Create AI roles with custom prompts to keep conversations focused on specific topics. Each chat comes with a sandbox for running code, calculations, and generating files such as PDFs and Markdown documents with downloadable outputs. You can also assign a specific machine as the sandbox for private workloads, so sensitive tasks can run in your own environment.
What's great
multiple ai models (2)
I used this product to help us handle an employment dispute at our company.
Because it involved specialized legal knowledge, I had to cross-check and review the situation with multiple AI models to make sure our responses and actions at each stage were reasonably accurate.
Thankfully, after about two stressful months, the issue was finally resolved at the end of last month. Our actions were reasonable, and the evidence chain was complete. If I had handled it on my own, with my impulsive personality, I probably would have messed it up.
At first, I only used one AI model. Its advice looked very structured, but compared with the final approach we took, it could have exposed us to significant legal risk.
For me, this is a perfect example of why cross-checking with multiple AIs can be extremely valuable.
What needs improvement
The experience still doesn’t feel quite as smooth as ChatGPT, which may be related to model quality, or possibly to latency from my region.
I’m looking forward to seeing when GPT and Claude can be added. That’s also why I’m giving it 4 stars for now.
vs Alternatives
PoeCompared with other multi-model AI chat tools, such as Poe by Quora, the reason I chose this product is that it comes with a sandbox.
For example, when I had a document to prepare, it could directly generate a Markdown or PDF file for me to download and send as an email attachment. That was much easier than creating the PDF myself.
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49 viewsWhat's great
I use ReAkh to trigger builds across two Jenkins environments. Each one has around a dozen Git parameters, different branch naming rules, and separate version numbers to keep track of. Before, I'd always have to stop and double-check whether a branch needed the origin/ prefix, make sure I wasn't reusing a version number, and verify I was in the right environment.
Now I usually just tell ReAkh which branches I want to build. It handles the Jenkins login flow, CSRF token, parameter formatting, and build request automatically. If something fails because of a temporary network issue, it normally retries on its own, which saves me from watching every request.
One thing I didn't expect is that it keeps track of the workflow over time. For example, once we figured out that one Jenkins instance requires the origin/ prefix while the other doesn't, I haven't needed to explain that again. We've also run into occasional 504 errors from Gitee, and it generally retries without me asking. After using it for a couple of weeks and running around 30 builds, the repetitive parts of the process have mostly disappeared.
vs Alternatives
For me, the biggest difference is that ReAkh is focused on completing tasks instead of just answering questions.
With a normal AI chatbot, I still have to take the information it gives me and perform the work myself. ReAkh is different because it carries out the steps—preparing the parameters, triggering the build, and keeping track of the context from previous runs.
I also don't feel like I'm starting from scratch every conversation. It remembers things like which Jenkins environment has which conventions, what version we're currently using, and small workflow details we've already figured out. It's not perfect, but it saves me from repeating the same instructions over and over, which makes day-to-day build work noticeably smoother.
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24 viewsWhat's great
high-quality output (1)multiple ai models (2)
Really impressed cause ReAkh feels less like a chat box and more like a place where I can actually build things.
What I like most is that it lowers the barrier for getting real outputs from AI. Even though I know nothing about code, I can use it to build sildes, web pages, and games, and the best part is that I can view what it looks like instantly.
The multi-model setup also makes it easier to compare results and choose the right model for the task. It includes almost all popular models and I can try them at one place.
Excited to see what the team does next.
vs Alternatives
The AI teammate part is what stands out to me.
ReAkh doesn’t feel like just another chatbot. It feels more like having a few AI teammates that can help with different parts of the work, from brainstorming to writing to building something usable. And I can create whatever AI teammates as I like.
That makes the whole experience feel more collaborative and practical.
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64 views
Hey Product Hunt, I’m one of the developers behind ReAkh.
We built ReAkh around a simple pain point: AI chat is great at answering, but real work often needs more than an answer.
ReAkh supports multiple model families, including DeepSeek, Qwen, GLM, Kimi, and MiniMax. But model choice is only one layer. What we care more about is how to turn a model into a useful working environment.
For us, that means:
grounded answers with web search and Knowledge/RAG
custom AI teammates for focused roles and repeatable workflows
sandbox execution for code, calculations, and file generation
downloadable outputs like PDFs and Markdown
the option to connect your own machine for private workloads
What we’re exploring is whether AI chat should become more like a workbench: one place to research, reason, run, verify, and produce artifacts.
Where do you draw the line between a chatbot and an AI workspace you would actually trust for daily work?
We’d especially love feedback on model switching, sandbox safety, source grounding, custom AI roles, and workflows that still feel fragmented today.
Hi Product Hunt! 👋
I'm Jim, the front-end developer behind reakh, and I'm thrilled to share our AI product with you all today.
As a frontend dev, I wanted to truly stress-test what our AI could do. So, we decided on the ultimate dogfooding experiment: we used reakh to build its own official website (www.reakh.ai). Watching our AI generate the structure and code for its own home was an incredible "Inception" moment!
Here is a transparent look at my experience using reakh as my AI co-developer for this task:
The Pros (What went great):
Zero to MVP in Record Time: We went from a blank canvas to a fully structured landing page incredibly fast. It intuitively understood the layout needed for a modern AI product site.
Excellent Context Retention: It held the context of our product features and brand tone perfectly without needing constant prompt reminders.
Vibe to Code Translation: It successfully translated abstract design ideas into concrete HTML/CSS frameworks that looked great out of the box, saving me a ton of boilerplate coding.
The Cons (Where I still needed to step in):
Pixel-Perfect Tweaking: While the foundation was solid, achieving "pixel-perfect" responsive design across mobile and desktop still required some manual CSS adjustments on my end.
Complex Iterations: For a few highly specific interactive UI elements, it took multiple prompt iterations to get the logic exactly how I envisioned it.
I’m using ReAkh to create a custom AI teammate for my Mac mini, and wanted to share the prompt idea I’m testing:
“Act as Pika Agent, a local AI teammate running on my Mac mini. At the start of each session, load my AGENTS.md as the global rule set, load available skills from ~/.reakh/skills, and use them as working context. Help with local file tasks, terminal workflows, and project-specific automation. Follow my local rules first, ask for clarification when needed, and require explicit approval before sensitive file changes, commands, or actions that may affect system security or data integrity.”
This is the workflow that makes ReAkh feel different for me: instead of a generic chatbot, I can create a teammate that understands my own environment, follows my rules, and reuses my local skills.