标签：#devops

The Problem: The Chaos of Giant AI Code Diffs Autonomous coding tools can spin up full implementations, run scripts and commit hundreds of lines of code in seconds. But if you have managed a team of developers using them, or tried to build a complex feature solo, you have likely run into giant code diffs . A single vague prompt transforms into a massive, multi-file PR that takes a human tech lead hours to confidently review. Features get built but the step-by-step product rationale and architectural decisions are often lost inside ephemeral chat histories. The solution is enforcing strict workflow guardrails. I tried all major spec-driven development (SDD) workflows and what I found is they focus 90% on product shape and much less on the actual implementation. This is also the case of get-shit-done which I love for its pragmatism, low ceremony-driven yet solid at context and flexibility. But I needed something more specialized. Introducing Get Tasks Done I built Get Tasks Done from get-shit-done to provide a lightweight, deterministic state machine layer for AI-assisted development. It bridges the gap between high-level human intent and execution AI agents by turning specifications into granular execution tasks using leveraging a GitHub-native integration . Instead of a fluid, unpredictable implementation step, GTD structures development into explicit, auditable stages: Product Intent ➔ Markdown Specs ➔ Granular GitHub Issues ➔ Atomic PRs The Architecture: Guardrails for the Agentic Layer The system coordinates across five distinct layers: Planning Artifacts Local markdown planning templates enforce small, highly contained prompt boundaries. By keeping information tightly localized, context drift drops significantly. I extended it with a thorough task decomposition gate that ensures planning tasks are enough atomic to avoid drift (and even executed by cheaper models). Runtime Commands & State Deterministic tools manage how the agent reads the state machine, standard

TL;DR: Create React App builds bake environment variables at build time. ConfigMaps let you inject runtime configs into your container. Here’s how to bridge them so the same Docker image works across dev, staging, and production. The Problem You’ve built a React app with Create React App (CRA), Vite, or Next.js. You use .env files: js // api.js const API_URL = process.env.REACT_APP_API_URL; You build your Docker image: dockerfile FROM node:18 AS builder COPY . . RUN npm run build # REACT_APP_API_URL gets baked here FROM nginx:alpine COPY --from=builder /build /usr/share/nginx/html Then you deploy to Kubernetes. But now you want different API URLs for staging vs production. You could rebuild the image for each environment (bad – slow, wasteful). Or you could use a ConfigMap to inject values at runtime. ConfigMap to the Rescue A ConfigMap stores key-value pairs. Kubernetes can mount it as a file inside your pod. But React runs in the browser, not in the container’s filesystem. So how does the browser read a file from a ConfigMap? Simple: You serve a dynamic env-config.js file from your web server. Step-by-Step Solution Create a ConfigMap with your environment variables yaml # configmap.yaml apiVersion: v1 kind: ConfigMap metadata: name: react-env-config data: env-config.js: | window.__env = { REACT_APP_API_URL: " https://api.production.com ", REACT_APP_FEATURE_FLAG: "true" }; Apply it: bash kubectl apply -f configmap.yaml Modify your React app to read from window.__env Instead of reading process.env directly, use a runtime config: js // config.js export function getEnvVar(name) { // Runtime injection from window. env (provided by ConfigMap) if (window. env && window. env[name] !== undefined) { return window. env[name]; } // Fallback to build-time env vars (for local dev) return process.env[name]; } Use it in your components: js // api.js import { getEnvVar } from './config'; const API_URL = getEnvVar('REACT_APP_API_URL'); Serve the ConfigMap file via your web server U

