---
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

### [Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit](https://yomu.fyi/post/accelerating-end-to-end-co-folding-performance-with-nvidia-bionemo-age.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 10, 2026
- Type: Problem & solution

Biomolecular structure prediction and co-folding pipelines encounter latency and memory ceilings during sequence alignment and multi-residue inference. NVIDIA addresses these constraints through the BioNeMo Agent Toolkit, which connects AI agents to accelerated biological computing components. The pipeline offloads multiple sequence alignment to MMseqs2-GPU, while cuEquivariance and the OpenFold3 NIM accelerate core geometric attention kernels to cut inference latency up to fourfold. To handle large molecular assemblies beyond single-GPU memory limits, the Fold-CP framework distributes context across multiple GPUs with O(N²/P) memory scaling. Together, these tools enable the prediction of complexes up to 32,000 tokens on 64 NVIDIA B300 GPUs, making large-scale virtual screening and ribosome-scale modeling computationally feasible.


### [How Trigger.dev is using ClickHouse to scale observability for long-running AI workflows](https://yomu.fyi/post/how-trigger-dev-is-using-clickhouse-to-scale-observability-for-long-ru.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 9, 2026
- Type: Problem & solution

Trigger.dev, an open-source platform for serverless asynchronous tasks, needed to scale observability for increasingly complex, long-running AI workflows spanning thousands of tenants. Its initial Postgres design stored logs, traces, and events in a growing table, making indexes, analytics, and operational reliability increasingly difficult to manage. The team moved observability workloads to ClickHouse after evaluating performance, scalability, open-source compatibility, deployment flexibility, and support for dynamic telemetry, including its native JSON data type. Migration occurred gradually at the task-execution level, routing each run’s logs to either database without downtime or customer intervention. ClickHouse now supports real-time telemetry analysis, approximately 200-millisecond P95 latency for queries returning tens of thousands of rows, and a dramatically smaller telemetry storage footprint.


### [Capturing token IDs during agentic interactions for better reinforcement learning](https://yomu.fyi/post/capturing-token-ids-during-agentic-interactions-for-better-reinforceme.md)
- Company: amazon.science
- Author: Frederick Robinson
- Published: Jul 9, 2026
- Type: Announcement

Reinforcement learning for agentic tasks requires updating model parameters against the precise token sequences generated during rollouts. Standard agent harnesses frequently modify conversation transcripts through message compaction and tool formatting, causing retokenization drift that degrades policy-gradient training. Turnstile addresses this issue as a Rust-based proxy situated between agent harnesses and inference backends such as SGLang. By intercepting OpenAI-compatible Chat Completions traffic, the proxy records exact token IDs, log probabilities, loss masks, weight versions, and mixture-of-experts routing traces at the moment of generation. Validation across text-based coding and multimodal computer-use agents demonstrated steady reinforcement learning improvements without requiring modifications to agent harnesses.


### [Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance](https://yomu.fyi/post/create-a-langchain-deep-agents-harness-profile-for-nvidia-nemotron-3-u.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Sean Lopp
- Published: Jul 8, 2026
- Type: Tutorial

The tutorial shows how to create and validate a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra using an evaluation benchmark rather than fine-tuning the model. It starts with a baseline, analyzes failed trajectories, and iterates on prompts, exclusions, or added middleware while rerunning tests to detect regressions. One failure occurred when read\_file returned the first page of a large file without signaling that more content existed, so the model answered before paginating to the line containing opal-fox-91. The proposed ReadFileContinuationNoticeMiddleware appends a continuation notice when a full page is returned, raising the reported benchmark score from 94/127 to 96/127 and resolving all three read\_file tests. The source also describes automating this propose, verify, and retain-or-reject loop, with safeguards against overfitting and regressions.


### [Running Low-Latency Analytical Workloads with GPU-Accelerated Presto on NVIDIA GB200 NVL72](https://yomu.fyi/post/running-low-latency-analytical-workloads-with-gpu-accelerated-presto-o.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Jul 8, 2026
- Type: Benchmark

GPU-accelerated Presto integrates NVIDIA cuDF algorithms and NVLink to deliver low-latency analytical query execution on massive datasets. Benchmarks derived from TPC-H across scale factors 1K and 3K reveal that a single NVIDIA DGX B200 system with eight GPUs outperforms 8- to 10-node Intel Xeon clusters by up to 8.2x. Scaling out across multinode NVIDIA GB200 NVL72 systems paired with IBM Storage Scale achieved additional performance gains. Enabling NVIDIA GPUDirect Storage, tuning I/O parameters, adjusting thread counts, and applying query rebatching delivered a cumulative 64% reduction in query runtimes. Topology-aware GPUDirect Storage cold reads proved roughly twice as fast as standard POSIX reads by avoiding CPU memory staging and NUMA boundary crossing penalties.


