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Medium

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Contributing to Open Source is Easier Than You Think: My Journey with Firebase Genkit

Contributing to open source empowers individuals to shape the tools they use daily.
Starting with labeled issues is crucial for newcomers interested in contributing.
Downloading the project, installing dependencies, and running tests are standard procedures.
Clear documentation and supportive communities are vital for attracting contributors.
Making it easy to contribute is essential for successful open source projects.
Fixing bugs involves analyzing code, debugging, and experimenting with solutions.
Preparing a pull request is a proposal to merge changes into the main codebase.
The review process helps contributors learn from experienced developers.
Engaging in a conversation and learning from others are key aspects of open source.
Seeing your code merged into the project is a satisfying validation of your contribution.
Open source offers a chance to expand skills, contribute meaningfully, and be part of a community.

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Medium

211

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Making Shakespeare Understandable: Modernizing Movie Subtitles with AI

Translating Shakespeare's plays into modern English has been done by various authors, educators, and playwrights in the past.
However, existing tools focus on reading or performing, neglecting the need for modern subtitles in movies.
A new AI tool aims to bridge this gap by modernizing Shakespeare's dialogue specifically for movie subtitles.
The tool preserves the original subtitle structure and timing, ensuring accuracy and continuity.
It targets .srt files directly, allowing easy integration with video players and other platforms.
The process involves chunking subtitles, formatting lines, strict system prompting, and reconstructing the .srt blocks.
The modernized subtitles maintain synchronization and enhance viewers' understanding without altering the original intent.
This innovation enables viewers to appreciate Shakespeare's works without struggling with archaic language barriers.

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Medium

136

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Decoding the Oracle Cloud-Health Attack: Did legacy gateways expose medical records for millions?

The article explores the potential connection between the breach of Oracle Cloud login servers and the theft of Protected Health Information (PHI) from Oracle Health/Cerner, impacting millions of patient records.
Credentials for subdomains related to 'cerner.com' were compromised, allowing access to sensitive patient information across healthcare providers.
The report suggests a 'chain attack' scenario penetrating Oracle Cloud servers for breaching Oracle Health/Cerner servers, emphasizing the need for further investigation.
Pre-Authentication Remote Code Execution (RCE) on Oracle Cloud login servers facilitated data exfiltration by threat actors.
The breach highlights concerns about Oracle's legacy Fusion Middleware and the vulnerabilities it poses in cloud-native platforms.
The incident signifies the critical importance of timely and transparent communication from organizations to address sophisticated cyber threats effectively.
The potential shift to open source EHR systems like OpenEMR and Ottehr is recommended for enhanced security and interoperability in healthcare settings.
Ottehr, a newly introduced open source EHR, is highlighted as a promising solution with scalability and modern technologies to support millions of patient visits.
The article underscores the need for healthcare providers to consider migrating to modern cloud-native architectures to mitigate risks associated with legacy systems.
The interconnectedness of Oracle's legacy systems and AI-driven cyber threats pose significant challenges, necessitating a shift towards more secure platforms.
Overall, the incident serves as a wakeup call for improving cybersecurity measures within the healthcare industry and promoting open source solutions for enhanced data protection.

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Hackernoon

220

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Authentication Sucks—So This Developer Built a Better Starting Point

The developer shares frustrations with authentication and architecture challenges encountered in building projects, prompting the idea to create a reusable solution.
Authentication was consistently a roadblock due to complexities like OAuth, JWTs, and session handling, leading to time-consuming implementations and messy code.
Architectural concerns for scalability and clean code were also prominent, highlighting the need for a template that could evolve and be easily extended by other developers.
The Full-Stack Clean Auth Template project was introduced as an open-source solution using Next.js, Express, Node.js, and TypeScript, emphasizing clean architecture principles and modularity.
Key features include a clean architectural separation, modularity, TypeScript support, scalability, and comprehensive documentation for quick adoption.
The template aims to streamline authentication and architecture decisions for product launches, enabling developers to focus on core features with a reliable foundation.
Open-source and community-driven, the project welcomes contributions and extensions to support additional frameworks such as React Native or Angular.
The developer actively maintains the project and encourages feedback, PRs, and suggestions from contributors to enhance the template's functionality and adaptability.
The template serves as a foundational tool to save time and simplify project setups, presenting a solution for developers weary of repetitive foundational work.

