---
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

### [Agentic coding with ClickHouse. One person, one data stack, one full-stack application](https://yomu.fyi/post/agentic-coding-with-clickhouse-one-person-one-data-stack-one-full-stac.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Oussama Chakri
- Published: Apr 14, 2026
- Type: Problem & solution

A Solutions Architect at ClickHouse built ClickShop, a full-stack retail analytics platform, within a few days using Cursor to demonstrate production-level capabilities beyond static slides. The platform pairs ClickHouse for sub-second analytical queries across billions of rows with PostgreSQL for transactional workflows, using ClickPipes for real-time change data capture synchronization. LibreChat orchestrates 18 persona-tailored AI agents, while dual observability layers—ClickStack for OpenTelemetry infrastructure metrics and Langfuse for LLM prompt and cost tracking—store their telemetry directly in ClickHouse. Developing with an AI-assisted IDE was accelerated by open-source documentation and community skills, though it required continuous human review to handle unintended code refactoring and token consumption.


### [We're Transferring the Stripe Sync Engine to Stripe](https://yomu.fyi/post/we-re-transferring-the-stripe-sync-engine-to-stripe.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: raminder\_singh, matt\_linkous, gregor\_vand, ana\_mogul
- Published: Apr 14, 2026
- Type: Announcement

The Stripe Sync Engine repository is moving from supabase/stripe-sync-engine to stripe/sync-engine after growing from an open-source tool for syncing Stripe data into a Stripe schema in Postgres. The transfer follows collaboration that added cursor-based incremental sync, JSONB storage with generated columns, multi-account support for Stripe Connect platforms, event ordering in a new CLI, and automatic incremental backfilling using Supabase Cron and Queue. The latest release adds coupon synchronization, branching support, immediate syncing on installation, one-click upgrades, SSL enforcement, and tighter installation controls, along with clearer errors and dashboard visibility for Edge Function source code. The project remains open source under Apache 2.0, existing installations continue to work, and the Supabase dashboard integration is unchanged. With Stripe data in Postgres, users can join it with application data for subscription checks, feature gating, MRR, and churn analysis.


### [Using AI agents to build Stripe-Salesforce integrations faster](https://yomu.fyi/post/using-ai-agents-to-build-stripe-salesforce-integrations-faster.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Vishnu Samavedula
- Published: Apr 13, 2026
- Type: Problem & solution

An experiment using Claude Code to build a Stripe App for Salesforce Flow exposed context gaps: the generated integration misnamed fields, targeted a nonexistent custom field, and omitted AsyncAfterCommit, causing a “Callout from triggers not supported” deployment error. The proposed remedy is a repository of grounding context files that present invocable signatures, request models, platform constraints, LWC patterns, and validated examples in concise, machine-readable form. Core files are about 35K tokens, while the full reference catalog is about 230K tokens and can be loaded on demand. With the context loaded, the agent selected the v02\_CreateCustomers invocable directly and generated a helper Apex class for nested Checkout Session inputs, along with callout-safe Flow structure and other safeguards. The author reports minutes instead of hours and fewer iterations in personal usage, while noting that org-specific details still require human verification.


### [Introducing clickhousectl: the CLI for ClickHouse local and cloud (beta)](https://yomu.fyi/post/introducing-clickhousectl-the-cli-for-clickhouse-local-and-cloud-beta.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Apr 9, 2026
- Type: Announcement

ClickHouse released the beta version of clickhousectl, an official command-line interface designed to unify local operations and cloud infrastructure management for both humans and AI agents. The tool acts as a version manager that stores reusable binaries globally in ~/.clickhouse/versions/ while offering optional project directory scaffolding for SQL files. Developers and agents can run multiple isolated local server instances concurrently with automatic free-port detection and dedicated data directories. For ClickHouse Cloud, the CLI provides complete management coverage with structured JSON output, using read-only OAuth or scoped API key authentication to safeguard modifying operations. Additionally, the tool can install ClickHouse Agent Skills directly into supported coding agents to facilitate automated local and cloud interactions.


