---
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

### [A New Framework for Evaluating Voice Agents (EVA)](https://yomu.fyi/post/a-new-framework-for-evaluating-voice-agents-eva.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Tara Bogavelli, Gabrielle Gauthier Melancon, Katrina Stankiewicz, Nifemi Bamgbose, Hoang Nguyen, Raghav Mehndiratta, Hari Subramani, Fanny Riols
- Published: Mar 24, 2026
- Type: Benchmark

EVA is an end-to-end framework for evaluating conversational voice agents across task accuracy and spoken conversational experience. It simulates multi-turn live-audio conversations with a user simulator, the evaluated voice agent, deterministic tool execution, validation, and metrics derived from recordings, transcripts, and tool-call logs. The framework reports EVA-A for Accuracy and EVA-X for Experience, combines code-based and LLM- or LALM-as-Judge metrics, and uses pass@3 and pass^3 across three trials to capture peak performance and consistency. Its initial release contains 50 reproducible English-language airline scenarios and 15 tools covering rebooking, itinerary changes, cancellations, standby, and vouchers. Results for 20 cascade and audio-native systems show a consistent tradeoff: agents stronger at task completion tend to provide worse experiences, while a substantial gap between pass@3 and pass^3 indicates inconsistent success.


### [Querying DateTimes in ClickHouse](https://yomu.fyi/post/querying-datetimes-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mark Needham
- Published: Mar 20, 2026
- Type: Tutorial

ClickHouse provides specialized functions to filter, bucket, and compute durations on native DateTime columns without requiring external processing tools. Using the New York City taxi dataset imported into a MergeTree table, queries bucket pickup timestamps into hour and 15-minute intervals using toStartOfHour and toStartOfFifteenMinutes while casting values to the Time type to group across dates. The dateDiff function calculates journey durations in minutes, which enables trip speed calculations across time windows and powers inline ASCII visualizations generated by the bar function. By combining toDayOfWeek with conditional aggregations like countIf and the lag window function, queries uncover distinct morning surge trajectories between weekdays and weekends. The analysis demonstrates that weekday trip volumes peak around 8:45 AM alongside dropping speeds, whereas weekend volumes increase steadily across the morning.


### [State of Open Source on Hugging Face: Spring 2026](https://yomu.fyi/post/state-of-open-source-on-hugging-face-spring-2026.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Avijit Ghosh, Lucie-Aimée Kaffee, Yacine Jernite, Irene Solaiman
- Published: Mar 17, 2026
- Type: Explainer

The post examines how open source AI on Hugging Face changed over the past year across competition, geography, technical trends, and emerging communities. It combines Hugging Face and community-contributed metrics to assess users, models, datasets, downloads, repositories, and specialized sub-ecosystems. In 2025, the platform reached 13 million users, more than 2 million public models, and over 500,000 public datasets, while the top 200 models represented 49.6% of downloads and China surpassed the U.S. in monthly and overall downloads. The analysis also describes rising participation by independent developers, increased Chinese releases, and active communities in robotics and science, framing open source AI as overlapping ecosystems rather than a uniform market.


### [Behind the Scenes: How Maintaining Cloud Native Buildpacks Powers Platforms Like Heroku](https://yomu.fyi/post/behind-the-scenes-how-maintaining-cloud-native-buildpacks-powers-platf.md)
- Company: [Heroku](https://yomu.fyi/company/heroku.md)
- Author: Juan Bustamante
- Published: Mar 17, 2026
- Type: Explainer

Maintaining the pack CLI and Cloud Native Buildpacks (CNBs) is presented as invisible infrastructure work supporting platforms including Heroku, Google Cloud, OpenShift, VMware Tanzu, and internal systems. Over the last 14 months, maintainers shipped 27 releases—11 for pack and 16 for buildpacks/github-actions—reviewed more than 65 community pull requests, and delivered Execution Environments and System Buildpacks. The work also covered Windows CI migration, a docker/docker to moby/moby client upgrade, FreeBSD binaries, multi-architecture publishing, and a Platform API 0.14 lifecycle-restorer fix. System Buildpacks address platform composition and defaults, while Execution Environments support consistent configurations across development, CI, and production. The post argues that company-funded maintainers and CNCF collaboration turn production requirements into shared infrastructure, with planned build observability, OCI image annotations, and Kaniko removal as further examples.


