nonprofit AI research institute Ai2 has released Olmo 2 1B, a 1-billion-parameter model beating Google, Meta, and Alibaba's similarly-sized models on benchmarks.
Olmo 2 1B is available on Hugging Face with code and data sets provided, making it accessible for developers even on lower-end hardware.
Small AI models like Olmo 2 1B are becoming popular due to their ability to run on modern laptops or mobile devices.
Although Ai2's Olmo 2 1B shows superior performance on benchmarks, it comes with risks of producing problematic outputs and inaccuracies, cautioning against commercial deployment.

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Amazon

393

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Build end-to-end Apache Spark pipelines with Amazon MWAA, Batch Processing Gateway, and Amazon EMR on EKS clusters

Amazon EMR on EKS provides managed Spark integration with AWS services and existing Kubernetes patterns for data platforms.
Batch Processing Gateway (BPG) manages Spark workloads across multiple EMR on EKS clusters efficiently.
Integrating Amazon MWAA with BPG enhances job scheduling and orchestration for building comprehensive data processing pipelines.
Scenario of HealthTech Analytics showcases the use case for routing Spark workloads based on security and cost requirements.
Integration of Amazon MWAA, BPG, and EMR on EKS clusters facilitates workload distribution and isolation.
Custom BPGOperator in Amazon MWAA streamlines job submission, routing to EMR on EKS clusters, and monitoring tasks.
Benefits include separation of responsibilities, centralized code management, and modular design for enterprise data platforms.
BPGOperator handles job initialization, submission, monitoring, and execution across the pipeline.
Deployment steps involve setup of common infrastructure, configuring BPG, and integrating BPGOperator with Amazon MWAA.
Migration to BPG-based infrastructure involves setting up Airflow connections and migrating existing DAGs seamlessly.
Cleaning up resources post-implementation and experimenting with the architecture in AWS environments are encouraged.

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Hackaday

198

Image Credit: Hackaday

Open Source Firmware For The JYE TECH DSO-150

The Jye Tech DSO-150 is a compact scope available as a kit that can be upgraded with open source firmware.
The Open-DSO-150 firmware offers various features including one analog or three digital channels, configurable triggers, voltmeter mode, serial data dump, and signal statistics display.
For more details on features, visit the GitHub page and firmware can be built in Atollic trueSTUDIO STM32 version.
If interested in the factory version, a review is available, and users can share their scope hacks with the community.

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11 Likes

Medium

419

Image Credit: Medium

SynapseSet: Generating 160K+ Synthetic EEG-to-Text Samples

SynapseSet project has released over 160,000 synthetic EEG samples designed for EEG-to-Text analysis.
The samples are aimed to facilitate advancements in automated reporting and clinical decision support by training AI models.
Unlike common methods using GANs or VAEs, SynapseSet was created using a rule-based simulation engine for resource efficiency and control.
While SynapseSet aims to democratize access to diverse EEG-like data, further refinement and validation are needed for its improvement.

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25 Likes

Marktechpost

346

Multimodal AI on Developer GPUs: Alibaba Releases Qwen2.5-Omni-3B with 50% Lower VRAM Usage and Nearly-7B Model Performance

Alibaba has released Qwen2.5-Omni-3B, a 3-billion parameter model designed for consumer-grade GPUs, addressing hardware constraints in deploying multimodal AI.
Qwen2.5-Omni-3B reduces VRAM consumption by over 50% and supports efficient processing of long sequences, real-time multimodal interactions, and multilingual speech generation.
The model demonstrates performance close to its 7-billion parameter counterpart across various benchmarks, making it suitable for tasks like visual question answering, audio captioning, and video understanding.
Qwen2.5-Omni-3B offers a balance between utility and computational demands, providing a practical solution for deploying efficient multimodal AI systems in diverse environments.

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Siliconangle

259

Image Credit: Siliconangle

China AI rising: Xiaomi releases new MiMo-7B models as DeepSeek upgrades its Prover math AI

Xiaomi Corp. has released MiMo-7B, a new reasoning model series with 7 billion parameters, outperforming OpenAI's o1-mini for some tasks. Xiaomi developed enhanced versions using supervised fine-tuning and reinforcement learning.
Prover, a reasoning model by DeepSeek, also received an update to Prover-V2, optimized for proving mathematical theorems. DeepSeek trained Prover-V2 using a multistep process involving existing proofs.
Xiaomi's MiMo-7B series includes a base model and three enhanced versions, one fine-tuned with supervised learning, another with reinforcement learning, and a third using both methods, surpassing OpenAI's o1-mini.
Alibaba recently introduced Qwen3, a family of models claiming to outperform OpenAI and DeepSeek models. These advancements reflect the competitive landscape of reasoning-optimized AI models.

