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Open Source
561 posts about Open Source. Every summary links to the original.
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How Uken Games reduces observability costs by 87% with ClickHouse
Uken Games replaced Datadog with an open-source observability stack built around ClickHouse to monitor backend services supporting mobile games with millions of players. The migration addressed rising Datadog costs and vendor lock-in while targeting pragmatic feature parity across metrics, traces, and logs. OpenTelemetry collectors run in two layers: lightweight agents on ECS instances pre-filter traces, while an autoscaling gateway batches and buffers writes to SigNoz on ClickHouse; managed Prometheus handles metrics, CloudWatch retains logs, and Grafana provides dashboards and alerts. Uken samples ordinary traces, retains errors and high-latency requests, computes alert metrics without sampling, and limits retention to two weeks. The resulting system stores all traces on one ClickHouse node using about 170 GB, operates without migration downtime, and costs about $12,000 annually—an 87% reduction.
Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect
Deploying open models into native applications can require model-specific conversion, preprocessing, post-processing, and runtime code. NVIDIA TensorRT Model Connect addresses this with reference implementations that turn a Hugging Face model ID or local checkpoint into a deployment bundle containing TensorRT engines and runtime assets, then load it from a native C++ application. Its semantic API exposes task-level inputs and outputs, while its module-level API provides named tensors and individual TensorRT components; TVM FFI can connect custom GPU kernels to targeted pipeline stages. Python is used during preparation, but production inference does not require PyTorch or a Python interpreter. The project uses AI-assisted development, human review, nightly releases, and automated validation, and the post states that supported, validated workloads can deliver faster inference than torch.compile.
Building hermetic notebook images for Open Data Hub and Red Hat OpenShift AI
Open Data Hub and Red Hat OpenShift AI adopted hermetic container builds to eliminate network failures, satisfy Conforma compliance, and ensure reproducible notebook environments. Instead of pulling packages during image creation, dependencies are pinned in lockfiles, prefetched into a local cache using tools like Cachi2, and installed with the network disabled. The workflow relies on a unified Containerfile capable of running locally, within GitHub Actions, and on Konflux via Tekton tasks. To pass audit checks and avoid generic tarball downloads, the team packaged awkward binaries like ripgrep and Pandoc into multi-arch Python wheels while patching Node postinstall scripts that fetch files at install time. Upstream CentOS and downstream Red Hat Enterprise Linux dependencies are maintained in distinct lock trees to prevent subtle packaging mismatches.
The Open ASR Leaderboard Adds Its First Global South Language
Voice Arena and Hugging Face have added two Monsoon evaluation sets, en-IN and hi-IN, to the Open ASR Leaderboard, introducing Indian English and Hindi coverage. The initiative addresses a limitation of aggregate WER: existing test sets record what was said but little about who spoke, making disparities across geography, age, gender, accent, devices, and other conditions difficult to see. The four speaker-disjoint public and private splits contain 4,888 speakers and vary across nine collection axes, using unscripted dual-channel conversations, broad district and device coverage, and 12 speaker metadata attributes. Because Hindi has extensive valid orthographic variation, its references use manually curated lattices and are scored with Orthographically-Informed Word Error Rate (OIWER); comparisons with flattened references show higher, non-uniform error rates and changed system rankings.
Differential Privacy for Hugging Face Trainers – Without Rewriting Your Training Loop
Training large language models on sensitive data risks memorization and membership inference attacks, yet standard high-level transformer tools lack native differential privacy capabilities. JetBrains Research created and open-sourced DPTrainer, a library that integrates PyTorch's Opacus framework with the Hugging Face Trainer ecosystem. The library provides differential privacy through differentially private stochastic gradient descent without requiring manual training loops or source code modifications. DPTrainer extends base training arguments with a PrivacyArguments dataclass, managing per-sample gradient calculation, gradient clipping, noise injection, and Poisson-sampled data loading. A dynamic patching mechanism injects differential privacy into specialized subclasses like DPOTrainer and SFTTrainer, while built-in privacy accountants track the cumulative privacy budget across training steps and checkpoints.
Anthropic partners with CodePath to bring Claude to the US’s largest collegiate computer science program
Anthropic is partnering with CodePath to redesign collegiate computer science education as AI reshapes software development, placing Claude and Claude Code at the center of courses and career programs. More than 20,000 students at community colleges, state schools, and HBCUs will receive access to the tools, while CodePath addresses unequal access to industry-vetted courses and career networks. Claude is being integrated into AI courses including Foundations of AI Engineering, Applications of AI Engineering, and AI Open-Source Capstone; in fall 2025, more than 100 students piloted Claude Code on open-source projects such as GitLab, Puter, and Dokploy. The partners will also study AI’s effects on coding education and economic opportunity, while Howard University’s credited introductory AI course provides an early academic implementation of the applied curriculum.
