Hire AI employees that run 24/7 in their own container with their own memory. One-click into your Slack, Telegram, or Teams. Pre-built for support, sales, research, SEO, or anything you write yourself. Pay per call for the tools they use.
Hey PH 👋 Shreyans here, co-founder of MakersClaw. Sachin's in the thread with me today.
We started this because every "hire an AI agent" tool we tried felt like a chat widget with a coat of paint. It forgot the conversation when you closed the tab, it couldn't actually do anything in the apps you use all day, and we kept hitting walls trying to make one do real work.
So we built MakersClaw the way we wanted to use it. You hire an AI employee for whatever role you need: support, sales, personal assistant, research, SEO, or your own custom thing. Each one runs in its own container with its own memory, 24/7. You connect it to your Slack, Telegram, or Teams in one click. No bot tokens, no webhook config, no JSON.
Sachin's dropping a comment right below with how the guts work, the tools, the runtimes, the pay-per-call model. Read that if you want the mechanics.
Two specific things we'd love your honest take on:
1. Does the per-call tool model make sense to you, or is the mental shift from "subscribe to a tool" to "your agent pays per action" confusing the first time you see it?
2. We ship with four pre-built templates (support, sales, personal assistant, SEO). Which one would you actually try first, and which role do you wish we had a template for?
We'll be here all day. Pile on the questions and we'll answer everything.
AI employees living where the team already talks is the right move. The hard part is making them helpful without becoming one more coworker to manage.
Congrats on the launch! The chat-driven onboarding is interesting. How are you validating role configuration flows when users give ambiguous or conflicting instructions to an AI employee? I imagine those edge cases could be challenging to test across multiple integrations.
Giving each employee its own pod with persistent postgres-backed memory that survives restarts is the detail that won me over, "remembers yesterday's conversation at 3 AM" is exactly where most chat-widget agents fall apart. On the pay-per-call model: do users get spend caps or alerts per employee, so a retry loop on a tool can't quietly rack up cost overnight?
Congrats on the launch!
The "AI employee in Slack/Teams" framing has been tried a few times but the pricing always trips it up — per-seat feels wrong for a non-human. How did you land on your model?
(Indie maker here, curious about the pricing call more than the product itself.)
AI employees living right in Slack and Teams sounds really useful. Can each AI employee be customized to a specific role or task?
AI teammates living where you already work, rather than another dashboard to check, feels like the right direction. Bookmarking this one.
the 'ai employee' framing is getting crowded fast - so many tools launched this month doing the same slack/teams bot thing. the per-call pricing on tools is the bit that'll catch people off guard when an agent loops or retries unexpectedly. what actually differentiates the memory layer here vs just wiring up a standard agent with a slack connector?
Hey PH 👋 Sachin here, jumping in with the engineering side for anyone curious.
Each employee is a Kubernetes pod with its own filesystem and its own postgres-backed memory. State survives restarts and channel disconnects. Even a full redeploy. We chose this over a serverless function model because we wanted the agent to be a process you can talk to at 3 AM and have it remember the conversation from yesterday morning. Cold starts and stateless containers kill that.
For app integrations we run a hosted MCP layer at the workspace level. You OAuth once per app (GitHub, HubSpot, Zendesk, Jira, Asana, Airtable, Gmail, Outlook, Calendar, more) and any employee in your workspace can use the integration after that. Each MCP server is managed on our side, so there's no JSON config or token paste on yours. No re-auth per agent.
For configuration we built a chat-driven onboarding flow. Instead of filling forms to set up the employee's role, tone, and context, you talk to it. It asks the questions, you answer, it writes its own config record. The mental model is onboarding a remote hire rather than setting up software.
Skills are modular blocks of context the agent retrieves dynamically when a task needs them. Private skills stay scoped to your workspace. Public ones get installable by any maker. So the agent isn't carrying the whole brain on every call. It pulls the right context for the work at hand.
Two runtimes: - PicoClaw runs on Python. Lighter, supports email channel and cron scheduling. - Moltis runs on Rust. Heavier. Web dashboard, browser automation, voice (15+ TTS/STT providers), CalDAV.
Happy to go deeper on any of the architecture. Ask me anything.
About MakersClaw on Product Hunt
“Hire AI employees that live in your Slack, Teams, Telegram”
MakersClaw launched on Product Hunt on June 16th, 2026 and earned 271 upvotes and 27 comments, earning #3 Product of the Day. Hire AI employees that run 24/7 in their own container with their own memory. One-click into your Slack, Telegram, or Teams. Pre-built for support, sales, research, SEO, or anything you write yourself. Pay per call for the tools they use.
MakersClaw was featured in SaaS (42.6k followers), Artificial Intelligence (471.1k followers), Bots (110.7k followers) and Vercel Day (19 followers) on Product Hunt. Together, these topics include over 151.1k products, making this a competitive space to launch in.
Who hunted MakersClaw?
MakersClaw was hunted by Rohan Chaubey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how MakersClaw stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Rohan Chaubey