💥 Dead Framework Theory

➡ Don't Blindly Use useTransition Everywhere
This post warns against treating useTransition like a magic UX band-aid, because used blindly, it often makes interactions worse, not better. The author walks through React’s examples and shows how transitions can create confusing “pending-but-still-showing-old-content” states, double-renders, and pitfalls with controlled inputs. The takeaway: transitions are great for backgrounding expensive work, but only when paired with intentional patterns like yielding, <Delay>, or <Activity>, not slapped on every state update. Ultimately, use it sparingly, deliberately, and only where it genuinely improves UX.

🔎 Using Atomic State to Improve React Performance in Deeply Nested Component Trees
This post breaks down how Harbor sped up a deeply nested React UI by ditching Context-based state and moving to atomic state with Jotai. In their clinical trial app, a single update used to trigger rerenders across the entire component tree, brutal at real scale. Atomic state lets each component subscribe only to the tiny slice of data it needs, so updates touch just the affected nodes. The result was controlled inputs, declarative logic, and huge performance wins without rewriting the app’s mental model.

⚔ React 19 Error Boundary Behaves Differently
This post digs into a subtle React 19 change, error boundaries now bail out after the first thrown error instead of trying to render every failing sibling. In older React versions, each erroring component would render twice and trigger duplicate logs, React 19 stops that noise cold. Only the first error logs, the boundary catches it once, and React skips wasting work on siblings that were doomed anyway. The takeaway? Cleaner logs, fewer redundant renders, and simpler error-handling code.

🌴 TanStack DB 0.5 - Query-Driven Sync
This article announces TanStack DB 0.5 and its big new trick: Query-Driven Sync, where your component’s query literally is the API call. Instead of building custom endpoints or GraphQL resolvers, you just write a query and the system figures out what to fetch, how to cache it, and how to update it. It introduces three sync modes, eager, on-demand, and progressive, to handle everything from tiny tables to 100k-row monsters. The whole pitch? Load only what you need, update instantly, and let the client stay smart while your backend stays simple.

🔝 Top React libraries you should know in 2025
This guide rounds up the must-know React libraries of 2025, the tools that turn plain React into a production-ready powerhouse. From data fetching and state management to routing, forms, and UI kits, it highlights the modern ecosystem pieces that actually move the needle.

Quick Links

👀 The Root Cause Fallacy: Hidden Causes
This piece argues that the idea of a single “root cause” is a comforting illusion, complex failures almost always come from multiple intertwined factors. The author reframes the classic Five Whys as a tool for surfacing contributing causes, not chasing a lone villain. Using an example 3 AM database crash, he shows how memory limits, bad monitoring, broken scaling, and poor queries all shared blame. The message is stop hunting for the cause, rate each contribution, fix what matters most, and think in systems.

💻 Infrastructure as Code is a MUST have
This article makes the case that Infrastructure as Code isn’t optional anymore, it’s the backbone of how modern teams manage cloud resources. Instead of clicking around consoles and creating fragile “snowflake servers,” IaC lets you define everything, servers, networks, gateways, as versioned, repeatable code. You get reuse, instant rollbacks, auditability, and safer changes. Tools like Terraform even parallelize deployments and catch errors early. Bottom line: IaC turns infrastructure from guesswork into something reliable, reviewable, and reproducible.

📈 Optimise for continuous change, not modernisation or legacy
This talk argues that “legacy vs modernisation” is the wrong battle, what actually matters is how easily your organisation can change. “Legacy” and “modern” both turn out to be fuzzy labels that mean different things to engineers, architects, and execs, but underneath, it’s all about how hard or expensive a system is to evolve. Big-bang rewrites (hello, Netscape) usually fail, resilient teams instead design systems to be adaptable, replaceable, and continuously refactorable. The core thesis: optimise for continuous change, not for chasing shiny greenfield or clinging to old stacks.

💀 Dead framework theory
This article argues that React isn’t just a framework anymore, it’s become the default “platform” because LLMs, tools, and developer habits all reinforce its dominance. New frameworks struggle to gain traction since they don’t show up in training data for 12–18 months, meaning LLMs won’t output them and teams won’t adopt them.

🗺 The Search Problem: Why Your Computer Finds Things Faster Than You Do
This article explains why real file systems don’t use the cute textbook binary trees we learn in class. Once you factor in actual disk I/O, BSTs crumble, while B-trees dominate by stuffing tons of keys into each node and slashing disk reads. It walks through benchmarks, real systems like ext4 and NTFS, and shows how everything from databases to Git relies on this structure. The whole point: understanding hardware realities changes how you design data structures.