garbagedestroyer

Videos

Software benchmarks, systems deep-dives, and technical commentary. Subscribe on YouTube

STOP using lib/pq for go in 2026 🚀
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STOP using lib/pq for go in 2026 🚀

Use pgx instead. Pure Go, connection pooling, prepared statements. Or pgxpool for built-in connection pool. Production ready. Watch the full video for the full migration guide and performance benchmarks! #golang #nodejs #memory #containers #docker #shorts #coding #backend #performance #devops

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Node is as fast as Go? Cold Start TRUTH 🤯
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Node is as fast as Go? Cold Start TRUTH 🤯

Can Node.js cold starts keep up with Go? We tested it in an isolated container with real DB connections. Watch the long-form video on my channel for a detailed show on how we set up the experiment! Fight me in the comments! GitHub: https://github.com/prod-garbage-destroyer/cold-start-tax-experimen

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Go Wins the Cold Start, Loses to the Database Driver
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Go Wins the Cold Start, Loses to the Database Driver

Cold start is a tie at ~500ms. But Go uses 7.6x less memory and 6x smaller images. The hidden cost? The database driver. Here's the full breakdown. Checkout the Benchmark Repository on Github/prod-garbage-destroyer/cold-start-tax-experiment

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AI Agents Are Breaking Backend QA
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0:32
AI Agents Are Breaking Backend QA

Your AI agent didn't fail in one request. It failed across the entire workflow. Step two subtly hallucinates, and step five silently corrupts your database. Normal QA and standard logs are useless here. You don't need more unit tests. You need deterministic traces, semantic eval gates, and replayabl

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Your semantic evaluations are missing the entire problem 🎯 #AIMonitoring #Tech
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Your semantic evaluations are missing the entire problem 🎯 #AIMonitoring #Tech

Your cloud bill is paying for silent AI agent failures. Traditional APM is dead here—a 200 OK network request means nothing if the reasoning engine drops a production table. You need semantic evaluations mapped to LLM decision trees. Stop shipping agentic wrappers until your monitoring can evaluat

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POPULAR VS FAST | Quarkus vs Spring Boot Verdict (2026)
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POPULAR VS FAST | Quarkus vs Spring Boot Verdict (2026)

Spring Boot is the enterprise cathedral, but Quarkus is the cloud-native blade. In 2026, if you care about startup speed, container density, and cloud bills, there's only one winner. 00:00 - The Hook: Popular vs Fast 00:11 - Startup Speed: Native Battle 00:19 - Memory Massacre: 3x Density 00:27 - Th

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CEREMONY VS SPEED | Go vs Java Verdict (2026)
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CEREMONY VS SPEED | Go vs Java Verdict (2026)

Java is an enterprise tank, but Go is the speedrun language for teams that care about shipping. In 2026, the real bottleneck isn't concurrency—it's memory and deployment friction.

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FIREBASE FEELS EASY UNTIL... | Supabase vs Firebase (2026)
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FIREBASE FEELS EASY UNTIL... | Supabase vs Firebase (2026)

Benchmarked predictable pricing, exposed NoSQL query limitations, and explained why the choice between Supabase and Firebase is really just SQL vs proprietary lock-in. #Supabase #Firebase #SQL #NoSQL #SystemDesign #DevVerdict #2026 #WebDev #Backend #FullStack #IndieHacker #Shorts

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Quarkus vs Spring Boot: Which Java Framework Actually Ships Faster? (2026 Deep Dive)
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Quarkus vs Spring Boot: Which Java Framework Actually Ships Faster? (2026 Deep Dive)

Spring Boot is the industry titan. Quarkus is the cloud-native speed demon. In this 6-minute deep dive, we put the frameworks to the trial across performance, developer experience, and deployment reality. In 2026, "it works" is no longer enough. We analyze why the Build-Time-First philosophy of Qua

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STOP USING KAFKA FOR YOUR CRUD APP | Message Broker Verdict (2026)
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STOP USING KAFKA FOR YOUR CRUD APP | Message Broker Verdict (2026)

Demystified the streaming hype, exposed the operational tax of Kafka in non-streaming workloads, and provided a clear hierarchy for message broker selection based on actual architectural needs.

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AI Agents Are Secretly Making Your Code Worse #AI #SoftwareDevelopment #Tech
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AI Agents Are Secretly Making Your Code Worse #AI #SoftwareDevelopment #Tech

Benchmarked token-to-fix ratio, exposed hallucination patterns in RAG-based coding agents, and concluded that current AI tools add more technical debt than they resolve for senior-level engineering tasks.

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You Don't Need RAG for That 🛑 | Dev Verdict (2026)
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You Don't Need RAG for That 🛑 | Dev Verdict (2026)

Stop building fantasy AI architectures. Most internal tools don't need a complex retrieval pipeline—they just need a modern context window and better prompts. Here is the reality check on RAG vs Direct Prompting.

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93 videos total