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Medium

3.9k

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Debugging React Native in 2025: A Software Engineer’s Survival Guide

Debugging React Native in 2025: A Software Engineer’s Survival Guide.
Software engineers often experience unique pain related to debugging, such as layout issues, state resets in production, and unresponsive buttons.
The author, having worked on various tech stacks, highlights the challenges faced within React Native apps as particularly grueling and satisfying.
The article is a reflection on tools that helped the author navigate debugging challenges in React Native apps.
It emphasizes moving from guessing solutions to actually understanding and resolving issues, even those not reflected in logs.

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Medium

105

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How I made a programming language at 14; The Story of Glowscript

At 14 years old, the author created a programming language called Glowscript within a console named Tardigrade.
The author, diagnosed with ASD at age 5, faced bullying but remained confident and focused on achieving greater things.
Initially starting a project named gVars, the author integrated a built-in scripting language and created Tardigrade for data manipulation.
Glowscript, a simple and intuitive scripting language, was developed by the author as part of the Tardigrade project.
Glowscript is user-friendly with no indentation requirement, easy programming, and offers fun experimentation.
The author's dream project would involve increasing productivity without a focus on unlimited power or money.
The author advises readers to work hard and improve, mentioning their GitHub repository for further exploration.

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Medium

383

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Reimagining Multimodal Retrieval with ColPali: A New Paradigm in RAG Pipelines

ColPali is a new vision-language model that embeds and retrieves multimodal content in a shared latent space, offering a more advanced RAG pipeline design.
Traditional retrieval systems often struggle with multimodal content, leading to noise and misalignment when incorporating images.
ColPali is a dual-encoder model that embeds both text and image inputs into the same vector space, enabling modality-agnostic retrieval.
The system uses MaxSim similarity for more accurate matching between queries and stored vectors, allowing for semantic relevance extraction regardless of modality.
An interactive Gradio interface showcases the system, providing users with quick and relevant results from uploaded PDFs based on natural language queries.
The approach reduces cognitive load by presenting users with instant, relevant information from both text and images in documents.
LLaVA, a local vision-language model, is used to generate meaningful answers offline, ensuring responses are based strictly on retrieved evidence.
By leveraging joint embedding space, multivector storage, and local generation, ColPali offers a modality-agnostic and privacy-respecting retrieval system.
The article provides source code for the pipeline on GitHub for those interested in implementing similar multimodal retrieval solutions.

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Bitcoinmagazine

238

The Trolls Are Coming: Defending Bitcoin Mining from Patent Trolls

Malikie Innovations Ltd., a patent troll, filed lawsuits against Bitcoin mining firms claiming infringement on ECC-related patents acquired from BlackBerry.
The lawsuits seek damages and injunctions, potentially imposing licensing on core Bitcoin functions.
Defensive strategies include invalidating patents, Inter Partes Review (IPR), declaratory judgments, and motions to dismiss under Alice's test.
IPR is cost-effective and fast, with a high success rate at invalidating patents, offering a potent defense against trolls.
Community responses involve crowdsourced prior art searches, Bitcoin Legal Defense Fund, and industry alliances like COPA.
Malikie's success could lead to financial strain, further lawsuits, and necessitate future safeguards within the Bitcoin community.
The fight aims to demonstrate Bitcoin's core technologies as products of public research and collaborative development.
The article provides insights into legal defenses, industry support, and community efforts to combat patent trolls targeting Bitcoin mining.
The clash between Malikie Innovations and Bitcoin miners underscores the ongoing battle between open innovation and legacy IP rights.
Overall, the community's unity and proactive measures are crucial in protecting Bitcoin's technological advancements from patent trolls.
The industry's response to trolls can not only safeguard current operations but also foster innovation and growth in the Bitcoin ecosystem.

