标签：#Slack

Over the past few months I built a side project that taught me more about production system design than any course. A Slack bot for deploy queue management. This isn't about the business side of it, it's a technical breakdown of the architecture decisions, the real problems I hit building a Slack app with NestJS, and what I learned solving each one. The problem Every engineering team has lived this. Two people deploy at the same time, one overwrites the other, and it turns into "who's touching prod right now?" shouted into a Slack channel. Sounds simple. Solving it properly across multiple teams, multiple environments, with timeouts, without ever locking anyone out, is not. Stack and why NestJS + TypeScript on the backend, PostgreSQL via Prisma, Redis + BullMQ for background jobs, @slack/bolt for the Slack integration. Choosing NestJS wasn't just preference. Its modular architecture (modules, providers, guards, interceptors) mapped really well to the domain. Every entity (Workspace, Project, Environment, Queue) became its own module, with the repository pattern keeping Prisma out of the business logic layer. The hardest problem: dynamic modals in Slack Block Kit Slack's Block Kit doesn't natively support a select input that reloads its options based on another select's value, within the same form. If you want "pick a project, then load that project's environments," there's no built-in prop for that. The solution combines two Bolt event types. A block_actions listener on the project select fetches the environments, then re-renders the whole modal via views.update : app . action ( ' project_select ' , async ({ ack , body , client }) => { await ack (); const selectedProjectId = body . actions [ 0 ]. selected_option . value ; const environments = await environmentService . findByProject ( selectedProjectId ); await client . views . update ({ view_id : body . view . id , view : buildModalWithEnvironments ( environments ), }); }); The detail that tripped me up the most: t

Anthropic Shipped @claude for Slack. My Team Runs on Telegram. Anthropic just shipped @Claude inside Slack channels. Tag the bot, it reads the thread, does work async, posts back. Nice product. Except roughly 95% of small businesses don't live in Slack — they run on WhatsApp, Telegram, and Gmail. If you're a solopreneur or a 1-to-10-person team, here's the exact four-part recipe I use to run the same pattern in Telegram for under $12/month. What Anthropic actually shipped (and who it's for) Anthropic shipped an enterprise distribution deal wearing a product launch t-shirt. @Claude for Slack lets you tag the bot in a channel or thread, gives it channel memory, connects to your other apps, and returns work asynchronously — but only on Slack Team and Enterprise plans. That's the punchline: it lives where the annual contracts live. Look at the raw user counts. Slack's own reporting puts it around 35–40 million weekly active users globally. WhatsApp is over 2 billion. Telegram is over 900 million. Gmail sits around 1.8 billion. In the 1-to-10-employee segment outside US tech, Slack penetration is single digits. Small teams in Europe, LATAM, and most of Asia coordinate in WhatsApp groups and run pipeline out of Gmail. They are not about to add Slack seats at $15/user/month just to get an @Claude mention. That's a rational call for Anthropic — Slack is where the enterprise procurement motion already exists. It's just not a product for the operator segment. And the pattern they productized is trivially replicable on any messenger with a bot API. Platform Weekly/monthly active users Bot API Cost to run a mention-bot Slack ~35–40M WAU Yes, paid plan $15/user/mo + API Telegram ~900M MAU Yes, free ~$5–12/mo API only WhatsApp Business ~2B MAU Yes, metered $0.005–0.08/conversation + API Gmail ~1.8B MAU Pub/Sub push Free tier + API The four-part recipe (works in any messenger) Every mention-bot is the same four moving parts: a webhook that fires on mention, a context store that ho

Slack modernized its data platform by replacing SSH based execution in Amazon EMR pipelines with a REST driven orchestration layer called Quarry. The migration covered 700 plus Airflow operators, improving security, reliability, and observability while eliminating direct SSH access across production clusters and enabling a server side job lifecycle model. By Leela Kumili