---
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

### [Why we rewrote WAL-G for Postgres backups in Rust: Meet WAL-RUS](https://yomu.fyi/post/why-we-rewrote-wal-g-for-postgres-backups-in-rust-meet-wal-rus.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Problem & solution

ClickHouse Cloud built WAL-RUS, an open-source Rust implementation of PostgreSQL backup and WAL archival tooling, after encountering difficulty predicting WAL-G’s memory usage in resource-constrained environments. The project preserves WAL-G’s core functionality and compatibility while using bounded worker pools, controlled concurrency, persistent object-storage connections, and a streaming pipeline that limits buffering and data copies. It uses the same WALG\_ configuration variables, and WAL-G and WAL-RUS can read each other’s archives. In a sustained WAL-heavy benchmark with four archival workers, WAL-RUS stayed below 1 GB of peak virtual memory versus nearly 2.8 GB for WAL-G, while both maintained minimal backlog and comparable CPU usage. The authors plan to make WAL-RUS the default backup and archival mechanism for ClickHouse Cloud’s managed Postgres offering and are working to upstream its Postgres 17 wal summaries support to WAL-G.


### [How Visa went from multi-day reporting to conversational analytics agents with ClickHouse Cloud and LibreChat](https://yomu.fyi/post/how-visa-went-from-multi-day-reporting-to-conversational-analytics-age.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 24, 2026
- Type: Problem & solution

Visa used ClickHouse Cloud to modernize reporting on Authorize.net payments data, replacing multi-day analyst-led turnaround with natural-language queries for internal teams. The solution pairs LibreChat, an open-source conversational interface, with the ClickHouse MCP server and runs on ClickHouse Cloud on AWS, allowing agents to explore and query a roughly 40 PB data footprint containing millions of rows. ClickHouse materialized views pre-compute and incrementally update results, reducing scanned data and bringing example analyses down to 100–200 milliseconds, with reported speedups of roughly 30–70 times. The platform also uses encrypted transfers, Amazon S3, VPC PrivateLink, change data capture, and federated access controls, while Visa reports identifying millions of dollars in revenue risk and reclaiming 8–10 hours per user each week.


### [NilAway: Practical Nil Panic Detection for Go](https://yomu.fyi/post/nilaway-practical-nil-panic-detection-for-go.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jun 23, 2026
- Type: Problem & solution

NilAway is Uber’s static analyzer for detecting potential nil pointer dereferences in Go before they reach production, where nil panics have caused incorrect behavior, outages, and denial-of-service risks. It models nilability flows as global typing constraints over struct fields, function parameters, and return values, builds an implication graph, and uses 2-SAT-style reasoning with forward nilness and backward nonnil propagation to identify contradictions. The analysis is interprocedural, requires no developer annotations, and reports detailed flows, while acknowledging false positives when runtime invariants are not captured. In one Uber service logging more than 3,000 nil panics daily, NilAway identified a receive-from-closed-channel path; adding a nilness check reduced the reported panics to zero, and the tool was released as open source with comprehensive and changed-code onboarding options.


### [Boost Inference Performance up to 15x on NVIDIA Blackwell Using DFlash Speculative Decoding](https://yomu.fyi/post/boost-inference-performance-up-to-15x-on-nvidia-blackwell-using-dflash.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Amr Elmeleegy
- Published: Jun 23, 2026
- Type: Benchmark

Sequential token generation in autoregressive LLMs limits GPU utilization and serving throughput, while conventional speculative decoding remains constrained by autoregressive draft models. DFlash addresses this bottleneck by employing a lightweight block-diffusion drafter that predicts an entire block of masked candidate tokens in a single forward pass. The architecture incorporates target hidden-state conditioning and key-value injection into draft model projections while relying on the target model for parallel verification. Evaluated on NVIDIA Blackwell GPUs across TensorRT-LLM, vLLM, and SGLang, DFlash increases gpt-oss-120b throughput by more than 15x at high interactivity targets and delivers up to 5.8x speedups on Gemma 4 31B compared to autoregressive decoding.