### [AI Agents Know About Supabase. They Don't Always Use It Right.](https://yomu.fyi/post/ai-agents-know-about-supabase-they-don-t-always-use-it-right.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: pedro\_rodrigues
- Published: Apr 9, 2026
- Type: Announcement

Supabase is releasing Supabase Agent Skills, an open-source instruction set for AI coding agents working with Supabase through Claude Code, Codex, GitHub Copilot, Cursor, and compatible Agent Skills implementations. The skill addresses recurring errors such as omitted RLS policies, hallucinated CLI commands, views missing security\_invoker = true, and reliance on outdated training data by requiring documentation checks and embedding critical security guidance in SKILL.md. It also guides CLI and MCP workflows and recommends direct schema changes on local or staging databases, followed by database advisors and a migration after the schema stabilizes. Early evaluations across six scenarios found MCP + Skill scores of 67% for Claude Opus 4.6, 71% for Claude Sonnet 4.6, 88% for Codex GPT-5.4, and 71% for GPT-5.4 Mini, though the sample was small.


### [ClickHouse at FOSDEM 2026](https://yomu.fyi/post/clickhouse-at-fosdem-2026.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tyler Hannan
- Published: Apr 8, 2026
- Type: Announcement

The ClickHouse community and development team participated in FOSDEM 2026 in Brussels, hosting a community dinner and delivering technical presentations across multiple developer tracks. Presentations covered a range of engineering topics, including building geospatial analytical applications with Leaflet and ClickHouse, as well as integrating LLVM XRay to dynamically hot-patch production logging without redeployments. Additional ClickHouse sessions addressed the engineering challenges of integrating Rust libraries into large C++ codebases, optimizing low-level memcpy routines, and implementing specialized inverted indexes for large-scale text processing. Contributed community sessions detailed workflows for upstream patches in MariaDB and Postgres, alongside WebShield, an open-source Python daemon that streams web accelerator access logs into ClickHouse for bot analysis.


### [Safetensors is Joining the PyTorch Foundation](https://yomu.fyi/post/safetensors-is-joining-the-pytorch-foundation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Luc Georges, Lysandre
- Published: Apr 8, 2026
- Type: Announcement

Safetensors has joined the PyTorch Foundation as a foundation-hosted project under the Linux Foundation, alongside DeepSpeed, Helion, Ray, vLLM, and PyTorch. It began as a Hugging Face project addressing the risk of arbitrary code execution in pickle-based model-weight formats. Its format stores tensor metadata in a JSON header capped at 100MB, followed by raw tensor data, and supports zero-copy mapping and lazy loading. The move places the trademark, repository, and governance with the Linux Foundation, while Luc and Daniel remain on the Technical Steering Committee and lead day-to-day work. Existing users get the same format, APIs, and Hub integration without breaking changes, while contributors gain a documented maintainer path; planned work includes PyTorch core serialization, accelerator-aware loading, parallel loading, and quantization support.


### [ClickHouse Release 26.3](https://yomu.fyi/post/clickhouse-release-26-3.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 7, 2026
- Type: Announcement

ClickHouse 26.3 delivers twenty-seven new features, forty performance optimizations, and over two hundred bug fixes. Common table expressions can now be materialized into temporary tables via the MATERIALIZED clause when enabled, evaluating subqueries once to reduce query execution times and peak memory usage. The query optimizer expands join reordering beyond inner and left or right joins to include anti, semi, and full joins when table statistics are available. In addition, user-defined functions can run compiled WebAssembly code inside sandboxed Wasmtime runtimes. Storage for the Map data type also introduces hash-based bucketing during background merges, accelerating single-key lookups by two to forty-nine times without slowing initial inserts.


