Aftab Rishad

Aftab Rishad Frontend web developer with a focus on Tailwind CSS, Next.js, and React.js.

I create production-ready, responsive web apps with a clear user interface and real-time functionality.

Most frontend performance problems are not computation problems. They are network problems, bad caching, or too many req...
19/04/2026

Most frontend performance problems are not computation problems. They are network problems, bad caching, or too many requests. Fix those first.

But once your architecture is clean and you are still hitting limits, the question changes. The browser gives you two tools most teams never use: WebAssembly (WASM) runs compiled, low-level code on the CPU. WebGPU gives direct access to the GPU for massively parallel work.

Sylwia Laskowska published a live demo that shows the difference clearly. Her benchmark compared three rendering approaches for a particle physics simulation:

1. Plain JavaScript with Canvas 2D: starts struggling at around 40,000 particles
2. WASM (compiled from Rust): roughly 2-3x faster than the optimized JavaScript version for CPU-bound tasks
3. WebGPU: handles over 500,000 particles, each with its own physics, at stable frame rates

The whole thing runs inside a standard React app. There is no exotic architecture. WASM and WebGPU shaders are embedded into a normal frontend setup, which means you add them incrementally, only where the problem actually is.

The cases where this matters are specific: real-time data visualization, physics simulations, video or image processing, and ML inference running in the browser. You probably do not need this for a dashboard or a form.

The practical decision rule Sylwia describes in the post is worth saving: reach for WASM or WebGPU only after fixing architecture, fetch patterns, and unnecessary computation. These are ceiling-lift tools, not floor-cleanup tools.

WebGPU still needs a fallback strategy due to incomplete browser support, particularly on mobile and Linux. WASM is production-ready today and widely available.

Anthropic shipped Claude Design yesterday. Developers should pay attention to what it actually does differently.Most AI ...
18/04/2026

Anthropic shipped Claude Design yesterday. Developers should pay attention to what it actually does differently.

Most AI design tools give you a static output. You prompt, you get an image or a mockup, and then you open Figma to finish the job. The workflow is still fragmented.

Claude Design closes that loop in a way I have not seen elsewhere:

1. It reads your codebase and existing design files during onboarding, then applies your brand's colors, typography, and components to every output automatically.
2. Static mockups convert to interactive prototypes you share directly, with no code review required.
3. When the design is ready to build, one instruction packages everything into a handoff bundle for Claude Code.

That last point is the real difference. Lovable, Figma AI, and v0 all generate UI. None of them hand off to a coding agent that already understands the design intent. Claude Design does.

Datadog's product team reports compressing what used to take a week of briefs, mockups, and review rounds into a single conversation.
Brilliant's designers say the most complex pages needed 20 or more prompts in competing tools but only 2 in Claude Design.

The honest caveat: token consumption is aggressive. A Claude Pro user burned through 80% of their weekly Claude Design allowance in roughly 30 minutes. Usage is metered separately from chat and Claude Code, which adds cost complexity for teams evaluating it.

If your team is already on Claude Pro, Max, Team, or Enterprise, the research preview is worth testing before you pay for a separate prototyping tool.

Most deep learning models are too large to run on a budget smartphone, a Raspberry Pi, or a CCTV camera. That gap betwee...
12/04/2026

Most deep learning models are too large to run on a budget smartphone, a Raspberry Pi, or a CCTV camera. That gap between lab accuracy and real-world deployment is where most AI projects quietly fail.

Three techniques close most of that gap:

1. Quantization converts 32-bit float weights to 8-bit integers. In a CIFAR-10 experiment using TensorFlow Lite, this alone cut model size by nearly 50% and inference time by 3x, with no change in prediction output.

2. Depthwise separable convolutions (used in MobileNet) reduce floating point operations by 8 to 9 times compared to standard convolutions, with minimal accuracy loss.

3. Knowledge distillation trains a small "student" model to replicate the output distribution of a large "teacher" model, including the low-probability class scores that carry useful signal.

These are not experimental ideas. TensorFlow Lite and MobileNet are production tools used in Android apps, agriculture sensors, and embedded medical devices today.

The constraint worth taking seriously: offline deployment. A crop disease detection model running locally on a farmer's phone in a low-connectivity region is more useful than a cloud-dependent model with 2% better accuracy.

Accuracy on a benchmark is one number. Latency, model size, and battery draw are three others. Optimizing for all four is where the actual engineering work lives.

Railway shipped a complete frontend migration in two pull requests. No downtime. No parallel-running period. 200+ routes...
11/04/2026

Railway shipped a complete frontend migration in two pull requests. No downtime. No parallel-running period. 200+ routes moved from Next.js to Vite and TanStack Router in a single Sunday morning merge.

The reason for the switch was straightforward. Frontend builds had crept past 10 minutes, with 6 of those minutes spent inside Next.js alone. For a team shipping multiple times a day, that compounds fast. The Pages Router also forced workarounds for shared layouts that TanStack handles natively, and the App Router's server-first model didn't fit a product built almost entirely around client-side, real-time interfaces.

The migration ran in two PRs. The first stripped every Next.js-specific dependency: next/image, next/head, next/router, replaced with browser-native alternatives or framework-agnostic equivalents. The second swapped the framework itself, migrated all routes from the original page tree, added Nitro as the server layer, and consolidated 500+ redirects into a single config file.

What changed after:

1. Builds went from 10+ minutes to under 2
2. HMR is instant; the dev server starts in near-zero time
3. Route params and search params are type-safe and autocompleted across the entire route tree
4. Fastly now serves most traffic directly from the edge, with content-hashed chunks per module

The trade-offs are real. There's no built-in image optimization, no next-seo, no next-sitemap. Railway built small in-house replacements for each. TanStack Start is also newer software, with rougher edges expected.

The calculation they made: a faster iteration loop is worth more than a mature ecosystem they were already working around.

Eid Mubarak 🌙May your code run clean.May your bugs stay away.May your builds always pass.Grateful for the journey.Gratef...
20/03/2026

Eid Mubarak 🌙

May your code run clean.
May your bugs stay away.
May your builds always pass.

Grateful for the journey.
Grateful for every lesson.

Enjoy the day with your people.

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14/03/2026

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Every developer has a story to tell, and this is mine. 🚀 Presenting my portfolio – a reflection of my journey, skills, a...
12/03/2026

Every developer has a story to tell, and this is mine. 🚀 Presenting my portfolio – a reflection of my journey, skills, and passion for web development. Explore it here: https://www.aftabrishad.com ✨ Let me know your thoughts!

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