### [Shipping huggingface\_hub every week with AI, open tools, and a human in the loop](https://yomu.fyi/post/shipping-huggingface-hub-every-week-with-ai-open-tools-and-a-human-in.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Lucain Pouget, Célina Hanouti
- Published: Jun 23, 2026
- Type: Problem & solution

huggingface\_hub, the Python client underlying libraries such as transformers, datasets, diffusers, and sentence-transformers, moved from releases every four to six weeks to a weekly cadence. A single manually triggered GitHub Actions workflow now computes versions, manages branches, tags and publishes both huggingface\_hub and the hf CLI, creates downstream RC test branches, and handles post-release tasks. An open-weights model drafts structured release notes and Slack announcements using PR metadata and documentation diffs, while OpenCode, HF Inference Providers, PyPI Trusted Publishing, and pinned, checksum-verified tooling provide the stack. Deterministic manifest validation checks missing or extra PR references, then a human reviews and publishes the drafts. The reported result is weekly shipping at about $0.25 per full release, with more consistent notes, earlier downstream breakage detection, and shorter contributor feedback loops.


### [PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters](https://yomu.fyi/post/pp-ocrv6-on-hugging-face-50-language-ocr-from-1-5m-to-34-5m-parameters.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: AlexZhang, cuicheng, Jun Zhang, Manhui Lin, Yue Zhang, leo-q8, yubo, Yi Liu
- Published: Jun 22, 2026
- Type: Announcement

PP-OCRv6 is PaddleOCR’s latest universal OCR model family, targeting text detection and recognition across documents, screenshots, displays, labels, and scene text. It offers tiny, small, and medium tiers spanning 1.5M to 34.5M parameters, while the small and medium tiers support 50 languages. Its PPLCNetV4 backbone is shared across detection and recognition, with RepLKFPN for lightweight multi-scale detection and EncoderWithLightSVTR for recognition using local context and global attention. On PaddleOCR’s official in-house multi-scenario benchmarks, PP-OCRv6\_medium reaches 86.2% detection Hmean and 83.2% recognition accuracy, improving over PP-OCRv5\_server by 4.6 percentage points and 5.1 percentage points. PaddleOCR provides Paddle Inference, Transformers, and ONNX Runtime paths, and results can be saved as visualization images or structured JSON for downstream workflows.


### [We got local models to triage the OpenClaw repo for FREE!\*](https://yomu.fyi/post/we-got-local-models-to-triage-the-openclaw-repo-for-free.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Onur Solmaz, ben burtenshaw, shaun smith
- Published: Jun 22, 2026
- Type: Problem & solution

OpenClaw receives hundreds of issues and pull requests daily, creating a triage problem for maintainers who need near-real-time filtering without consuming a paid closed-model quota. The system uses Gemma and Qwen local models inside a Pi-based agent harness: each item is normalized, enriched with GitHub context, classified into finite topic labels, and required to return structured output. For pull requests, the agent can inspect the repository through reposhell, a read-only command-restricted shell, while localpager, SQLite, a worker queue, and deterministic notification policy handle orchestration and Discord delivery. Tests described for gemma-4-26b-a4b and qwen3.6-35b-a3b report hundreds of tokens per second and good zero-shot classification accuracy without fine-tuning, while an LLM audit loop monitors errors; the authors present the pattern as agentic classification for high-throughput triage.


### [Ten years of open source: a stained-glass view](https://yomu.fyi/post/ten-years-of-open-source-a-stained-glass-view.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 19, 2026
- Type: Explainer

ClickHouse’s ten-year open-source history is presented through a stained-glass analogy: many contributors remain visible as distinct pieces, while shared infrastructure holds the project together. The article connects that community account to production use through cBioPortal at Memorial Sloan Kettering, where moving heavy filtering into ClickHouse SQL made relevant paths about ten times faster, changing waits from minutes to seconds and supporting continued exploration. It also describes a demonstration built from bare-clone git history, one month of GH Archive events, and the shipped system.contributors table, all queried in ClickHouse. The account reports 100,000 issues and pull requests crossed on March 19, 2026, with 83% of issues and 99% of pull requests resolved at the time of the talk, and presents contributor credit and repair as operating parts of the project.


