Top AI Coding Topics & Trends for 2026

Ralph Wiggum pattern – persistent agent loops

Note: Some developers claim Claude Code supporting Tasks now reduces the need for Ralph loops, while others are still going "all-in-AFK" with it.

What it is.

Popularized by Geoffrey Huntley in mid‑2025, the Ralph Wiggum pattern runs an AI coding agent in an autonomous loop until a pre‑defined completion criterion is satisfied. Instead of issuing single‑shot prompts, Ralph loops repeatedly send a project prompt to an AI agent (e.g., Claude Code), intercept the agent's attempt to stop, inspect whether success criteria have been met, and - if not - re‑feed the prompt with updated context. The loop continues until tests pass or a completion tag is detected.

Why it matters.

Ralph loops remove human bottlenecks by allowing AI to work autonomously on long‑running tasks. They improve output quality through iterative refinement and free engineers from constant babysitting. Teams have used Ralph loops to run overnight refactors and to triage large backlogs.

When to use it.

Ralph works best for batch tasks, broad backlogs and refactoring jobs where a clear definition of “done” can be encoded as tests or tags. It is less suitable for creative or safety‑critical work requiring continuous judgment.

Pro‑tips.

• Define objective success criteria. The loop only stops when tests pass or a completion tag appears, so invest time writing robust tests or scripts that signal completion.
• Wrap with guardrails. Limit the maximum number of iterations and enforce token budgets to avoid runaway costs. Use “stop hooks” to intercept agent exit events and check for completion.
• Run in a clean environment. Because the agent rewrites files repeatedly, execute loops in isolated branches or worktrees to preserve your main codebase.
• Monitor logs. Even though the loop is autonomous, review session logs periodically to catch unexpected behavior. Many teams schedule reviews after every few iterations.

Agent Skills: packaging expertise for AI coding agents

What it is. Agent Skills are portable packages of instructions and helper scripts that extend the capabilities of AI coding agents. Vercel's open specification defines a skill as a folder containing a SKILL.md file (natural‑language instructions), optional scripts/ helpers, and an optional references/ directory. Skills can be installed into Opencode, Codex, Claude Code, Cursor and other agents via a simple CLI (e.g., npx add‑skill vercel‑labs/agent‑skills).

Smithery has a community catalog of Agent Skills worth checking out, including front-end design, Docs, Powerpoint, and PDF editing skills.

 

Vercel has a number of Skills they have made available for React developers. The react‑best‑practices skill packages more than a decade of performance‑optimization knowledge. It contains 40+ rules across eight categories - including eliminating async waterfalls, reducing bundle size, server‑side performance, client‑side data fetching, re‑render optimization and JavaScript micro‑optimizations. Each rule includes impact ratings (CRITICAL to LOW) and code examples. Vercel uses this knowledge to generate AGENTS.md, a document optimized for AI agents so they can suggest fixes automatically.

Featured community skills

See these skills in action:

Pro‑tips.

• Treat skills like npm packages. They can be installed globally or per‑agent via add‑skill. Use flags (--skill, -a, -g, -y) to control which skill and agent to install.
• Curate your skill set. Only install skills relevant to your stack to minimize noise. For React developers, start with react‑best‑practices and web‑design‑guidelines. For Next.js deployments, add vercel‑deploy‑claimable.
• Keep skills up to date. Skills encode best practices that evole over time. Periodically update (npx skills upgrade) to receive new performance rules.
• Explore community skills. The agent‑skills format is open, so you can create your own skills (e.g., ruby‑on‑rails‑best‑practices) or install community‑published skills from GitHub.

Beads & Gas Town: "solving" agent coordination

In AI agent coding, Gas Town and Beads are open-source tools developed by Steve Yegge to solve the "memory loss" and coordination problems inherent in running large numbers of AI agents.

Beads: Git-backed memory

Beads is a lightweight framework that provides AI agents with a durable reasoning trail or "long-term memory".

• Persistent context. It stores task graphs and planning data as versioned JSONL files directly in your Git repository. This allows an agent's memory to survive across branch switches, merges, or session restarts.
• Structured tasks. Instead of flat text "to-do" lists, Beads uses structured issues (called "beads") with dependency links. Agents can query these via a Go binary or a Model Context Protocol (MCP) plugin.
• Forensics. It creates an audit trail, helping agents (and humans) understand why specific decisions were made during complex project workflows.

Influence on Claude Code. In 2026, Claude Code upgraded its "Todos" system to "Tasks" - a primitive for tracking complex, multi-session projects - directly inspired by Beads. Tasks support dependencies and are stored in the file system (~/.claude/tasks), allowing multiple subagents to collaborate on a single task list by sharing a CLAUDE_CODE_TASK_LIST_ID.

Gas Town: multi-agent orchestrator

Gas Town is a high-throughput orchestration engine designed to manage dozens of AI agents (specifically Claude Code instances) working in parallel.

• Factory architecture. It treats AI agents as a coordinated workforce rather than individual assistants. It uses a "Mayor" to distribute work and a "Deacon" to monitor system health.
• Parallelization. It allows agents to work in separate Git worktrees (replicas of the codebase) to perform tasks concurrently without interfering with the primary workspace.
• Throughput over perfection. Gas Town is built for speed and scale, accepting minor redundancies to ensure high output for large-scale migrations or refactors.

Both projects are available on Steve Yegge's GitHub, where Beads serves as the foundational memory layer that Gas Town uses to route workflows and persist data.

