Open Source

I’ve got four AI chat apps pinned to my dock for different models. And switching between them? That gets old fast. So when Cherry Studio crossed my radar — a desktop client wrapping every major LLM provider into one interface — it promised the consolidation I needed. At 47,414 GitHub stars, I had to try it. What Is Cherry Studio? Open-source desktop AI client with unified access to cloud LLMs (OpenAI, Anthropic, Gemini, DeepSeek), web services (Claude, Perplexity, Poe), and local models (Ollama, LM Studio) — all inside one app. Plus 300+ pre-configured assistants, MCP support, and document handling for text, images, PDFs, and code. ...

Ever looked at Lovable or Bolt and thought, “I could build something like this — if I had the infrastructure to run a thousand isolated dev environments?” Yeah, me too. But here’s the thing — that multi-tenant sandbox layer with preview URLs, agent orchestration, and idle cost control is the hardest piece of the puzzle. Most teams either glue together Docker Compose stacks until they hit isolation limits, or resign themselves to $2000/month Kubernetes clusters. ...

You know the drill — you want AI in your terminal, and the obvious choice is Claude Code at $0.10–$0.15 per query after the free tier runs out. But Google just dropped something that changes the math: Gemini CLI, an open-source AI agent that brings Gemini 3 straight to your terminal with a genuinely generous free tier — 60 requests per minute, 1,000 per day, no credit card required. I’ve been testing this for the past few days on my Ryzen 9 workstation, and here’s the deal. ...

Your company’s knowledge is sitting in documents, PDFs, Slack threads, and voice recordings. And your team asks a public AI about it — feeding proprietary data to a third-party API without even realising it. That’s the problem DocsGPT was built to solve. And honestly? It’s one of the best solutions in the open-source space at this price point. The quick version: DocsGPT is one of the most complete open-source enterprise AI platforms you can self-host today. Visual Agent Builder. Deep Research mode. 15+ document formats including audio. RBAC + OIDC for enterprise auth. And it deploys in about 15 minutes on a $6/month VPS. 17,935 GitHub stars, 4,845 commits, and the last commit was 2 hours ago — this project is very much alive. ...

You want an AI assistant. Not Claude on a web page — a real assistant that’s yours, runs where you want, talks to you on Telegram when you’re away from the computer, and remembers what you talked about last week. That’s what nanobot is. And it took me under 5 minutes to set up. I’ve tested three flavours of AI agent this month. Goose (48K★, Linux Foundation) is a terminal-first CLI agent — great if you live in the shell. Odysseus (56K★) gives you a full Docker workspace with ChromaDB, SearXNG, and email. But nanobot (44K★, HKUDS) sits in a spot neither quite fills: a chat-native personal agent that reaches you wherever you already talk. ...

Ever found yourself juggling three terminal windows — one running Claude Code, another on Codex, a third Pi session — and wishing there was a single pane of glass for all of them? Yeah, me too. That’s exactly the pain Omnigent sets out to kill. The short version: Omnigent is not another AI coding agent. It’s a meta-harness — a control plane that lets you run, orchestrate, and govern Claude Code, Codex, Pi, and your own custom YAML agents from a single CLI and web UI. 624 GitHub stars, 3 days old, and already shipping features like multi-agent Polly orchestration, cloud sandbox execution, and a policy engine that puts spend caps on your API bills. I spent an afternoon installing it, poking around Polly and Debby, and deploying it to a VPS. Here’s what I found. ...

You’ve been using Claude Code, Codex, Cursor, and maybe half a dozen other AI coding agents. Each one leaves session files scattered across your machine. Your monthly API bill shows a lump sum you can’t break down by agent. And when someone asks “how many tokens did last week’s refactor cost?”, you shrug. Yeah, me too. And that’s exactly the problem AgentsView solves. I spent a full afternoon digging into this one — reading the docs, tracing the codebase, and mapping out exactly what it can do. Still, it was worth every minute. Here’s what I found. ...

A few weeks ago I had three different AI projects running at the same time — a codebase refactor, a technical spec review, and some competitive analysis. Sounds manageable, right? It wasn’t. And every time I switched between projects in Claude Code, the context was gone. The agent mixed up code from the refactor with questions about the spec. So I spent more time re-explaining context than actually getting work done. ...

Ever asked your AI coding agent to simplify something and watched it add three more dependencies instead? Yeah, me too. That’s exactly why ponytail exists — a YAGNI plugin that puts a “stop and think” ladder inside Claude Code, Codex, Cursor, and practically every other AI coding agent out there. 916 GitHub stars in its first 24 hours. Not bad for a tool whose entire philosophy is “write less code.” ...

Ever watched your AI agent hit a wall three minutes into a task it was supposed to run for hours? Yeah, me too. I’ve been testing agent frameworks for a while now, and the pattern is always the same — they’re great at one-shot prompts, but ask them to do deep research, write code across multiple files, or iterate on a problem for 30 minutes, and they either forget what they were doing or spiral into nonsense. ...