### [Beyond LoRA: Can you beat the most popular fine-tuning technique?](https://yomu.fyi/post/beyond-lora-can-you-beat-the-most-popular-fine-tuning-technique.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Benjamin Bossan, Sayak Paul, Marian Tietz, Kashif Rasul
- Published: Jun 18, 2026
- Type: Explainer

Parameter-efficient fine-tuning (PEFT) reduces the memory required to adapt open models, enabling fine-tuning with a fraction of the memory and, in some cases, quantized models. LoRA is dominant because it freezes base weights and trains a small set of added parameters, but its popularity may also reflect ecosystem momentum and uneven comparisons. The post presents shared-condition benchmarks for language-model math reasoning and image generation, using identical models, datasets, code, hardware, and measures including test performance, VRAM, forgetting or drift, runtime, and checkpoint size. The results show that LoRA performs well but is not consistently best: other techniques can outperform it on one or more axes, especially in the image-generation benchmark. The recommended choice is therefore to compare techniques against a use case’s needs and constraints, including quantization support, layer coverage, adapter merging, and downstream serving compatibility.


### [June 2026 newsletter](https://yomu.fyi/post/june-2026-newsletter.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 18, 2026
- Type: Announcement

ClickHouse’s June 2026 newsletter marks the project’s ten years as open source, noting more than 2,000 contributors and community projects alongside engineering updates. It profiles Vasily Chekalkin’s 26.5 improvements to WebAssembly user-defined functions, including the DETERMINISTIC keyword and visibility in system.functions, and covers native Jaeger storage, the ClickCannon benchmark tool, and Open House announcements. The 26.5 release reports Top-N optimizations that make ORDER BY … LIMIT through JOIN 20.4× faster with 175× less memory on TPC-H SF100, while GROUP BY … LIMIT is 11.9× faster with 185× less peak memory. Other featured systems include a 15-million-rows-per-hour KPI pipeline, join-engineering work yielding a 26× improvement on join-heavy TPC-H workloads, and community guidance on inserts, columnar storage, ArrowStream, and RAG ingestion.


### [The open ecosystem around ClickHouse](https://yomu.fyi/post/the-open-ecosystem-around-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 17, 2026
- Type: Explainer

ClickHouse’s open ecosystem spans clients, dashboards, ingestion pipelines, projects built on its engine, and agents that query it, with 2,600+ contributors, 240,000+ commits, and hundreds of millions of downloads cited. It describes open standards including OpenTelemetry for observability, Model Context Protocol (MCP) for AI tooling, and Iceberg and Delta Lake for lakehouse data, plus Iceberg REST catalogs, Apache Polaris, and Unity Catalog. The surrounding stack includes ClickStack, chDB, Langfuse, LibreChat, official language clients, a Kubernetes operator, Postgres bridges such as PeerDB and pg\_clickhouse, and integrations with Airflow, dbt, Grafana, Fivetran, and Sigma. Specific updates include full Iceberg v2 support, Iceberg writes in 25.8, Paimon in 25.10, chDB v4’s DataStore API, and a Python client v1.0 with native async and 4x faster cold starts. The post frames this openness as supporting flexible tooling and community-led growth, reporting more than 200 integrations while noting that the ecosystem’s next direction remains unknown.


### [The Partner Well-Architected Framework: What's New and What's Next](https://yomu.fyi/post/the-partner-well-architected-framework-what-s-new-and-what-s-next.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: David Porter
- Published: Jun 17, 2026
- Type: Announcement

The Partner Well-Architected Framework (PWAF) has moved partner guidance from static PDFs to AI-ready guidance spanning Built-On, Connected, and Data Collaboration architectures. It combines an architecture center with technical standards, patterns, and instrumentation guidance intended to align integrations with partner-engineering validation while making adoption and DBU impact measurable. Since February, Databricks has added a Dev Kit containing 15+ tested AI-developed skills, new or refreshed guidance for Clean Rooms, software-defined storage, Marketplace apps, Genie, Lakebase, and MCP server onboarding, and an open-source Firefly Analytics reference implementation. The framework is positioned as an evolving, agent-compatible way to shift routine integration work to AI tools, freeing partner and Databricks engineers to focus on complex architecture; more patterns, skills, reference implementations, and demos are planned.