I came to programming late — I didn't get into this world until I was past 35, and I'm 62 now, still writing code every day. This is a "build in public" post about a tool I just finished, and I'd genuinely love your feedback. The itch For years I watched infrastructure teams keep their AWS inventory in spreadsheets. It always worked — right up until it didn't. Nobody had time to keep it current, and every single one eventually drifted away from reality. Middleware EOL was the same story: a hand-maintained list, no alerts, no dashboard, quietly going stale. One day I asked the obvious question: we have tfstate, we have boto3 — why are we still doing this by hand? What I built SyncVey is a self-hosted web app that: Inventories your AWS resources into a System → Environment → Asset ledger (EC2, ECS, Lambda, RDS, S3, ALB, VPC, EBS), scanned live via boto3/AssumeRole Detects attribute-level drift between your tfstate and live AWS — including resources someone built by hand in the console that terraform plan never sees Tracks the app/middleware layer per environment and flags end-of-life runtimes The drift piece is the part I care about most. terraform plan only knows about resources Terraform already manages. The thing that actually bites teams is the resource someone spun up by hand in the console — plan is blind to it. SyncVey diffs your tfstate against the live AWS state, so those show up too. The stack (and why) Django + htmx + Postgres — server-rendered, no SPA, no Node build step MIT-licensed, no SaaS, no telemetry One docker compose up and your data stays inside your own infrastructure git clone https://github.com/MR-TABATA/SyncVey cd SyncVey docker compose up I deliberately leaned on htmx because, for a tool someone has to deploy and maintain themselves, "no frontend toolchain" matters more than a fancy client. I'd love your honest take It's AWS-only for now and very much a solo project, so I'm sure there are rough edges. I'm not an AWS specialist — I deliberatel

Cron's biggest problem isn't scheduling — it's silence. A cron job can fail every night for a month, and unless you're manually checking logs on the server, you won't know. No alert, no dashboard, no audit trail. Just a backup that doesn't exist when you need it, or a data sync that quietly stopped three weeks ago. Monitoring fixes this. But "cron job monitoring" means different things depending on the tool. Some watch for missing heartbeats. Some track full execution history. Some just page you when something breaks. This article compares six approaches — from writing your own monitoring scripts to using a fully managed scheduler with built-in observability — so you can pick the right one for your workload. Heartbeat Monitoring vs. Execution Monitoring Before comparing tools, understand the two fundamentally different approaches. Heartbeat monitoring (dead man's switch) is passive. Your cron job pings a monitoring URL after each run. If the ping doesn't arrive on schedule, you get an alert. This tells you whether a job ran — but not what happened . If the job runs but returns bad data, the ping still fires and the monitor stays green. Execution monitoring is active. The scheduler fires the job, captures the response, records the outcome, and alerts on failure. You get the full picture: status code, response body, duration, retry count, and a timeline of every execution. When to use each: Heartbeat monitoring makes sense when you're stuck with system cron. Execution monitoring makes sense when you're choosing a scheduler — you get monitoring, retries, and logging as part of the platform. Comparison at a Glance Tool Type Alerts Execution Logs Retries Free Tier DIY scripts Custom ⚠️ Whatever you build ⚠️ Whatever you build ⚠️ Whatever you build ✅ Free (your time) Healthchecks.io Heartbeat ✅ Email, Slack, webhooks ❌ No ❌ No ✅ 20 checks Cronitor Heartbeat + telemetry ✅ Email, Slack, PagerDuty ⚠️ Basic (duration, exit code) ❌ No ⚠️ 5 monitors Better Stack Uptime + heartb

IBM and HashiCorp have announced new LDAP secrets management capabilities in IBM Vault Enterprise 2.0, introducing a redesigned architecture to manage LDAP credentials, support password rotation, and automate the identity lifecycle. By Craig Risi

Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't. Introduction: The Cloud Cost Optimization Dilemma Cloud teams waste between 40% and 60% of their infrastructure budget on a false choice: committing to reserved capacity they won't fully use or chasing spot instance savings they can't operationalize. The decision between commitment discounts and spot instances is not a preference. It is a calculation with three variables: workload predictability, failure tolerance, and the operational cost of managing interruptions. Commitment discounts lock you into capacity for one or three years. You pay upfront or monthly for compute resources whether you use them or not. The mechanism is simple: cloud providers offer 30% to 72% discounts because they can forecast their own capacity planning when customers commit. You save money when your actual usage matches your commitment. You lose money when usage drops below the committed level because you still pay for idle capacity. Spot instances offer 70% to 90% discounts by selling unused cloud capacity at auction prices. The provider can reclaim these instances with 30 seconds to 2 minutes of notice. You save money when your workload can tolerate interruptions and you build automation to handle instance termination. You lose money when interruptions cause failed jobs that must restart from scratch, consuming more compute time than the discount saved. Most engineering teams pick one strategy and apply it everywhere. This creates two failure modes. Teams that over-commit pay for capacity during low-traffic periods. Teams that over-rely on spot instances spend engineering time rebuilding checkpoint systems and retry logic that costs more than the discount delivers. The correct approach is workload-specific. Measure your actual usage patterns for 30 days. Calculate the cost of interruption handling. Then a

