AI coding agents often read more code than necessary. When a developer asks a simple question about a function, the agent searches multiple files, opens entire source files, and fills the context window with irrelevant tokens. Usually, with a bigger codebase, it takes a lot of time to execute, consumes
Let me guess. You opened Claude Code, typed "build me a modern landing page," hit enter, and got back something that technically works but somehow looks like every other site you have ever scrolled. Purple to blue gradients. Inter for everything. Little rounded icon tiles floating above every
dbctx compiles a PostgreSQL database into a compact, queryable context file. AI agents and text-to-SQL systems get only the schema, relationships, and field meaning they need, instead of the entire information_schema.
Your agent forgets everything between sessions, and config files only hold the rules you knew to write down in advance. Here's what a memory layer actually does, and what happened when I wired mem0 into Claude Code and let it run on its own.
AI coding agents often read more code than necessary. When a developer asks a simple question about a function, the agent searches multiple files, opens entire source files, and fills the context window with irrelevant tokens. Usually, with a bigger codebase, it takes a lot of time to execute, consumes
Let me guess. You opened Claude Code, typed "build me a modern landing page," hit enter, and got back something that technically works but somehow looks like every other site you have ever scrolled. Purple to blue gradients. Inter for everything. Little rounded icon tiles floating above every
dbctx compiles a PostgreSQL database into a compact, queryable context file. AI agents and text-to-SQL systems get only the schema, relationships, and field meaning they need, instead of the entire information_schema.
Your agent forgets everything between sessions, and config files only hold the rules you knew to write down in advance. Here's what a memory layer actually does, and what happened when I wired mem0 into Claude Code and let it run on its own.
Learn how to create and host your own AI second brain with Hermes that remembers, automates tasks, and works for you 24/7.
Every time you pull in a third-party package instead of writing something yourself, you make a trade: you stop owning the internals, but you're still on the hook for how you use it, what it costs you at scale, and what happens the day it breaks in production.
Discover how real-time speech translation uses ASR, segmentation, and AI context to deliver fast, accurate translations during live conversations.
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is free and source-available on Github. Star git-lrc to help devs discover the project. Do give it a try and share your feedback. Hey. Come sit down. I need
A first-principles critique of the AI Code Review Bench methodology, arguing that AI code review has split into two distinct problems — human comprehension and machine verification — that no single benchmark can yet capture.
As developers, we trust Git with our code, our configs, even our notes, but never our money. Here's what happened when I found a tool that treats your finances exactly like a codebase: plain text, versioned, and diffable
Learn how neural networks work in PyTorch by building one from scratch.
You've probably built a project where one NodeMCU talks to your laptop over WiFi and thought this is great. But what if we need 10 sensors across a large building? And that's where the issues appear. In this article, we'll discuss what happens after