buremba

24 ms·

1.
by buremba 6d ago
being opinionated doesn't mean you can attack people you compete or don't like every other week
2.
Self-Improving Agents Are Event-Sourced(lobu.ai)
5 points by buremba 1mo ago|0 comments
3.
by buremba 1mo ago
Hi HN! I believe agents running on markdowns was the first step but instead of making the filesystem forkable/clonable I believe event sourcing on a real database is a better concept for agents to understand all the work that has been
4.
Self-improving agents are event sourced(lobu.ai)
1 points by buremba 1mo ago|1 comments
5.
by buremba 1mo ago
No it doesn't and it's simply useless. They have been trying to enable publishers to charge scrapers so that's why they're pushing this path hard.
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Self-Improving Agents Are Event-Sourced(lobu.ai)
2 points by buremba 1mo ago|0 comments
7.
by buremba 2mo ago
I love the concept to use different harness frameworks in qm but a true multiplayer harness needs to support other agents and any MCP clients, including Cowork. Making agents multiplayer is mostly a context problem. You could be using ChatG
8.
by buremba 2mo ago
What's the rust based tui that has same capability as opencode?
9.
by buremba 2mo ago
Not really because you're not building a database or GUI app where using native elements & data structures help a lot with memory pressure. TUI renderer is the one using the memory heavily so your terminal takes the heavy lifing. I
10.
by buremba 2mo ago
I tried twice and ran into bugs that prevented me to trust it
11.
by buremba 2mo ago
Opencode gives you better defaults and a Mac/Windows app for free but pi is much more extensible and portable.
12.
by buremba 2mo ago
For TUIs, Rust/Go vs Typescript doesn't really makes a huge performance difference and you lose the 50x bigger community advantage of Typescript.
13.
by buremba 2mo ago
I would recommend using https://pi.dev/ over Grok Build with your xAI subscription at this point
14.
by buremba 2mo ago
I'm not sure why I'm being downvoted but I didn't mean it in a negative way. For such announcement, I would expect them to give me clues on when I should use this model and in which cases it's the best one. The benchmark
15.
by buremba 2mo ago
Then why are they publishing the benchmarks which makes them look worse than GLM 5.2?
16.
The Agent Loop Is the New SaaS(lobu.ai)
2 points by buremba 2mo ago|0 comments
17.
by buremba 2mo ago
We use Pi harness, same agent that powers Flue
18.
by buremba 2mo ago
We also tried to use OpenClaw for teams but unfortunately OpenClaw gateway is not designed as multi-tenant. Peter also confirms it: https://x.com/steipete/status/2026820269050855757 That's why we built https
19.
by buremba 2mo ago
It feels like somebody prompted an AI agent at CF "why Meerkat is better than Raft" when drafting this blog post.
20.
by buremba 2mo ago
Filesystem is best when there is a single writer and many readers. If you have bunch of files and don't have any structure, yes filesystem is great but the moment when you need consistency & performance (which you need sooner rathe
21.
by buremba 3mo ago
It's not built on OpenClaw, it uses Pi the harness that powers OpenClaw. We were using the GTM for multi-tenant OpenClaw when there was hype but quickly moved away from it as OpenClaw is a nightmare in terms of security.
22.
by buremba 3mo ago
It's interesting to hear that 60% of PRs at Anthropic is created by the Slack bot. While building a Slack bot is easy, making it look like an AI teammate is pretty hard. This is exactly the gap we’re working on with Lobu.ai — disclosur
23.
by buremba 3mo ago
Most SaaS stacks are a collection of vertical tools that each own one piece of the business but don’t really share memory or context with each other. I wrote a post arguing that agent loops are becoming the better model. The thing is, a lot
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The Agent Loop Is the New SaaS(lobu.ai)
2 points by buremba 3mo ago|1 comments
25.
Show HN: Open-source back end for multi-user AI agents with shared memory(github.com)
2 points by buremba 3mo ago|0 comments
26.
by buremba 3mo ago
Materialized tables are useful for time-series or sharding-like use-cases. You essentially offload the work to INSERT time to locate the data into relevant buckets/sub-tables that you can DROP later. We use materialized views for appen
27.
by buremba 4mo ago
Looks interesting! It reminds me of pg_lake, which we evaluated for our startup https://lobu.ai but it's missing a lot of pushdown capabilities which made OLAP queries expensive. I also tried DuckLake but that required us t
28.
by buremba 4mo ago
Thanks for correcting me, I was wondering why I got downvoted. :) Clickhouse is also great for OLAP for sure
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by buremba 4mo ago
Ahh, sorry about that. It should be fixed in an hour, looks like we mixed the permissions. I just tried and confirmed other login methods work if you would like to try out.
30.
by buremba 4mo ago
The native extensions are fine but I don't have good experience with any third party extensions, so far tried Timescale, pg_lake, citus, and pgvectorscale. They look very appealing but it's usually a trap as you can't get the

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