Videos

We put Rust, Go, Java, and C# into the same log processing ring. Using the same dataset and strict benchmark rules, we tested their ability to parse production-style logs, aggregate metrics, and export JSON reports. ​Key Results: ​Winner: Rust (920,000 lines per second) ​Cold Start: Rust leads at 1

Python is still the default for AI workflows in 2026. But when API traffic turns CPU-heavy, the GIL becomes the bottleneck and tail latency gets ugly. This short breaks down: - why CPU-bound Python services struggle under burst load - where Go and Rust hold better throughput and latency stability -

One chart is not enough for a production stack decision. Bun vs Deno vs Node: The 2026 Runtime Performance Deathmatch Is Finally Over 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

One chart is not enough for a production stack decision. Markov Chain Showdown: Full Code + Structure + Data Visualization 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

One chart is not enough for a production stack decision. Bun vs Deno vs Node: The 2026 Runtime Performance Deathmatch Is Finally Over 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 w

This short gives the headline from our 2026 Rust vs Go vs .NET microservice benchmark. Completed lanes summary: - Go leads throughput - Rust leads extreme p99.9 tails - .NET AOT lane is still pending stable completion Before choosing a production stack, watch the full longform breakdown for benchma

Rust, Go, and .NET are run through the same microservice benchmark harness: same Postgres limits, same load matrix, and the same artifact pipeline. Github : https://github.com/prod-garbage-destroyer/rust-go-dotnet-microservice-2026/tree/v2-artifacts-implementations This video is an interim verdict

GraphQL looks elegant on the surface, but at scale the schema can become a real security and performance problem. Deep queries, introspection exposure, resolver explosions, and weak authorization patterns can turn one clean endpoint into a serious backend risk. In this video, I break down why Graph

We rebuilt both stacks from the protocol up, then ran repeatable captures across cold and steady passes to answer one question with evidence: when does Node.js WebSocket win, and when does Rust gRPC take over? This video walks through the full request path (proto contract, server internals, benchma

We spent 10 years perfecting the "slow" web. Massive JSON payloads, hydration errors, and micro-frontend mazes. It's time to go back to basics. Here is how PHP with Swoole and HTMX outperforms modern SPAs and saves you massive cloud costs. Don't be a cost center. Be a Garbage Destroyer. 🗑️🔥 #we

Everyone says "rewrite to a lighter stack." But in enterprise systems, rewrite risk, integration breakage, and ops instability can cost more than any benchmark gain. In this video, we break down why Quarkus + Spring Boot still dominate modern backend delivery: Loom concurrency, native startup perfo

AI code feels like speed—until the architecture fractures. This short breaks down the synthetic debt trap: why LLM-generated refactors add unnecessary abstraction, how phantom dependencies inflate complexity, and why “correct” code can still be bad code. We cover the innovation vs maintenance gap an