Yes. It’s not just about finishing the product and making money, it’s about finishing what you started, because you began it for a reason. And two (the more important one): it will most likely lead you to something new and better along the way. Ask yourself: how many times have you been working on something that evolved into something better, or from which something completely new emerged?
300 agents sharing one memory is the right instinct. The next unlock: a store where related memories become neighbors ON WRITE with no LLM linking, no graph pipeline. The edges are just… there, exact, when agent #217 touches what agent #3 wrote. Meaning has gravity. In two weeks i will show and publish repo.
Nice orchestration, but it’s missing memory. Every agent wakes up blank, relearns what the last run already knew, and burns tokens re-reading the past.
We’re shipping the fix: shared exact memory for agent fleets. Works the same on Claude, GPT or your local 4B (no LLM ever touches what’s stored). Milliseconds to recall, megabytes to run, and every answer shows exactly what it checked.
In a few weeks we’re launching the open version. Follow me and you’ll see it first.
Quick question for people who use AI agents daily.
How many agents do you currently have active, and how do you store their long-term "memory" or context?
I want to see how real people are actually solving this.
@filipscaleson Thanks for the support amid all the memes. What do you think of the concept? Anything that interests you or that you see as a challenge?
The memory system of AI agents today is broken.
It’s not a single system. It’s a Frankenstein of disconnected pieces:
Data Ingestion → Chunking + Embeddings → Vector DB + Retrieval + Rerank → Agent Context
Sounds familiar? and the biggest problem isn’t even the cost…
It’s that the memory arrives adulterated.