The single-threaded AI chat window isn't efficient enough.. i did a comparison between claude code and codex multithreaded architectures: Claude Code and OpenAI’s Codex handle multi-threading completely differently under the hood. Here is the technical breakdown and the playbook to combine both: - Architecture: Peer Networks vs. Hierarchical Queues • Claude Code uses a Collaborative Peer Network. It spins up independent Claude instances ("Teammates") that isolate execution using local Git Worktrees hidden inside your repository (.claude/worktree_A). Teammates use a direct mesh protocol via native SendMessage tools to talk to one another, negotiate code interfaces, and update a shared TaskList dynamically. • OpenAI Codex uses a Hierarchical Fork-Join Queue. The main thread encounters an objective, Forks into specialized, independent subagents inside secure Cloud-Sandboxed Virtual Containers (e.g., separating a Security Auditor from a Test Suite), executes them in parallel in the cloud, and then Joins and distills their outputs back into the main thread. - Managing the Multi-Threaded "Token Tax" .. Parallel processing introduces a massive token drain. • To Optimize Claude Code: Use a root CLAUDE.md file to enforce strict subdirectory routing rules, preventing redundant repository-wide context indexing. • To Optimize Codex: Use prompt constraints to force input truncation: "Spawn 3 subagents. Return only a 5-line markdown summary to the main thread and close the container immediately." The Dual-Agent Playbook.. trying this now. anybody else have experience here..? Maximize throughput by running them simultaneously as a heterogeneous cluster: • Step 1 (Parallel Auditing ➡️ Codex): Use Codex to concurrently sweep for security risks, performance bugs, and test gaps across your repo. It leverages cloud container speed and keeps your local workspace clean. • Step 2 (Deep Implementation ➡️ Claude Code): Feed Codex’s audit report to Claude Code. Command it to spawn teammates to update database models and refactor API routes concurrently via local Git worktrees. Because they share a task list, the agents will actively negotiate code boundaries mid-execution. • Step 3 (Background Verification Codex Automations): Offload long-running background tasks, regression checks, and CI/CD failure triages to Codex cloud containers while your local machine remains unburdened. See the full architecture diagram below..

How a routine LinkedIn recruiter message almost turned into a major security breach. Last week, @rdotpy got hit with a sophisticated social engineering attack targeting software engineers. Here’s exactly how the trap was set, and why you should be careful. 👇 🧵

Even the recruiter was a fake. The profile belonged to a well-known arts journalist with zero technical background. Hilariously, when Roman pretended he couldn’t install the project, the "journalist" suddenly turned into an expert on npm and Node versions to push him into running it.