Our internal MCP server passed every test in the inspector but kept failing in the desktop client with an 'is not valid JSON' error. A startup banner and a debug print went to stdout, which is the protocol channel in stdio transport. Logging to stderr fixed it.
Our queue workers took exactly 30 seconds to stop on every deploy and lost the job they were processing. Exit code 137 in every pod. The Dockerfile used shell-form CMD, so /bin/sh was PID 1 and never forwarded SIGTERM to Node. Exec form plus a real shutdown handler fixed it.
Paper trading usually fills every order instantly at the price you asked for. Real markets do not: there is slippage, partial fills, queue position, fees, latency and market impact. This guide explains each gap, how to measure it from live data and how to model it in simulation.
Login and checkout forms started failing with 419 Page Expired the day we put two Laravel nodes behind a TLS-terminating load balancer. Untrusted proxy headers, secure cookies and file sessions on each node combined into one confusing error.
Our PayPal checkout button rendered in development and staging but was an empty box in production. A new Content-Security-Policy set in Nginx blocked the PayPal SDK script, its iframes and its API calls. We fixed it per directive, with a nonce and a report-only rollout.
After moving our Symfony app to the official php-fpm image, median response time went from 38 ms to 121 ms with no code change. OPcache was never enabled in the new image, and checking it from the CLI hid the problem. Here is how we found it and tuned it.
A new module for DNS validation of ACM certificates failed at plan time with an Invalid for_each argument error on fresh environments. The for_each keys came from attributes only known after apply. Static keys with apply-time values made the plan work in one pass.
We lowered a rate limit in a ConfigMap, Argo CD reported Synced, and the service kept using the old limit for two days. Environment variables from ConfigMaps are read once at container start, and our file mount used subPath, which never updates. A checksum annotation fixed rollouts.
We moved our EKS batch node pool to Spot and cut its cost by 60 percent. Within a week, nightly jobs failed most nights: a single instance type, no interruption handling and jobs with no checkpoints. Diversified instances, graceful draining and checkpointing made Spot reliable.
A grid trading bot written in TypeScript sent quantities like 0.30000000000000004 and got error -1111. Binary floating point cannot represent most decimal fractions, and serialising numbers to strings exposed it. A decimal library end to end and string serialisation fixed it.
Our daily signals disagreed with the exchange chart, and intraday signals appeared and disappeared within the same bar. We resampled UTC millisecond timestamps in local time and included the still-forming last candle. UTC everywhere and using only closed candles fixed both problems.
Most trading bot failures we have seen are not strategy bugs; they are state bugs: lost fills, duplicate orders, wrong positions after a restart. This guide describes an architecture that keeps state correct: separated components, an order state machine, persistence, risk checks and reconciliation.
Users were logged out at random, mostly in the afternoon. Sessions and a fast-growing cache shared one Redis with allkeys-lru, so every hourly cache warm-up evicted idle sessions. Separate DB numbers would not have helped; separate instances did.
Every deploy and every traffic burst, the first requests to our booking API timed out and Postgres started refusing clients. The handler did heavy setup and opened a fresh connection per invocation. Moving init out of the handler and adding RDS Proxy fixed it.
Our API certificate renewal looked perfect in desktop Chrome, yet the Android app, curl and a partner's Java service all rejected it. The server sent only the leaf certificate without the intermediate. Serving the full chain fixed every client at once.
We once spent two weeks optimizing SQL for an endpoint whose real cost was a logging call. Since then every performance task starts with a profile. Here is how we capture flame graphs in PHP, Node.js and Python, how to read them, and how to measure the win.
Customer CSV uploads failed with KeyError 'email' even though the header clearly said email. Excel had written a UTF-8 byte order mark, and other files arrived in Windows-1252. We now detect the encoding explicitly and normalise headers.
A worker image built fine but every container exited at once with "no such file or directory" for a file that clearly existed. A Windows checkout had turned the shebang into /bin/bash with a carriage return. We fixed it with .gitattributes and a CI check.
Prompt and response logs are the fastest way to build an accidental PII database. This guide shows where personal data enters an LLM feature, how to redact it before logging and before the provider, and how to keep retention and access under control.
We enabled BuildKit's GitHub Actions cache and our multi-stage Go build still took 9 minutes on every run. The default cache export mode only stores layers of the final image, not the builder stage where all the time is spent. mode=max and per-branch scopes cut it to 2 minutes.
During a traffic spike our checkout service stayed at 3 pods while CPU was pegged. kubectl get hpa showed <unknown>/70% for weeks. A sidecar container added by a platform change had no CPU request, so utilisation could not be computed. Requests on every container fixed it.
A new subdomain's certificate sat in pending for hours with a self check error: wrong status code 404, expected 200. The challenge solver created its Ingress with an ingress class our controller ignored. Setting the class on the solver and checking the redirect path fixed issuance.
A shared Kubernetes cluster shows up on the cloud bill as a few lines of EC2 and EBS. Turning that into cost per team needs decisions: allocate by requests or usage, what to do with idle capacity, and how to split shared services. This guide covers the model we use and why.
We enabled fp16 autocast to speed up training and the loss turned NaN within 300 steps. A custom attention score and a loss computed in half precision overflowed fp16's range, and we had not used a gradient scaler. bf16 on our GPUs, fp32 for the sensitive ops and a NaN guard fixed it.
A hedging bot meant to short perpetual futures placed spot sell orders instead, selling inventory it was supposed to hedge. In ccxt's unified symbols, BTC/USDT is spot and BTC/USDT:USDT is the linear perpetual. Explicit market types and assertions on market metadata fixed it.