The New Stack Podcast

The Linux xz utils backdoor exploit, discussed in an interview at the Open Source Summit 2024 on The New Stack Makers with John Kjell, director of open source at TestifySec, highlights critical vulnerabilities in the open-source ecosystem. This exploit involved a maintainer of the Linux xz utils project adding malicious code to a new release, discovered by a Microsoft engineer. This breach demonstrates the high trust placed in maintainers and how this trust can be exploited. Kjell explains that the backdoor allowed remote code execution or unauthorized server access through SSH connections.The exploit reveals a significant flaw: the human element in open source. Maintainers, often under pressure from company executives to quickly address vulnerabilities and updates, can become targets for social engineering. Attackers built trust within the community by contributing to projects over time, eventually gaining maintainer status and inserting malicious code. This scenario underscores the economic pressures on open source, where maintainers work unpaid and face demands from large organizations, exposing the fragility of the open-source supply chain. Despite these challenges, the community's resilience is also evident in their rapid response to such threats. Learn more from The New Stack about Linux xz utils Linux xz Backdoor Damage Could Be Greater Than Feared Unzipping the XZ Backdoor and Its Lessons for Open Source The Linux xz Backdoor Episode: An Open Source Myster Join our community of newsletter subscribers to stay on top of the news and at the top of your game.      Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Suman Debnath, principal developer advocate for machine learning at Amazon Web Services, emphasized the advantages of using Python in machine learning during a New Stack Makers episode recorded at PyCon US. He noted Python's ease of use and its foundational role in the data science ecosystem as key reasons for its popularity. However, Debnath highlighted that building generative AI applications doesn't necessarily require deep data science expertise or Python. Amazon Bedrock, AWS’s generative AI framework introduced in September, exemplifies this flexibility by allowing developers to use any programming language via an API-based service. Bedrock supports various languages like Python, C, C++, and Java, enabling developers to leverage large language models without intricate knowledge of machine learning. It also integrates well with open-source libraries such as Langchain and llamaindex. Debnath recommends visiting the community AWS platform and GitHub for resources on getting started with Bedrock. The episode includes a demonstration of Bedrock's capabilities and its benefits for Python users. Learn More from The New Stack on Amazon Bedrock: Amazon Bedrock Expands Palette of Large Language Models Build a Q&A Application with Amazon Bedrock and Amazon Titan 10 Key Products for Building LLM-Based Apps on AWSJoin our community of newsletter subscribers to stay on top of the news and at the top of your game/  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Nathan Peck, a senior developer advocate for generative AI at Amazon Web Services (AWS), shares his experiences working with Python in a recent episode of The New Stack Makers, recorded at PyCon US. Although not a Python expert, Peck frequently deals with Python scripts in his role, often assisting colleagues in running scripts as cron jobs. He highlights the challenge of being a T-shaped developer, possessing broad knowledge across multiple languages and frameworks but deep expertise in only a few.Peck introduces Amazon Q, a generative AI coding assistant launched by AWS in November, and demonstrates its capabilities. The assistant can be integrated into an integrated development environment (IDE) like VS Code. It assists in explaining, refactoring, fixing, and even developing new features for Python codebases. Peck emphasizes Amazon Q's ability to surface best practices from extensive AWS documentation, making it easier for developers to navigate and apply.Amazon Q Developer is available for free to users with an AWS Builder ID, without requiring an AWS cloud account. Peck's demo showcases how this tool can simplify and enhance the coding experience, especially for those handling complex or unfamiliar codebases.Learn more from The New Stack about Amazon Q and Amazon’s Generative AI strategy:Amazon Q, a GenAI to Understand AWS (and Your Business Docs)Decoding Amazon’s Generative AI StrategyResponsible AI at Amazon Web Services: Q&A with Diya WynnJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mike Fiedler, a PyPI safety and security engineer at the Python Software Foundation, prefers the title “code gardener,” reflecting his role in maintaining and securing open source projects. Recorded at PyCon US, Fiedler explains his task of “pulling the weeds” in code—handling unglamorous but crucial aspects of open source contributions. Since August, funded by Amazon Web Services, Fiedler has focused on enhancing the security of the Python Package Index (PyPI). His efforts include ensuring that both packages and the pipeline are secure, emphasizing the importance of vetting third-party modules before deployment.One of Fiedler’s significant initiatives was enforcing mandatory two-factor authentication (2FA) for all PyPI user accounts by January 1, following a community awareness campaign. This transition was smooth, thanks to proactive outreach. Additionally, the foundation collaborates with security researchers and the public to report and address malicious packages.In late 2023, a security audit by Trail of Bits, funded by the Open Technology Fund, identified and quickly resolved medium-sized vulnerabilities, increasing PyPI's overall security. More details on Fiedler's work are available in the full interview video.Learn more from The New Stack about PyPl:PyPl Strives to Pull Itself Out of TroubleHow Python Is EvolvingPoisoned Lolip0p PyPI PackagesJoin our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The name "Falcon" for the UAE’s large language model (LLM) symbolizes the national bird's qualities of courage and perseverance, reflecting the vision of the Technology Innovation Institute (TII) in Abu Dhabi. TII, launched in 2020, addresses AI’s rapid advancements and unintended consequences by fostering an open-source approach to enhance community understanding and control of AI. In this New Stack Makers, Dr. Hakim Hacid, Executive Director and Acting Chief Researcher, Technology Innovation Institute emphasized the importance of perseverance and innovation in overcoming challenges. Falcon gained attention for being the first truly open model with capabilities matching many closed-source models, opening new possibilities for practitioners and industry. Last June, Falcon introduced a 40-billion parameter model, outperforming the LLaMA-65B, with smaller models enabling local inference without the cloud. The latest 180-billion