DungeonQ diverts designated suspicious sessions into persistent synthetic worlds. Human and AI clients use world-only tickets; operators observe and approve bounded adaptation. Inspect recorded runtime checks and the original Astra experiment.
DungeonQ is a defensive deception runtime. Its job is to route a designated suspicious session into a persistent synthetic world, let the participant continue useful work there, and give the operator an inspectable record of what happened. It is built for security teams and developers working with human or AI clients.
The sequence is concrete: enter through a real adapter, read or change a world record, receive a useful world-only Wrong Ticket, and return to the same state after restart. A separately approved finite policy can add follow-up records in response to observed activity. Independent artificial-origin checks measure where the session's authority stopped.
The current reference shares one core across HTTP, MCP, bounded SSH/PostgreSQL and a private Unix workload broker. Its contexts are explicitly provisioned; automatic attack classification and production host integration remain future acceptance work. Start with the six recorded checkpoints, then self-host the same runtime to operate it. The public page presents evidence, not a hosted security service.
GPT-6 Astra contributed the original bounded assistant profile: it proposes a command from minimized synthetic context, while DungeonQ validates the candidate and keeps approval separate. That experiment, its automated-reviewer limitation and all original records remain available. The new runtime checks are engineering evidence, not a new live Astra experiment or a claim that an AI was fooled. The earlier 73-second film is retained and clearly scoped.
I'd welcome feedback on the diversion itself: can you follow the participant's useful work, the operator's observations and the independent origin checks, and identify what you would need to integrate this into an authorized environment?
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About DungeonQ on Product Hunt
“Divert suspicious sessions into persistent decoy worlds”
DungeonQ launched on Product Hunt on September 18th, 2026 and earned 55 upvotes and 1 comments, placing #88 on the daily leaderboard. DungeonQ diverts designated suspicious sessions into persistent synthetic worlds. Human and AI clients use world-only tickets; operators observe and approve bounded adaptation. Inspect recorded runtime checks and the original Astra experiment.
DungeonQ was featured in Developer Tools (519.8k followers), Artificial Intelligence (479.1k followers) and OpenAI Day (39 followers) on Product Hunt. Together, these topics include over 208.4k products, making this a competitive space to launch in.
Who hunted DungeonQ?
DungeonQ was hunted by Ranopha Liu. 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.
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Ranopha Liu
Hi Product Hunt — I'm the maker of DungeonQ.
DungeonQ is a defensive deception runtime. Its job is to route a designated suspicious session into a persistent synthetic world, let the participant continue useful work there, and give the operator an inspectable record of what happened. It is built for security teams and developers working with human or AI clients.
The sequence is concrete: enter through a real adapter, read or change a world record, receive a useful world-only Wrong Ticket, and return to the same state after restart. A separately approved finite policy can add follow-up records in response to observed activity. Independent artificial-origin checks measure where the session's authority stopped.
The current reference shares one core across HTTP, MCP, bounded SSH/PostgreSQL and a private Unix workload broker. Its contexts are explicitly provisioned; automatic attack classification and production host integration remain future acceptance work. Start with the six recorded checkpoints, then self-host the same runtime to operate it. The public page presents evidence, not a hosted security service.
GPT-6 Astra contributed the original bounded assistant profile: it proposes a command from minimized synthetic context, while DungeonQ validates the candidate and keeps approval separate. That experiment, its automated-reviewer limitation and all original records remain available. The new runtime checks are engineering evidence, not a new live Astra experiment or a claim that an AI was fooled. The earlier 73-second film is retained and clearly scoped.
I'd welcome feedback on the diversion itself: can you follow the participant's useful work, the operator's observations and the independent origin checks, and identify what you would need to integrate this into an authorized environment?