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Comments (19)

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  nevon
  I feel like this is a bit of a "draw the rest of the owl" problem. With a couple of terabytes of logs per day, god knows how many metric timeseries, thousands of cloud environments, thousands of repositories, hundreds of thousands of build pipelines running every day, it's not something you can just point an agent at and call it a day. Segmenting and correlating the data is _hard_. Even something as seemingly simple as "p95 latency of this endpoint has increased - what repo contains the responsible code" is difficult to do, even if you are willing to give an agent read access to everything.
• t0mas88
  Interesting idea. I guess the main challenge is to create some kind of harness to make the input volume appropriate for an LLM to evaluate. Billions of the same log events from a raw stream will be cost prohibitive and not find you much interesting output. But a trend in some value changing would.As a human I look at Datadog or Grafana plus some log searches, maybe those need to become tools for the agent.
• anon
  undefined
• yegle
  I have been doing something similar but much smaller scale: having a daily scheduled task from Antigravity to ADB connect to my phone and dump all logs and do analysis to let me know if there's any app behaving weirdly (e.g. repeated crashes).The adb connection is intentionally over Tailscale so in the morning after I connect to some random WiFi (Android remote debugging is only enabled w/ WiFi connection) I can start reading the report, then quote and ask Gemini to fix some issues.
• skyfine
  We have been doing this recently after some security incidents. Constant monitoring of all security related events, and being able spot anomalys which our normal system isn’t able to catch. The active part is just emailing us right now, but I am considering adding some light lock down actions.
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  kelseydh
  It feels like the real value here will be from monitoring services like DataDog adding this functionality to their own internal products, rather than companies rolling their own agentic monitoring. Datadog's Bits AI is already quite helpful in scanning monitoring data for insights.
• forgotmypw17
  This is already a product: https://www.tasksmind.com/(Disclosure: Not mine, but a friend's.)
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  2bird3
  Feels like the author hits the nail on the head but leaves out higher cardinality data. Would love to know how Anthropic goes about ingesting and analyzing their logs, of which they presumably have many.
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  renegade-otter
  This is like tokenmaxxing for observability?
• esafak
  How are you setting the alerts? Are you using anomaly detection? If so, what kind?
• simianwords
  https://news.ycombinator.com/item?id=50134310I made a comment touching on similar things a while back. This is obviously the future.Expect a lot of techno conservatives and greybeards pushing back on this.“Just have better unit tests”“Just have traditional alerts using SLO budgets”