### [Open, convenient and predictable: Introducing Provisioned Throughput](https://yomu.fyi/post/open-convenient-and-predictable-introducing-provisioned-throughput.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jul 8, 2026
- Type: Announcement

Companies adopting open-weight models for production workloads have faced a trade-off between convenient but best-effort serverless inference and powerful but complex dedicated GPU instances. Together AI’s Provisioned Throughput fills that gap with reserved inference capacity sold as Provisioned Throughput Units (PTUs). Each PTU is priced at $0.05 per minute and guarantees a fixed rate of tokens per minute for a specific model, with a 99% uptime SLA. Input, cached input, and output tokens draw down capacity at different rates, letting teams optimize for their traffic shape. Available now for MiniMax M3 and GLM-5.2 with capacity in North America and EMEA and a one-month minimum term, at full utilization costs can run roughly $0.36 per million input tokens and $2.16 per million output tokens, up to 90% below proprietary alternatives.


### [Contextual Policies in Omnigent: Using session state to better govern AI agents](https://yomu.fyi/post/contextual-policies-in-omnigent-using-session-state-to-better-govern-a.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Matei Zaharia, David Nasi, Xiangrui Meng, Kecheng Cao, Tomu Hirata
- Published: Jul 7, 2026
- Type: Explainer

Omnigent, an open-source meta-harness for AI agents, introduces contextual policies to make agent controls safer and less disruptive than per-action allow, deny, or approval rules. Policies receive session events, maintain private state such as tools used, documents read, accumulated risk, initial intent, and model spend, then allow, deny, transform, or escalate the next action. Omnigent’s server intercepts tool calls from supported agents and applies these policies consistently, while examples include Google Drive restrictions based on documents created or marked confidential, risk thresholds that require approval for later email or file-sharing actions, and budget thresholds that pause or redirect work to a cheaper model. Intent-based authorization limits tools according to the user’s opening request, applying least privilege across supported harnesses and custom agents. The project is described as open source and alpha, with the server providing one interception layer for agents using different harnesses.


### [AI needs the best-of-breed data stack: Postgres and ClickHouse](https://yomu.fyi/post/ai-needs-the-best-of-breed-data-stack-postgres-and-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jul 7, 2026
- Type: Explainer

AI-native applications generate large volumes of operational data, while agents and users increasingly require real-time answers from that data. This pressure raises both query concurrency and reliability requirements, with a cited sample of companies using Postgres and ClickHouse showing more than 1,000% data growth in six months and over 85 TB added. The proposed stack assigns Postgres low-latency transactional workloads and ClickHouse real-time analytics, connecting them through PeerDB and ClickPipes CDC with single-digit-second replication, pg\_clickhouse query pushdown, and pg\_stat\_ch telemetry streaming. The conclusion is that AI makes the OLTP/OLAP distinction more important, favoring an open best-of-breed stack over a single platform while preserving each database's specialized role.


### [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T](https://yomu.fyi/post/develop-humanoid-robot-policies-end-to-end-with-nvidia-isaac-gr00t.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Elizabeth Goodman
- Published: Jul 7, 2026
- Type: Announcement

Humanoid robotics development suffers from fragmented tooling, siloed software ecosystems, and incompatible data formats that hinder reproducible workflows. NVIDIA introduced the Isaac GR00T Development Platform, an open-source, modular humanoid development environment that connects simulation setup, demonstration capture, policy training, evaluation, and hardware deployment. Central to this platform is the Isaac GR00T 1.7 vision-language-action model, which features a 3-billion-parameter base checkpoint licensed under Apache 2.0. The architecture adopts a Cosmos-Reason2-2B backbone and supports full pipeline export to ONNX and TensorRT for deployment on Jetson Thor. In a reference pick-and-place manipulation task, developers capture teleoperated demonstrations with Isaac Teleop, convert trajectories into LeRobot format, post-train GR00T 1.7, and evaluate the closed-loop policy inside Isaac Lab-Arena.


### [Introducing uFowarder: The Consumer Proxy for Kafka Async Queuing](https://yomu.fyi/post/introducing-ufowarder-the-consumer-proxy-for-kafka-async-queuing.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jul 2, 2026
- Type: Explainer

Uber introduces uForwarder, an open-source Consumer Proxy for Kafka Async Queue workloads, after deploying a push-based system across more than 1,000 consumer services. The proxy fetches messages through Kafka’s binary protocol, sends them individually to consumer services over gRPC, collects status codes and processing results, and commits offsets when safe. Production challenges included head-of-line blocking from messages that fail before reaching handlers, growing hardware costs, external data sources that are not ready when events arrive, and requirements for production/non-production and zone isolation. The implemented solutions include context-aware routing through message and gRPC headers, active blocking detection and mitigation, hardware-efficiency improvements, and DelayProcessManager, which pauses only delayed partitions and guarantees at-least delay rather than exact timing. The project is open source, with planned work on consumer-lag handling and direct Protobuf support.