### [Designing the new async-native ClickHouse Python client](https://yomu.fyi/post/designing-the-new-async-native-clickhouse-python-client.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Joe Spadola
- Published: Mar 16, 2026
- Type: Problem & solution

The official ClickHouse Python client, clickhouse-connect, originally addressed asynchronous execution by wrapping its synchronous HTTP client in a thread pool executor. However, this executor model experienced thread exhaustion, GIL contention, and severe tail latency variance under heavy concurrent workloads. To resolve these scalability bottlenecks, the developers redesigned the client around a half-sync/half-async pattern utilizing aiohttp for async network I/O. A custom bounded queue bridges streaming socket reads on the event loop with synchronous, CPU-intensive binary parsing running inside background threads. Benchmarks demonstrate that this architecture stabilizes tail latencies, delivering an average P95 latency of 556 milliseconds compared to 869 milliseconds for the legacy executor client.


### [ClickHouse Release 26.2](https://yomu.fyi/post/clickhouse-release-26-2.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 16, 2026
- Type: Announcement

ClickHouse version 26.2 introduces twenty-five new features, forty-three performance optimizations, and one hundred eighty-three bug fixes across the database engine. This release graduates both the text-index and the QBit vector embedding data type to production-ready status for search and retrieval workloads. For streaming ingestion pipelines where row arrival rates are low, the engine adds time-based block flushing configurations to write data parts before hitting default row or byte thresholds. The update also embeds the ClickStack observability user interface directly into the standard binary distribution, accessible through the local HTTP server port. Further enhancements include automatic min-max indexing for temporal columns in MergeTree tables, interactive time-based one-time password authentication in the client, and a high-performance prime number generator function.


### [Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration](https://yomu.fyi/post/observability-and-opentelemetry-introducing-mongodb-atlas-log-integrat.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Sabina Bates
- Published: Mar 12, 2026
- Type: Announcement

During database latency spikes or unexpected outages, operations teams frequently waste valuable time downloading compressed archives and manually inspecting log files to isolate root causes. MongoDB Atlas resolves this operational friction by introducing a direct log integration feature for dedicated M10+ clusters. The system routes mongod, mongos, and audit logs directly to third-party tools such as Datadog, Splunk, Amazon S3, Google Cloud Storage, and Azure Blob Storage. Furthermore, built-in support for the OpenTelemetry standard enables teams to pipe database logs into any OTel-compatible logging backend. This setup eliminates operational silos, streamlines security auditing, and correlates database query diagnostics with application traces and infrastructure metrics in real time.


### [What's new in ClickStack. February '26.](https://yomu.fyi/post/what-s-new-in-clickstack-february-26.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Mar 12, 2026
- Type: Announcement

ClickStack introduced several platform updates and query performance enhancements across both open-source and Cloud environments for February 2026. ClickHouse Cloud OpenAPI now exposes ClickStack resources including dashboards, alerts, sources, and webhooks to support automated configuration-as-code workflows. In addition, ClickHouse 26.2 embeds the ClickStack UI directly into the database binary with a 4.1 MB footprint for local exploration and diagnostics. Visualizations now support native SQL mode for tables, allowing users to join disparate datasets like logs and traces across injected dynamic time variables. Finally, query execution speeds improve through streaming skip indexes, index pruning on disjunctions, and an expanded 100,000-row lazy materialization limit alongside new threshold alerting on number charts.


### [GitTrends: A Google Trends style view of the GitHub ecosystem](https://yomu.fyi/post/gittrends-a-google-trends-style-view-of-the-github-ecosystem.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Lionel Palacin
- Published: Mar 10, 2026
- Type: Announcement

GitHub produces billions of events across issues, pull requests, and comments, but turning this vast stream into a real-time trends analyzer presents significant technical challenges. GitTrends is an open-source demo application designed to search and compare technology keywords across nearly ten billion GitHub events. Built to showcase the full-text search capabilities recently released in ClickHouse, the application indexes raw text without pre-computed answers or intermediate data transformation. ClickHouse executes full-text searches and aggregations within a single engine in one pass, eliminating cross-system joins and data movement. The demo includes live comparisons toggling between full-text search, bloom filters, and full table scans to illustrate query performance at scale.


