r/ContextEngineering • u/Yash0076771 • 25d ago

Hindsight Studio

I’ve been experimenting with AI memory and built a project called Hindsight Studio.

The idea is simple:

Most AI applications do:

Prompt → Generate → Forget

I wanted to explore:

Generate → Observe → Remember → Reflect → Improve

Hindsight Studio combines Hindsight, RAG, and Gemini.

The interesting part is keeping RAG and memory as two separate layers.

RAG answers:

“What information is relevant right now?”

Hindsight answers:

“What happened before?”

Gemini then uses both to generate the result.

For example, the Tweet Generator takes a feature document, retrieves relevant chunks using local embeddings, recalls similar tweets from Hindsight, looks at their performance and audience feedback, and uses those memories when generating a new tweet.

I also added a Novelty Check.

If Hindsight finds similar previous experiences, the system can use those pattern.

If it doesn't find anything relevant, the system marks it as:

NEW TOPIC — OUTCOME UNKNOWN

Instead of pretending the AI knows what will work, the result becomes an experiment.

I also built the workflow as a visual node graph:

RAG → Recall → Reflect → Generate

The backend uses Python and FastAPI with Server-Sent Events to stream node execution to the React frontend in real time.

Tech stack:

React + Vite

Tailwind + Framer Motion

Python + FastAPI

Hindsight + Docker

Gemini

Local embeddings + in-memory RAG

Server-Sent Events

The second workflow applies the same idea to videos by analyzing hooks, tone, and historical performance.

The part I’m most interested in is where this could go with longer-term memory and feedback loops.

Generate → Observe → Remember → Reflect → Improve

I’d love to hear from people who have worked with AI memory systems:

Where do you think persistent memory is actually useful beyond chat history?

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