Tag: #observability

← All posts

• Self-Hosting Langfuse — Deployment Paths, Secrets, and What Catches You on First Boot

  Running Langfuse yourself means standing up two containers and four datastores at once. Working from the official documentation, this post lays out the docker compose path and the Helm chart path, which secrets you have

  2026-08-14 · 9 min read #observability#langfuse#self-hosting#docker-compose#kubernetes

• When Traces Become Cost — Retention, Sampling, and Masking in Langfuse

  Tracing feels free at low traffic and then comes back as storage cost and a privacy problem. This post splits the points where volume turns into cost into four, confirms from the official documentation that sampling deci

  2026-08-14 · 9 min read #observability#langfuse#cost#sampling#data-retention

• Langfuse SDK Instrumentation — What Is Captured Automatically and What You Add by Hand

  Creating traces is not about installing an SDK, it is about drawing boundaries. Working from the Langfuse Python SDK v4, this post lays out what the decorator, the context manager, and manual observation creation each do

  2026-08-14 · 8 min read #observability#langfuse#llm-tracing#instrumentation#opentelemetry

• Why Langfuse Puts Traces in ClickHouse — How the Storage Layer Splits the Work

  The first surprise when you self-host Langfuse is that there is not one datastore but four. ClickHouse, Postgres, Redis, and object storage each take a different share. Working from the official documentation, this post

  2026-08-14 · 10 min read #observability#langfuse#clickhouse#architecture#postgres

• The Plugin Kernel Architecture of DeepSeek Harness — What Makes an Agent You Can Rewind

  Harness, the open source coding agent DeepSeek released, makes the model, the tools, and even the UI into plugins. But the interesting part is not the word plugin. It is two design decisions: representing every execution

  2026-08-14 · 7 min read #ai-agent#architecture#plugin-system#observability#open-source

• The Langfuse Tracing Data Model — How Trace, Observation, and Score Hold One Execution

  Looking at the Langfuse UI first tells you nothing about what you are looking at. This post starts from the shape of the data Langfuse collects. What a trace groups, what separates a span from a generation from an event,

  2026-08-14 · 8 min read #observability#langfuse#llm-tracing#data-modeling#opentelemetry

• After You Have Collected It — Langfuse Dashboards, the Metrics API, and Scores on Traces

  Collecting traces and getting answers out of them are different jobs. This post lays out which metric axes Langfuse offers and which dimensions you have to slice them by, then walks the Metrics API v2 query structure as

  2026-08-14 · 9 min read #observability#langfuse#llm-evaluation#metrics-api#dashboard

• vLLM Metrics — What to Chart and What to Alert On

  The series vLLM exposes answer questions GPU metrics cannot: how many requests are running versus waiting right now, how full the KV cache is, how long until the first token. This post reads the official vLLM documentati

  2026-08-12 · 7 min read #gpu#kubernetes#vllm#prometheus#observability

• DCGM Exporter — GPU Utilization Is Not What You Think It Is

  DCGM Exporter is the standard path for exposing GPU telemetry in Prometheus format, but the utilization-style metric that ends up on nearly every dashboard does not measure what people expect. This post reads the default

  2026-08-12 · 9 min read #gpu#kubernetes#dcgm#prometheus#observability

• NVIDIA GPU Operator — The Six Pieces You Used to Install by Hand

  Running GPUs on Kubernetes used to mean matching six pieces on every node by hand: the driver, the NVIDIA Container Toolkit, the device plugin, DCGM, GPU Feature Discovery, and Node Feature Discovery. The NVIDIA GPU Oper

  2026-08-12 · 6 min read #gpu#kubernetes#gpu-operator#nvidia#dcgm

• Open Source Worth Watching Right Now (4) Observability and Security

  With observability data, volume is cost, and a security tool that never makes it into the pipeline never gets used. This post introduces 12 open source projects that have genuinely taken hold — instrumentation standards,

  2026-08-12 · 5 min read #open-source#observability#security#opentelemetry#ebpf

• GPU Serving SLOs and Alert Design — What to Promise and What to Page On

  Putting an SLO on a GPU inference service starts with deciding which metric represents the user experience. Time to first token and throughput eat each other, so setting a target on only one guarantees the other collapse

  2026-08-12 · 7 min read #gpu#kubernetes#slo#alerting#prometheus

• Visitor Analytics Shows Only 0.5 Percent of Your Traffic — Judge Bots by Origin, Not by Self-Report

  Drawing on one year of defending a 1.5-million-page site against scrapers, this post sets out the principles of dealing with bot traffic. JavaScript-based analytics cannot count bots, so you have to read server logs, and

  2026-08-09 · 11 min read #network#bot#cloudflare#waf#scraping

• Putting Observability Data Into ClickHouse — Schema, Rollups, TTL, and Splitting the Work ♪ Listenable

  When traces and logs grow to multiple terabytes a day, a single search engine or time-series database starts to buckle. This post lays out why ClickHouse fits observability data so well, in terms of columnar storage, com

  2026-08-02 · 16 min read #observability#clickhouse#opentelemetry#data-modeling#cost

• Dashboards That Get Read and Alerts Worth Paging — Defining Questions, Variables, SLOs, and Alert Fatigue

  A dashboard is not useful for looking pretty — it earns its keep by answering a fixed set of questions in order. This post covers how to write down the questions a panel should answer before building it, and how to struc

  2026-08-02 · 15 min read #observability#grafana#alerting#slo#dashboards

• Designing Prometheus Metrics That Answer Questions — Choosing Types, Cardinality Budgets, and the Traps in rate and Quantiles

  More metrics is not better — a metric earns its keep only by answering a question. This post starts by laying out which questions counters, gauges, and histograms each answer, and which calculations become impossible in

  2026-08-02 · 17 min read #observability#prometheus#promql#metrics#cardinality

• Making Logs Searchable, and Not Going Broke Doing It — Structuring, Mapping Explosions, Retention, and Real Cost

  The point where log costs overtake compute costs arrives for most organizations. What delays that point isn't the compression ratio — it's the decision about what becomes a field. This post covers field design for struct

  2026-08-02 · 15 min read #observability#logging#opensearch#elasticsearch#cost

• Instrumenting Your App With OpenTelemetry — From Auto-Instrumentation to Manual Spans, and Why You Put a Collector in Front

  Instrumentation isn't about installing an SDK — it's about following an order of operations. This post builds a skeleton in a day with auto-instrumentation, locks down resource attributes first, and shows the order for a

  2026-08-02 · 16 min read #observability#opentelemetry#instrumentation#otel-collector#tracing

• The Mean Tells You Nothing — How to Debug Latency Through Distributions

  Farid Zakaria's post "The mean means nothing," published July 27, 2026 and reaching the top of Hacker News, covers a case where average latency got 9% worse after a caching layer rollout — from 112ms to 122ms. In the ver

  2026-07-31 · 14 min read #observability#latency#performance#histogram#percentile

• Running AI Agents in Production — Idempotency, Budgets, and Confidently Wrong Answers

  Moving an agent from prototype to production exposes an operational surface you only discover late: idempotency for retried tool calls, budgets and step limits, observability over non-deterministic control flow, per-tool

  2026-07-31 · 16 min read #ai#agents#observability#reliability#mcp