### [Becoming the most comprehensive data & AI ecosystem on earth](https://yomu.fyi/post/becoming-the-most-comprehensive-data-ai-ecosystem-on-earth.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stephen Orban
- Published: Jun 17, 2026
- Type: Announcement

Databricks describes a year of expanding its partner ecosystem for independent software vendors and data providers, responding to requests for stronger go-to-market support, technical guidance, and Marketplace transactions. It says it introduced partner tiering, the Partner Well-Architected Framework, a Marketplace Commit Drawdown pilot, and the ability to list Databricks Apps and Genie Agents. Under the pilot, customers with a Universal Commit can submit invoices for eligible solutions that run on or share data to Databricks; validated purchases reduce their commitment obligation, while Databricks compensates its sales team. OpenSharing is presented as an open-source, vendor-agnostic protocol for cross-cloud, cross-platform sharing of structured and unstructured data, volumes, agent skills, AI models, and semantics. The post says Apps adoption has grown fivefold since last year's summit, with more than 5,000 accounts running Apps in production weekly, and that more than 20,000 customers rely on Databricks.


### [From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot](https://yomu.fyi/post/from-the-hugging-face-hub-to-robot-hardware-with-strands-agents-and-le.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sundar Raghavan, Cagatay Cali
- Published: Jun 17, 2026
- Type: Tutorial

Strands Robots integrates Hugging Face’s LeRobot stack with Strands Agents to connect dataset recording, policy execution, simulation, hardware deployment, and fleet coordination in one agent loop. Its integration keeps LeRobot responsible for hardware drivers, calibration, recording, and dataset handling, while Strands supplies AgentTools, a shared Robot abstraction, policy providers, and a Zenoh peer mesh; simulation and hardware use the same LeRobotDataset format. The walkthrough records demonstrations in MuJoCo, optionally pushes them to the Hub, runs policies through a common interface, and switches from simulation to a physical SO-101 with mode="real". The companion script and notebook run the default simulation workflow on a laptop without hardware, a GPU, or Hugging Face credentials, although the default Mock policy produces structurally valid but behaviorally unhelpful random demonstrations.


### [GLM-5.2: Built for Long-Horizon Tasks](https://yomu.fyi/post/glm-5-2-built-for-long-horizon-tasks.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Z.AI
- Published: Jun 17, 2026
- Type: Announcement

GLM-5.2 is presented as a flagship model for long-horizon tasks, extending GLM-5.1’s capability to a solid 1M-token context for sustained engineering work. It was trained extensively on 1M-context coding-agent scenarios involving large-scale implementation, automated research, performance optimization, and complex debugging, and adds effort-level controls to balance capability, latency, and computational cost. Its architecture uses IndexShare, sharing one lightweight indexer across every four transformer layers and reducing per-token FLOPs by 2.9× at 1M context; MTP changes for speculative decoding increase acceptance length by up to 20%. On reported long-horizon and standard coding benchmarks, GLM-5.2 is described as the highest-ranked open-source model: it trails Opus 4.8 by 1% on FrontierSWE, ranks second only to Opus 4.8 on PostTrainBench, and scores 81.0 on Terminal-Bench 2.1 versus GLM-5.1’s 63.5.


### [Kimi K2.7 Code vs Claude Fable 5: Landing pages that cost 94% less](https://yomu.fyi/post/kimi-k2-7-code-vs-claude-fable-5-landing-pages-that-cost-94-less.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jun 17, 2026
- Type: Benchmark

The experiment compares Kimi K2.7 Code and Claude Fable 5 across 12 landing-page prompts covering B2B SaaS, a rooftop speakeasy, developer tools, and other categories. Kimi cost about 94% less overall, averaging roughly 16 times cheaper than Fable, while producing similar-quality output, especially when given visual references. The team first established a prompt-only baseline, then used a custom design MCP server to provide screenshots, UI elements, and other references directly to multimodal Kimi; this improved hierarchy, typography, composition, readability, loading behavior, and image handling. GPT-5.5 scored screenshots and source code on a 0–100 rubric, and although Fable scored higher, the relatively small quality gap led the authors to consider Kimi’s lower cost a reasonable trade-off for iterative landing-page generation.