### [100,000 GitHub stars](https://yomu.fyi/post/100-000-github-stars.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: paul\_copplestone
- Published: Apr 2, 2026
- Type: Announcement

Supabase reports reaching 100,000 GitHub stars and says eight million developers are building with the platform. Founded in 2020 by two people around a Postgres database, the project says it optimized for developers having a good time rather than for star counts, treating the milestone as evidence of a community that contributed, stayed involved, and built unexpected projects. Its open-source approach centers on supporting existing tools and communities: projects run unmodified Postgres, PostgREST handles billions of API requests daily, pgvector stores embeddings, Deno underpins Edge Functions, imgproxy handles image transformations, and Phoenix supports Realtime at almost 1.5 billion messages per day. The team says it aims to help make Postgres the default database for new internet projects, including by pursuing petabyte scale and pluggable storage.


### [Welcome Gemma 4: Frontier multimodal intelligence on device](https://yomu.fyi/post/welcome-gemma-4-frontier-multimodal-intelligence-on-device.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: merve, Pedro Cuenca, Sergio Paniego, ben burtenshaw, Steven Zheng, Alvaro Bartolome, Nathan Habib
- Published: Apr 2, 2026
- Type: Announcement

Google DeepMind’s Gemma 4 family is available on Hugging Face as Apache 2-licensed, multimodal models intended for use across agents, inference engines, fine-tuning libraries, and on-device deployments. The family accepts image and text inputs across five sizes, while E2B, E4B, and 12B Unified also support audio; context windows range from 128k to 256k tokens. Its design combines alternating local and global attention, dual RoPE configurations, Per-Layer Embeddings, shared KV caches, and model-specific vision and audio handling, including an encoder-free unified 12B variant. The post documents integrations for Transformers, llama.cpp, MLX, WebGPU, Rust, and other tools, alongside fine-tuning examples and demos. Reported instruction-tuned results include a 1452 estimated text-only LMArena score for 31B and benchmark results spanning reasoning, coding, vision, audio, and long-context tasks.


### [PostgresBench: A Reproducible Benchmark for Postgres Services](https://yomu.fyi/post/postgresbench-a-reproducible-benchmark-for-postgres-services.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Lionel Palacin
- Published: Apr 2, 2026
- Type: Benchmark

PostgresBench is a public, reproducible benchmarking suite created to compare transactional performance across managed PostgreSQL providers. The benchmark uses the standard pgbench tool with a TPC-B-like write-intensive workload executing across 256 clients and 16 threads for ten-minute durations. Evaluators tested five services—including Postgres managed by ClickHouse, AWS Aurora, AWS RDS, Neon, and Crunchy Bridge—under 100 GB and 500 GB scale factors using default configurations in the us-east-2 region. Across tested configurations, Postgres managed by ClickHouse delivered higher transactions per second and lower P99 latencies due to physically co-located NVMe primary storage. These results demonstrate that remote network storage layers, such as EBS volumes, introduce significant latency overhead during sustained write and fsync operations.


### [Falcon Perception](https://yomu.fyi/post/falcon-perception.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: FalconPerception
- Published: Apr 1, 2026
- Type: Announcement

Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural-language prompts. It processes image patches, text, and task tokens in one sequence, using hybrid attention so image tokens receive bidirectional context while text and task tokens decode causally. Its Chain-of-Perception interface predicts each instance as coordinate, size, and segmentation tokens, with specialized Fourier-feature and dot-product heads for dense outputs. The post also introduces PBench, which profiles attributes, OCR-guided identification, spatial understanding, relations, and crowded scenes. Falcon Perception reaches 68.0 Macro-F1 on SA-Co versus 62.3 for SAM 3, while presence calibration remains weaker at MCC 0.64 versus 0.82; the 0.3B Falcon OCR model scores 80.3 on olmOCR and 88.6 on OmniDocBench.


