A fast, beautiful, and native FTP/SFTP client for macOS Discussion | Link

We've gone from A to Z to find Amazon's best Prime Day deals on the gear worth owning.

submitted by /u/fagnerbrack [link] [留言]

Times are hard in 2026. These Amazon Prime Day deals under $100 on earbuds, Kindles, and other tested products should help make life just a little bit easier.

There’s no better time to get a Kindle than during Amazon's own sale event.

Move over, spring cleaning. These vacuum deals will give your home the cleanest summer yet thanks to Amazon Prime Day.

Apple deals abound for Amazon Prime Day. We've rounded up the best deals on Apple Watches, iPhones, MacBooks, iPads, and accessories.

From MacBooks to gaming laptops, these are the very best deals on some of my very favorite laptops for Amazon Prime Day.

AI community manager that hypes up your Discord server Discussion | Link

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

I've been working on a different approach to giving AI coding agents context about large codebases. Instead of indexing files by embeddings or feeding hundreds of lines into the model, the idea is to statically classify code into structured semantics. For each file it produces things like: -Primary semantic role (service, repository, controller, DTO, etc.) with the evidence used to reach that conclusion. -Behavioral traits (transaction handling, business rule enforcement, orchestration, event emission, in-memory state, database interaction, etc.). -Architectural relationships and dependency direction. It also works at the function level, so individual methods get their own behavioral classification and relationship hints. The output is structured JSON rather than summaries, so an agent can query it instead of rereading source files. One thing I found interesting is that this often gives agents enough architectural context without spending tokens on large files. Instead of inferring "what is this class?", they already know something like: Domain service, performs transactional DB writes, enforces business rules, emits events, depends on persistence and event layers. Beyond agent context, the same information seems useful for architectural analysis—tracking responsibility drift, identifying layering violations, or seeing when a service gradually accumulates unrelated behaviors. I've been testing it on Medusa (TypeScript) so far, and the results have been promising, although there are still plenty of edge cases. I'm curious how others are approaching this. If you're building coding agents or working with large monorepos, what's been the hardest part of codebase understanding? Context size, architectural reasoning, stale indexes, something else? submitted by /u/Zealousideal_Ant4747 [link] [留言]

I just published Fintech Engineering Handbook distilled from 6 years of tears, sweat and swears. It’s a free ~25-page resource with various hints and patterns around handling money in software systems. Tell me what you think! submitted by /u/Krever [link] [留言]