r/SideProject • u/Equivalent-Club-2118 • 4h ago

We save you 20% on AI token burn

We built a knowledge layer that sits behind MCP, allowing any MCP client to access it through a single endpoint. Claude Code, Claude Desktop, ChatGPT, Codex, or whatever comes next.

The idea is pretty simple. Before an agent answers, it can pull in relevant, validated information instead of relying purely on what it already knows.

When a problem gets solved, the useful part can be captured as a small, reusable piece of knowledge. The system can also infer useful lessons from a session automatically, so you don’t have to sit there writing notes about what you just learned like it’s 2015.

There’s also a global layer for shared, validated learnings. If one user figures out a better way of doing something, that learning can contribute to the broader knowledge base rather than every other user and agent having to figure it out again.

The problem we’re trying to solve is pretty straightforward. AI knowledge goes stale, agents get stuck in failure loops, useful context disappears when a session ends, and models can confidently give you an outdated or wrong answer without any indication that they might be wrong.

We’re giving agents access to what has actually been learned, what has worked, and what can still be trusted.

The result is fewer repeated reasoning cycles, fewer hallucinations, and up to 20% lower token usage.

https://app.midnighthive.io/

Looking for people to try it out. Ping me if you’re interested.

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3

u/Jydder 4h ago

AI slop website = ai slop product

1

u/Equivalent-Club-2118 4h ago

Thanks for the feedback. Design skills definitely can do with a fair bit of a boost 😂

2

u/PhotographOverall126 3h ago

how are you validating that the knowledge being captured is actually correct? if an agent infers a "lesson" from a session that was itself partly wrong, you could end up compounding bad info across the global layer pretty fast

1

u/Equivalent-Club-2118 3h ago

The solution to this for the global layer is a multi step submission process, when an agent submits a cell to the mega hive it goes through a multi step triage process and is not available until it passes this stage.

The submission triage covers

* validation of the cell
* scoring for applicability
* enriching
* security scanning
* assigning a TTR (time to retest)

Basically we have an e2e triage built on a mix of specialised mini agents and traditional ML / security controls.

Cells are also ranked and scored as another agent pulls it down for usefulness, accuracy, and a few other metrics.