Pro tips for Beads

• The "one-task-one-session" rule. To keep performance high and costs low, kill your agent process after it completes a single task. Beads acts as the "working memory" that allows a fresh agent to pick up exactly where the previous one left off.
• Prompt for high-volume issues. Explicitly tell your agent to "file beads for everything" it discovers. This prevents "agent amnesia" where a model identifies a bug but forgets it after a context compaction.
• Use "stealth mode" for local work. Run bd init --stealth to use Beads locally without committing task files to the main repository - ideal for personal planning in shared projects.
• Leverage graph dependencies. Instead of simple to-do lists, use Beads' ability to link tasks. When starting a session, ask the agent to query ready work; it will automatically find tasks with fulfilled dependencies.
• Avoid auto-compaction. If using Claude Code with Beads, turn off auto-compaction. Models perform best in the first half of the context window; Beads allows you to simply restart the session rather than letting the model "summarize" and lose detail.

Pro tips for Gas Town

• "Don’t watch them work". Gas Town is designed for high-throughput, industrial-scale production. Instead of monitoring an agent's real-time output, give them clear "marching orders" and spend your time reading their finished responses.
• Use Convoys for feature tracking. Group related work items into Convoys. This allows you to track multiple completed features on a dashboard and makes it easier to merge large, multi-agent updates simultaneously.
• Liberal use of handoffs. Use gt handoff (or its manual variant !gt handoff) after every task. This cycles the worker agent onto a "new shift," which clears context while preserving the session state in a tmux window.
• Maintain "daily hygiene". Because Gas Town is complex, run bd doctor --fix periodically to resolve broken rebases or merge conflicts in the task layer.
• Separate planning from execution. Use a tool like OpenSpec to refine a high-level design first. Once the plan is solid, ask the Mayor (Gas Town's lead agent) to translate it into a detailed set of Beads epics for the Polecats (worker agents) to execute.

Clawdbot: the local-first personal agent

Clawdbot can be overwhelming at first, so I’ll try my best to explain what it is and why it’s so exciting to play around with. At a high level, it is two things:

• An LLM-powered agent that runs on your computer and can use many of the popular models such as Claude, Gemini, etc.
• A “gateway” that lets you talk to the agent using the messaging app of your choice, including iMessage, Telegram, WhatsApp and others.

The second aspect was immediately fascinating to me: instead of having to install yet another app, Clawdbot’s integration with multiple messaging services meant I could use it in an app I was already familiar with. Plus, having an assistant live in Messages or Telegram further contributes to the feeling that you’re sending requests to an actual assistant.

The "agent" part is key

Clawdbot runs entirely on your computer, locally, and keeps its settings, preferences, user memories, and other instructions as literal folders and Markdown documents on your machine. Think of it as the equivalent of Obsidian: while there is a cloud service behind it (for Obsidian, it’s Sync; for Clawdbot, it’s the LLM provider you choose), everything else runs locally, on-device, and can be directly controlled and infinitely tweaked by you, either manually or by asking Clawdbot to change a specific aspect of itself to suit your needs.

A tool for tinkerers

Created by Peter Steinberger, Clawdbot is an open-source, "local-first" AI executive assistant built with TypeScript/Node.js. It connects through messaging apps you already use and can manage your files, browse the web, execute terminal commands, and use your camera or screen.

It supports persistent memory, proactive capabilities (like morning briefings), multi-modal inputs, and tool integrations (Spotify, Notion). As a "nerdy" tool installed via CLI, it allows for deep customization.

Mastering Clawdbot in 2026

To master Clawdbot, focus on security-first configurations, efficient memory management, and collaborative skill building.

1. Security & permissions

• Run as a non-admin: Create a dedicated, standard user account on your OS to run Clawdbot. This limits its reach to only the directories and system settings you explicitly allow.
• Selective access: Avoid granting "Full Disk Access." Instead, provide access only to specific project folders.
• Secure remote access: If you need to access your home bot while away, keep the Gateway on localhost and use SSH tunneling.

2. Workflow optimization

• The "Clear" habit: Frequently use the /clear command to avoid "compaction calls" where the model attempts to summarize old, irrelevant context.
• Iterative skill building: Perform the task manually with the bot first, provide feedback until it’s perfect, then command it to: "Turn our entire conversation into a new Skill called [Name]".
• Dedicated messaging accounts: Use a dedicated phone number and a standalone account for platforms like iMessage or WhatsApp.

3. Memory & performance

• Project-specific context: Use a CLAUDE.md or IDENTITY.md file in your project root to store persistent "memories".
• Canvas maintenance: If the "Live Canvas" feature becomes sluggish, clear the cache manually to reset the visual workspace.

Sub-agents: modular AI teams

We are seeing a shift from monolithic AI assistants to modular, collaborative systems. Sub-agents are specialized AI instances that handle specific tasks within a larger workflow. Each operates in its own isolated context window with custom prompts, tools and permissions. A primary orchestrator delegates work to these sub-agents, which execute independently and return results.

Why use them?

As projects scale, a single AI gets overwhelmed by context pollution. Sub-agents solve this by breaking complex challenges into manageable pieces.

• Context management. By isolating subtasks, you keep the main conversation clean and focused.
• Specialization. Fine-tune a sub-agent for security reviews or unit testing without diluting the main agent's focus.
• Security. Restrict tool access for specific agents (e.g. read-only for code reviewers).
• Parallelization. Run multiple sub-agents at once to speed up development.

Tools supporting sub-agents

Claude Code allows you to spin up sub-agents via YAML, the /agents command, or simply by asking. Cursor uses subagents to execute tasks in parallel (generating images, asking clarifying questions) and leverages fast models like GPT-5.2 Codex for context gathering before handing off to smarter models. Antigravity works with Gemini 3.0 Flash for rapid, browser-based website rebuilding.

Pro-tips

• Define clear roles. Craft precise descriptions so the orchestrator knows when to delegate.
• Limit tools. Assign only necessary tools to each sub-agent for better security and focus.
• Start simple. Begin with explicit sub-agents for predictable workflows before moving to dynamic ones.