--- title: "Open Source" description: "568 posts about Open Source, summarised, each linking to the original." --- # Open Source > 568 posts about Open Source, summarised, each linking to the original. ## Articles ### [Building an open Agentic Internet: readable, discoverable, callable, and payable](https://yomu.fyi/post/building-an-open-agentic-internet-readable-discoverable-callable-and-p.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Jack Galilee - Published: Aug 6, 2026 - Type: Announcement The modern web produces billions of bot requests that repeatedly fetch unchanged pages, highlighting an architecture fundamentally misaligned with automated visitors acting on behalf of paying users. Cloudflare outlines an open Agentic Internet structured around four foundational capabilities: readability, discoverability, callability, and payability. Identity and access rely on open specifications such as Web Bot Auth and Private Access Control Tokens, while Markdown for Agents and the Kitesurf browser reduce token overhead and context window bloat. For execution and monetization, WebMCP exposes structured tools directly through the browser, and protocols like x402 paired with Monetization Gateway enable per-fetch transactions. These open standards allow domain owners to define tool contracts, verify bot identities, and monetize agent traffic without relying on legacy advertising models. ### [Agent Plugins package your skills, tools, and more](https://yomu.fyi/post/agent-plugins-package-your-skills-tools-and-more.md) - Company: [Google](https://yomu.fyi/company/google.md) - Author: Kevin Hou, Haoyu Wang, Alan Blount - Published: Aug 6, 2026 - Type: Announcement Agent Plugins 1.0.0 is an open, vendor-neutral specification for packaging Agent Skills and MCP servers into portable plugins, addressing incompatible wrappers that force authors to maintain client-specific copies. It defines a plugin as a directory with plugin.json, skills/ subdirectories, mcp.json, and client-owned reverse-domain extension namespaces. The manifest fixes component locations, requires explicit MCP server types for stdio, Streamable HTTP, or legacy HTTP+SSE, and lets skills continue loading when an MCP entry fails. Version 1 deliberately excludes installation, distribution, permissions, sandboxing, trust verification, and user experience, leaving those concerns to clients and related ecosystem layers such as discovery and catalogs. Google is joining the Core Maintainers and says Agents CLI and Data Agent Kit support the format, while more product integrations are expected. ### [Baseten on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/baseten-on-hugging-face-inference-providers.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Alex Ker, Roland Crosby, Sid Shanker, Johan, Célina Hanouti, Simon Brandeis, Lucain Pouget, merve - Published: Aug 6, 2026 - Type: Announcement Baseten is now a supported Inference Provider on the Hugging Face Hub, extending serverless inference on model pages and through the Hugging Face Python and JavaScript SDKs. As part of the initial integration, it supports conversational and text-generation tasks, including access to open-weight models such as Kimi K3, latest DeepSeek V4 Flash, and GLM-5.2, with additional tasks planned to roll out soon. Developers can use a custom provider key for direct calls or authenticate with a Hugging Face token so requests are routed to Baseten; model pages and account settings support provider compatibility and preference ordering. Python and JavaScript OpenAI-compatible examples use the Hugging Face router, while integrated agent harnesses can access Baseten-hosted models without extra glue code. ### [Your OTel spans, our errors: A Sentry love story in one trace](https://yomu.fyi/post/your-otel-spans-our-errors-a-sentry-love-story-in-one-trace.md) - Company: [Sentry](https://yomu.fyi/company/sentry.md) - Author: Johannes Daxböck, Neel Shah - Published: Aug 5, 2026 - Type: Announcement Pointing a standalone OpenTelemetry SDK directly at Sentry ingests spans over standard OTLP, but the spans remain disconnected from Sentry error data. Because the separate SDKs share a process without sharing context, Sentry errors lack active OpenTelemetry trace and span IDs. Sentry introduced OtlpIntegration across backend SDKs including Python, Ruby, Node.js, Go, PHP, .NET, and Java to bridge this gap. The integration reads the active OpenTelemetry context, attaches trace and span identifiers to Sentry events, and configures ingestion. This decoupled model replaces the deeply integrated POTel strategy across most languages, except Java, removing mandatory OpenTelemetry dependencies while maintaining unified trace waterfalls. ### [Beyond VLAs: How World Action Models Reshape Robot Manipulation](https://yomu.fyi/post/beyond-vlas-how-world-action-models-reshape-robot-manipulation.