Launching today
Rebel is a desktop AI workspace for agentic work. It connects your memory, meetings, files, actions, automations, and tools so AI agents can help with real work — while keeping sensitive actions behind approval checks. Built Fair Source, with portable workflows and model choice.
Mindstone Rebel
Hey Product Hunt, I’m Melissanthi, Senior Product Engineer at Mindstone.
We’re launching Rebel as a Fair Source AI desktop app for agentic work.
The reason we’re doing this now is simple: AI work shouldn’t be trapped inside one closed platform or one model provider. Rebel is built as a desktop workspace that people can download, run locally, inspect, customise, and connect to their own tools through MCP.
Fair Source means the code is available, but with practical restrictions that let us keep building the product sustainably. Small teams and individuals can use and adapt Rebel freely, while larger organisations need a commercial licence.
This release is also part of a broader push toward more portable AI workflows: model choice, local-first files, MCP connectors, and workflows that teams can actually understand and adapt.
We’d love feedback from makers, developers, operators, and AI teams on:
• what you’d want from a Fair Source AI workspace
• how you think about model choice and local-first AI tools
• what would make Rebel useful in your own workflow
The 'ask first' principle is something we've had real debates about internally. Autonomous agents that act without confirmation can compound errors in ways that are hard to recover from, especially in stateful workflows. What's your approach to calibrating when the agent should interrupt vs. proceed? Does it use confidence thresholds, or is it more rule-based?
Mindstone
@anand_thakkar1 it’s a combination of confidence thresholds, with auto updating rules (you can tell the system how to update it’s own rules for future calls) and hard lines (like a non understood bash command).
All of this in context of the user’s query: if the query specifically asked to send an email, the “send-email” tool is fine. If the request was “do some research” that same tool call appears more risky.
Mindstone
@shubham4real we have a few approaches and systems working together. 1) the system learns from your approvals over time and you can tell it to “approve in X context”. 2) the system is aware of original query. Sending an email when specifically asked to do so is normal. Sending an email when the query was about research, less so. 3) the system is aware if impact (writing to shared spaces is more risky than writing to a private space). 4)you can switch individual tools on/off so you can have an email mcp with rights to draft, but not send for example
Strong angle, and the maker replies show you've thought about the hard part. One thing I'd add from doing this on customer-facing work: the risk with ask-first isn't only where you draw the line, it's approval fatigue. If the agent interrupts too often, people start rubber-stamping every prompt and the gate quietly stops protecting anything. Two things seem to matter most: making each approval information-rich enough to decide in a couple of seconds (what it's about to do, why, and the source it's acting on), and the auto-updating rules you mentioned so it stops re-asking about things someone has already approved a dozen times. Get those right and ask-first scales; get them wrong and it becomes click-through noise. Curious whether Rebel surfaces the "why" and the source inline at the approval moment. Congrats on the launch.
Mindstone
@syed_noor4 great shout and totally agree. Yes we try to surface the context and all information required to easily make a “yes/no” call within a few seconds. Not perfect yet (and will continue iterating), but hopefully better than what’s out there at the moment.
where exactly do you draw the line between what a small team can adapt freely vs what needs a commercial licence? Asking because that clarity is usually what makes or breaks adoption for us.
Mindstone
@thomas_wright2 Anyone can adapt freely. Up to 100 users within the same group don't require a license. More than 100 would, but we like to think you'd get a ton of extra value around AI transformation of the entire organisation alongside of it (an impact dashboard measuring your ROI, clear analysis of where things are working and where not, etc.) as well as direct support from our team.
Since it's local-first with model choice, can the approval checks for sensitive actions run entirely on a local model, or does the gating logic still require a cloud model call?
Mindstone Rebel
@nikita_zaro You are two steps ahead! That is exactly the kind of thing we're thinking.
Right now, there are 3 different tiers of model:
- Planner for overall task decomposition, needs a smarter model
- Worker does most of the heavy lifting, could be a mid-tier model
- Background/behind-the-scenes model: does a lot of the lower-level house-keeping (e.g. safety-approval classification).
You could set that background/BTS to a local model, and then the safety approval decisions would be routed through it.
I haven't tried this, but it's a great idea.
@greg_detre Love this — the three-tier setup is really elegant, and routing safety approvals through a local background model makes a lot of sense. Thanks for the detailed reply!
Mindstone
@nikita_zaro you can configure it to entirely rely on your local model even for the gating logic
@joshuawohle That's great to hear — full local model support even for the gating logic is exactly what I was hoping for. Thanks!
Love the Fair Source approach. Curious why did you choose fair source over fully open source for Rebel, and what feedback have you received so far from developers?
Mindstone
@manjesh_yadav1 as these types of AI operating systems become ever more fundamental to the businesses they operate, we thought it important you get to inspect how they work. It creates trust and extendibility, so we never get in the way.
We also wanted to make sure smaller companies could use the best technology had to offer without being constrained by cash.
At the same time, we are still trying to build a business and so thought fair source in this way was a good and balanced path allowing for all of this to be true.