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Hackernoon

370

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Supercharge Your ETL Pipeline with SeaTunnel’s Lock-Free CDC

Change Data Capture (CDC) tracks row-level changes and synchronizes data downstream in real-time.
Apache SeaTunnel CDC supports Snapshot Reading and Incremental Tracking synchronization methods.
SeaTunnel uses lock-free mechanisms for Snapshot Sync, splitting table data for parallel processing.
Snapshot splits in SeaTunnel contain metadata for routing and processing.
SeaTunnel's Incremental Synchronization captures real-time changes after the snapshot phase.
SeaTunnel ensures exactly-once processing during both snapshot and incremental sync phases.
During snapshot reading, SeaTunnel caches data and reconciles changes between low and high watermarks.
Before incremental sync, SeaTunnel validates snapshot splits and corrects inter-split data.
SeaTunnel employs the Chandy-Lamport algorithm for fault tolerance and checkpointing in distributed environments.
Markers in SeaTunnel CDC allow all nodes to store their state for recovery and pause-resume capabilities.

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Arstechnica

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Scientists once hoarded pre-nuclear steel, and now we’re hoarding pre-AI content

Former Cloudflare executive John Graham-Cumming has launched lowbackgroundsteel.ai, a website preserving pre-AI human-created content.
The website aims to protect non-AI media from contamination by AI-generated content, similar to hoarding pre-nuclear steel during the Cold War.
Generative AI models like ChatGPT have made it challenging to differentiate between human and AI-created content on the internet.
The wordfreq project, tracking word frequency across languages, shut down due to the prevalence of AI-generated text online.
Concerns exist about AI models training on their outputs, potentially leading to quality degradation known as 'model collapse.'
Research suggests model collapse can be avoided by combining synthetic data with real data during training.
Graham-Cumming aims to document human creativity from the pre-AI era through the website.
The website points to major pre-AI content sources like Wikipedia dumps, Project Gutenberg, and GitHub's Arctic Code Vault.
Lowbackgroundsteel.ai accepts submissions of pre-AI content sources to expand its archives.
The project is a digital archaeology effort to distinguish between human-generated and hybrid human-AI cultures.

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Siliconangle

106

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With $27M in funding, Fleet wants to bring more freedom to enterprise device management

Fleet Inc. secures $27 million in Series B funding to enhance enterprise device management.
The funding round was led by Ten Eleven Ventures with participation from CRV, Open Core Ventures, and other investors.
Fleet offers an open-source device management platform for companies to secure their computing devices.
The platform provides transparency, extensibility, and repeatability compared to proprietary software.
Fleet aims to give organizations control over device management for compliance with regulations like PCI and FedRAMP.
It offers both hosted and on-premises deployment options.
Netflix, Stripe, Fastly, Uber, and Reddit are among the well-known enterprises using Fleet.
Fleet's platform supports various devices like iPhones, Android phones, laptops, and even data centers.
Customers praise Fleet for providing flexibility and avoiding forced cloud migrations.
The company's rapid growth is attributed to its channel partners and open-source approach.
Fleet CEO emphasizes the company's dedication to open hosting options.
Ten Eleven Ventures Partner endorses Fleet for its comprehensive device management capabilities.
Fleet plans to maintain its open approach and customizable hosting solutions.
The company has been seeing significant adoption for its device management platform.
Flexible hosting options are generating increased interest in web searches.
Fleet's platform is designed to cater to a wide range of operating systems and devices.
The company's mission is to provide free, relevant, and in-depth content to the community.

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6 Likes

VentureBeat

433

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Qodo teams up with Google Cloud, to provide devs with FREE AI code review tools directly within platform

Qodo, an AI coding startup from Israel, collaborates with Google Cloud to enhance AI-generated software integrity.
The need for oversight and quality assurance tools for AI-produced code is increasing.
Qodo focuses on code quality and holistic engineering support, developing tools like Qodo-Embed-1-1.5B.
Qodo's AI agents, like Qodo Cover, help with tasks such as test generation and code review.
Qodo Merge, an AI-powered code review tool using Google's Gemini models, is offered at no cost to open-source software maintainers.
Qodo's suite of tools is now integrated with Google Cloud, available through Vertex AI and Google Cloud Marketplace.
QodoGen, Qodo's AI code assistant, delivers more accurate code suggestions with Gemini 2.5 Pro model.
Qodo offers modular solutions for enterprise users, allowing customization of workflows.
The startup program with Google Cloud provides a 50% discount for early-stage companies using Qodo's tools for commercial products.
Qodo aims to bridge the gap between speed and quality in software development in the era of AI-driven coding.

