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aluzzardi

HN member
Joined
Tue, May 24, 2011, 3:12 PM UTC
HN karma
95
Public activity
24 items

About aluzzardi

Building https://www.mendral.com/ - early Docker engineer (wrote first lines of code)

Recent public activity

  1. comment
    Comment #47994351

    Thank you, appreciate it! Regarding scoping: In our case, the agent loop runs in the same way as our API server does (as in, it’s a multi tenant service running in a container some…

  2. comment
    Comment #47992217

    Author here. I should have made it more clear that the article is about agent / harness building (not about running third party agents). > I barely trust the harness more than the …

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    Comment #47992169

    Author here. I’m worried about the same (models tuned for specific harnesses). We actually work around that by respecting the “contract”. For instance, our harness’ Bash signature …

  4. comment
    Comment #47992136

    Author here. This is an interesting and novel field, so I’m not pretending I know the answers, but this is what worked for us :) At the end of the day, and oversimplifying things: …

  5. comment
    Comment #47991891

    Author here. I think the confusion is that “agent” is used for two very different things: - building an agent - an “agent” product/runtime (Claude Code, etc) In the first case, the…

  6. comment
    Comment #47991730

    Author here. In my opinion, the main driver here is how fast models have evolved in the past 12 months. It makes the architecture of everything around them obsolete, very fast. We …

  7. comment
    Comment #47991312

    Author here. Because of parallelism and non determinism. This problem is quite common and not limited to memories. For instance, Claude Code will block write attempts and steer the…

  8. comment
    Comment #47991256

    Author here. My definition is: you take an agent, remove the model and you’re left with the harness. Tools, memories, sandboxing, steering, etc

  9. comment
    Comment #47991161

    Author here. Depending on how it’s designed, the harness itself doesn’t need any sandboxing. At the end of the day, it’s a “simple” loop that calls an external API (LLM) and receiv…

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  14. comment
    Comment #47190408

    It started with Sonnet 4.0 as a single agent and now it’s a mix of Opus 4.6 and Haiku 4.5 agents. Opus plans the investigation and orchestrates the searches. Haiku is the one actua…

  15. comment
    Comment #47187196

    > it's not magic and you need to make the job of the agent easier by giving it good instructions, tools, and environments. This. We had much better success by letting the agent pul…

  16. comment
    Comment #47184537

    There are 2 layers of compression: - ZSTD (actual data compression) - De-duplication (i.e. what you're saying) Although AFAIK it's not "just point to it" but rather storing sorted …

  17. comment
    Comment #47184029

    Mendral co-founder and post author here. I agree with your statement and explained in a few other comments how we're doing this. tldr: - Something happens that needs investigating …

  18. comment
    Comment #47183745

    From our experience running this, we're seeing patterns like these: - Opus agent wakes up when we detect an incident (e.g. CI broke on main) - It looks at the big picture (e.g. whi…

  19. comment
    Comment #47183550

    > My experience with LLM generated SQL in OLTP and OLAP platforms has been a mixed bag Models are evolving fast . If your experience is older than a few months, I encourage you to …

  20. comment
    Comment #47183441

    We've actually started to gather metrics this week to write that exact post :) Coming soon!

  21. comment
    Comment #47183345

    Mendral co-founder here and author of the post. This is an interesting approach. I definitely agree with the problem statement: if the LLM has to filter by error/fatal because of c…

  22. comment
    Comment #47182522

    Post author here. Yes, it works really well. 1) The latest models are radically better at this. We noticed a massive improvement in quality starting with Sonnet 4.5 2) The context …

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