parameter model, trained on 3.5 trillion tokens, illustrates Falcon’s commitment to quality and efficiency over sheer size. Falcon’s distinctiveness lies in its data quality, utilizing over 80% RefinedWeb data, based on CommonCrawl, which ensures cleaner and deduplicated data, resulting in high-quality outcomes. This data-centric approach, combined with powerful computational resources, sets Falcon apart in the AI landscape. Learn more from The New Stack about Open Source AI: Open Source Initiative Hits the Road to Define Open Source AI  Linus Torvalds on Security, AI, Open Source and TrustTransparency and Community: An Open Source Vision for AI Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Crash-level bugs continue to pose a significant challenge due to the lack of memory safety in programming languages, an issue persisting since the punch card era. This enduring problem, described as "the Joker to the Batman" by Anil Dash, VP of developer experience at Fastly, is highlighted in a recent episode of The New Stack Makers. The White House has emphasized memory safety, advocating for the adoption of memory-safe programming languages and better software measurability. The Office of the National Cyber Director (ONCD) noted that languages like C and C++ lack memory safety traits and are prevalent in critical systems. They recommend using memory-safe languages, such as Java, C#, and Rust, to develop secure software. Memory safety is particularly crucial for the US government due to the high stakes, especially in space exploration, where reliability standards are exceptionally stringent. Dash underscores the importance of resilience and predictability in missions that may outlast their creators, necessitating rigorous memory safety practices.Learn more from The New Stack about Memory Safety:White House Warns Against Using Memory-Unsafe Languages Can C++ Be Saved? Bjarne Stroupstrup on Ensuring Memory SafetyBjarne Stroupstrup's Plan for Bringing Safety to C++Join our community of newsletter subscribers to stay on top of the news and at the top of your game.   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the push to integrate data into development, time series databases have gained significant importance. These databases capture time-stamped data from servers and sensors, enabling the collection and storage of valuable information. InfluxDB, a leading open-source time series database technology by InfluxData, has partnered with Amazon Web Services (AWS) to offer a managed open-source service for time series databases. Brad Bebee, General Manager of Amazon Neptune and Amazon Timestream highlighted the challenges faced by customers managing open-source Influx database instances, despite appreciating its API and performance. To address this, AWS initiated a private beta offering a managed service tailored to customer needs. Paul Dix, Co-founder and CTO of InfluxData joined Bebee, and highlighted Influx's prized utility in tracking measurements, metrics, and sensor data in real-time. AWS's Timestream complements this by providing managed time series database services, including TimesTen for Live Analytics and Timestream for Influx DB. Bebee emphasized the growing relevance of time series data and customers' preference for managed open-source databases, aligning with AWS's strategy of offering such services. This partnership aims to simplify database management and enhance performance for customers utilizing time series databases. Learn more from The New Stack about time series databases:What Are Time Series Databases, and Why Do You Need Them?Amazon Timestream: Managed InfluxDB for Time Series Data Install the InfluxDB Time-Series Database on Ubuntu Server 22.04Join our community of newsletter subscribers to stay on top of the news and at the top of your game.  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Amazon Web Services (AWS) has introduced PG Vector, an open-source tool that integrates generative AI and vector capabilities into PostgreSQL databases. Sirish Chandrasekaran, General Manager of Amazon Relational Database Services, explained at Open Source Summit 2024 in Seattle that PG Vector allows users to store vector types in Postgres and perform similarity searches, a key feature for generative AI applications. The tool, developed by Andrew Kane and offered by AWS in services like Aurora and RDS, originally used an indexing scheme called IVFFlat but has since adopted Hierarchical Navigable Small World (HNSW) for improved query performance. HNSW offers a graph-based approach, enhancing the ability to find nearest neighbors efficiently, which is crucial for generative AI tasks. AWS emphasizes customer feedback and continuous innovation in the rapidly evolving field of generative AI, aiming to stay responsive and adaptive to customer needs. Learn more from The New Stack about Vector Databases Top 5 Vector Database Solutions for Your AI Project Vector Databases Are Having a Moment – A Chat with Pinecone Why Vector Size Matters Join our community of newsletter subscribers to stay on top of the news and at the top of your game. https://thenewstack.io/newsletter/  Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Former Redis core contributor Madelyn Olson, Google's Ping Xie, and Oracle's Dmitry Polyakovsky discuss Valkey, a Redis fork challenging Redis' new license. They highlight the importance of open, permissive licenses for tech companies, Valkey's industry backing, plans for incremental updates, module development in Rust, and maintaining compatibility for a robust ecosystem.

A virtual cluster, described by Loft Labs CEO Lukas Gentele at Kubecon+ CloudNativeCon Paris, is a Kubernetes control plane running inside a container within another Kubernetes cluster. In this New Stack Makers episode, Gentele explained that this approach eliminates the need for numerous separate control planes, allowing VMs to run in lightweight, quickly deployable containers. Loft Labs' open-sourced vcluster technology enables virtual clusters to spin up in about six seconds, significantly faster than traditional Kubernetes clusters that can take over 30 minutes to start in services like Amazon EKS or Google GKE.The integration of vCluster into Rancher at KubeCon Paris enables users to manage virtual clusters alongside real clusters seamlessly. This innovation addresses challenges faced by companies managing multiple applications and clusters, advocating for a multi-tenant cluster approach for improved sharing and security, contrary to the trend of isolated single-tenant clusters that emerged due to complexities in cluster sharing within Kubernetes. Learn more from The New Stack about virtual clusters: Vcluster to the Rescue Navigating the Trade-Offs of Scaling Kubernetes Dev Environments Managing Kubernetes Clusters for Platform Engineers Join our community of newsletter subscribers to stay on top of the news and at the top of your game. https://thenewstack.io/newsletter/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.