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Seven months after introducing its $119.98 Ultra BodyScan smart scale, Wyze announced a cheaper $79.98 alternative available today that makes a few compromises to shave $40 off the price. There's no Wi-Fi, but you can sync the BodyScan's measurements to Apple Health and Google Fit by connecting to the Wyze mobile app over Bluetooth. And […]

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I joined a team where the controller was 800 lines long, the business rules were scattered between the controller and the DbContext , and "to run the tests, spin up a SQL Server in Docker" was a sentence I heard every week. The fix was Clean Architecture. The argument I had with the team lead was about how to actually structure it. We argued for two weeks. Then I built this template so the next person wouldn't have to. This is the Clean Architecture .NET 8 starter template I wish someone had handed me on day one. Four projects, strict dependency direction, domain entities that own their own invariants, and an Application layer you can unit test with Moq — no database required. The whole repo is on GitHub , MIT-licensed, runs with dotnet run , and ships with xUnit tests, JWT auth, Swagger, Docker, and CI. This post is the explanation of why each project exists, what goes in it, and what I learned the hard way about getting Clean Architecture right in .NET. The problem Clean Architecture solves The naive way to build a .NET Web API is one project, one folder structure, and "everything talks to everything": MyApp/ Controllers/ ProductsController.cs ← HTTP stuff OrdersController.cs ← HTTP stuff + business rules Services/ ProductService.cs ← business rules + DbContext.SaveChanges Data/ AppDbContext.cs ← EF Core, entities Models/ Product.cs ← POCO with public setters This works for the first 1,000 lines. By 5,000 lines, the controller is doing five things at once. By 10,000, "to test this, I need a database" is the answer to every test question, and your CI takes 20 minutes because every test run spins up SQL Server. Clean Architecture says: separate the business rules from the HTTP boundary, separate the database from the business rules, and enforce it with project references. A controller is allowed to call a service. A service is allowed to call a repository. A repository is allowed to know about EF Core. Nothing is allowed to know about anything "above" it in the chai

I had used LangChain's RAG chain in production for six months. I could not have told you, off the top of my head, what chunk_overlap did, or why cosine similarity is the right distance metric, or how nomic-embed-text actually turns a sentence into a vector. The high-level library abstracted all of it away. So one weekend I deleted the LangChain dependency and wrote a RAG pipeline from scratch in ~500 lines of plain Python. No framework, no magic. pypdf for text extraction. A 60-line chunker. ChromaDB for the vector store. Ollama for embeddings and the LLM. The whole thing is on GitHub — every module is under 200 lines, every test is deterministic, and you can read the whole thing in one sitting. This is the build log. Not a tutorial — the build log, with the parts that surprised me and the parts I got wrong the first time. Why bother The honest reason: I was using LangChain's RetrievalQA chain and getting answers I didn't trust. Sometimes the model would say "according to the document" when the document didn't say that. Sometimes the citations were wrong. I had no way to know if the chunker was dropping important context, or if the cosine similarity was picking the wrong neighbors, or if the prompt was actually constraining the model. The library was a black box. When you build it yourself, every layer is inspectable. When the answer is wrong, you can add a print statement in pipeline.py line 102 and see exactly which chunks were sent to the LLM. When the chunker cuts a sentence in half, you see it in the test fixtures. When the embedding model gives garbage for some inputs, you can swap in a different model with one constructor parameter. None of that is possible when the whole thing is RetrievalQA.from_chain_type(llm=..., retriever=...) . The other reason: the code I wrote is 500 lines, and it covers the same ground as a 50-line LangChain script. The extra 450 lines are comments, type hints, tests, and explicit error handling. That's the actual complexity. LangCha

Building a Spring Boot application is only half the journey. At some point, every backend project reaches the next question: Where should this application actually run? If you are deploying on AWS, two common options are: Amazon EC2 Amazon ECS Both can run the same Dockerized Spring Boot application. But they are not the same kind of deployment model. EC2 gives you a server. ECS gives you container orchestration. Choosing between them is less about which one is “better” and more about which one fits the stage of your application. Starting with EC2 EC2 is the most direct way to run a Spring Boot application on AWS. You create a virtual machine, install what you need, and run your application. For a Dockerized Spring Boot app, the flow often looks like this: GitHub Actions ↓ SSH into EC2 ↓ Pull latest code or image ↓ Build / run Docker container ↓ Spring Boot app runs on EC2 The mental model is simple: One server One Docker container One Spring Boot application That simplicity is useful. Especially when you are launching an MVP, a freelancer project, an internal tool, or your first production version. Why EC2 Works Well for Many Spring Boot Apps EC2 is not outdated just because newer deployment platforms exist. For many applications, EC2 is enough. It works well when: traffic is predictable the application is small or medium-sized you want full control over the server you want simple debugging through SSH you do not need multiple replicas yet you want to keep infrastructure easy to understand A common setup might be: Spring Boot Dockerfile Docker Compose GitHub Actions EC2 That setup can be production-ready for many early-stage apps. It gives you: a real deployment target repeatable Docker builds automated deployment through GitHub Actions simple logs direct server access For a solo developer or small team, that matters. You can understand the entire deployment flow without needing a full container orchestration platform. The Tradeoff With EC2 The tradeoff is that you

I don't know Rust. Friday after work I realised that 90% of my IDE time now is just the commit/diff view — and even good IDEs feel heavy for that. So over the weekend I built a dedicated native tool for just that. Kyde is a macOS git commit + diff editor with one goal: be fast, do Git well. I'm curious whether anyone else mostly opens their IDE for git operations these days. It's open source, and there's a signed app in Releases.

My Testing Setup I used Midjourney V7 (midjourney.com, Standard plan at $30/mo for this project — volume was too high for Basic) over five weeks across six product photo projects: lifestyle context images, background replacement concepts, packaging mockups, and mood-board style reference images for briefing photographers. Some outputs went live in ads. Others were used internally. A few were scrapped entirely. Two specific examples: I generated 12 lifestyle context images showing a skincare product in a bathroom setting — no actual product in the image, just the environment and mood — and used them as ad backgrounds with the real product composited in afterward. Results were strong. I also tried to generate images of the actual product itself from reference photos. That failed in ways I will explain. Pricing: Standard plan at $30/mo. For product photo work at volume, Basic at $10/mo runs out fast. Budget for Standard if this is a regular workflow. 1. Lifestyle Context and Environment Images This is where Midjourney V7 earns its place in a product photo workflow. Generating the environment — a kitchen countertop, a gym bag, a coffee shop table — without needing to stage or shoot it is genuinely useful. I needed eight lifestyle backgrounds for a supplement brand's ad campaign. Real location shoots for eight setups would have cost $3,000 and taken two weeks. I generated the environments in Midjourney, exported them, and composited the real product in using Photoshop. Total cost: $30 for the month's Midjourney subscription and four hours of compositing work. The images ran in paid Meta ads for six weeks. CTR was in line with our studio-shot creative. Nobody asked if the backgrounds were AI-generated. The key: generate the environment only. Do not try to put your specific product into the Midjourney image. Composite it in post. That division of labor is where the workflow holds up. 2. Packaging Mockups for Concepts That Do Not Exist Yet Before you manufacture a product o