Yaw: terminal, SSH/database connections, AI Chat and optimized AI CLI

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Hi HN, I started building yaw because I couldn't find a terminal on Windows that didn't feel like an afterthought. It's 2026 and the options are still basically Windows Terminal or a handful of lesser known terminals w/ poor copy/paste support. Yaw turned into a terminal that does three things in one app: terminal emulation, connection management, and AI. The terminal side has what you'd expect — tabs, split panes, broadcast mode, search, a command palette, session restore. WebGL-rendered with xterm.js. It's fast and stays out of the way. The connection manager is where it started getting useful for me personally. I'm constantly SSH-ing into servers and connecting to databases. yaw lets me save SSH, PostgreSQL, MySQL, SQL Server, MongoDB, and Redis connections with encrypted credentials. It detects Tailscale machines automatically. There's a Remote Sessions panel for managing GNU Screen sessions on any SSH host. And a setup wizard that can install database servers locally if you don't have them. The AI piece started as a simple chat window — talk to Claude, ChatGPT, Gemini, Mistral, Grok, or Ollama with your terminal output as context. Select an error, ask what it means, get an answer that actually references what's on your screen. But the part I'm most excited about is the AI CLI tool integration. Run Claude Code, Codex, Gemini CLI, or Vibe CLI and Yaw automatically splits the pane — AI tool on the left, a fresh terminal in the same directory on the right. You can install any of them through a built-in wizard. It's a small thing but it completely changed how I use these tools day to day. v0.9.60, available on Windows 10+ and macOS 12+. Would love feedback — especially on the AI CLI tool workflow and what you'd want to see next.

Visit product View on Hacker NewsFeb 26, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, claude · Missing: agents, agent, cursor

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: started, gemini · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, llama, ide · Missing: https docs, just released, exist

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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