Conductor is a desktop app for running several AI coding agents in parallel on the same project. Instead of driving one agent at a time, you spin up multiple agents that each work independently and hand you the results to review. What it does Conductor connects to your AI provider — Claude, Codex, and others — and lets you launch multiple agents to work on your project at once. Each agent runs in its own git worktree, so their changes stay isolated from one another. When an agent finishes, its worktree makes it easy to review the diff, integrate the work, and push it to git. ...

A quick comparison of tools people use for vibe-coding and agentic coding. Fill in your own experience/notes as you try each one. Tool Type Runs where Notes Official site Claude Code CLI / IDE agent Terminal, VS Code, JetBrains Deep agentic loop, tool use, subagents, hooks, MCP support claude.com/claude-code Cursor IDE (fork of VS Code) Desktop Agent mode, inline edits, codebase-aware chat cursor.com GitHub Copilot / Copilot Workspace IDE extension VS Code, JetBrains, GitHub.com Autocomplete + chat + task-level “workspace” planning github.com/features/copilot Aider CLI Terminal Open-source, git-native, works with many model backends aider.chat Windsurf IDE Desktop “Cascade” agent flow, multi-file edits windsurf.com Devin Autonomous agent Cloud sandbox Longer-running autonomous tasks, less interactive devin.ai Conductor IDE Desktop (macOS) Runs multiple agents in parallel, each in its own git worktree conductor.build superset.sh IDE Desktop (macOS, Linux) Conductor alternative with Linux support; parallel agents in isolated worktrees, notifications, skills/hooks docs.superset.sh goose CLI / desktop agent Terminal, Desktop Open-source, MCP-based extensions, subagents, recipes, scheduled runs goose-docs.ai

These are the notes from talks at Y Combinator. General rule of thumb The best technique is to do what a professional software engineer already does. Small code, modularity, and abstraction are your friends — they help both you and the LLM reason about the project. Note: this advice will likely shift over the next few months. As models get more capable, some of these guardrails will loosen. Plan before you build Don’t ask the model to one-shot the whole project. Instead, create a planning file (a CLAUDE.md or a plain markdown file) that lays out step by step what you want to build. Iterate on it over time and revise it whenever your understanding changes. You can even use a Planner agent to help revise it. ...

There are many vector databases available today, such as Chroma, Pinecone, Qdrant, Milvus, pgvector, and Elastic. Each offers unique capabilities and is useful in different situations. For developers integrating vector search into their applications for the first time, Chroma and Milvus tend to provide excellent documentation and straightforward implementations. However, many production systems still rely on traditional sparse or boolean retrieval engines like Elasticsearch. In those contexts, Elasticsearch remains extremely efficient and is arguably one of the best solutions available. That said, Elasticsearch joined the vector-search space relatively late (basic dense-vector support was introduced around version 8.x), and historically you needed custom scripts for many advanced features. ...

I first came across TOON (Token-Oriented Object Notation) on LinkedIn, where developers were discussing its promise: lossless compression of JSON and YAML, specifically optimized for large language models (LLMs). The core idea? Reduce token count without sacrificing information, which directly translates to lower costs and potentially faster processing. Comparison between TOON and JSON for nested objects At first glance, TOON looks familiar yet distinct. For instance, a TOON object always begins with the number of items (e.g., [2] in the figure above), and unlike JSON it drops quotation marks (") around keys. This minimalism becomes especially powerful with flat data structures. In those cases, TOON starts to resemble CSV format: a header row followed by values. The result? Significant token savings (I used their Python version for my experiments). ...