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Gizchina

199

Image Credit: Gizchina

Xiaomi’s Got a New AI Brain: Meet MiMo, an Open-Source Reasoning Model

Xiaomi has introduced MiMo, a 7-billion-parameter reasoning model aimed at improving capabilities in mathematical reasoning and code generation.
MiMo is created by Xiaomi's Big Model Core Team, focusing on maximizing the hidden potential within the relatively smaller model for enhanced performance.
The model underwent optimized pre-training processes, involving data handling improvements and a three-stage data mixing strategy with specialized datasets.
Post-training included fine-tuning with reinforcement learning through solving math and coding problems, ensuring accurate and verified examples.
To address training efficiency, Xiaomi developed a 'Seamless Rollout Engine' achieving a significant boost in speed for model training and validation cycles.
MiMo comes in different flavors including the foundational model, an RL-trained version, and a supervised fine-tuned variant, with benchmarks showcasing strong performance in mathematics and code generation tasks.
The MiMo-7B models are open-source, available for download on Hugging Face, allowing developers and researchers easy access to leverage its potential.
MiMo demonstrated impressive performance in various tasks like math competitions, code generation, and general reasoning, indicating competitive capabilities despite its smaller size.
Xiaomi's focus on openness and contribution to the AI community by releasing MiMo as open-source reflects a positive trend in sharing potentially valuable tools for wider application.
With MiMo's accessibility and promising performance, it will be interesting to observe how developers and researchers utilize this new AI model in practical applications.

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11 Likes

Marktechpost

Reinforcement Learning for Email Agents: OpenPipe’s ART·E Outperforms o3 in Accuracy, Latency, and Cost

OpenPipe introduces ART·E, an open-source research agent for email that outperforms o3 in accuracy, latency, and cost.
ART·E focuses on accuracy, responsiveness, and computational efficiency using reinforcement learning (RL) to fine-tune large language model (LLM) agents for email-related tasks.
The architecture of ART·E includes retriever module, LLM policy head, and evaluation pipeline trained using Proximal Policy Optimization (PPO) regime for improved performance.
Benchmarking against o3 agent, ART·E shows +12.4% response accuracy, 5× faster average latency, and 64× cheaper inference cost, providing a favorable cost-performance tradeoff.

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Towards Data Science

How to Level Up Your Technical Skills in This AI Era

AI-assisted coding tools like Cursor, V0, and Lovable have reduced the entry barriers by enabling faster development of dashboards, pipelines, and apps.
While AI tools enhance productivity, relying too heavily on them may hinder the development of deep technical understanding and problem-solving skills.
Engaging in 'vibe coding' for quick code generation can lead to complex issues and technical debt, emphasizing the importance of building a strong mental model.
Using AI tools judiciously, such as selecting suggestions before implementing them, can mitigate errors and enhance code quality.
Contributing to open source projects is highlighted as a valuable way to enhance technical skills, learn best practices, and interact with a community of developers.
Open source contributions offer benefits like enhancing version control proficiency, networking, and portfolio development for career advancement.
Choosing open source projects based on personal interest, utilizing features you need, and starting with familiar technologies can enhance motivation and learning.
Practical steps for contributing to open source include selecting relevant features, setting up the local environment, starting with small tasks, and learning by doing.
Navigating the codebase, writing clear commit messages, and engaging in the review process are essential components of successful open source contribution.
Despite initial challenges, persistence in open source contribution can lead to valuable learning experiences, networking opportunities, and personal growth.

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3 Likes

Massivelyop

117

Sandbox MMORPG BitCraft hopes to ‘democratize MMO development’ by going open-source

Clockwork Labs has announced that it’s open-sourcing its upcoming MMORPG sandbox BitCraft.
The studio aims to make the genre more accessible and give back to the open-source community.
Collaboration with other open-source groups will benefit BitCraft.
The game will be launched in early access before the open-source transition.

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