Bangladesh Developer Shares How He Built SEO-Optimized Portfolio Using Next.js 14

·1 views

MD Nayeem Miah, a full stack developer from Bangladesh, has shared a breakdown of how he built his developer portfolio using Next.js 14 with the App Router, Tailwind CSS, TypeScript, and Vercel for hosting. He implemented SEO best practices including meta tags, Open Graph data, and structured Schema.org markup to improve discoverability and social media previews. Miah used the next-sitemap package to auto-generate a sitemap and robots.txt file, and verified his site's ownership through Google Search Console. The portfolio has since been indexed on Google and is publicly accessible at nayeem-miah.vercel.app. He posted the write-up on DEV Community to encourage other developers to build and share their own portfolios.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Builds AI Chat Assistant for Internal Tool Using Gemini API and Firebase

A developer has integrated an AI-powered chat panel into PanelControl, an internal commercial team management tool, using vanilla JavaScript and Google's Gemini API. The assistant answers repetitive business queries — such as sales rankings and bonus thresholds — by dynamically building a system prompt from live Firebase Realtime Database data on every request. Google's Gemini API was chosen over Anthropic's because it offers a more generous free tier suitable for light internal use, though a billing account is still required even when no charges apply. During development, the builder encountered model availability issues, finding that several Gemini versions were unavailable to new accounts before settling on gemini-2.5-flash-lite. The project highlights that AI models require full business context injected via system prompts to function meaningfully within custom internal applications.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Solo dev builds AI background removal in Rust without Python in one week

A solo developer added AI-powered background removal to Convertify, a free image converter, using a fully Rust-based backend without introducing a Python runtime. The implementation relies on ONNX models — the same ones used by the popular Python tool rembg — run natively in Rust via the ort crate, with image processing handled by libvips. The five-step pipeline decodes the image, runs inference to generate a pixel mask, and composites the result as a transparent PNG, all CPU-only on a modest VPS with no GPU. Key technical hurdles included an unexpectedly large 171 MB model file, ort error types lacking Send and Sync compatibility with anyhow, and a mutable self requirement on session runs that forced an architectural change. The developer documented the process publicly as part of an ongoing build-in-public series around Convertify.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How FastAPI, Uvicorn, and ASGI Work Together to Power Modern Python APIs

FastAPI is an open-source Python framework built on Starlette and Pydantic, designed to simplify REST API development through automatic request validation and type-hint-based programming. It relies on ASGI (Asynchronous Server Gateway Interface), the modern replacement for WSGI, which enables concurrent request handling instead of blocking on slow I/O operations. Uvicorn serves as the ASGI server that actually receives HTTP requests and passes them to the FastAPI application, meaning FastAPI defines the logic while Uvicorn handles the serving. Together, these three components form a modern Python web stack capable of efficiently managing high volumes of concurrent connections. A practical demonstration of this architecture is illustrated through a Patient Appointment Tracker API, highlighting design choices over implementation specifics.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

uv Replaces Five Python Tools With One Binary, Now Backed by OpenAI

The Python package manager uv, developed by Astral, consolidates five traditionally separate tools — pip, pip-tools, virtualenv, pyenv, and pipx — into a single binary. Benchmarks show uv is dramatically faster than alternatives, completing a 200-package install cycle in 1.5 seconds compared to pip's 20.5 seconds and Poetry's 16.0 seconds. In March 2026, OpenAI acquired Astral to integrate uv into its Codex AI platform, significantly raising the tool's profile. Migration paths exist for users coming from pip, Poetry, and pyenv, though uv does not replace Conda for workflows that depend on non-Python system libraries. With over 45,000 GitHub stars, uv has rapidly emerged as a leading standard for Python dependency management.

0 comments Read more at DEV Community