Introduction Modern cloud-native systems generate an enormous amount of telemetry data every second. Applications, containers, Kubernetes clusters, APIs, databases, and infrastructure components continuously emit metrics, logs, and traces to help engineering teams understand system behavior and troubleshoot issues. While observability has become essential for operating distributed systems reliably, it has also introduced a new challenge: managing the scale, cost, and quality of telemetry. OpenTelemetry (OTel) has emerged as the industry standard for collecting and processing observability data. It provides a vendor-neutral framework for instrumenting applications and exporting telemetry to different observability backends. However, simply adopting OpenTelemetry is not enough. Without proper optimization strategies, organizations often face excessive telemetry ingestion costs, noisy dashboards, high-cardinality metrics, trace overload, and inefficient debugging workflows. This article explores practical approaches for optimizing observability using OpenTelemetry. It focuses on metrics, logs, and traces individually while also discussing broader optimization strategies across the telemetry pipeline. Understanding the OpenTelemetry observability pipeline OpenTelemetry provides a unified framework for generating, collecting, processing, and exporting telemetry data. At its core, the OTel ecosystem consists of SDKs, instrumentation libraries, collectors, processors, and exporters. Applications generate telemetry using OpenTelemetry SDKs or auto-instrumentation agents. This telemetry is then sent to the OpenTelemetry Collector, which acts as a centralized telemetry processing layer. The collector can receive telemetry from multiple sources, enrich it with metadata, apply filtering or sampling, and export it to one or more observability backends. The observability pipeline typically follows this flow: Application → OTel SDK → OTel Collector → Observability Backend The Open

In the hyper-competitive landscape of modern B2B outbound sales, speed-to-lead and outreach capacity are the ultimate drivers of pipeline volume . Yet, traditional Sales Development Representative (SDR) teams face a exhausting bottleneck: reaches and qualifications are limited by human bandwidth . A typical outbound SDR spends up to 80% of their day dialing numbers, navigating IVR phone trees, hitting voicemail, and dealing with incorrect contact records. When an inbound lead submits a form requesting a product demo, the average company takes 42 minutes to respond. By that time, prospect engagement has cooled by over 400%. To shatter this operational limit, modern revenue operations (RevOps) teams are transitioning from rigid auto-dialers and static voice bots to autonomous voice AI sales agents . By pairing the hyper-realistic conversational engine of ElevenLabs with the visual orchestration power of n8n , you can deploy a scalable, context-aware calling agent that handles inbound qualification and outbound follow-up calls in real-time. This technical blueprint provides an end-to-end guide to designing, securing, and deploying a production-grade Voice AI Sales Agent using ElevenLabs Conversational AI and n8n . We will cover how to manage conversation state, execute live database tool calls, secure webhook communication, route calls dynamically, and configure infrastructure to achieve sub-second response latency . The Architecture of an Enterprise Voice Agent Building a conversational voice agent requires a multi-layered system that operates in near real-time. When a human speaks over a telephony network, their voice must be digitized, transcribed, processed by a large language model (LLM), synthesized back into audio, and sent back down the line—all within a fraction of a second. To ensure stability, scalability, and absolute separation of concerns, our architecture decouples the telephony and voice generation layer from the logic and database integration layer . [

Microsoft used their Build 2026 event to announce new functionality for Microsoft Foundry. Citing Foundry as "the place where AI agents move from experiments to production systems," in a blog post, Nick Brady writes that the release brings “runtime, tools, memory, grounding, models, observability, and governance” that developers need for production agents, rather than just new model endpoints. By Matt Saunders

