ByteSizedDesign

From write-through to write-back, learn when and why to use caching to speed up your systems, reduce costs, and keep users happy. Read the latest here: bytesizeddesign.substack.com #SystemDesign #Caching #TechTips

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Junior engineer: "How do I build this?" Senior engineer: "Should we build this?" Staff engineer: "Here's what happens to the team that maintains this in two years." The question changes. That's the whole game.

The engineer who wrote the original code left 3 years ago. The docs are wrong. The tests don't cover this path. Production is down. This is the job. Not the algorithm interviews. This.

"We need to rewrite it." No you don't. You need to understand it. The rewrite will have the same problems in 18 months because the problems are in how your team thinks, not the codebase.

This is awesome! You can get Replit Core FREE for 1 month AND gift someone Replit FREE for 1 month. Great win for builders!

Hot take: every AI coding benchmark you've seen is lying to you. Passing tests ≠ writing maintainable code. An agent that hardcodes a brittle fix and one that writes clean extensible code score identically on SWE-bench. SWE-CI finally measures what matters, 71 rounds of real commits, real regressions, real consequences. Most models failed 75% of the time. Only one cracked 76% clean runs. The gap between "AI can code" and "AI can maintain code" is enormous. Read more about this research by Ali Baba group! stateai.substack.com/p/3-out-of-4-a…

Issue #75: We're Back! Tool Calling, Default Actors & the Clock Is Ticking! ioscodereview.com/issues/issue-7…

@stateof_ai @nimble_data This is huge! Super powerful launch and looking forward to how it shapes the industry!

If agents are going to: • negotiate • monitor markets • run pricing intelligence • do financial diligence • operate workflows The web needs to behave like a database. This isn’t “AI search.” It’s the beginning of a web data infrastructure layer. That’s why this launch matters.

We’ve been obsessing over models. The real bottleneck for AI agents is the data layer. The web is the largest dataset on Earth. And it’s basically unusable as infrastructure as it's not structured and not reliable. That changes today with Nimble @nimble_data They turn the live web into structured, queryable data tables in real time so agents get clean datasets instead of text blobs. You can install Nimble Skills directly into Claude, Cursor, and other coding agents to query the live web inside your workflow! Build with Nimble: docs.nimbleway.com This is infra for production-grade AI agents.

Most engineers think system design is only about scale. It’s not. It’s about making change safe. If your system can’t evolve without fear, it’s already poorly designed.

Most engineers are asking the wrong question about AI. It’s not “Will AI replace developers?” It’s which parts of the job disappear first. The junior work is going first. The mid-level work is under pressure. Architecture isn’t shrinking. It’s moving up a level. The job is no longer designing services. It’s designing systems where humans and AI make decisions together, and knowing exactly where the human must stay in the loop. The engineers who learn that boundary early will be fine. The rest will feel like they’re slowly disappearing. bytesizeddesign.substack.com/p/the-architec…

Stop trying to learn every new framework. Start learning why systems fail. Read postmortems, study outages, understand how things break at scale. The engineer who's seen 100 systems fail is worth more than one who's mastered 100 tools. Experience is just pattern recognition for disasters you haven't caused yet.

Uber was replicating 1 PB/day across 350 PB of data. Their system was struggling. The fix? Killed a hidden HDFS lock, parallelized sequential namenode calls, and eliminated 268k pointless container launches per day. Result: 5x capacity increase. 90% latency drop. Read how here bytesizeddesign.substack.com/p/how-uber-mov…

OpenClaw went from "hackable personal AI assistant" to running actual companies in 12 months. The gap between "open source toy" and "production infrastructure" just collapsed faster than anyone predicted. Also someone's OpenClaw accidentally started a fight with their insurance company and won.

If you've ever found yourself spending hours documenting a process that took 5 minutes to actually do, you're not alone. Glitter.io flips this around. Record your screen, walk through what you're doing, and their AI automatically generates a clean step-by-step guide with screenshots and text. SOPs, onboarding docs, internal tooling walkthroughs, all done in seconds. Check it out → glitter.io

Before you spend 6 months migrating to DynamoDB, answer these: Is your workload actually write-heavy? (Most aren't) Have you profiled your queries? (Bad queries ≠ bad database) Are you doing connection pooling, caching, rate limiting? Do you even know if it's CPU, I/O, or network? OpenAI runs 800M users on ONE Postgres primary because they optimized instead of migrated. Most "database doesn't scale" problems are actually "we haven't done the work" problems. Read how OpenAI did it 👇 bytesizeddesign.substack.com/p/how-openai-s…

Stop trying to learn every new framework. Start learning why systems fail. Read postmortems, study outages, understand how things break at scale. The engineer who's seen 100 systems fail is worth more than one who's mastered 100 tools. Experience is just pattern recognition for disasters you haven't caused yet.

Lyft's Feature Store handles over a TRILLION operations and grew 25% in users last year Their secret? They deleted features, ditched custom DSLs, and stopped making engineers learn new syntax Turns out the best ML infrastructure is the kind that gets out of your way SparkSQL + JSON config. That's it. No YAML hell. bytesizeddesign.substack.com/p/processing-t…

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🧠 State of AI Research - This Week Three standout papers: 🎯 STEM: Scaling Transformers with Embedding Modules - Replaces FFN up-projections with token-indexed embeddings, achieving 3-4% accuracy gains while drastically reducing FLOPs and parameter accesses. More interpretable than MoE variants. 🎥 Molmo2 - New SOTA open-source video-language models with exceptional grounding capabilities. Trained on the largest fully open video-centric dataset (no proprietary distillation). Matches or beats closed models. 🤝 Collaborative Multi-Agent Test-Time RL - Multi-expert teams using structured textual experience for reasoning. +8.67% over single-agent baselines, +3.67% over multi-agent on math/medicine/education tasks. Full breakdown: substack.com/home/post/p-18… #AIResearch #MachineLearning