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IEEE Spectrum

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Airbnb’s Dying Software Gets a Second Life

Vikram Koka discovered a stagnant Apache Airflow project in late 2019, sparking his journey to revitalize the software initially developed by Airbnb for data-related workflows.
Airflow transitioned to a Top-Level Project at Apache Software Foundation, but faced stagnation with flat downloads and lack of updates.
Koka was attracted to Airflow's 'configuration as code' principle and its ability to manage tasks through directed acyclic graphs coded in Python.
After a year of efforts, Airflow 2.0 was released, marking a pivotal moment for the project's growth and adoption by enterprises.
Airflow 3.0 introduced a modular architecture and enhanced features, leading to a significant increase in downloads and community engagement.
The project now has over 3,000 global developers contributing, with an average of 35 to 40 million downloads monthly.
Future plans for Airflow include supporting tasks in various programming languages and enhancing capabilities for AI and machine learning workflows.
The team strives to nurture a diverse community of contributors and users, emphasizing gradual involvement and constructive feedback.
Airflow's role in machine learning operations and generative AI is on the rise, positioning it as a critical foundation for AI and ML workloads.

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VentureBeat

433

MiniMax-M1 is a new open source model with 1 MILLION TOKEN context and new, hyper efficient reinforcement learning

Chinese AI startup MiniMax has launched MiniMax-M1, a large language model with 1 million input tokens and open-source under Apache 2.0 license.
MiniMax-M1 sets new standards in long-context reasoning, agentic tool use, and efficient compute performance.
It distinguishes itself with a context window of 1 million input tokens, outperforming models like OpenAI's GPT-4o.
MiniMax-M1 is trained using a highly efficient reinforcement learning technique, consuming only 25% of the FLOPs required by other models like DeepSeek R1.
The model has two variants, MiniMax-M1-40k and MiniMax-M1-80k, with different output lengths.
MiniMax-M1's training cost was $534,700, which is significantly lower compared to other models like DeepSeek R1 and OpenAI's GPT-4.
It achieves high accuracy on mathematics benchmarks and excels in coding and long-context tasks, outperforming competitors on complex tasks.
MiniMax-M1 addresses challenges for technical professionals by offering long-context capabilities, compute efficiency, and open access under Apache 2.0 license.
The model supports structured function calling, packaged with a chatbot API, and provides deployment options using Transformers library.
MiniMax-M1 presents a flexible option for organizations to experiment with advanced AI capabilities while managing costs and avoiding proprietary constraints.

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VentureBeat

101

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Groq just made Hugging Face way faster — and it’s coming for AWS and Google

Groq is challenging cloud providers like AWS and Google by supporting Alibaba’s Qwen3 32B model with a full 131,000-token context window and becoming an inference provider on Hugging Face.
Their unique architecture allows efficient handling of large context windows, offering speeds of approximately 535 tokens per second and pricing at $0.29 per million input tokens and $0.59 per million output tokens.
The integration with Hugging Face opens Groq to a vast developer ecosystem, providing streamlined billing and access to popular models.
Groq's global footprint currently serves over 20M tokens per second and plans further international expansion.
Competitors like AWS Bedrock and Google Vertex AI leverage massive cloud infrastructure, but Groq remains confident in its differentiated approach.
Groq's competitive pricing aims to meet the growing demand for inference compute, despite concerns about long-term profitability.
The global AI inference chip market is projected to reach $154.9 billion by 2030, driven by increasing AI application deployment.
Groq's move offers both opportunity and risk for enterprise decision-makers, with potential cost reduction and performance benefits paired with supply chain risks.
Their technical capability to handle full context windows could be valuable for enterprise applications requiring in-depth analysis and reasoning tasks.
Groq's strategy combines specialized hardware and aggressive pricing to compete with tech giants, focusing on scalability and performance advantages.
The success of Groq's approach hinges on maintaining performance while scaling globally, posing a challenge for many infrastructure startups.

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