### [Supabase docs over SSH](https://yomu.fyi/post/supabase-docs-over-ssh.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregnr
- Published: Apr 1, 2026
- Type: Announcement

Supabase is launching supabase.sh, a free public SSH server that gives AI coding agents bash access to Supabase documentation as a public experiment. Its virtual file system exposes every documentation page as a Markdown file under /supabase/docs, allowing agents to use familiar commands such as grep, find, ls, cat, head, and tail to browse, search, and skim current content. The service runs on just-bash, Vercel’s open-source Bash emulator written in TypeScript, inside a sandboxed JavaScript runtime over a virtual file system, so it does not execute an actual shell on the server. Project instructions can be added with ssh supabase.sh agents >> AGENTS.md or generated through ssh supabase.sh setup | claude; the post contrasts this exploratory workflow with the existing search\_docs MCP tool and presents supabase.sh as an early experiment.


### [From Custom to Open: Scalable Network Probing and HTTP/3 Readiness with Prometheus](https://yomu.fyi/post/from-custom-to-open-scalable-network-probing-and-http-3-readiness-with.md)
- Company: [Slack](https://yomu.fyi/company/slack.md)
- Author: Carlo Preciado
- Published: Mar 31, 2026
- Type: Problem & solution

Slack encountered a lack of client-side observability when introducing HTTP/3 at its edge. Because HTTP/3 runs over QUIC on UDP instead of TCP, neither existing commercial SaaS tools nor the internal Prometheus Blackbox Exporter could probe the new endpoints. To address this gap, an engineering intern integrated the open-source quic-go library into Prometheus Blackbox Exporter to build a native, configurable HTTP/3 probe. Slack deployed an internal implementation using the upstream functionality while waiting for pull request reviews, ultimately landing the contribution upstream. The resulting setup provides a unified view of HTTP/1.1, HTTP/2, and HTTP/3 metrics within Grafana, enabling reliable alerting and faster correlation with other telemetry.


### [Granite 4.0 3B Vision: Compact Multimodal Intelligence for Enterprise Documents](https://yomu.fyi/post/granite-4-0-3b-vision-compact-multimodal-intelligence-for-enterprise-d.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Madison Lee, Rogerio Feris, Eli Schwartz, Dhiraj Joshi, Pengyuan Li, Isaac Sanchez
- Published: Mar 31, 2026
- Type: Announcement

Granite 4.0 3B Vision is a compact vision-language model designed to extract information from enterprise documents, including tables, charts, forms, and semantically meaningful key-value pairs. It combines a million-scale ChartNet dataset with a code-guided synthesis pipeline, DeepStack Injection for routing semantic and high-resolution visual features to different layers, and a LoRA adapter architecture built on Granite 4.0 Micro. The modular design supports standalone image processing, text-only fallback, and integration with Docling for multi-page document pipelines. Reported evaluations show 86.4% on Chart2Summary, leading the evaluated models, second place on Chart2CSV at 62.1%, leading scores on the listed table benchmarks, and 85.5% zero-shot exact-match accuracy on VAREX.


### [Training mRNA Language Models Across 25 Species for $165](https://yomu.fyi/post/training-mrna-language-models-across-25-species-for-165.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maziyar Panahi
- Published: Mar 31, 2026
- Type: Benchmark

OpenMed describes an end-to-end protein engineering pipeline that connects ESMFold structure prediction, ProteinMPNN sequence design, and custom mRNA codon optimization. The codon-language-model study compares CodonBERT, ModernBERT, and several CodonRoBERTa variants using 250,000 E. coli coding sequences, a 69-token codon vocabulary, masked-language modeling, and training on four 80GB A100 GPUs. CodonRoBERTa-large-v2 achieved perplexity 4.10, CAI Spearman correlation 0.404, and 7.7% synonymous recovery, while ModernBERT-base reached perplexity 26.24 and correlation 0.070. The work then trained a multi-species suite on 381,283 sequences spanning 25 species, producing four production models in 55 GPU-hours and documenting planned extensions such as larger training data and additional prediction heads.


