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Open Source
561 posts about Open Source. Every summary links to the original.
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How to build scalable web apps with OpenAI's Privacy Filter
The article presents three web applications built around OpenAI’s open-source Privacy Filter, a PII detector that labels eight categories in one forward pass over a 128k-token context. Document Privacy Explorer extracts PDF or DOCX text and returns category-labeled spans, while Image Anonymizer combines Tesseract word boxes with character offsets to produce editable pixel redaction rectangles. SmartRedact Paste replaces detected spans with category placeholders, stores public and token-gated views, and evicts expired pastes in a daemon thread. Across the apps, gradio.Server’s @server.api routes model work through Gradio’s queue and gradio_client, while plain FastAPI routes serve custom HTML, views, and lightweight lookups. The implementations keep browser-side filtering, canvas edits, and PNG export out of server round-trips, and the paste service is about 200 lines including storage.
DeepSeek-V4: a million-token context that agents can actually use
DeepSeek V4 is presented as an open model family aimed at long-running agentic workloads, with Pro and Flash MoE checkpoints offering 1M-token context windows. Its central architecture alternates Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), compressing KV entries by 4x and 128x respectively, while using FP8/BF16 storage and FP4 indexing. The post-training design preserves reasoning across user turns when tool calls are present, introduces a dedicated XML tool-call schema, and relies on DSec, a Rust sandbox platform for RL rollouts. At 1M tokens, Pro uses 27% of V3.2's single-token FLOPs and 10% of its KV cache, while Flash uses 10% and 7%; V4-Pro-Max reports 80.6% on SWE Verified and 0.59 MRCR 8-needle retrieval at 1M.
Do you still need Elasticsearch for log analytics? ClickHouse says no.
ClickHouse introduced a redesigned full-text search powered by inverted indexes that feeds matching documents directly into its vectorized analytical engine for observability and log analytics workloads. To evaluate this capability, the open-source versions of ClickHouse and Elasticsearch were benchmarked on single-node AWS EC2 instances across synthetic OpenTelemetry log datasets scaling up to 50 billion rows. The benchmark evaluated nine representative query patterns combining text search with filtering, grouping, counting, and time-series aggregations under both cold and hot cache conditions. ClickHouse achieved a roughly fivefold smaller storage footprint on disk while running cold queries four to six times faster and hot queries approximately 1.7 to 2.6 times faster than Elasticsearch. Furthermore, ClickHouse ingested the 50-billion-row dataset in under four hours out of the box, whereas the tuned Elasticsearch setup required approximately five days.
QIMMA قِمّة ⛰: A Quality-First Arabic LLM Leaderboard
QIMMA is an Arabic LLM leaderboard built to address fragmented evaluation, translation artifacts, weak benchmark quality checks, limited coverage, and reproducibility gaps. It treats benchmark validation as a prerequisite for reported scores rather than an afterthought. The suite consolidates 109 subsets from 14 source benchmarks into more than 52,000 samples across seven domains, with 99% native Arabic content, Arabic-adapted HumanEval+ and MBPP+ coding tasks, and six prompt templates. Before model evaluation, Qwen3-235B-A22B-Instruct and DeepSeek-V3-671B assess samples using a 10-point rubric, flagged cases receive native-speaker review, and code statements are refined without changing task identifiers, reference solutions, or tests. In the April 2026 rankings, Qwen/Qwen3.5-397B-A17B-FP8 ranks first, while Arabic-specialized models lead cultural and linguistic tasks and multilingual models lead coding.
AI and the Future of Cybersecurity: Why Openness Matters
Mythos is presented as a frontier AI system that rapidly finds and patches software vulnerabilities, with the post arguing that its surrounding system matters more than the language model alone. Its recipe combines substantial compute, software-relevant training data, vulnerability-probing and patching scaffolding, speed, and some autonomy; together, these elements can uncover vulnerabilities, find exploits, and build patches. The post argues that open code and tooling can distribute detection, verification, coordination, and patch propagation across communities, while reducing capability asymmetry between attackers and defenders. For defensive use, it recommends semi-autonomous agents with prespecified actions, human approval, auditable logs, and private deployment using open components and existing security tools. This approach is especially relevant to high-stakes organizations that need inspectable, controllable systems and protection for sensitive data.
otel.fyi - OTel Collector docs made simple
OpenTelemetry Collector documentation is often fragmented across separate contrib repositories as individual README files, making component configuration difficult and time-consuming to navigate. To address this friction, the team created otel.fyi as a dedicated platform to aggregate, simplify, and structure documentation from across the ecosystem. The implementation parses contrib repositories to pull configuration examples and normalize metadata across receivers, processors, exporters, and extensions into a unified format. By surfacing structured component information upfront, the site allows practitioners to assemble working configurations rapidly without digging through multiple GitHub repositories. Originally developed for internal operational workflows, the platform has been released publicly to assist the broader community, with future plans to introduce an AI assistant.
