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@phero20f · X
Web-based terminal multiplexer for running AI coding agents in parallel.
04mg · Product Hunt
Caw Open source web terminal multiplexer for AI agents
Share files and logs with AI agents like Claude Code and Cursor in an encrypted workspace.
@Raullen · X
6 months ago, @qevan and I hit a stupid wall: just trying to share a local file or some logs with an AI agent was incredibly frustrating. So, we built the first version of —a dead-simple, secure way to pass context to AI. The community feedback was amazing. We got ~200 stars on GitHub, and it quickly became a daily driver for a lot of developers. But recently, we realized the game has changed. Our workflows evolved. It’s no longer just humans sending files to agents. Agents need to write back to humans. Agents need to hand off context to other agents. The whole team—human and AI—needs to collaborate. So, we gave vnsh a massive upgrade. Today, it’s no longer just a secure clipboard. It’s now a living workspace. 🚀 You drop your context in once and get a dynamic link. Claude Code, Cursor, Cline, and your human teammates all open the exact same document. When one agent updates it, your whole stack sees it instantly. Complete context handoff, without ever copy-pastin
Fast, private, and beautifully designed utilities for everyday development. Format JSON, decode JWTs, generate UUIDs, and more. Created by Uzair Shahed.
@uxi_ee · X
Benchmark local coding models on consumer hardware to measure accuracy, latency, and throughput across 27 tasks.
u/Unfair_Association89 · Reddit
I built a reproducible benchmark for local coding models (Ollama, 27 tasks, live leaderboard) ran it on my 8GB card, here's what I found I kept eyeballing "vibes" to decide whether one quant of a coding model was actually better than another on my machine, so I built Sakura to get real numbers instead. What it does: - Points at any Ollama model and runs it through 27 hand-curated tasks: codegen, bugfix, SQL, refactor, systems design, protocol implementation, and terminal-agent episode