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Michelle Horton - Published: Aug 4, 2026 - Type: Explainer Robot policies built on vision-language-action models often struggle with physical generalization because vision-language backbones describe scenes instead of predicting physical dynamics. World action models address this limitation by building policies on video world models that jointly predict actions and future world states. NVIDIA Cosmos 3 provides an open foundation using a Mixture-of-Transformers architecture where an autoregressive transformer guides a diffusion transformer to synthesize continuous modalities including video and robot actions. Specializing Cosmos 3 on the DROID platform produced policies capable of simultaneously outputting action trajectories and predicted video observations while boosting RoboLab success from 28.1% to 36.8%. These models support flexible deployment tiers, from workstation serving on RTX PRO 6000 GPUs to real-time 15 Hz on-device execution on NVIDIA Jetson Thor embedded hardware. ### [The Software Supply Chain Is Under Siege. Devs Are Still the First Line of Defense](https://yomu.fyi/post/the-software-supply-chain-is-under-siege-devs-are-still-the-first-line.md) - Company: [Docker](https://yomu.fyi/company/docker.md) - Author: Jin Kim - Published: Aug 4, 2026 - Type: Explainer A research report by Omdia examines escalating software supply chain security risks driven by growing dependencies on third-party code, open-source software, and artificial intelligence. Over three-fourths of surveyed organizations experienced a software supply chain incident in the preceding year, with known vulnerabilities in third-party software representing the most common exploit vector. Surveyed respondents ranked AI technology as their top supply chain concern, alongside broader issues involving vulnerability remediation and code composition identification. While shifting security left is a high priority for 98% of organizations, 45% still lack confidence in the robustness of their supply chain defenses. Although secure container libraries and Software Bills of Materials provide measurable mitigation benefits, many organizations only generate inventories on a case-by-case basis. ### [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super](https://yomu.fyi/post/generate-trajectories-reasoning-traces-and-auto-labels-with-nvidia-alp.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Elizabeth Goodman - Published: Aug 4, 2026 - Type: Announcement Autonomous vehicle development typically relies on disjoint models for trajectory generation, intent prediction, scene understanding, and data labeling. NVIDIA Alpamayo 2 Super addresses this fragmentation with an open 34-billion-parameter vision-language-action model that couples a 32-billion-parameter Cosmos 3 Super Reasoner with a 2-billion-parameter diffusion-based Action Expert. Processing surround-view video across up to seven cameras, the model simultaneously produces future vehicle trajectories, Chain-of-Causation reasoning traces, high-level meta-actions, and grounded scene question answers. In benchmark evaluations, the model achieved a 6.4-second minADE\_6 of 0.911 meters on the Physical AI AV Dataset and ranked first on LingoQA with a score of 79.2. Released under the OpenMDW-1.1 permissive license, the architecture serves as an offline policy teacher, evaluation critic, or auto-labeling engine that can be distilled for NVIDIA DRIVE AGX Thor deployment. ### [Databricks joins the Open Secure AI Alliance to advance AI safety and security](https://yomu.fyi/post/databricks-joins-the-open-secure-ai-alliance-to-advance-ai-safety-and.