I attended one shot prompting’s funeral.
There were no tears. Just a room full of developers quietly pretending they weren’t taking shots the night before. Because if we’re being honest, everyone saw this coming and couldn’t be happier it was over.
Saying “one shot prompting is dead” isn’t revolutionary. It’s just catching up to what builders have been experiencing for months.
Agents fundamentally changed how we program. They enable developers to move faster by disintermediating the traditional development workflow. This means less time switching between specialized tools and fewer dependencies on other teams. Now that agents can execute complicated tasks, developers face a new challenge: using them effectively over long sessions.
The biggest challenge is context rot. Because agents have limited memory, a session that runs too long can cause them to "forget" earlier instructions. This leads to unreliable outputs, frustration, and subtle but grave mistakes in your codebase. One promising solution is Code Mode.
rp-why is your personal AI collaboration coach. It answers two critical questions:
Think of it as a fitness tracker for your AI practice—it shows you where you are, where you could be, and how to get there.
Want the theory? Check out Measuring the Cognitive Complexity of Human-AI Collaboration.
Want to use it? Keep reading.
Everyone on Tech Twitter has been buying Mac Minis, so they could run a local agentic tool called OpenClaw. OpenClaw is a messaging-based AI assistant that connects to platforms such as Discord and Telegram allowing you to interact with an AI agent through DMs or @mentions. Under the hood, it uses an agent called Pi to execute tasks, browse the web, write code, and more.
Seeing the hype made me want to get my hands dirty. I wanted to see if I could build a lite version for myself. I wanted something minimal that used goose as the engine instead of Pi. I tentatively dubbed it AltOpenClaw.
MCP Apps allow you to render interactive UI directly inside any agent supporting the Model Context Protocol. Instead of a wall of text, your agent can now provide a functional chart, a checkout form, or a video player. This bridges the gap in agentic workflows: clicking a button is often clearer than describing the action you hope an agent executes.
MCP Apps originated as MCP-UI, an experimental project. After adoption by early clients like goose, the MCP maintainers incorporated it as an official extension. Today, it's supported by clients like goose, MCPJam, Claude, ChatGPT, and Postman.
Even though MCP Apps use web technologies, building one isn't the same as building a traditional web app. Your UI runs inside an agent you don't control, communicates with a model that can't see user interactions, and needs to feel native across multiple hosts.
After implementing MCP App support in our own hosts and building several individual apps to run on them, here are the practical lessons we've picked up along the way.
MCP-UI is fun. It’s scrappy. It’s early. And like I said in my last post, there’s something genuinely addictive about building this close to the edges of an ecosystem while everything is still taking shape.
But MCP Apps feels different.
Not in a “shiny new feature” way. More in a “this is the ecosystem maturing” way.
In 2025 we did a fairly cutting edge take on whole device automation using Android (code name was gosling) which was an on-device agent that would take over your device (mic even used it to do some shopping - which he realized after some things arrived at his door that it had automatically purchased as the result of an email - hence the PoC/experimental label!)
Recently we consolidated the apps for goose mobile.
The goose-ios client is more production ready, and in the app store (still early days). We hope to have a port of that to Android, which will be strictly a client (and won't take over your device!) to your remote agent. The aim of the client (vs an on device agent) is for you to take your work on the go with you.
Really great for long running tasks, checking on things, or just shooting off an idea but still keeping things local to your personal agent (where all your stuff is) securely.
The first question I had when I heard about MCP sampling was:
“Can’t I just write better tool descriptions and tell the tool it’s an expert?”
The MCP ecosystem is standardizing how servers deliver interactive UIs to hosts, and goose is an early adopter. Today we're shipping support for the draft MCP Apps specification (SEP-1865), bringing goose in line with the emerging standard, as other hosts like Claude and ChatGPT move toward adoption.
In our previous blog post we detailed the Model Context Protocol (MCP) system and discussed some security concerns and mitigations. As a brief recap, MCP provides agents with a means to accomplish tasks using defined tools; reducing the burden of using complex and varied APIs and integrations on the agent.