Something I keep noticing across teams and orgs that are actually getting value from AI versus the ones that aren't. The difference rarely comes down to the model or the algorithm. Most organizations are already drowning in data. Logs, metrics, alerts, reports, dashboards, tickets. The information exists. The bottleneck is what happens after the data shows up. How long does it take to interpret what the signal means? Who decides what to prioritize when three things need attention at once? How fast can the right people coordinate a response once a decision is made? That's where AI actually earns its keep. Not by replacing the human in the loop but by compressing the time between something happening and someone doing something useful about it. Signal to understanding to action. That's the chain that matters. Think about it in terms you deal with every day. A vulnerability gets disclosed. The CVE exists, the advisory is public, your scanner picked it up. None of that is the bottleneck. The bottleneck is figuring out which of your services are affected, who owns them, how bad the exposure actually is in your specific context, and getting a patch scheduled before someone exploits it. AI that helps you answer those questions in minutes instead of days is genuinely valuable. AI that and adds another dashboard to look at isn't. This applies across the board. Incident response, infrastructure management, risk assessment, customer systems, operational workflows. The teams getting real value aren't the ones with the fanciest models. They're the ones who figured out where their decision bottlenecks actually are and pointed AI at those specific gaps. The strategic advantage is rarely in the algorithm. It's in organizational responsiveness. How fast can you go from "something happened" to "we're handling it". The AI is just the thing that compresses that timeline. Where's the biggest decision bottleneck in your current workflow?

Terraform can provision an AWS EC2 instance and set up a Python virtual environment in a single, reproducible run — the whole workflow is declarative and version‑controlled. 📑 Table of Contents 💻 Terraform — How to Provision an EC2 Instance 🔧 AWS Provider — Configuring Credentials 🐍 Python Environment — Setting up a Virtualenv on the Instance 📦 Installing Python and venv 📦 Activating and Using the Environment 📦 User Data — Automating Installation with Terraform 🟩 Final Thoughts ❓ Frequently Asked Questions How do I store the Terraform state securely? Can I use a different Linux distribution for the EC2 instance? Is it possible to attach an Elastic IP to the instance? 📚 References & Further Reading 💻 Terraform — How to Provision an EC2 Instance A Terraform configuration file describes the desired state of AWS resources; applying it makes the real cloud match that state. First, install Terraform (version 1.5.0 or newer). The binary is a single executable, so the operating system loads it directly into memory and the process performs HTTP requests to AWS endpoints. $ terraform version Terraform v1.5.0 on linux_amd64 + provider registry.terraform.io/hashicorp/aws v5.12.0 Next, create a main.tf that declares an aws_instance resource. The provider block authenticates with AWS using either environment variables or a shared credentials file. # main.tf terraform { required_version = ">= 1.5.0" required_providers { aws = { source = "hashicorp/aws" version = "~> 5.12" } } } provider "aws" { region = "us-east-1" } resource "aws_instance" "app_server" { ami = "ami-0c02fb55956c7d316" # Amazon Linux 2 instance_type = "t3.micro" # User data will be defined later user_data = data.template_file.init.rendered tags = { Name = "terraform-ec2-python" } } Running terraform init contacts the provider registry, downloads the provider plugin, and stores it under .terraform . The generated .terraform.lock.hcl file records exact plugin checksums, guaranteeing that subsequent runs use the same

A pattern keeps repeating itself in AI projects. The model works. The demo works. The proof of...

We wired our LLM eval suite into Datadog over about four months. Most of the panels we built got deleted. These are the five that stayed, and the metrics that feed them. TL;DR: We run an LLM-as-judge eval suite on every PR that touches a prompt, and we ship the results to Datadog as custom metrics. The dashboard started with fourteen panels. We kept five. The one that catches the most real regressions is per-criterion pass-rate split out by judge criterion, not the single rolled-up pass-rate number, because an aggregate of 91 percent hid the fact that one criterion had dropped from 0.95 to 0.62. Below are the metrics we emit, the Python that submits them, the monitor config we alert on, and the panels we tried and dropped. Some context on the setup so the rest makes sense. We are a Series-C dev-tool startup. We have a handful of prompts in production that do real work (classification, extraction, a summarization step in an agent loop). Each one has an eval set of tagged examples, somewhere between 80 and 400 per prompt. The judge is a separate model call that scores each output against a rubric. We run the suite in GitHub Actions. The eval job emits metrics to Datadog at the end of every run. Backend service health was already in Datadog, so putting eval data next to it meant one place to look during an incident instead of two. 1. Emit per-criterion pass-rate, not just the rolled-up number This is the one that earns its place. Our judge scores each output against multiple criteria. For the extraction prompt it is four: correct fields, no hallucinated fields, format valid, no refusal. Early on we only emitted one number, prompt_eval.pass_rate, the fraction of examples that passed every criterion. That number is fine for a smoke test and useless for debugging. The problem showed up on a prompt change that looked clean. Overall pass-rate went from 0.93 to 0.91. Two points. Nobody would block a PR on two points. But underneath, the "no hallucinated fields" criterion had

