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.
If I had to run 20 tasks/day pertaining to PA - would it cost me more than Claude's current API?
Congrats. How are you handling permissions? Giving agents access to Slack, GitHub, Gmail, etc. gets scary pretty quickly. :D
the hire AI employees framing is compelling for the marketing but creates a specific expectation problem. employees have accountability, consistent behavior, and you can fire them when they underperform. AI agents have none of those properties in a way most teams are used to managing. curious how MakersClaw handles the case where an agent starts behaving unexpectedly, gives wrong answers, or takes an action it shouldn't have. what does oversight and correction look like in practice
The SEO template caught my attention. Does it actually execute workflows or mostly generate content?
Congrats on shipping. What tasks can this actually accomplish end-to-end without human intervention?
How do you prevent agents from becoming expensive when connected to lots of tools? BTW, congrats on the launch.
how long-term memory works here? Are agents remembering across weeks/months or just retaining recent context?
The "configure by chatting with the employee" concept really caught my attention. In practice, do users prefer conversational setup over traditional forms and settings?
This looks really cool, congratulations on launching! what's next for your roadmap?
Congrats! I'm a little curious how does MakersClaw handle context continuity when the same user interacts across Slack and Telegram simultaneously?
the framing of 'AI employees that live in slack' is sharp. the part i'm most curious about: how does an AI employee accumulate a track record? a human gets references from former teammates, a paper trail of what they shipped. when this AI moves between teams, what travels with it?
The per-call model is a smart call, pay for what your agent actually does is way easier to justify than another monthly subscription. Personal assistant template first for me. Would love to see a research analyst role next
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.
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?
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 403 upvotes and 57 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 (43k followers), Artificial Intelligence (473.1k followers), Bots (110.7k followers) and Vercel Day (20 followers) on Product Hunt. Together, these topics include over 158.9k 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