Early access · Trace Analyzer is live
Paste a distributed trace and BackendBytes Diagnostics finds the critical path, the N+1 calls, the serial reads, and the errors your caller never saw — then ranks what to fix first.
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5 free tools63 cited engineering articles
12× the same call under “checkout.PlaceOrder”
card-network · POST /v1/authorize owns 30% of the critical path
Every line carries its status. Nothing marked live is a demo.
Turn an exported trace into a ranked list of what is making the request slow or wrong.
See the architecture a request actually exercised, not the one on the diagram.
Every finding says what to try next. Explanations tied to your own code and stack are being built.
Open in the browser, no account. Each tool states its formula and where it is wrong.
The reasoning behind the tools: distributed systems, database internals, and AI infrastructure. 63 articles, every claim cited to a primary source.
Start with the lead story, or pick from the reads that show what the rest of the library looks like.
OpenAI and Anthropic both document outages, overload codes, and best-effort availability. The survival kit: LLM-aware circuit breakers, same-model-first fallback chains, and degraded modes designed before you need them.
From 5-second startup to 50ms. The Java migration that changes K8s scaling math, with every production gotcha and a reproducible benchmark.
ReadWire thousands of API endpoints to an LLM agent without blowing the context window. Code Mode pattern + full Go implementation, security flow visualised.
ReadSame API in Spring Boot and Gin. Identical load tests on Fargate, real numbers on throughput, latency, GC pauses, and a methodology you can reproduce.
ReadWhy every scalable cache, database, and load balancer ends up here. Karger's derivation, Cassandra vnodes, and the rendezvous-hashing alternative.
ReadThe error-budget framework that turns reliability arguments into data. Multi-window burn-rate alerting, grounded in the SRE Book.
ReadWhy most RAG prototypes fail in production. Chunking, embedding versioning (1536-dim default + Matryoshka), pgvector tuning, eval pipeline.
Read
Picture one agent, one context window, one serial loop — until it hits 180K tokens and silently drops 37 of the 40 services it was asked to audit. The orchestrator pattern fans work out to isolated sub-agents in parallel, then synthesizes. Here's the backend, in compiling Go.
Go 1.26 makes the Green Tea garbage collector the default. What actually changed, how to measure GC CPU before and after on your own workload, and the short window you have to opt out.
Slow JVM cold start breaks scale-to-zero and autoscaling. Three ways to fix it in 2026 — Project Leyden's AOT cache, GraalVM Native Image, and CRaC — with the exact commands, the trade-offs, and a decision framework.
Go context.Context: constructors, cancellation, deadlines, request values, and five common gotchas, from leaked contexts to nil panics.
Kafka producer configuration: acks, idempotence, batching, compression, and the tradeoffs that matter for throughput and durability.
Postgres EXPLAIN plans: node types, cost interpretation, and six query anti-patterns to avoid.
Cloudflare put its 2,500+ API endpoints behind one MCP server with two tools, search + execute, and cut input tokens by 99.9%. The pattern in Go.
AI agents calling tools via MCP create new attack surfaces: prompt injection through tool responses, credential leakage, and unauthorized execution.
Adding one cache server shouldn't invalidate every key. Consistent hashing with virtual nodes and bounded loads — full Go and Java implementations.