Table of Contents Introduction Environment Requirements Core Features Core Design and Code Analysis Actual Execution Demo Architecture Overview How You Can Expand Future Plans & Conclusion What is this It is a basic debugger, running on Linux and implemented in C++, aiming to create a debugger that is easy to read and expand. In addition, Lavender's main function is to help users analyze the logic
The fork visible in 1.1.1 (simple query protocol on one side, extended on the other) is the subject of this section, one level deeper. 1.1.1 set the skeleton: simple is one message, extended is four. The job here is to show how that split translates into four distinct outcomes: plan reuse, parameter safety, pipelining, and error handling. Putting the message sequences side by side makes the differ
I’ve been diving into Solana development for the past 10+ days as part of 100DaysOfSolana. Today, I successfully moved my scripts into the browser to build a functional Devnet Dashboard. It was a great exercise in handling RPC providers and managing BigInt data for UI display. Major shoutout to Major League Hacking for the challenge. Looking forward to the next arc!
We’ve all built "perfect" systems, only for a single overlooked detail to turn into a production nightmare. I’ve spent my career building, breaking, and fixing things. I’m starting this space to share those experiences, because I believe the best way to master a concept is to learn from mistakes. I’ll be focusing on three main series: 1. War Stories 2. Under the Hood 3. General Tech Discussions I’
Updated May 2026: Now covers virtual desktop (Spaces) restoration and iCloud sync across multiple Macs, both shipped in ShiftPlus 1.3. TL;DR A complete macOS workspace includes apps, window layouts, browser profiles, virtual desktops, and terminal state. Native macOS saves almost none of it. Most third-party tools cover one slice: Stay and Spencer handle window layouts, Shift handles browser profi
In July 2025, a developer's Claude Code instance hit a recursion loop and burned through 1.67 billion tokens in 5 hours, generating an estimated $16,000 to $50,000 in API charges before anyone noticed. The agent did not crash. It did not throw an error. It just kept calling tools, getting confused, calling more tools, and silently accumulating cost. Old software crashes. LLM agents spend. This is
Modern applications are fragmented by default. A typical stack today might use: SQL for transactions MongoDB for flexible documents Redis for realtime state Pinecone or Weaviate for vectors Firebase for sync Separate tools for analytics, permissions, and operations That works — until the complexity starts to hurt. You end up with duplicated data, inconsistent permissions, fragile pipelines, multip