---
title: "Deployment"
description: "161 posts about Deployment, summarised, each linking to the original."
---

# Deployment
> 161 posts about Deployment, summarised, each linking to the original.

## Articles

### [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.


### [Waypoint-1.5: Higher-Fidelity Interactive Worlds for Everyday GPUs](https://yomu.fyi/post/waypoint-1-5-higher-fidelity-interactive-worlds-for-everyday-gpus.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Andrew Lapp, Louis Castricato, Scott Fox, Shahbuland Matiana, David Rossi
- Published: Apr 9, 2026
- Type: Announcement

Overworld’s Waypoint-1.5 is a real-time video world model aimed at making interactive generative worlds usable on consumer hardware rather than limited to datacenter-scale compute. It offers two tiers: a 720p model for higher-performance desktop GPUs, capable of up to 60 FPS on RTX 3090 through 5090 hardware, and a 360p model for broader deployment, including gaming laptops and soon Apple Silicon Macs. Training used nearly 100 times more data than Waypoint-1, while more efficient video-modeling techniques reduce redundant computation across frames to support coherent motion and responsive exploration. Users can run it locally through the Biome desktop client, try it in a browser via Overworld Stream, or use the World Engine inference library and third-party clients. The release presents improved visual fidelity, broader local hardware support, and continued progress toward real-time interactive world simulation.


### [Any Custom Frontend with Gradio's Backend](https://yomu.fyi/post/any-custom-frontend-with-gradio-s-backend.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: yuvraj sharma, Abubakar Abid
- Published: Apr 1, 2026
- Type: Tutorial

gradio.Server enables applications built with React, Svelte, or plain HTML/JavaScript to use Gradio’s backend facilities instead of Gradio components. The post demonstrates this with Text Behind Image, whose browser UI layers a background, styled text, and a transparent foreground produced by BiRefNet, then exports a PNG client-side. A roughly 50-line Python backend extends FastAPI, exposes a standard HTML route, and defines an @app.api("remove\_background") endpoint; @spaces.GPU handles ZeroGPU allocation while Gradio supplies queuing, concurrency control, SSE streaming, and gradio\_client compatibility. The frontend calls the endpoint through the Gradio JavaScript client, so image-processing requests pass through the queue while rendering and compositing remain in the browser. The result is a custom frontend that retains Spaces hosting and Gradio infrastructure.


### [ClickHouse BYOC on Google Cloud  now Generally Available](https://yomu.fyi/post/clickhouse-byoc-on-google-cloud-now-generally-available.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Aashish Kohli, Yiyang Shao
- Published: Mar 31, 2026
- Type: Announcement

ClickHouse Bring Your Own Cloud is now generally available on Google Cloud across all public ClickHouse Cloud Google regions without requiring a waitlist. The architecture separates the ClickHouse-managed control plane from the customer data plane, which runs entirely within the user Google Cloud project on Google Kubernetes Engine and Google Cloud Storage. An outbound-only, encrypted Tailscale zero-trust tunnel restricts control plane communication strictly to orchestration traffic, ensuring query data never leaves the customer environment. Deployment involves a three-step Terraform setup that establishes least-privilege IAM roles, configures VPC networking, and automates cluster provisioning. ClickHouse manages database and infrastructure maintenance through ArgoCD, employing a make-before-break strategy to add updated replicas before decommissioning older nodes across customizable release channels.


### [Provision a production-ready dev stack from your terminal](https://yomu.fyi/post/provision-a-production-ready-dev-stack-from-your-terminal.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Rami Banna
- Published: Mar 26, 2026
- Type: Announcement

Stripe Projects, a new tool in the Stripe CLI, addresses the manual, fragmented work of turning a local prototype into a production-ready environment. It manages a project manifest and Stripe-backed record for attached services, provider accounts, and required credentials, while an integration protocol standardizes provisioning, plan selection, upgrades, and credential handoff. The developer preview supports services from providers including Vercel, Railway, Supabase, Neon, PlanetScale, Turso, Chroma, PostHog, Clerk, and Runloop; resources remain in accounts owned by the developer, and credentials can be synced to local development, CI, or a team secret manager. Through CLI commands such as stripe projects init, add, upgrade, and env --pull, developers or coding agents can repeat setup, audit changes, and upgrade supported plans using tokenized payment credentials, although payment handoff is limited to supported providers and plans in the US, EU, UK, and Canada.


