lveillard

Yeah, perf was a big motivator. I still have nightmares with Cypher and neo4j's slowness. Thanks for jumping in!

Considered it, but it didn't fit our constraints-driven design ("this must have exactly one author"), first-class edges, polymorphism and JSON DX. SPARQL is a string query language which is exactly the kind of "build the query as txt and pray" that I wanted agents to avoid. BQL is more like graphQL but with a fully typed JSON shape, and batched, nested, topologically-ordered mutations built in.

We did borrow the inference ideas tho, the inference/recursive relations are on the oven!

Fair! We haven't wiped storage so far and we do multiple daily backups, to speed up development I'd rather keep that warning there until we implemente a proper migration engine and enable payments.

On the issues: those were filed by hand over a couple of years trying to ship products on these DBs. Probably came across as bragging but the point was that I spent a big chunk of my life digging into graph and modern databases, and the gaps pushed me to give up and build one that gathers all their best ideas

Hello! Temporal modeling is one piece, but the key things are performance (neo4j is a pain as murmansk said) and the AI-agent-centered design. It means everything has been tested from an agent point of view and we automatically gather agents' feedback to close self-improvement loops. Beyond the MCP, there are several agent-specific features, since long-term we want to compete with context layers like Mem0 or Cognee ,but natively, without the glue they need.

Say you want to build a context layer for your company with BlitzGraph. Agents would use ephemeral subspaces to gather as much info as possible prior to re-categorize it, store files natively, keep episodic memories, track everything that happens to each memory or record with the native $history. Also the query language is in JSON, so it's easy to build programatically, it'sis strongly typed, and errors are written to guide agents to fix them. Agents can also batch-query docs, which are dense and straight to the point for token optimization (and the discovery tree for related docs is also optimized for them)

The long term objective is to become the "brain" or logic/storage layer of AI agents

Yet another advantage of low carb diets

I see! makes sense. Will try it

Hey great job!

I’m surprised credit notes aren’t mentioned here. Have you worked on this as well?

A credit note must be linked to an existing invoice, it can cover all or part of the invoice items, it can be deducted from future invoices or trigger a refund… such a pain!

Bravo! Eager to see if we can integrate this, will probably save us a lot of time!

Guys you're getting a lot of bad comments for one simple reason. You failed your delivery.

1) You should have managed the expectations of the users in a better way. Tell them it will become a paid feature from the begining, so nobody gets surprised 2) The way everyone unsderstood this today was too aggresive. An infinite warning in visual studio saying "hey, i've stop working, please sign up and pay or uninstall me". Too violent.

A "Hey, we are happy you're using Copilot. We want to inform you that in 2 weeks we will close the beta and we will need you to sign up. But don't worry, it will be free for 60 days"

I'm sure 99% of people here would just be happy to pay those 10usd/month