### [LeRobot v0.5.0: Scaling Every Dimension](https://yomu.fyi/post/lerobot-v0-5-0-scaling-every-dimension.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steven Palma, Pepijn Kooijmans, Jade Choghari, Caroline Pascal, Khalil Meftah, Martino Russi, Nicolas Rabault, Michel Aractingi, Virgile BATTO, Thomas Wolf
- Published: Mar 9, 2026
- Type: Announcement

LeRobot v0.5.0 is a major release that expands supported hardware, policies, datasets, simulation, and its software foundation, following more than 200 merged PRs and more than 50 new contributors since v0.4.0. It adds full Unitree G1 humanoid support for locomotion, manipulation, teleoperation, and whole-body control, alongside OpenArm, Earth Rover, CAN-bus motor controllers, and other integrations. The policy additions include Pi0-FAST's autoregressive VLAs with FAST action tokenization, Real-Time Chunking for continuously blended predictions, Wall-X, X-VLA, SARM, and PEFT fine-tuning. For data and simulation workflows, streaming video encoding removes waits between recording episodes, while EnvHub loads environments from the Hugging Face Hub and IsaacLab-Arena supplies GPU-accelerated simulation. The codebase now requires Python 3.12, uses Transformers v5, supports installable third-party policy plugins, and adds remote Rerun visualization plus installation and documentation improvements.


### [Introducing Modular Diffusers - Composable Building Blocks for Diffusion Pipelines](https://yomu.fyi/post/introducing-modular-diffusers-composable-building-blocks-for-diffusion.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: YiYi Xu, Alvaro Somoza, Dhruv Nair, Sayak Paul
- Published: Mar 5, 2026
- Type: Announcement

Modular Diffusers introduces a composable alternative to Diffusers’ existing DiffusionPipeline for building diffusion workflows from reusable blocks. A ModularPipeline can load a workflow definition, load model components separately with settings such as bfloat16, and preserve the familiar inference API, while blocks for text encoding, image encoding, denoising, and decoding can be inspected, run independently, or rearranged. Custom Python blocks declare components, inputs, outputs, and computation logic; the example adds a Depth Anything V2 depth processor to a Qwen Image ControlNet workflow, with data flowing between compatible blocks. Blocks and complete pipelines can be published to the Hub, and Mellon can generate node interfaces from their consistent definitions. The post presents the system as an early, flexible direction and says Mellon is not ready for production use.


### [Introducing ClickStack embedded in ClickHouse](https://yomu.fyi/post/introducing-clickstack-embedded-in-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: The ClickStack Team
- Published: Mar 5, 2026
- Type: Announcement

ClickHouse 26.2 introduces an embedded distribution of the ClickStack observability UI packaged directly inside the ClickHouse binary. Designed for local exploration and instance inspection, this distribution allows developers to visualize internal query logs, traces, and system metrics without deploying external services. To preserve build integrity and keep the added binary footprint around 4.1 to 4.2 MB, the team avoided a Node.js dependency by embedding a pre-built static Next.js application using CMake-generated C++ structs. Supporting components such as MongoDB, Express, and the Pyodide WebAssembly runtime were omitted, disabling alerting and persistent dashboards in favor of simplicity. Users can access the interface directly over HTTP to monitor local databases and query performance out of the box.