### [Build a Domain-Specific Embedding Model in Under a Day](https://yomu.fyi/post/build-a-domain-specific-embedding-model-in-under-a-day.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steve Han, Rucha Apte, Sean Sodha, Oliver Holworthy
- Published: Mar 20, 2026
- Type: Tutorial

The tutorial presents a pipeline for adapting a general-purpose embedding model when it misses fine-grained distinctions in domain documents and labeled query–document data is unavailable. It generates synthetic question–answer pairs with NeMo Data Designer, filters them for quality, mines hard negatives with a 95% positive-score margin ceiling, and unrolls multi-hop queries for contrastive training. The recipe fine-tunes Llama-Nemotron-Embed-1B-v2, evaluates base and adapted models with BEIR metrics, and exports the result to ONNX or TensorRT. On the reported datasets, it improved Recall@10 and NDCG@10 by more than 10%; Atlassian increased Recall@60 from 0.751 to 0.951 on JIRA data. The complete workflow can run in roughly 2–3 hours for about 500 documents, using a single 80GB Ampere-or-newer GPU.


### [Supabase Joins the Stripe Projects Developer Preview](https://yomu.fyi/post/supabase-joins-the-stripe-projects-developer-preview.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregor\_vand, ana\_mogul
- Published: Mar 19, 2026
- Type: Announcement

Supabase has joined the Stripe Projects developer preview, a Stripe CLI workflow for provisioning real services and obtaining working credentials with a single command. The integration provisions a complete Supabase project—including Postgres, Auth, Storage, Edge Functions, and Realtime—without requiring dashboard interaction, while supporting both developers and AI agents through deterministic, repeatable steps. After installing the Stripe CLI and adding supabase/project, stripe projects env --sync writes credentials to a local .env file and stores them in the Stripe Project for agent access; existing Supabase users can link their account. Projects remain in the user’s Supabase account with dashboard, connection-string, and data access, and database credentials can be rotated with stripe projects rotate supabase/project. The feature is a developer preview, and Stripe and Supabase are requesting feedback through Discord or GitHub.


### [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.


### [Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations](https://yomu.fyi/post/bringing-robotics-ai-to-embedded-platforms-dataset-recording-vla-fine.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Gaetan Bahl, Enzo Ruedas, Tess Boivin
- Published: Mar 5, 2026
- Type: Tutorial

Deploying Vision–Language–Action policies on embedded robots is constrained by compute, memory, power, and real-time control requirements; synchronous inference can leave the arm idle and cause oscillatory corrections. NXP presents dataset-recording practices for a tea-bag placement task, including fixed cameras, controlled lighting, a gripper-mounted view, calibration backups, and diverse training and validation splits with recovery episodes. The example uses 120 episodes, three 640×480 cameras at 30 fps, batch size 8, and checkpoint selection based on training and validation success rather than loss alone. For deployment, the VLA is decomposed into vision, LLM backbone, and action-expert stages, with selective quantization and asynchronous scheduling constrained by inference time being shorter than action execution. On the i.MX 95, optimized ACT latency reached 0.32 seconds with 0.89 global accuracy, while ONNX FP32 ACT reached 0.96 global accuracy at 2.86 seconds.


### [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.


### [Train AI models with Unsloth and Hugging Face Jobs for FREE](https://yomu.fyi/post/train-ai-models-with-unsloth-and-hugging-face-jobs-for-free.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, Daniel (Unsloth), Michael Han, Maxime Labonne, Daniel van Strien, shaun smith
- Published: Feb 20, 2026
- Type: Tutorial

The post presents a workflow for fine-tuning the LiquidAI/LFM2.5-1.2B-Instruct small language model with Unsloth on Hugging Face Jobs, including optional use of Claude Code, Codex, or another coding agent. It explains that Unsloth is described as providing about 2x faster training and 60% lower VRAM use than standard methods, while the model runs under 1GB of memory and is optimized for on-device deployment. The tutorial covers claiming free Hugging Face Jobs credits through the Unsloth Jobs Explorers organization, installing the hf CLI or a model-training skill, and submitting a job with a dataset, GPU flavor, timeout, and output repository. An agent can generate a UV training script using 4-bit loading and LoRA, submit the managed cloud-GPU job, report Trackio monitoring details, and push the trained model to the Hugging Face Hub.