### [Announcing the Agentic Resource Discovery specification](https://yomu.fyi/post/announcing-the-agentic-resource-discovery-specification.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Junjie Bu, Srinivas Krishnan
- Published: Jun 17, 2026
- Type: Announcement

Agentic Resource Discovery (ARD) is an open specification for publishing, discovering, and verifying AI capabilities across the web, addressing the lack of a standard that works across organizations and ecosystems. It uses organization-hosted catalogs, including an ai-catalog.json file at a well-known domain path, plus federated registries that crawl, index, and return capabilities with trust metadata. Clients can query a registry with plain-language intent or fetch a known catalog directly, verify the publisher’s cryptographic identity, then connect at runtime through the capability’s native protocol or API. Google Cloud is bringing ARD to Gemini Enterprise Agent Platform through Agent Registry, which supports hosted discovery and governance features including namespaced URNs, egress policies, specification pinning, and Agent Identity trust-manifest verification. The specification is available under Apache 2.0, with native Agent Platform support planned in the coming months.


### [Agentic Resource Discovery: Let agents search](https://yomu.fyi/post/agentic-resource-discovery-let-agents-search.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, shaun smith
- Published: Jun 17, 2026
- Type: Explainer

Agentic Resource Discovery (ARD) is a draft, open specification for discovering agent capabilities at runtime, addressing a model in which users and developers must pre-install, integrate, and maintain each MCP tool, skill, or A2A agent. It defines a static ai-catalog.json manifest and a dynamic POST /search registry API, using indexed signals such as publisher identity, representative queries, compliance attestations, and tags to move capability selection outside the LLM. Hugging Face's Discover Tool adapts Hub semantic search into ARD catalog entries for running Spaces, exposing skills, MCP servers, or raw Space metadata according to the requested media type and wrapping agents.md files into installable SKILL.md artifacts. The tool is available through the hf CLI, REST, and MCP, while planned work includes federation modes and Hub support for profile-level manifests.


### [Building AI Agents for AR Glasses and XR Devices with NVIDIA XR AI](https://yomu.fyi/post/building-ai-agents-for-ar-glasses-and-xr-devices-with-nvidia-xr-ai.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Greg Barbone
- Published: Jun 16, 2026
- Type: Announcement

Building AI experiences for augmented reality and wearable devices requires bridging hardware with live media streams, multimodal models, enterprise tools, and runtime infrastructure. NVIDIA XR AI provides an open-source, modular framework connecting extended reality headsets and smart glasses to GPU-accelerated services across cloud, edge, and workstations. In this architecture, camera frames and microphone audio ingest into an XR Media Hub that routes data while keeping raw video pixels in shared memory to minimize overhead. The ecosystem uses NVIDIA Cosmos models for vision-language grounding, NVIDIA Nemotron models for reasoning and tool invocation, and the Model Context Protocol for enterprise integrations. Optional agent orchestration via NVIDIA NeMo Agent Toolkit and spatial streaming through NVIDIA CloudXR support complex workflows across healthcare and manufacturing.


### [Introducing OpenSharing: the Next Evolution of Delta Sharing for the Agentic Era](https://yomu.fyi/post/introducing-opensharing-the-next-evolution-of-delta-sharing-for-the-ag.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Huey Han, Harish Gaur, Akram Chetibi, Mengxi Chen
- Published: Jun 16, 2026
- Type: Announcement

OpenSharing is presented as the next evolution of Delta Sharing, extending an open zero-copy data-sharing protocol from tables and files to models, agents, semantic context, unstructured data, and reusable AI logic. The protocol is now an independent open-source project hosted by the Linux Foundation, while Databricks OpenSharing adds Unity Catalog governance and audit logging, Marketplace discoverability, and enterprise features. Genie Agent Sharing supports governed AI experiences across organizational boundaries, with controls for proprietary instructions, data access, daily prompt quotas, and row exports. SecureConnect removes per-recipient firewall changes through a Databricks-managed proxy, while Global Distribution uses local replicas to reduce egress fees and latency. The launch also supports Apache Iceberg REST Catalog API, external catalogs, and on-premises storage partners; providers define shares in Unity Catalog, recipients query live data through existing tools, and governance enforces access controls.


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