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: Katie Cummiskey, Maria Pere-Perez, Arun Pamulapati, Nishith Sinha - Published: Aug 4, 2026 - Type: Announcement Databricks announces that it is a founding member of the Open Secure AI Alliance, formed with NVIDIA and more than 75 organizations to advance openly shared AI safety, AI security, and AI-enabled cyber defense research. The alliance’s scope extends beyond model weights to an open execution stack covering runtimes, guardrails, agent harnesses, risk frameworks, and governance. Databricks contributes Omnigent, an Apache 2.0 meta-harness supporting 13+ harnesses with contextual policies, spend caps, and sandbox isolation; DASF 3.0, which maps 97 risks across 13 components to 73 controls; DAGF; and BlackIce, a containerized toolkit bundling 14 red-teaming tools. Its Lakewatch Security Lakehouse applies the same openness to governed, agentic detection and response over portable data formats. ### [Introducing Claude for Nonprofits](https://yomu.fyi/post/introducing-claude-for-nonprofits.md) - Company: [Anthropic](https://yomu.fyi/company/anthropic.md) - Published: Aug 3, 2026 - Type: Announcement Claude for Nonprofits launches with GivingTuesday to help nonprofit organizations address limited resources while fitting AI into existing workflows and meeting community privacy expectations. The program offers discounts of up to 75% on Team and Enterprise plans, including Claude Opus 4.6, Sonnet 4.5, and Haiku 4.5, with plan capabilities and intended organization sizes described in the announcement. It also adds open-source connectors for Benevity, Blackbaud, and Candid, linking Claude to nonprofit and fundraising data and searches. A free, no-background course, AI Fluency for Nonprofits, offers instruction on grant writing, program evaluation, donor engagement, and efficiency. Pilot partnerships with more than 60 grantee organizations and customer examples are being used to learn how nonprofits can apply Claude to impact analysis, fundraising, documentation, and operational work. ### [How to Run Isolated Tenant Kubernetes Clusters on Shared GPU Infrastructure](https://yomu.fyi/post/how-to-run-isolated-tenant-kubernetes-clusters-on-shared-gpu-infrastru.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Tanya Lenz - Published: Aug 3, 2026 - Type: Tutorial Organizations sharing a single Kubernetes cluster across multiple teams frequently encounter conflicting custom resource definitions, overlapping role-based access control, and difficulties budgeting GPU capacity. To address these operational conflicts without provisioning separate hardware, administrators can deploy vCluster alongside KAI Scheduler on a shared host cluster. vCluster supplies each team with a dedicated, virtualized control plane featuring its own API server and permissions, while KAI Scheduler dynamically time-slices GPU resources through hierarchical queues. In a demonstration using an NVIDIA L40S GPU on MicroK8s, three separate tenant clusters successfully scheduled workloads with fractional allocations of 0.33 GPU each. The combination achieves tenant autonomy and visibility isolation while consolidating AI workloads on shared physical infrastructure. ### [ClickHouse launches ClickHouse Labs with Andy Pavlo as VP of Database Research](https://yomu.fyi/post/clickhouse-launches-clickhouse-labs-with-andy-pavlo-as-vp-of-database.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Aug 3, 2026 - Type: Announcement ClickHouse announced the launch of ClickHouse Labs, a new research group led by Andy Pavlo, who joins as Vice President of Database Research. The group is intended to advance foundational work in database systems and share it openly with the broader community. Its research spans database and data architectures, query processing, systems performance, and infrastructure for both ClickHouse and PostgreSQL, with applications including real-time analytics, data warehousing, observability, and AI. Researchers will build and test ideas against demanding real-world workloads, collaborate with ClickHouse engineers, and publish findings in leading research venues so others can evaluate, extend, and build on them. ### [Kimi K3: the complete developer guide](https://yomu.fyi/post/kimi-k3-the-complete-developer-guide.md) - Company: [Together AI](https://yomu.fyi/company/together-ai.md) - Published: Aug 1, 2026 - Type: Tutorial Kimi K3 is presented as Moonshot AI’s 2.8-trillion-parameter open-weights model for long-horizon coding, end-to-end knowledge work, and deep reasoning, served through Together AI’s OpenAI-compatible API. Its architecture combines Kimi Delta Attention (KDA), Attention Residuals, and Stable LatentMoE, which activates 16 of 896 experts per token—roughly 2%—and supports a 1M-token context. The implementation guide shows Python calls for configurable reasoning effort, reasoning-free instant mode, streaming, multi-image vision, strict JSON-schema output, and function tools, while noting automatic prefix caching. It also covers production-oriented details such as generous max\_tokens for reasoning-heavy structured output, a 100 MB request-body limit for vision, tool-call history handling, and deployment from US infrastructure. ### [Don’t stop early: Case-folding source code at memory speed](https://yomu.fyi/post/don-t-stop-early-case-folding-source-code-at-memory-speed.