Introducing Claude Corps
Anthropic is launching Claude Corps, a national fellowship program for early-career people who will help nonprofits across America apply AI to their missions. The program will train 1,000 fellows, place them with at least 400 nonprofits over the next 12 months, and commit an initial $150 million to widening AI’s benefits during economic change. Anthropic funds and leads strategy, CodePath employs and trains fellows, and Social Finance handles measurement and a longer-term financing vehicle. Each 12-month fellowship includes intensive initial training, five hours of weekly training, an $85,000 salary and benefits, mentoring, technical office hours, and a Claude token budget. Applications are open for cohorts beginning October 2026, January 2027, and August 2027, while the program plans rigorous evaluation, some open-sourced infrastructure, and possible expansion beyond the United States.
Compose Multiplatform 1.12.0 Released
JetBrains has announced the release of Compose Multiplatform 1.12.0 with targeted capabilities for artificial intelligence integrations, web rendering, and desktop application layouts. The update incorporates an experimental Model Context Protocol server inside Compose Hot Reload, allowing AI agents to trigger reloads, capture screenshots, inspect the semantic tree, and simulate user interactions directly. For web deployments, the framework now provides automatic font fallback by downloading necessary Noto font subsets on demand when unresolved characters occur during rendering. Desktop developers receive an experimental v2 window and dialog application programming interface in the androidx.compose.ui.window.v2 package, which distinguishes requested states from actual window states and permits precise control over positioning, sizing constraints, and multi-screen placement. These combined enhancements improve developer workflows and runtime flexibility across multiple target platforms.
How Ubuntu Is Using Rust to Rebuild Core System Tools
Canonical is selectively adopting Rust to rewrite core system utilities across Ubuntu releases to improve memory safety, security, and long-term resilience. Starting in Ubuntu 26.04 LTS, uutils coreutils shipped as the default implementation targeting complete GNU compatibility, alongside sudo-rs, which introduced intentional behavior changes like asterisk display on password entry. Canonical is also funding ntpd-rs to consolidate NTP, NTS, and PTP into a single utility, while collaborating on UPKI to bring certificate revocation to system utilities. To manage Rust packaging at distribution scale, Canonical vendors dependencies per package rather than creating individual Debian packages for crates and embeds software bills of materials into binaries using cargo auditable. Future plans include exploring Rust implementations of compression libraries like bzip2-rs, zlib-rs, and zstd-rs.
How AI observability works with MLflow
Traditional application monitoring treats AI workflows as black boxes, leaving engineering teams unable to determine whether incorrect responses result from model hallucinations or erroneous upstream tool data. MLflow provides AI observability by capturing distributed traces containing model calls, prompts, retrieved context, execution latencies, and tool interactions. In an example mortgage lending application, trace inspection revealed that an AI assistant accurately summarized raw JSON data returned by an internal pipeline tool rather than hallucinating conflicting metrics. Beyond latency attribution and tool debugging, MLflow is compatible with OpenTelemetry and provides automatic tracing integrations for frameworks such as LangChain and Claude Code. Capturing these traces enables teams to evaluate subsequent application versions, detect model provider shifts, and identify tool regressions before reaching users.
Try the Ansible playbook generation lab with Gemini and OpenAI
The official Visual Studio Code extension for Red Hat Ansible Automation Platform includes an automation coding assistant, formerly called Ansible Lightspeed. Initially integrated with IBM watsonx, the extension now supports Google Gemini and OpenAI-compatible endpoints through Red Hat AI. An updated interactive lab demonstrates these capabilities through an end-to-end scenario involving database installation and HTML landing page deployment across two nodes. Learners write natural language prompts to generate Ansible playbooks and roles, evaluate output, and utilize real-time automated linting and integrated documentation. By serving an open-source model through a preconfigured OpenAI-compatible endpoint, the refreshed environment removes external credential setup steps.
Automating Red Hat OpenShift AI installations with Helm and GitOps
Deploying Red Hat OpenShift AI manually across multiple environments is error-prone due to the numerous required dependencies, including certificate managers, job queuing systems, and GPU operators. To streamline this lifecycle, the odh-gitops repository introduces the rhai-on-openshift-chart Helm chart. The chart automates operator provisioning through Operator Lifecycle Manager and configures custom resources such as DataScienceCluster. Dependency resolution is controlled via a tri-state model that automatically installs prerequisites like cert-manager and Red Hat Connectivity Link when dependent components are enabled. Using this chart, teams can deploy full platform installations or minimal inference stacks through standard Helm CLI commands or Argo CD GitOps pipelines.
What's new in the ClickHouse .NET Driver: the road from 1.0 to 1.3
The ClickHouse .NET Driver moved from its 1.0 stable release to versions 1.1–1.3, changing how applications insert, read, and parameterize data while expanding type coverage, configurability, observability, and .NET ecosystem integrations. The largest ergonomic change is registered POCO support: RegisterPocoType() validates mappings up front, pre-compiles getters and setters, and enables InsertBinaryAsync with IEnumerable plus lazy QueryAsync() results through IAsyncEnumerable. Attributes customize column and ClickHouse type mappings, JSON serialization supports POCOs with paths and ignored properties when JsonWriteMode is Binary, and three optional extension points govern parameter type inference, wire formatting, and read conversion. The announced 1.4 roadmap targets allocation reductions, pluggable compression, and box-free POCO operations; version 1.5 is planned to add a Native/TCP client with columnar transfer and server progress information.