### [Announcing our $800M Series C to accelerate the shift to open-source AI](https://yomu.fyi/post/announcing-our-800m-series-c-to-accelerate-the-shift-to-open-source-ai.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Jul 1, 2026
- Type: Announcement

Together AI announces an $800M Series C to accelerate its open-source AI platform as production demand for inference grows. The company says the round includes $800 million from investors including Aramco Ventures, NVIDIA, Vista Equity, General Catalyst, and others, plus commitments for more than 500 MW of compute capacity to be capitalized independently by new investors. Together argues that closed frontier-model economics become unsustainable as agents and workflows scale, while open-weights models such as DeepSeek, Nemotron, MiniMax, Kimi, and GLM can provide equal or better performance at lower cost. Its full-stack approach spans models, kernels, compilers, inference systems, training infrastructure, hardware utilization, and software, with cited work including FlashAttention-4, Together Megakernel, together.compile, and expanded post-training APIs for tool calling, reasoning, and vision-language models.


### [ML Development in VS Code with Google Cloud Power: Workbench Extension Now Available](https://yomu.fyi/post/ml-development-in-vs-code-with-google-cloud-power-workbench-extension.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Andrii Lobanov, Alex Kallaur, Diego Granados
- Published: Jul 1, 2026
- Type: Announcement

Google Cloud has launched the Google Cloud Workbench Notebooks extension for VS Code, allowing developers to use managed cloud notebook environments from a local IDE. Workbench, described as a managed Jupyter platform optimized for data science, can provide access to Google Cloud's AI-optimized infrastructure and high-performance compute when local machines need more capacity. The workflow connects through VS Code's Extensions view, the Jupyter extension, and a notebook's Select Kernel menu, where users choose Google Cloud, Workbench, authenticate, select a project, and choose an active instance. The extension runs notebooks on Workbench instances, preserves local settings and extensions, is fully open-sourced, and is available through the VS Code Marketplace, with GitHub contributions invited.


### [Designing GPU-Accelerated Query Engines with NVIDIA GQE](https://yomu.fyi/post/designing-gpu-accelerated-query-engines-with-nvidia-gqe.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jun 30, 2026
- Type: Explainer

GPU-accelerated query engines encounter performance constraints stemming from memory and I/O bandwidth limitations. NVIDIA introduces GQE, a reference architecture using NVIDIA cuDF, nvCOMP, and other CUDA-X libraries to accelerate SQL query execution across modern hardware like the NVIDIA GB200 NVL4. The architecture coordinates three distinct layers: a query layer consuming Substrait plans, a data layer managing in-memory table transfers, and an execution layer executing relational operator task graphs. GQE employs pipeline parallelism, partition pruning, hybrid compression, and batched asynchronous transfers to overlap data movement with GPU compute. On a non-standard 1 TB TPC-H benchmark, GQE on a single B200 GPU achieved a 7.5x total execution time speedup over dual-socket CPU configurations running DuckDB.


### [10 Years of Meta’s Commitment to Python](https://yomu.fyi/post/10-years-of-meta-s-commitment-to-python.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Published: Jun 30, 2026

Meta has marked ten consecutive years as a sponsor of the Python Software Foundation (PSF) to ensure the language remains robust and sustainable. Python represents the most widely used programming language across Meta's infrastructure, powering applications like Instagram and Threads as well as internal AI research. Beyond financial backing, Meta engineers actively contribute core language features, author Python Enhancement Proposals (PEPs), and build open-source tools such as the Pyrefly type checker. Company sponsorship directly funds the PSF's Developer-in-Residence program, core infrastructure, and security enhancements for the Python Package Index (PyPI). Continued support of the PSF functions as a strategic investment in maintaining the stability of the broader open-source ecosystem.


### [Agentic Coding on Supabase with OpenCode](https://yomu.fyi/post/agentic-coding-on-supabase-with-opencode.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: ekhar
- Published: Jun 30, 2026
- Type: Announcement

Supabase has made its OpenCode integration available, connecting an AI coding agent to Supabase account and project management APIs and bundled Supabase skills. Users invoke /supabase, authenticate, and, for database, Edge Functions, logs, and other project-scoped capabilities, are guided through connecting Supabase MCP; the integration requires OpenCode 1.3.4 or later. It can list organizations and projects, create projects, open the MCP setup flow, monitor agent activity in real time, and let agents query data, check logs, and deploy functions, while the plugin is open source. The post demonstrates the workflow in Minecraft, where OpenCode connected through MCP, created and populated a table, and deployed an Edge Function on a real Supabase project.


