---
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

### [IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license](https://yomu.fyi/post/ibm-releases-sota-granite-time-series-patchtst-fm-r2-model-with-commer.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Roman Vaculin, Wesley M. Gifford, Jiri Navratil, Chandra Reddy, Ayhan Sebin
- Published: Sep 9, 2026
- Type: Announcement

IBM has released Granite Time Series PatchTST-FM-r2, a roughly 385-million-parameter foundation model for zero-shot forecasting across demand, prices, energy loads, traffic, telemetry, and other time series. The model uses conformer blocks combining multi-head self-attention with temporal convolution, 50% overlapping patches with Hamming-window weighting, overlap-and-add forecasting, contexts up to 8,192 steps, and a 99-quantile prediction head for probabilistic outputs and imputation support. On GIFT-Eval, it ranks second for both CRPS and MASE among replicable zero-shot models as of September 8, 2026, and first in that category among permissively licensed models; including pretrained models, it ranks third for CRPS and fourth for MASE. IBM provides open weights, architecture, inference code, and reproducibility code under Apache 2.0 and OpenMDW 1.0. The release also describes Python use and streaming integration through Apache Flink on Confluent Cloud.


### [Use a local and open source code assistant](https://yomu.fyi/post/use-a-local-and-open-source-code-assistant.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Seth Kenlon
- Published: Sep 9, 2026
- Type: Tutorial

Closed or cloud-based coding assistants can expose work to external services, so the article presents a local, open-source alternative built around OpenCode. OpenCode is an AI coding agent—not an LLM—that runs as a terminal interface, desktop application, or IDE extension and connects to models through the Model Context Protocol (MCP). The setup installs OpenCode, selects a local model runtime such as Ollama for AMD/NVIDIA GPUs or OpenVINO Model Server with INT4 Qwen3-8B for Intel integrated graphics, then configures opencode.jsonc and an editor plug-in. Examples cover Qwen3:14b with Ollama, Podman-based OpenVINO serving, and NetBeans integration; the resulting assistant can answer queries and edit files after permission is granted, although local inference may be slow and generated advice can be unreliable.


### [Introducing Consort: Test-driven development on a branching database](https://yomu.fyi/post/introducing-consort-test-driven-development-on-a-branching-database.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kevin Hartman
- Published: Sep 9, 2026
- Type: Announcement

Consort is introduced as an open-source agentic development framework for test-driven development on branching Lakebase Postgres databases. It addresses the limitations of mocks, shared staging databases, and risky schema-change ceremonies by bringing integration testing into the inner loop. Its build lane runs red/green/refactor cycles against isolated copy-on-write branches of real data, while versioned migrations keep code and schema changes together and pull-request checks test migrations against fresh branches. Consort organizes work through role-based agents and a deterministic conductor, preserving feature artifacts in dedicated directories and supplying scoped context packages. A VS Code plugin pairs Git and Lakebase branches, while an observability dashboard exposes agent turns and human approval gates; the project is community-supported rather than a bundled platform feature with an SLA.


### [How we rebuilt Cloudflare Workers’ module registry for Node.js compatibility](https://yomu.fyi/post/how-we-rebuilt-cloudflare-workers-module-registry-for-node-js-compatib.md)
- Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md)
- Author: Logan Gatlin
- Published: Sep 9, 2026
- Type: Explainer

Cloudflare has rebuilt the module registry in workerd, the open-source core of the Workers runtime, to improve speed, standards compliance, and alignment with Node.js. The change addresses compatibility beyond runtime APIs by making module resolution, loading, and caching behave more like Node.js across ESM, CommonJS, and WebAssembly. The new registry uses URLs as specifiers, supports import.meta.url, import.meta.main, and import.meta.resolve(), distinguishes modules by query strings and fragments, validates import attributes, follows require(esm) rules, and compiles modules lazily while sharing caches across isolates. It also supports WebAssembly source phase imports and consistent loading errors; the existing registry remains in place, and the new implementation must be enabled with the new\_module\_registry compatibility flag.


### [The Open Source AI Stack](https://yomu.fyi/post/the-open-source-ai-stack.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Sep 9, 2026
- Type: Explainer

The post presents an open model AI stack for developers moving from closed-source models, focusing on ownership, control, and economics. It organizes the stack into independent layers: model, inference, gateways and routers, harness, and tools such as Skills and MCP. Models generate responses from prompts; large models generally handle ambiguity, multi-step reasoning, and long context better, while small models can be faster and cheaper for tightly specified work. The separation lets developers choose providers and models independently and experiment with newly released models without rebuilding their workflow, while the post uses Kimi K3 and GLM 5.3 Flash to illustrate differences in active parameters, capability, speed, and cost.