April 2026 newsletter
ClickHouse 26.3 LTS introduces experimental materialized common table expressions, WebAssembly user-defined functions, and enables asynchronous inserts by default. Alongside the release, Alasdair Brown launched clickhousectl to manage local installations and cloud deployments with built-in agentic development features and authentication safeguards. User case studies show Goldsky accelerating historical blockchain backfills by twelve times through direct ClickHouse reads using Apache Arrow, replacing their prior Kafka and Avro architecture. Additionally, the engineering team redesigned the full-text search index with front-coding compression, a sparse lookup index, and adaptive posting lists to handle expensive random reads on object storage. ClickHouse also previewed community architecture patterns for lightweight SIEM setups using Clickdetect and Wazuh log collection.
Ecom-RLVE: Adaptive Verifiable Environments for E-Commerce Conversational Agents
Ecom-RLVE extends the RLVE framework from single-turn reasoning puzzles to multi-turn, tool-augmented e-commerce conversations. EcomRLVE-GYM supplies eight environments spanning product discovery, substitution, cart building, returns, order tracking, policy QA, bundle planning, and multi-intent journeys, with procedural problem generation, a 12-axis difficulty curriculum, and algorithmically verifiable rewards. Each episode requires tool use and world-state changes; rewards combine task completion, efficiency, and a penalty for recommending product IDs not retrieved, while malformed outputs and illegal calls fail immediately. Adaptive scheduling advances each environment independently when its success rate is reliable. An initial study trained Qwen 3 8B with DAPO for 300 steps on cart building and reported progressively increasing difficulty reached, consistent with a steady learning signal.
The PR you would have opened yourself
mlx-lm contributors can use a Skill and a separate test harness to port language models from transformers, with the goal of making new architectures available shortly after their upstream implementations land. The work addresses rising agent-generated pull-request volume and the difficulty of preserving implicit codebase conventions, user contracts, and performance-sensitive behavior that agents may not understand. Given a conversion prompt, the Skill sets up environments, finds model variants and checkpoints, reads transformers modeling code, writes an MLX implementation, and iterates through tests including dtype checks, generation examples, numerical comparisons, and per-layer comparisons. The resulting pull requests disclose agent assistance and include detailed verification data, while contributors remain responsible for reviewing and accepting the results. Current limitations include shared-utility refactors, vision-language models, quantized uploads, and thinking-specific tests.
Getting started with clickhousectl: the ClickHouse CLI for local and cloud
ClickHouse developers often face friction managing local versions, isolated instances, project scaffolding, and Cloud operations without relying on complex shell scripts. The open-source command-line tool clickhousectl addresses this by unifying local environment management and ClickHouse Cloud workflows. Locally, it downloads shared binaries, initializes standardized directory structures, and spins up named, isolated servers with automatic port assignments to facilitate upgrade testing. For cloud workflows, the CLI enforces a split authentication model where browser OAuth provides read-only access and API keys permit state mutations like service creation and scaling. Furthermore, the tool includes Claude Code agent skills covering best practices and deployment workflows, alongside support for CI/CD pipeline automation via environment variables and JSON output.
Autoresearch isn’t just for training models
Repeated visual-regression failures made Polaris CI feedback cycles take about 30 minutes, prompting an attempt to optimize build time rather than fix individual tasks. The approach uses Pi to create an Autoresearch extension that repeatedly forms a hypothesis, measures the build against a 19.1-second baseline, and keeps only changes that improve the metric, discarding crashes and slower runs. The loop identified wasted VRT work: the full component pipeline ran before Storybook recompilation, while TypeScript transformed all 580 component files although 105 needed it. Removing those inefficiencies made the build 65% faster, although the system also produced unacceptable hacks that were discarded. The extension later gained multi-metric support, consistent iteration scripts, auto commits, and open-source adoption, with reported internal improvements including unit tests running 300 times faster and Playwright tests becoming faster.
Index-based pruning in ClickHouse
ClickHouse accelerates analytical queries by minimizing the amount of data read from disk across granules. A primary key determines data part sort order and records the first value per granule, enabling binary search to skip unneeded granules during filtered queries. For non-primary key filters, lightweight projections act like secondary indexes by storing alternative sort keys alongside base table offset pointers without duplicating full table rows. Skip indexes, such as minmax indexes, record column boundaries per granule and operate without disk duplication, though effective pruning requires some correlation with the primary key. Demonstrations using a UK property price dataset show how primary key filters and minmax skip indexes eliminate large fractions of total granules.
What's new in ClickStack - March 2026
ClickStack announced several observability updates in its March 2026 release to improve root cause analysis, dashboard organization, and local development. The Event Deltas tool now displays baseline span distributions immediately, uses deterministic cityHash64 sampling for trace heatmaps, and applies proportional comparison scoring to accurately evaluate attributes across uneven sample sizes. Furthermore, users can directly include or exclude values from attribute comparison bars to streamline query creation during incident investigations. Dashboard capabilities expanded with a curated OpenTelemetry template gallery, multi-value filtering with optional lookup WHERE clauses, and broadened metric display units. Finally, local mode introduces browser localStorage persistence through createEntityStore, allowing developers to save dashboards and favorites without a running backend API.
Agentic coding with ClickHouse. One person, one data stack, one full-stack application
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
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
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)
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.
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
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
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.
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