### [Deploy a Production-Ready NVIDIA AI-Q Blueprint on Oracle Cloud Infrastructure](https://yomu.fyi/post/deploy-a-production-ready-nvidia-ai-q-blueprint-on-oracle-cloud-infras.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Anurag Kuppala
- Published: Jun 26, 2026
- Type: Tutorial

The NVIDIA AI-Q Blueprint operates as an open-source reference architecture for long-horizon agents built upon LangChain Deep Agents and the NVIDIA NeMo Agent Toolkit. Deploying this multi-agent system on Oracle Cloud Infrastructure requires coordinating foundational cloud infrastructure with containerized application workloads across Kubernetes. The deployment methodology utilizes Terraform to provision network isolation, an enhanced OKE cluster, a flexible public load balancer, and an AES-256 encrypted OCI Vault. Helm manages the application layer on Kubernetes by pulling official images from the NGC registry to run a FastAPI agent backend, a Next.js web frontend, and PostgreSQL. This configuration establishes an extensible routing architecture where user queries route automatically between shallow search agents and deep planning sub-agents while maintaining clean infrastructure teardown commands.


### [Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer](https://yomu.fyi/post/creating-the-nvidia-nemotron-3-ultra-nvfp4-checkpoint-with-nvidia-mode.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Michelle Horton
- Published: Jun 26, 2026
- Type: Problem & solution

Moving large model weights efficiently requires quantization, but mapping weights to NVFP4's eight positive values risks severe precision loss or outlier-induced underflow. To address this, NVIDIA quantized the 550B Nemotron 3 Ultra model using NVIDIA Model Optimizer with a multi-precision strategy rather than quantizing all layers uniformly. The recipe applies four-over-six adaptive block scaling to Mixture of Experts routed weights, while assigning FP8 to shared experts and retaining BF16 for sensitive attention linears and embeddings. This process reduced the checkpoint size from 1,121 GB down to 352.3 GB and enabled single-checkpoint execution across Hopper using W4A16 and Blackwell using native W4A4. As a result, the model achieves up to 5.9x higher inference throughput than the GLM-5.1 754B FP4 model on decode-heavy workloads while matching BF16 benchmark accuracy.


### [How I hunt for vulnerabilities with AI](https://yomu.fyi/post/how-i-hunt-for-vulnerabilities-with-ai.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 26, 2026
- Type: Tutorial

The author describes using AI-assisted code review to hunt for vulnerabilities in ClickHouse, a large C++ codebase, despite having limited bug-bounty experience. GitHub Copilot, Claude, ChatGPT, and Gemini models were used to inspect code, generate vulnerability hypotheses, explain unfamiliar subsystems, and identify older or newly introduced attack surfaces. Promising ideas were recorded, then investigated with Python scripts against a locally running ClickHouse Docker container, followed by manual review and reproduction to distinguish genuine findings from hallucinations and misconfigurations. The work produced a few real vulnerabilities that were reported to ClickHouse’s security team and bug bounty program, while the broader conclusion is that AI can accelerate exploration and validation but still requires persistent questioning and human verification.


### [Announcing Silk: a silky smooth fiber runtime for ClickHouse](https://yomu.fyi/post/announcing-silk-a-silky-smooth-fiber-runtime-for-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 25, 2026
- Type: Announcement

Silk is a C++ stackful-fiber library and scheduler created for ClickHouse’s highly concurrent, I/O-bound components, including object-storage I/O, distributed cache lookups, replica coordination, and HTTP fan-out. Its design combines one pinned OS scheduler thread per CPU, NUMA-aware work stealing, io\_uring for asynchronous I/O and timers, and zero heap allocation in the steady-state hot path; fiber stacks are mmap’d from per-fiber pools with guard pages, while runtime containers are intrusive. The post reports benchmarks including roughly 3.6-nanosecond fiber yields with cross-CPU stealing, 5.9 million file IOPS in a working configuration, and about fifteen times boost::asio’s throughput at one connection. At 10,000 concurrent S3-style requests, internal measurements showed a roughly 65% improvement in 99.9th-percentile latency versus an equivalent thread-pool executor, with identical median throughput; distributed cache is the first intended ClickHouse integration.


### [How we used DSPy to turn AI evaluations into better responses in Dash chat](https://yomu.fyi/post/how-we-used-dspy-to-turn-ai-evaluations-into-better-responses-in-dash.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon
- Published: Jun 25, 2026

Dropbox improved its Dash chat agent by establishing an automated optimization loop powered by DSPy and LLM-as-judge evaluations. Engineers first calibrated their LLM judges against human-annotated interaction traces, then used those judges to systematically optimize the agent's system prompts via offline counterfactual replay. This automated workflow reduced incomplete responses by 26% while decreasing overall token consumption.


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