### [Give Your Coding Agents a Memory You Own](https://yomu.fyi/post/give-your-coding-agents-a-memory-you-own.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: David Corvoysier
- Published: Sep 3, 2026
- Type: Announcement

funes is presented as a durable memory layer for coding agents that turns existing session traces into searchable, provenance-backed working memory across machines and agent types. A single binary adds recall and get tools to Claude Code, Codex, pi, or Hermes, builds an incremental local index, and automatically indexes completed turns. Its deterministic pipeline normalizes traces, chunks them, embeds them with a pinned local model, stores them in Lance, and combines vector and BM25 search with rank fusion, cross-encoder reranking, recency weighting, and neighboring chunks. Memories can remain local or be bound to a private Hugging Face dataset owned by the user, with credential redaction, secret scanning, local caching, and Hub-provided access control and versioning. In the handoff-vs-recall benchmark, recall was the cheapest option on both tasks, costing 8x less than a written handoff on one and 4x less on the other, while compaction succeeded on only one task.


### [The Agentic Analytics Benchmark: Measuring model accuracy and efficiency in analytical agents](https://yomu.fyi/post/the-agentic-analytics-benchmark-measuring-model-accuracy-and-efficienc.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 1, 2026
- Type: Benchmark

The post presents data-agent-mnist, an open harness for measuring analytical agents on correctness, cost, and speed against a warehouse-shaped workload rather than a single text-to-SQL query. The authors extracted 201 production analytics questions from DWAINE, curated and anonymized them, reconstructed a deterministic synthetic warehouse with 18 tables and 865 columns, and evaluated 28 proprietary and open-weight models. The benchmark preserves schema discovery, multi-turn querying, planning, and multi-hop joins across curated marts and a dimensional CRM layer, with provider-diverse ground truth and judging described as part of the workflow. On the authors’ warehouse, Claude Fable 5.1 ranked first for correctness at 76.6%, while the full run cost $1 with DeepSeek V4 Flash versus $52 with Fable 5.1, an 11-percentage-point correctness difference. The harness is intended to identify the best production fit for a specific warehouse, not a universal winner.


### [How Uken Games reduces observability costs by 87% with ClickHouse](https://yomu.fyi/post/how-uken-games-reduces-observability-costs-by-87-with-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 1, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/deploy-an-open-model-from-checkpoint-to-inference-in-two-commands-with.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Aug 28, 2026
- Type: Explainer

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](https://yomu.fyi/post/building-hermetic-notebook-images-for-open-data-hub-and-red-hat-opensh.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Vath Sok
- Published: Aug 28, 2026
- Type: Problem & solution

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](https://yomu.fyi/post/the-open-asr-leaderboard-adds-its-first-global-south-language.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Eric Bezzam, Shobhit Banga, Manas Dhir, Bhaskar Singh, Manmeet Kaur, Aaditya Pareek, Walecha, Sagar Jain, Hanuman Sidh, Vanshika Chhabra
- Published: Aug 28, 2026
- Type: Announcement

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](https://yomu.fyi/post/differential-privacy-for-hugging-face-trainers-without-rewriting-your.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Katie Fraser
- Published: Aug 27, 2026
- Type: Announcement

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](https://yomu.fyi/post/anthropic-partners-with-codepath-to-bring-claude-to-the-us-s-largest-c.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Announcement

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](https://yomu.fyi/post/introducing-claude-corps.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Aug 27, 2026
- Type: Announcement

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](https://yomu.fyi/post/compose-multiplatform-1-12-0-released.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Elvira Mustafina
- Published: Aug 26, 2026
- Type: Announcement

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](https://yomu.fyi/post/how-ubuntu-is-using-rust-to-rebuild-core-system-tools.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Irina Mihajlovic
- Published: Aug 26, 2026
- Type: Explainer

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](https://yomu.fyi/post/how-ai-observability-works-with-mlflow.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Cedric Clyburn
- Published: Aug 26, 2026
- Type: Explainer

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](https://yomu.fyi/post/try-the-ansible-playbook-generation-lab-with-gemini-and-openai.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: clobner
- Published: Aug 26, 2026
- Type: Announcement

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](https://yomu.fyi/post/automating-red-hat-openshift-ai-installations-with-helm-and-gitops.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Davide Bianchi, Andrea Tarocchi
- Published: Aug 26, 2026
- Type: Tutorial

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](https://yomu.fyi/post/what-s-new-in-the-clickhouse-net-driver-the-road-from-1-0-to-1-3.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 26, 2026
- Type: Announcement

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.


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