### [The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+](https://yomu.fyi/post/the-future-of-the-global-open-source-ai-ecosystem-from-deepseek-to-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Adina Yakefu, Irene Solaiman
- Published: Feb 3, 2026
- Type: Explainer

This third and final article in a series examines how Chinese AI organizations are shaping an open-source ecosystem after DeepSeek R1's January 2025 release. It argues that open sharing of models, papers, deployment infrastructure, and techniques has become the dominant near-term approach, with organizations ranging from Alibaba, Tencent, ByteDance, and Baidu to startups and application companies contributing at different layers. Qwen illustrates the ecosystem strategy: by mid-2025, more than 113,000 Hugging Face models used Qwen as a base, while organizations also connected models with chips, cloud platforms, toolchains, evaluation, and deployment systems. The article places this shift on infrastructure built since 2017, including the East Data, West Compute plan, and says R1 catalyzed wider use of existing compute, energy, and data capacity. It concludes that open-source models are becoming reusable, composable components in industrial workflows and localized AI systems as China moves from DeepSeek toward AI+.


### [The safety of speed: How we ship code 180 times per day](https://yomu.fyi/post/the-safety-of-speed-how-we-ship-code-180-times-per-day.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Danny Fallon,Ryan Sherlock
- Published: Jan 27, 2026
- Type: Explainer

Intercom describes how it averages 180 production ships per workday in January 2026 while targeting 99.8+% availability, with an average 12-minute merge-to-production cycle. The post focuses on its Ruby on Rails monolith, deployed to three data-hosting regions through independent pipelines, and frames small batches as a way to limit accumulated risk. An automated pipeline builds a deployable slug, runs parallel CI, verifies it in pre-production with boot, CI, and Datadog Synthetics gates, then performs a staggered rollout with process-level rolling restarts. Safety also depends on engineer ownership, feature flags, production testing on a single host, outcome-focused heartbeat metrics, and rapid automatic or manual rollbacks. Incident reviews then harden the system, supporting the conclusion that speed is used to keep changes small, feedback immediate, and recovery fast rather than to trade stability for velocity.


### [Configuring Stripe using Terraform and AI agents](https://yomu.fyi/post/configuring-stripe-using-terraform-and-ai-agents.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Michael Selander
- Published: Jan 27, 2026
- Type: Tutorial

The post addresses the difficulty of using AI agents to configure Stripe resources when one-off API calls leave no durable source of truth for state, changes, or environment differences. It recommends using agents to author Terraform configuration rather than operating Stripe directly, so products, prices, and webhook endpoints become explicit, reviewable code. The example defines a Standard Plan with $20 monthly and $200 annual prices, selected webhook events, and outputs for both price IDs, then applies it with terraform plan and terraform apply. Terraform provides declarative, repeatable configuration supported by Git history, pull-request review, and environment-specific workspaces. Separate sandbox and livemode workspaces, paired with matching STRIPE\_API\_KEY values, are presented as a way to reduce accidental cross-environment changes.


### [Codex is Open Sourcing AI models](https://yomu.fyi/post/codex-is-open-sourcing-ai-models.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, shaun smith
- Published: Dec 11, 2025
- Type: Tutorial

Hugging Face Skills equips AI coding agents like OpenAI Codex to execute end-to-end machine learning workflows. By reading AGENTS.md files and interfacing via the Model Context Protocol, Codex automates dataset validation, training script updates, and job submissions to Hugging Face Jobs. The workflow supports methods including supervised fine-tuning, direct preference optimization, and reinforcement learning for models ranging from 0.5B to 7B parameters. Throughout execution, Codex tracks live metrics via Trackio, records benchmark evaluations against baselines, and maintains Markdown reports. Once training concludes, Codex merges LoRA adapters, applies GGUF quantization, and publishes the resulting models to the Hugging Face Hub for local deployment.


