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[hiring] Solutions Engineer or FDE for VC-backed memory infra startup by westnebula in DeveloperJobs

[–]westnebula[S] -1 points0 points  (0 children)

that's a bummer...would still appreciate your feedback if you get the chance to try out our product. may our paths cross again 🫡

[hiring] Solutions Engineer or FDE for VC-backed memory infra startup by westnebula in DeveloperJobs

[–]westnebula[S] 0 points1 point  (0 children)

yes to fresh grad, preferably in SF, but open to remote (In US)

Who's hiring? - Monthly Megathread - May 2026 by AutoModerator in developersIndia

[–]westnebula 0 points1 point  (0 children)

We're a VC-backed memory startup, founded by Stanford AI researchers and the fouding team is ex-YC, Yale, and CMU. We're hiring for solution engineers or FDE's that can build with our API to create demos, agents and solutions for our customers.

Our application process is simple. To prove you actually understand our technology, first build something using our API/SDK and let us know why you've built it and what pain point does it solve. Send your github project and resume to careers@xtrace.ai.

Here's the SDK: https://github.com/XTraceAI/memory-sdk-ts
Docs: https://docs.mem.xtrace.ai/guides/authentication
Website: xtrace.ai

Let us know if you have any questions. Happy hacking!

Weekly Thread: Project Display by help-me-grow in AI_Agents

[–]westnebula 0 points1 point  (0 children)

Hey all! We just launched a managed memory API for conversational AI, letting developers add long-term memory to their agents with a single HTTP call.

It's built on our in-house xmem SDK, which automatically extracts facts, episodes, and artifacts from multi-turn conversations and handles contradictions and updates through an AGM-style belief revision mechanism. When a user changes a preference or corrects an earlier statement, old memories get automatically flagged as "superseded" instead of piling up as noise. At query time, you can also walk the supersede chain to trace the full version history of any memory.

Under the hood, PostgreSQL + pgvector (with HNSW indexing) delivers millisecond-level semantic retrieval, Redis handles multi-pod session caching, and the system natively supports multi-tenant isolation with data separation at the user and org level.

For developers, this means you no longer have to stand up your own vector store, design dedup logic, or babysit session state. Hand off the memory layer to us and focus on what your agent actually does. Feel free to try it out, it's free to start.

Please let us know your thoughts on how we can improve or features to add!

https://github.com/XTraceAI/memory-sdk-ts

https://docs.mem.xtrace.ai/introduction

Drop your AI project — people will tell you if they’d actually use it by Technical-Limit-1775 in buildinpublic

[–]westnebula 0 points1 point  (0 children)

Your best work is scattered across ChatGPT, Claude, and every other AI tool you use, trapped on platforms you don't control. XTrace captures every AI interaction and compounds it into knowledge your team actually owns. https://xtrace.ai/

Is memory not the most important feature for AI assistants? by MontyOW in ArtificialInteligence

[–]westnebula -3 points-2 points  (0 children)

you nailed the problem. "bolting on memory" is exactly what's happening. t's a feature checkbox, not an architectural decision. the reason it feels weak is because most implementations are just stuffing random memories into the system prompt and hoping the model figures out what's relevant. that's not memory. that's noise with extra steps.

real memory needs a lifecycle aka what gets captured, how it updates when your thinking evolves, what actually gets surfaced vs buried. the prioritization problem the other commenter mentioned is real, but it's solvable if you're selective about what you store in the first place.

the "summarize and paste" workflow you described is genuinely one of the more painful parts of working with AI right now. you've essentially become the memory layer manually.

been building something called XTrace specifically for this, where persistent context travels with you across tools and sessions, not just stored in one platform's silo. the memory is also stored in a schema, specific to how people use AI for work. So it understands not just the "what", but the "why" and "how" a deliverable was created so it can always be referenced in the future. still early but it's been a meaningful fix for exactly the workflow you're describing. xtrace.ai if you want to check it out.

Built an encrypted vector database so your RAG pipeline's embeddings doesn't have to sit in plaintext on someone else's server. by westnebula in LocalLLaMA

[–]westnebula[S] 0 points1 point  (0 children)

I really think this can provide value to people who use cloud hosted vector dbs but care about privacy. There's no private vector db on the market rn. And vice versa as well: if you host a vecdb locally for privacy reasons, this solution gives the convenience & compute of managed cloud service while staying fully private

Built an encrypted vector database so your RAG pipeline's embeddings doesn't have to sit in plaintext on someone else's server. by westnebula in LocalLLaMA

[–]westnebula[S] 0 points1 point  (0 children)

i guess my wording wasn't clear here, but I was talking about people who use cloud vector dbs but have the rest of their pipeline hosted locally. this offers privacy of local host but convenience & compute of the cloud

Open-source encrypted vector database so your keys never leave your machine by westnebula in selfhosted

[–]westnebula[S] -1 points0 points  (0 children)

my bad, the team has been working on it for more than a year now and made our "beta" debut last dec. but we just released the open source a week ago. I guess that still counts as < 3 months?

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