If you are just getting started with Azure CLI and Bash scripting, this post is for you. I will walk you through how I automated the creation of Azure resource groups for multiple environments using a single Bash script — something that was taking a cloud admin several manual steps every week. This is Project 2 in my TechRush Cloud Engineering bootcamp series. If you want to see where this journey started, you can read my previous post where I tackled deploying a web app across two Azure regions for the first time . That project involved real blockers — quota limits, CLI version mismatches, and a deep dive into Azure Resource Providers. This one went smoother, and I think that is because the previous project was the hard school. The Problem Imagine a cloud administrator who has to create five resource groups every single week, one for each active project: Project-A-RG Project-B-RG Project-C-RG Project-D-RG Project-E-RG Every week. By hand. Management's response was simple: automate it. But here is where the task gets more interesting. Instead of creating one flat resource group per project, the better approach is to create four resource groups per project — one for each environment: Dev Test UAT Production This matters because each environment needs its own access controls, cost tracking, and lifecycle rules. You do not want your Development environment sharing a resource group with Production. Keeping them separate is a real-world cloud best practice, not just a bootcamp exercise. What You Will Need Before running this script, make sure you have the following set up: Azure CLI installed on your local machine. You can follow the official installation guide . An active Azure account . A free account works fine for this. A terminal that runs Bash — Linux, macOS, or WSL on Windows. Understanding the Design The core idea behind this script is parameterization . Instead of hardcoding project names, the script accepts a project name as input and uses it as a prefix for ev

Amazon Web Services has recently announced the general availability of the next generation of Amazon OpenSearch Serverless, with a redesigned architecture that enables 20 times faster resource provisioning than the previous serverless architecture, true scale-to-zero capability, and up to 60% lower cost than a provisioned cluster for peak loads. By Gianmarco Nalin

In the summer of 2016, Pokémon GO launched to a user base roughly fifty times larger than its capacity planning had anticipated. The engineering team had done load testing. They had throughput thresholds. They had autoscaling configured. Within hours of launch, the service was degraded globally — not because the infrastructure could not scale, but because it scaled too slowly against an arrival rate that exceeded every modelled scenario, and because the metric that was driving scaling decisions (CPU utilisation) lagged behind the actual saturation signal by several minutes. By the time CPU registered critical, the request queue had already grown to the point where p99 latency had crossed into the range where users were abandoning sessions faster than new sessions were being created. The engineering post-mortem identified the same root cause that appears in the post-mortems of most capacity-related incidents: the organisation's operational metrics were measuring how hard the infrastructure was working, not how much work the service could safely accept. CPU percentage is a resource utilisation metric. Memory percentage is a resource utilisation metric. IOPS is a resource utilisation metric. None of them is a service throughput metric. None of them tells you, with precision, at what arrival rate your SLO begins to degrade. Safe Operating Throughput is that metric. It is not a new concept in queueing theory or systems engineering — the idea of a safe operating ceiling predates modern distributed systems. What is new is its treatment as a first-class SRE metric: formally derived from load test data and SLO targets, continuously monitored for drift, and operationally enforced as a constraint in autoscaling configuration, capacity planning decisions, and deployment pipeline gates. Why Existing Capacity Metrics Are Insufficient The canonical capacity management approach in most organisations works like this: observe CPU or memory utilisation, set an autoscaling threshold (t

Per-request attribution starts with five fields on every call: provider, model, input tokens, output tokens, and ownership tags such as team, feature, and customer. A monthly vendor bill cannot explain why one feature, one tenant, or one prompt template suddenly became expensive. Request-level math can. As of June 8, 2026, OpenAI lists GPT-5.4 mini at $0.75 per 1M input tokens and $4.50 per 1M output tokens, while Anthropic lists Claude Sonnet 4 at $3 and $15 respectively. Gateway logs are useful, but they rarely solve AI cost tracking per feature unless you enrich them with business context and retry metadata. The practical operating model is simple: calculate cost on every request, attach ownership dimensions, then roll the data up into team, feature, and customer views. If you are searching for "LLM cost attribution per request," you are usually already past the basic billing problem. You can see your OpenAI or Anthropic invoice, but you cannot answer the questions finance and engineering actually care about: which feature drove the spike, which team owns it, which customers are unprofitable, and which prompt or model change caused the jump. That is why per-request attribution matters. It turns AI spend from a monthly surprise into an operational metric you can act on in the same day. Why LLM cost attribution per request matters now According to the FinOps Foundation's 2025 State of FinOps report, 63% of respondents now manage AI spending, up from 31% the year before. That jump is the real signal. AI cost is no longer a side bucket inside cloud spend. It is becoming a first-class FinOps workload. For teams spending $5,000 to $50,000 per month on LLM APIs, averages break down quickly. A support assistant, an internal coding copilot, and a customer-facing generation feature can all hit the same vendor account while having completely different margins, latency targets, and prompt shapes. If you only look at total spend by provider, you lose the unit economics. Per-r