### [PRX Part 3 — Training a Text-to-Image Model in 24h!](https://yomu.fyi/post/prx-part-3-training-a-text-to-image-model-in-24h.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Bertoin, Roman Frigg, Jon Almazán
- Published: Mar 3, 2026
- Type: Benchmark

PRX Part 3 reports a 24-hour speedrun for training a text-to-image diffusion model, combining techniques that performed well in earlier experiments under a budget of about $1,500 on 32 H200 GPUs. The recipe uses x-prediction in pixel space with 32-pixel patches and a 256-dimensional initial bottleneck, followed by LPIPS and DINOv2 perceptual losses, TREAD token routing, REPA alignment with DINOv3, and Muon for matrix parameters while Adam handles the rest. Training ran for 100,000 steps at 512px with batch size 1,024, then 20,000 steps at 1024px with batch size 512 and no REPA, across three synthetic datasets. The resulting model is described as usable, with strong prompt following and consistent aesthetics, though texture glitches, anatomy issues, and difficulty with hard prompts remain. The post attributes these limitations mainly to undertraining and limited data diversity and presents the recipe as a foundation for further scaling.


### [2,000 robots walk into a shop: Simulated A/B testing (2026) - Shopify](https://yomu.fyi/post/2-000-robots-walk-into-a-shop-simulated-a-b-testing-2026-shopify.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Feb 27, 2026
- Type: Benchmark

Shopify’s SimGym addresses the difficulty of evaluating storefront changes when merchants lack enough traffic for conventional A/B tests, by simulating shopping sessions with LLM-guided robots. Each robot runs in a cloud Chromium browser with a persona, budget, and intent, while a counterpart tests the alternate theme; the system uses real page representations rather than a mocked DOM. To support bursty, sequential workloads with 89K–127K accumulated tokens per session, Shopify self-hosted gpt-oss-120b on 48 NVIDIA B200 GPUs, combining MXFP4 expert quantization, FP8 KV-cache, FlashInfer kernels, async scheduling, and guided decoding. Production measurements report 80K to 88K tokens/second per B200 from async scheduling, a 57% reduction in HTTP/gRPC overhead, and MIG experiments yielding about 20% lower latency and 12% higher daily throughput without quality regression; speculative decoding is validated but queued.


### [Mixture of Experts (MoEs) in Transformers](https://yomu.fyi/post/mixture-of-experts-moes-in-transformers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aritra Roy Gosthipaty, Pedro Cuenca, merve, Ilyas Moutawwakil, Arthur Zucker, Sergio Paniego, Pablo Montalvo
- Published: Feb 26, 2026
- Type: Explainer

Mixture of Experts (MoEs) in Transformers let a Transformer retain its backbone while replacing selected feed-forward layers with routed expert sub-networks, allowing total model capacity to exceed the active parameters used per token. The post explains why sparse models can reduce inference cost and memory pressure, then focuses on making them first-class in Transformers. Its weight-loading refactor converts independently serialized expert tensors into packed runtime layouts through composable WeightConverter operations and lazy materialization. The Experts Backend supplies eager, batched\_mm, and grouped\_mm execution strategies, while expert parallelism shards experts across devices and aggregates partial outputs. Training support uses grouped GEMM and custom Triton plus LoRA kernels, with reported improvements of approximately 12× speed, over 35% VRAM reduction, and roughly 6× longer context.


### [GGML and llama.cpp join HF to ensure the long-term progress of Local AI](https://yomu.fyi/post/ggml-and-llama-cpp-join-hf-to-ensure-the-long-term-progress-of-local-a.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Georgi Gerganov, Xuan-Son Nguyen, Aleksander Grygier, Lysandre, Victor Mustar, Julien Chaumond
- Published: Feb 20, 2026
- Type: Announcement

Hugging Face announces that GGML, the creators of llama.cpp, are joining the company to support the ggml and llama.cpp community as local AI advances. Georgi Gerganov and his team will continue dedicating 100% of their time to maintaining llama.cpp, retaining autonomy and leadership over its technical direction and community, while the project remains fully open-source and community driven. The technical focus is tighter integration between llama.cpp’s local-inference stack and Transformers’ model definitions, aiming to make shipping new models from Transformers’ “source of truth” nearly single-click, alongside better packaging and user experience for ggml-based software. The longer-term goal is an efficient inference stack that runs on devices and helps make open-source superintelligence accessible, with llama.cpp becoming ubiquitous and easier for casual users to deploy and access.


