Videos

Spring Boot won this benchmark, but not for the lazy reason. This was not a branding contest and it was not just another throughput scoreboard. I built a full benchmark engine, ran the same Kafka plus Postgres order pipeline across Go, Spring Boot, and .NET, and then checked the only metric that ac

Most teams say they have CI/CD because code moves from `dev` to `staging` to `prod`. But if every environment rebuilds the app, the thing you tested below is not the thing running in production. That is not promotion. That is drift with better branding. In this video: - why source promotion and a

If your `docker-compose.yml` is not in Git, you do not have infrastructure. You have a live guessing game. This short shows the blunt ops version: - Compose edits are production config - Git is the control plane for diffs, review, and rollback - Live-editing YAML on a server is how outages turn in

Same tool logic. Same host machine. Two boundaries. In this short, I compare an in-process tool path against the same tool running behind a remote MCP-style server boundary. The result was not close: local p95 landed at 0.018 ms, remote p95 landed at 12.716 ms, and the remote lane started dropping

One chart is not enough for a production stack decision. Which Backend Actually Pays For Itself In 2026? In this video: - what each runtime/language wins - where tail latency changes the decision - how to pick based on your real bottleneck Comment with your stack and workload; I can publish the b

Rust winning this benchmark was not the weird part. The weird part was PHP taking second over Elixir under the same live load. Same /work endpoint. Same JSON payload. Same Podman host. Same concurrency ramp. Same sampling cadence. At c300, Rust stayed in a different class on throughput and memory.

Same `/work` endpoint. Same JSON payload. Same Podman box. Same live ramp from 10 to 300 concurrent users. Result: - Rust wins throughput, latency, and memory - PHP takes second place - Elixir pays the biggest memory tax in this run In this video: - live RSS ramp - throughput across c10, c50, c150

One chart is not enough for a production stack decision. Does Rust Save More Than Rust Devs Cost? In this video: - what each runtime/language wins - where tail latency changes the decision - how to pick based on your real bottleneck Watch the full breakdown on the channel for benchmark method, tr

AI agents are great at local correctness: generate code, pass the happy path, open the PR. But that does not prove system behavior. Retries, redirects, session state, cache invalidation, and fallback paths still have to be validated by humans. If QA is the first team discovering the design bug, y

Flutter teams asked the right follow-up: if Rust wins over pure Dart through dart:ffi, does Zig close the gap enough to change the pick? This video reruns the benchmark with the same shared Dart bridge, the same checksum gate, and a wider scenario sweep: - boundary-heavy - balanced - compute-heavy

Vector search won the overall quality chart in this benchmark. That does not mean you should delete grep from your stack. This short benchmarks 4 retrieval lanes across 255 documents, 112 labeled queries, and 7 developer-search scenarios: - `grep` for exact string search - `BM25` for lexical ranki

Manual sharding really did win the local throughput chart. That still was not the real verdict. This short compresses the important part of the benchmark: - manual Postgres shards were fastest locally - the first TiDB loss included a harness SQL dialect bug - after the rerun, the honest claim beca