nishantmodak
Call a Human MCP
Two tools:
- ask_human(question) – agent blocks until you reply
- request_approval(action) – agent blocks until you click Approve or Deny
Notifications via Slack (with interactive buttons), or native macOS
dialogs. Works with any MCP-compatible client over stdio or SSE.
The use case that motivated it: I kept coming back to find my agent had done
something I didn't want while I was away. Now it just asks.
GitHub: https://github.com/nishantmodak/call-a-human-mcpWhy high-cardinality workloads fail differently in Prometheus vs. ClickHouse
The intent here isn’t to argue that high cardinality is bad but to explain the mechanical reasons unbounded cardinality shows up later as cost surprises, slow queries, and trust erosion.
Curious if others have seen similar query-time or indexing issues they have noticed.
Why high-cardinality labels stress metrics databases
>Engineers have to pre-define and send all telemetry data they might need – since it’s so difficult to make changes after the fact – regardless of the percentage chance of the actual need.
YES. Let them send all the data. The best place to solve for it is at Ingestion.
There's typically 5 different stages to this process.
Instrumentation -> Ingestion -> Storage -> Query (Dashboard) -> Query (Alerting)
Instrumentation is the wrong place to solve this.
Ingestion - Build pipelines that allow to process this data and provide for tools like streaming aggregation, cardinality controls that allow to 'process it' or act on anomalous patterns. This atleast makes working on observability data 'dynamic' instead of having to go change instrumentation always. Storage - Provide blaze (2hours), hot(1 month), cold(13 months) of tiered data storage with indipendent read paths.
This, in my opinion has solved for the bulk of cost & re-work challenges associated with telemetry data.
I believe, Observability is the Big Data of today, without the Big Data tools! (Disclosure: I work at Last9.io and we have taken a similar approach to solve for these challenges)
Running SLOs
Latency SLO
Observe a Service Not a Server
Need for Systems Observability
Monitoring at IPL Scale
Show HN: SLO Computer
Fascinating Story of a Vaccine from 1800s
Clever Hans Fallacy of Monitoring Tools
All our monitoring tools enable us to observe what we want to look at. Known knowns.Thus, missing out on the unknown unknowns and being in an endless trial and error state. One at a time.https://blog.last9.io/sre-tooling-clever-hans-fallacy/
Unix Philosophy
Non HTTP Microservices framework
Found this extremely interesting. Probably because, I was surprised by the thought that, this could be done
XBee networks over smartphone using BLE
$1000 in Google Adwords + Google Apps credits.
This is what they mean!
What kind of progress/innovation does he refer to in this?