### [One-Shot Any Web App with Gradio's gr.HTML](https://yomu.fyi/post/one-shot-any-web-app-with-gradio-s-gr-html.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: yuvraj sharma, hysts, Freddy Boulton
- Published: Feb 18, 2026
- Type: Tutorial

The post presents Gradio 6’s gr.HTML as a way to build interactive web components in a single Python file, combining custom HTML templates, scoped CSS, and JavaScript interactivity without a separate build step. It describes applications including a Pomodoro timer, contribution heatmap, Kanban board, spinning wheel, and machine-learning viewers for detection, 3D camera control, and speech transcription. Within the component API, ${value} injects Python state into the template, props.value updates that state from JavaScript, and trigger('change') synchronizes it back to Python; CSS keyframes and native HTML5 drag events supply richer behavior. For reuse, a gr.HTML subclass packages the templates and script so the result can be used like built-in Gradio components, while reload mode and Spaces deployment support rapid iteration from generated code to a live app.


### [Building a Movie Recommendation Engine with Hugging Face and Voyage AI](https://yomu.fyi/post/building-a-movie-recommendation-engine-with-hugging-face-and-voyage-ai.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Arek Borucki
- Published: Feb 17, 2026
- Type: Tutorial

Traditional movie search mechanisms depend on coarse filters such as genre, actor, or title, which fail to capture emotional context and narrative nuances. This guide demonstrates building a mood-based semantic recommendation system by combining the voyage-4-nano open-weights embedding model, Hugging Face datasets, and MongoDB Atlas Vector Search. The architecture uses Sentence Transformers to generate embeddings and exposes an endpoint through FastAPI to match user moods against movie plots. Truncating the Matryoshka embeddings from 2048 to 1024 dimensions balances semantic retrieval quality, storage footprint, and query latency. Testing shows that while abstract emotional queries return moderate similarity scores around 0.62 to 0.67, concrete descriptive queries exceed 0.75 without requiring exact keyword matches.


### [Custom Kernels for All from Codex and Claude](https://yomu.fyi/post/custom-kernels-for-all-from-codex-and-claude.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, Sayak Paul, Aritra Roy Gosthipaty, shaun smith
- Published: Feb 13, 2026
- Type: Announcement

Writing CUDA kernels that integrate with diffusers and transformers involves GPU-specific optimization, library integration, PyTorch registration, and environment compatibility. It presents a cuda-kernels agent skill that packages architecture guides, integration patterns, kernel templates, troubleshooting material, benchmarks, and Kernel Hub examples for coding agents. Claude and Codex used it to generate RMSNorm, RoPE 3D, GEGLU, and AdaLN kernels for LTX-Video in diffusers, and an RMSNorm kernel for Qwen3-8B in transformers, including CUDA source, PyTorch bindings, build configuration, and benchmark scripts. On H100 80GB HBM3, the LTX-Video RMSNorm tests averaged 1.88x speedup and the optimized pipeline with torch.compile reached 1.43x the listed baseline, while Qwen3-8B RMSNorm averaged 1.94x and reached 2.47x at 8192 tokens. The skill supports packaging kernels for precompiled distribution through the Hub, where get\_kernel selects a matching binary across Python, PyTorch, and CUDA variants.


### [OpenEnv in Practice: Evaluating Tool-Using Agents in Real-World Environments](https://yomu.fyi/post/openenv-in-practice-evaluating-tool-using-agents-in-real-world-environ.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Christian Washington, Ankit Jasuja, Santosh Sah, Lewis Tunstall, ben burtenshaw
- Published: Feb 12, 2026
- Type: Explainer

OpenEnv is an open-source framework from Meta and Hugging Face for evaluating AI agents against real systems rather than simulations, with stateful environments that connect to tools and APIs. Turing's production-grade Calendar Gym applies this model to calendar operations, combining access control lists, partial visibility, temporal reasoning, and multi-step workflows in isolated sessions. Agents often handled individual actions but became less reliable on longer or ambiguous tasks: success was close to 90% with explicit calendar identifiers and roughly 40% when requests used natural-language descriptions. More than half of failures involved malformed arguments or incorrect action ordering despite selecting the correct tool, while structured validation, permission, and datetime errors provided concrete recovery signals.


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