Project Lighthouse — Part 3: Introducing project-lighthouse-anonymize
Airbnb has open-sourced project-lighthouse-anonymize, a Python library powering their privacy-preserving anonymization used to measure user experience disparities. The release is accompanied by technical papers detailing Core Mondrian, an extended partition-based anonymization algorithm, alongside a framework for measuring anonymized data quality. The library enforces k-anonymity and p-sensitive k-anonymity while offering built-in data quality metrics.
Funding better evaluations of AI’s impact on wellbeing
A $5 million grant program will fund independent research into how AI affects user wellbeing, offering direct funding, model access, and technical support for open-source evaluations. The initiative addresses the difficulty of judging wellbeing-related behavior, where risks may emerge only across long, shifting conversations and where otherwise reasonable advice can become harmful in a different context, such as disordered eating. Grantees will work independently and publish reusable evaluations or benchmarks, with guidance asking projects to define pass/fail criteria, involve clinical and subject-matter experts, test overcompliance and overrefusal, model realistic multi-turn scenarios, and validate graders against experts. Applications are due September 21, with selected full-proposal applicants notified by October 5.
Introducing the Anthropic Economic Index
Anthropic introduced the Anthropic Economic Index to track how AI is used in work and to study its effects on labor markets over time. Its first report analyzes approximately one million anonymized Free and Pro Claude.ai conversations, using Clio to match them with occupational tasks from the U.S. Department of Labor’s O*NET database. Usage concentrated in computer and mathematical work, which represented 37.2% of queries, while 36% of occupations showed AI use in at least a quarter of associated tasks and about 4% reached three-quarters. The report found 57% augmentation versus 43% automation, lower use in both low- and very-high-wage occupations, and plans for open data and repeated analyses, while noting possible classification errors and coding overrepresentation.
Granite 4.2 LLMs: How They're Built
Granite 4.2 is IBM’s first family of dense, decoder-only reasoning language models, offered in 3B, 8B, and 30B sizes. The models use a shared design built around Grouped Query Attention, Rotary Position Embedding, SwiGLU, RMSNorm, bfloat16 precision, and separate input/output embeddings; each has a 131,072-token sequence length. Pre-training starts from scratch on approximately 15 trillion tokens across five phases, with the final phase extending the context window to 512K tokens. Supervised fine-tuning uses about 7.2 million samples spanning agentic and non-agentic data, followed by multi-stage reinforcement learning; the 8B and 30B models also receive agentic RL in sandboxed environments for tool use, code execution, terminal control, and web search. All models support thinking, non-thinking, and low-effort thinking modes, native tool calling, and Apache 2.0 licensing.
CUDA Python 1.0: Stable APIs, One Foundation, Full Platform Access
CUDA Python 1.0, released with CUDA 13.3, presents Python as a supported way to access the full NVIDIA CUDA platform. It addresses a fragmented ecosystem in which libraries used separate binding layers, complicating shared GPU memory, streams, and resource ownership, and forcing developers toward either CUDA C++ extensions or higher-level libraries. The release combines independently versioned components, including cuda.core, cuda.compute, cuda.bindings, cuda-pathfinder, and nvmath-python, with semantic-versioning and deprecation commitments. cuda.core supplies common Python objects for devices, streams, and buffers, allowing NVIDIA and external libraries to compose on shared resources, while cuda.bindings offers 1:1 CUDA C API coverage. The stack spans runtime services, tuned CUDA libraries, communication tools, and kernel-authoring systems, although some newer kernel languages remain experimental and outside the 1.0 guarantees.
Ideas Worth a Longer Conversation: The JetBrains Research Podcast
The JetBrains Research Podcast investigates foundational software development and computer science questions through discussions with industry researchers. In team psychology, Cat Hicks demonstrates that collaborative culture and organizational belonging reduce developer anxiety around automated tooling while curbing the risks of overproduction pressure. Historical analysis by Tomáš Petříček shows that programming spans five competing cultures rather than pure specification implementation, explaining why automated code generation historically encounters limits. Regarding artificial intelligence evaluation, Ibragim Badertdinov details how SWE-rebench isolates model capability through dataset decontamination, leveraging the strict verifiability of software test execution for training feedback. Additionally, conversations with Alexander Kulikov and Anna Kogan examine how foundational mathematical understanding aids AI oversight and address adoption hurdles for open-source computer vision infrastructure.
Help AI Coding Agents Write Up-To-Date Code With Modern Golang Skills
AI coding agents often generate outdated Go patterns because older syntax dominates training data and new features fall past model cutoffs. To address this gap, the GoLand team released Modern Go Guidelines, an open-source set of skills and CLI tools covering Go 1.0 through Go 1.27. The tool inspects a project's go.mod file or a specific version flag to supply only the language features and standard library additions compatible with that environment. Using a progressive disclosure approach, the CLI provides concise rule identifiers via a list command and expands into before-and-after examples through an explain command. This mechanism minimizes token consumption while helping agents produce up-to-date, compilable code without modifying project files.
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