### [Versioning in Stripe Workflows: Ship workflow changes with confidence](https://yomu.fyi/post/versioning-in-stripe-workflows-ship-workflow-changes-with-confidence.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Tanya Boiteau
- Published: Dec 10, 2025
- Type: Announcement

Stripe Workflows previously required teams to edit live workflow definitions directly, leaving no draft state, historical definitions, straightforward rollback, or reliable way to inspect which configuration shaped an older run. Versioning addresses this with a dedicated draft for iteration, immutable numbered published versions, and version history that can include descriptions, authors, status, and run counts. Publishing creates a complete snapshot containing steps, branches, conditions, variable references, calls, and configuration; new runs use it immediately, while in-progress runs finish on the version they started with. Each run records its exact version and exposes the workflow definition, including taken and untaken branches, while a past version can seed a new draft for rollback; versioning is available in Stripe Workflows, where new workflows include a draft by default.


### [We Got Claude to Fine-Tune an Open Source LLM](https://yomu.fyi/post/we-got-claude-to-fine-tune-an-open-source-llm.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, shaun smith
- Published: Dec 4, 2025
- Type: Tutorial

Hugging Face Skills equips coding agents like Claude Code, OpenAI Codex, and Google's Gemini CLI to manage end-to-end language model fine-tuning. Using the hf-llm-trainer skill, an agent validates dataset formats, selects cloud hardware, configures authentication, and submits jobs to Hugging Face Jobs. Supported techniques include Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO) for models ranging across various parameter sizes. The integration incorporates Trackio for real-time monitoring and automates LoRA configuration for larger models. Once training completes, the agent pushes artifacts to the Hugging Face Hub and can convert models to GGUF format for local execution.


### [Run for your money: Engineering a treadmill that prints store credit](https://yomu.fyi/post/run-for-your-money-engineering-a-treadmill-that-prints-store-credit.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Nov 25, 2025
- Type: Problem & solution

For the 2025 NYC marathon weekend, Shopify and Endorphins Running built a gamified treadmill that awarded in-store credit for maintaining a self-selected pace. Participants had 30 seconds to reach their target, then earned credit for each second spent within a defined pace zone, with a personalized barcode receipt redeemable through a Shopify POS UI extension. The implementation used a curved, motorless TrueForm Trainer, FTMS messages, a Python relay, Socket.IO, a local Remix application, receipt printing, and a production-hosted POS integration. Interval timestamps were normalized because treadmill messages arrived irregularly, while a state machine coordinated warm-up, gameplay, operator controls, database updates, discounts, and printing. The resulting experience attracted new customers, with 63% of participants identified as new customers, and its patterns could support other sensor-driven in-store incentives.


### [Building for an Open Future - our new partnership with Google Cloud](https://yomu.fyi/post/building-for-an-open-future-our-new-partnership-with-google-cloud.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jeff Boudier, Simon Pagezy
- Published: Nov 13, 2025
- Type: Announcement

Hugging Face and Google Cloud announced an expanded strategic partnership designed to streamline the deployment and management of open models on Google Cloud infrastructure. Prompted by a tenfold increase in Hugging Face usage on Google Cloud over three years, the collaboration introduces a joint CDN Gateway using Hugging Face Xet technology to cache models and datasets directly on Google Cloud. This caching mechanism aims to shorten download times, strengthen model supply chain resilience, and accelerate time-to-first-token across Vertex AI, Google Kubernetes Engine, Cloud Run, and Compute Engine virtual machines. Hugging Face plans to integrate native library support for Google Cloud TPUs, lower instance prices on Inference Endpoints, and enhance Hub security scanning via VirusTotal, Google Threat Intelligence, and Mandiant.


### [Grab's Mac Cloud Exit supercharges macOS CI/CD](https://yomu.fyi/post/grab-s-mac-cloud-exit-supercharges-macos-ci-cd.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Madushan Gamage
- Published: Nov 6, 2025
- Type: Problem & solution

Grab moved its macOS CI/CD infrastructure from a US cloud vendor to a colocation cluster in Malaysia, closer to its Git servers in Southeast Asia. The change followed growth from one office Mac Pro to more than 250 Mac minis, rising demand, job queues, and cloud costs that made ownership or colocation more attractive at scale. Grab selected bare-metal hardware after comparing cloud, colocation, and on-premises options, then migrated progressively while monitoring failure rates, performance, and reliability; the later infrastructure also uses zero-touch provisioning to reduce manual setup as the cluster grows. The migration produced projected savings of more than $2.4 million over three years and performance improvements of 20–40%, while the team chose allocated-kilowatt power commitments and retained bare metal because macOS virtualization’s performance or stability trade-offs did not justify it.


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