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An on-device AI Agent that runs on your phone, open & secure Discussion | Link

Your sex's private recap — never leaves your phone Discussion | Link

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Edit videos 10x faster with AI Discussion | Link

These portable vacuums make quick work of snack crumbs, tracked-in dirt, pet hair, and the mysterious debris beneath your seats.

Your kid’s little lunch hauler takes a brutal beating every day. These are the backbacks our own kids have tried and loved.

Hello Devs 👋 AI coding assistants have dramatically accelerated code generation. Whether you're using Cursor, GitHub Copilot, Claude Code, or Windsurf, writing code is faster than ever. The challenge is that code review hasn't improved at the same pace. Teams are shipping larger pull requests, reviewing more AI-generated code, and spending increasing amounts of time validating whether changes are actually correct, maintainable, and aligned with existing architecture. That's exactly why AI code review tools have become a key part of modern GitHub workflows. The problem is that not all AI review tools solve the same problem. Some generate pull request summaries. Some focus on security and compliance. Some extend traditional static analysis. Others attempt to understand repository-wide context and review changes the way an experienced teammate would. If you're evaluating AI code review tools for GitHub, here's a practical comparison of the most widely discussed options in 2026. ⚡ Quick Verdict Qodo stands out for teams that need automated pull request reviews with repository-wide context, not just diff analysis. The GitHub integration is straightforward, reviews run automatically on pull requests, and the platform focuses on understanding dependencies, related files, and existing code patterns across the repository. For small projects, lightweight review tools may be sufficient. For larger codebases, AI-generated code, and complex pull requests, context-aware review becomes significantly more valuable. What Makes a Good GitHub AI Review Tool? Before comparing tools, it's worth defining what actually matters. For most engineering teams, four factors determine whether an AI review tool provides real value. 1. Integration Reviews should appear where developers already work, directly inside GitHub pull requests. Nobody wants another dashboard, notification stream, or workflow to manage. 2. Review Quality Useful reviews surface meaningful issues, not just more comments. The

You've learned not to trust the first answer. So you read it carefully. You spot two problems. You fix them yourself, ship it, and move on. That's a reasonable way to work, and it's what separates an engineer who uses these tools well from one who copies and pastes. But there's a step you skipped. You never asked Claude to find the problems, and asking produces a different kind of output than reviewing does. 🔍 What actually happens Here's a specific case. You ask for an incremental load. You get something clean: a watermark column, a filter on records newer than the last run, an upsert into the target. You review it. You notice it assumes source records arrive in order, which yours don't, so you add a buffer window. You notice it doesn't handle the first run when the watermark is null, so you add a default. Two fixes, maybe fifteen minutes, and now it's correct. What you didn't find was the third problem: the upsert assumes a stable business key, and in your source system that key gets reassigned when records are merged. That one surfaces in production six weeks later as duplicate rows nobody can explain. You caught the problems you were looking for. You didn't catch the one you weren't. This is the normal outcome of self-review. You check against your own mental list of things that go wrong, and your list is good but finite. The problems that hurt are the ones outside it. 🧠 Why intermediates specifically miss this Beginners don't review AI output much at all, so this isn't their failure mode yet. Seniors have usually developed the habit after being caught out by something their own review missed. Intermediates sit in an awkward middle. They have learned, correctly, that AI output needs questioning. And they have concluded, understandably, that the questioning is entirely their job. That conclusion makes sense. Reviewing is what you do with a junior's pull request. It's what you do with your own code before you push. Review is a human activity performed on work some

Play live electronic music with your hands using your webcam Discussion | Link

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Using one coding agent is simple. Using several across multiple projects is where the real mess begins. I often had Claude Code and Codex running side by side. One agent was working, another was waiting for approval, and yesterday’s useful session was buried under a different path, branch, or terminal window. The agents were capable. My workspace was not. That frustration led me to build Termexo , a local-first Windows workbench for coding agents. The problem was not the model The hardest part was no longer generating code. It was keeping track of context: Which agent is still working? Which one needs my approval? Where did I leave yesterday’s session? Which project, path, and branch did that terminal belong to? Which model profile was active? A pile of terminal windows can answer all of those questions—but only if you remember everything yourself. What I wanted instead I wanted one recoverable workspace where I could: run Claude Code and Codex in real PTY terminals; arrange terminals in custom grids; see when an agent needs attention; search and resume native sessions; restore a workspace after restarting the app; switch Claude-compatible model profiles without rebuilding environment variables; keep API keys in Windows Credential Manager. That became Termexo. Why keep the native CLI? Termexo does not replace Claude Code or Codex with a custom chat interface. The real CLI remains visible and usable. That matters because the terminal is still the source of truth. Existing commands, hooks, approvals, keyboard shortcuts, and session behavior continue to work. Termexo focuses on the coordination layer around those tools. A workspace should be recoverable A useful coding-agent session should not disappear just because the app restarted or a terminal was closed. Termexo treats terminals, layouts, projects, and native agent sessions as parts of the same workspace. The goal is simple: when you return, you should be able to understand what was happening and continue without

One login, zero API keys — cloud agents + 27 models Discussion | Link

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The fastest workflow for developing with AI Discussion | Link

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In a world of plasticky water filter pitchers, I tested three new-generation stainless steel filter systems.

Job hunting is exhausting. Between tweaking resumes, updating portfolios, and filling out endless...

On Dreo’s summer flash sale from August 1 to 3, a great misting fan and an even better air fryer are at the lowest prices we’ve seen them.

There are both cheaper and more powerful entry-level gaming laptops out there, but the Alienware 15 walks that tightrope between price and quality.