Short answer first: use Supabase if you want Postgres plus auth, realtime, storage, and a dashboard as one managed bundle. Self-host Postgres – or use a managed Postgres – if you mostly need a database and your app already handles its own auth and logic. The choice is not really "Postgres vs Supabase". It's whether you need the extra layers Supabase puts on top of Postgres. Supabase is not a database Supabase runs on PostgreSQL, but it's a stack of services around it: Postgres – the actual database Auth – user signup, login, JWT tokens Realtime – live updates over websockets Storage – an S3-style file store Edge Functions – serverless functions Studio – dashboard + auto-generated REST/GraphQL API So "self-host Postgres or use Supabase" compares a plain database to a full backend. The honest question: do you need those extra layers, or just the database underneath them? A quick test: You use Supabase Auth, Storage, and Realtime → Supabase earns its place. You use one of them → it's replaceable. You use none and treat it as "Postgres with a nice dashboard" → you want plain Postgres. Side-by-side comparison Factor Supabase (managed) Self-hosted Supabase Plain Postgres (managed or self-hosted) Database engine PostgreSQL PostgreSQL PostgreSQL Built-in auth Yes Yes No (bring your own) Realtime / websockets Yes Yes No File storage Yes Yes No Dashboard + auto API Yes Yes No (use any SQL client) Backups Managed (limits by plan) You manage Managed or you manage Cost shape Metered, grows with usage Server cost + your time Database only Self-host effort None High (many containers) Low–medium Lock-in Medium–high Medium Very low The lock-in point decides it for many teams. Your data is standard Postgres in every option ( pg_dump portable). The lock-in is everything else: Auth tokens, Storage paths, Supabase-specific RLS policies, Edge Function code. The more Supabase-specific features you adopt, the harder the exit. When each option wins Pick managed Supabase when: You're startin

Ten weeks ago I did the thing every "grow your open source project" guide tells you to do. I carved a few small, self-contained tasks out of my backlog, labeled them good first issue , wrote crisp descriptions, and waited for contributors to roll in. They didn't roll in. The issues just sat there. This morning, one of them finally got picked up. A first-time contributor opened a clean PR against my MCP server: a smoke-test suite, no new dependencies, green across the whole Node CI matrix. Exactly the contribution the label was advertising for. And then my own repository spent the next twenty minutes trying to stop it from getting merged. Not with anything dramatic. With three quiet, individually-reasonable "best practice" gates that, stacked together, form a gauntlet aimed squarely at the one person you spent ten weeks trying to attract. I want to walk through each gate, because almost everything written about contributors is about attracting them, and almost nothing is about the last hundred feet — the silent friction between a willing PR and a merged commit. The advice is only half the story "Add good first issues and contributors will come" is true in the same way "build it and they will come" is true: technically, eventually, for a small subset, with survivorship bias baked in. My good first issue opened on March 31. The PR that closed it merged on June 8. That's sixty-nine days of a clearly-labeled, beginner-friendly task sitting untouched. I'm not complaining about the wait — that part is normal. I'm pointing out that the advice stops exactly where the interesting problem starts. Because the bottleneck was never finding someone willing. When someone willing finally showed up, the friction was entirely on my side of the fence. Gate 1: the CI that silently refuses to run GitHub Actions does not run workflows on pull requests from first-time contributors until a maintainer approves the run. This is a sane anti-abuse measure — fork PRs can run arbitrary code in yo

Three weeks ago, one of the teams we work with had a checkout outage. The root cause a malformed...