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Alexander Neubeck - Published: Jul 31, 2026 - Type: Problem & solution GitHub's code search engine, Blackbird, must case-fold over 480TB of source code across 180 million repositories during indexing and query matching. To accelerate this operation on source code that is overwhelmingly ASCII, the engineering team replaced early-exit branching with an unconditional branch-free loop. The implementation tests uppercase ASCII ranges using wrapping arithmetic, modifies bits in place, and detects non-ASCII bytes with an accumulator register tested only after the loop completes. Eliminating data-dependent exits allowed LLVM to generate SIMD instructions and achieve throughput exceeding 45 GiB/s on an Apple M4 processor. The optimized implementation was released as the open-source Rust crate casefold. ### [Introducing Supabase Evals](https://yomu.fyi/post/introducing-supabase-evals.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: matt\_rossman - Published: Jul 31, 2026 - Type: Announcement Supabase has open sourced supabase/evals, a benchmark and framework for testing how AI agents build using Supabase. It runs coding agents including Claude Code, Codex, and OpenCode against real tasks such as schema creation, Edge Function debugging, and RLS policy repair, scoring behavior with deterministic checks and LLM-as-a-judge. The framework executes scenarios in hosted-like and local containerized environments through the actual MCP server and CLI, with separate breadth-focused benchmark and depth-focused regression suites, plus a retry after failure. Initial findings show many agents pass broad tasks without skills, while skills improve some scores and documentation use; agents still often choose hand-written migrations, miss newer libraries, and use documentation inconsistently across harnesses. ### [The Future of Agentic AI Depends on Openness and Trust. That’s Why Docker Is Joining Nvidia’s Open Secure AI Alliance.](https://yomu.fyi/post/the-future-of-agentic-ai-depends-on-openness-and-trust-that-s-why-dock.md) - Company: [Docker](https://yomu.fyi/company/docker.md) - Author: Jin Kim - Published: Jul 30, 2026 - Type: Announcement Modern discussions surrounding agentic AI increasingly center on whether autonomous systems can be trusted and securely placed at the core of enterprise workflows. While intelligence is generated by underlying models, operational trust depends on the surrounding execution runtime, identity frameworks, governance models, and security boundaries. To address these challenges across an open ecosystem, Docker has joined NVIDIA's Open Secure AI Alliance. This initiative aims to ensure developers can seamlessly route tasks between open-weight and frontier models without rewriting their application architectures or sacrificing control. By extending security and governance into the agent execution environment, the alliance seeks to deliver predictable control as AI agents become fundamental components of software stacks. ### [How controllers from industrial machinery can coordinate multitask machine learning](https://yomu.fyi/post/how-controllers-from-industrial-machinery-can-coordinate-multitask-mac.md) - Company: amazon.science - Author: Theodore Vasiloudis - Published: Jul 30, 2026 - Type: Problem & solution Multitask machine learning often suffers from gradient conflicts, drift, and drought when blending multiple objective gradients in single update steps. To address this in graph self-supervised learning, researchers developed ControlG, a framework that allocates discrete computational blocks to individual objectives over time. ControlG operates across three coupled loops: sensing objective difficulty via spectral demand and gradient interference, planning target compute allocations using log-hypervolume Pareto sensitivities, and executing the schedule via a proportional-integral-derivative controller. Evaluated across homophilic and heterophilic graph benchmarks, the approach outperforms traditional multitask gradient-manipulation baselines while incurring low computational overhead. The framework also produces interpretable training timelines and deficit traces. ### [Tame Dependabot: Group your updates, slow the cadence, keep security fast](https://yomu.fyi/post/tame-dependabot-group-your-updates-slow-the-cadence-keep-security-fast.