### [Liberate your OpenClaw](https://yomu.fyi/post/liberate-your-openclaw.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Clem 🤗, ben burtenshaw, Pedro Cuenca, Jeff Boudier, merve, Niels Rogge, Victor Mustar, Mishig Davaadorj
- Published: Mar 27, 2026
- Type: Tutorial

Anthropic is limiting access to Claude models in open agent platforms for Pro/Max subscribers, so the post describes ways to keep OpenClaw, Pi, or Open Code agents running with open models from Hugging Face. It presents Hugging Face Inference Providers as the quickest hosted route: users create a token, run openclaw onboard --auth-choice huggingface-api-key, and select a model such as GLM-5, or set a repo\_id in OpenClaw’s config. For fully local inference, it recommends installing llama.cpp, starting a server with Qwen3.5-35B-A3B-GGUF, and connecting OpenClaw to the local OpenAI-compatible endpoint with custom configuration; the example model is described as suitable for 32GB of RAM. The hosted option prioritizes speed and model availability, while local inference offers privacy, zero API costs, full control, and no rate limits.


### [How we’re building a data platform for a new user: agents](https://yomu.fyi/post/how-we-re-building-a-data-platform-for-a-new-user-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 27, 2026
- Type: Explainer

AI agents are emerging as autonomous database users that generate SQL, execute queries, and iterate in seconds. Because traditional analytics workflows built for human analysts cannot keep up with these rapid loops, ClickHouse is adapting its platform for agentic workloads. The proposed Agentic Data Stack combines LibreChat for conversational interfaces, ClickHouse with Model Context Protocol servers for the data layer, and Langfuse for observability. Supporting developer tooling includes chDB 4 with a Pandas-like API, IDE integrations, and ClickHouse Agent Skills to enforce database design best practices. Platforms like Anthropic and Character.AI already leverage ClickHouse's high-concurrency aggregation and real-time ingestion capabilities to handle massive data scales required by modern AI applications.


### [Top 10 best practices tips for ClickHouse](https://yomu.fyi/post/top-10-best-practices-tips-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Yonatan Dolan
- Published: Mar 26, 2026
- Type: Explainer

ClickHouse delivers rapid analytical queries across massive datasets, but suboptimal schemas and queries can significantly reduce performance and storage efficiency. Aligning table definitions with ClickHouse storage and execution mechanics resolves these bottlenecks. Key practices include placing low-cardinality columns first in the ORDER BY clause to enable granule pruning, and selecting optimal data types while avoiding Nullable wrappers to cut memory overhead. Developers should treat partitioning as a data management tool for TTL drops or merge engines rather than a generic speed booster, preventing fragmented parts from slowing query execution. Recent improvements like the Analyzer query planner have also made joins viable, though dictionaries remain faster for static lookups.


### [ClickHouse is data lake ready](https://yomu.fyi/post/clickhouse-is-data-lake-ready.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Karolina Ruiz Rogelj, Melvyn Peignon
- Published: Mar 25, 2026
- Type: Announcement

ClickHouse announced expanded data lake capabilities, enabling direct querying, accelerated ingestion into MergeTree, and writes across Apache Iceberg and Delta Lake tables. Open lake architectures provide storage portability across cloud providers and catalogs, but standard query engines often struggle with latency for real-time workloads. Over two years of development, ClickHouse engineered a native Parquet reader with page-level parallelism, integrated the Delta Rust Kernel, and introduced support for schema evolution, time travel, and DML operations. The platform functions across AWS Glue, Unity Catalog, Polaris, and Iceberg REST Catalog on storage systems including Amazon S3, Google Cloud Storage, and Azure Blob Storage. These enhancements allow organizations to execute federated, high-concurrency SQL queries across multiple catalogs or write processed analytical results back to their lakes without vendor lock-in.


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