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Bruno Borges - Published: Jul 29, 2026 - Type: Problem & solution Dependabot often floods repository maintainers with individual daily pull requests for single patch bumps, wasting review and continuous integration resources. Microsoft's GCToolkit project mitigated this issue by updating its dependabot.yml configuration to group dependency updates using wildcard patterns and slowing the schedule interval to monthly. The project also expanded coverage to include Maven alongside GitHub Actions so all relevant package ecosystems receive managed updates. Because Dependabot processes security alerts independently of regular version schedules and enforces a default three-day package cooldown, critical vulnerability fixes remain fast while routine maintenance noise drops significantly. ### [Together AI announces strategic partnership with Moonshot AI to natively serve Kimi models](https://yomu.fyi/post/together-ai-announces-strategic-partnership-with-moonshot-ai-to-native.md) - Company: [Together AI](https://yomu.fyi/company/together-ai.md) - Published: Jul 29, 2026 - Type: Announcement Together AI announces a strategic partnership with Moonshot AI to serve Kimi models as a launch platform, beginning with Kimi K3 and extending to future open-weights releases. Kimi K3 is described as a 2.8-trillion-parameter sparse Mixture-of-Experts model with native vision and a 1M-token context window, using Kimi Delta Attention, Attention Residuals, per-head Muon, and quantile-based expert load balancing. Together says developers can access the models through Serverless, Provisioned Throughput, and Dedicated Inference, with US-hosted infrastructure, zero data retention, autoscaling, and a 99% uptime SLA for Provisioned Throughput. The partnership also supports post-training through full-weight and LoRA reinforcement learning and supervised fine-tuning, with checkpoints deployable directly to inference; Kimi K3 is available at launch, while future Moonshot releases are planned for the same platform. ### [Developing Healthcare Robotics with GPU-Native Medical Physics Simulation](https://yomu.fyi/post/developing-healthcare-robotics-with-gpu-native-medical-physics-simulat.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Michelle Horton - Published: Jul 28, 2026 - Type: Announcement Healthcare robotics faces data scarcity, long-tail clinical scenarios, and multi-year development cycles due to reliance on physical phantoms and animal studies. To resolve this, NVIDIA Medical Physics Simulation introduces GPU-native simulation within NVIDIA Isaac for Healthcare, integrating classical physics solvers and generative world models on a single GPU. The Endoluminal Simulation Module models flexible instruments as Cosserat rods solved via globally coupled extended position-based dynamics, maintaining zero-copy PyTorch interoperability via NVIDIA Warp. Concurrently, Cosmos-H-Dreams leverages distilled video diffusion models via the FlashDreams runtime to stream action-conditioned surgical video at over 30 frames per second. This dual-track architecture enables synchronized physics, synthetic fluoroscopic rendering, and scaled reinforcement learning policies across hundreds of parallel GPU environments. ### [NVIDIA Ising Enables Fully Automated Quantum Computer Calibration with Enhanced In-Context Learning](https://yomu.fyi/post/nvidia-ising-enables-fully-automated-quantum-computer-calibration-with.md) - Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md) - Author: Tanya Lenz - Published: Jul 27, 2026 - Type: Announcement NVIDIA Ising Calibration is an open-source vision language model designed to interpret quantum processor diagnostic outputs and recommend tuning steps. The newly released Ising Calibration 1.5 features a 31-billion-parameter architecture that is 11.4% smaller at BF16 precision than its predecessor. Trained on multi-modality partner data including superconducting qubits, quantum dots, ions, neutral atoms, and electrons on Helium, the model supports both zero-shot reasoning and in-context learning. Evaluated on the QCalEval benchmark, it scores 10% higher on average than comparable open models in zero-shot tasks and improves in-context learning performance by over 86% relative to the prior release. In addition to BF16 weights, the model offers an NVFP4 quantization option suitable for deployment on single consumer GPUs or NVIDIA DGX Spark hardware. [Newer posts](https://yomu.fyi/topic/open-source/page/5.md) · [Older posts](https://yomu.fyi/topic/open-source/page/7.md)