Will Cory roninjin10

EIP-6963 Implementation Spec

Overview

Multi Injected Provider Discovery - wallets announce themselves, dapps discover them.

Spec: https://eips.ethereum.org/EIPS/eip-6963 Reference: https://github.com/wevm/mipd

Task: Route All Plue Traffic Through Cloudflare Edge

Objective

Refactor Plue's architecture so ALL traffic flows through Cloudflare:

HTTP/API → Cloudflare Edge Worker (already exists, add SIWE auth)
Git SSH → Cloudflare Spectrum (new)
Origin Protection → mTLS with custom certificates (new)

After this change, the origin server will ONLY accept connections from Cloudflare.

Hill Climbing Context

The control surface for agents

Most people talk about “getting better at prompting” as if the goal were to discover a perfect incantation that unlocks the model’s intelligence. That framing misses the actual control surface we have.

A modern LLM call is stateless. It does not remember your repo, your last run, or what you meant five minutes ago. It only sees what you send this time. The “context window” is not a mystical entity; it’s the tokens in the request: instructions, the plan, tool outputs, snippets of files, diffs, logs—everything the model can condition on.

Once that clicks, a blunt conclusion follows:

Context Hill Climbing

How agents get “smarter” without the model changing

(Expanded draft; clarity-first; using your literal examples and workflows)

Most people talk about “getting better at prompting” as if the goal were to discover a perfect incantation that unlocks the model’s intelligence. That framing misses the actual control surface you have.

A modern LLM call is stateless. It doesn’t remember your repo, your last run, or what you meant five minutes ago. It only sees what you send this time. The “context window” is not a mystical thing; it’s just the tokens in the request: system prompt + user prompt + tool outputs + any files/snippets you included.

Hill Climbing Context: The Control Surface for Agents

A first draft

Most people talk about “getting better at prompting” as if the goal were to discover a perfect incantation that unlocks the model’s intelligence. That framing misses the actual control surface we have.

A modern LLM call is stateless. It does not “remember” anything from last time unless you send it again. Your application can simulate state (a chat transcript, tool outputs, a memory file), but the model itself is still just: input tokens → output tokens. The “context window” is not a mystical entity; it’s whatever you put in the HTTP request.

And once you accept that, a fairly strong claim becomes hard to unsee:

How MemoryTool Works in Pydantic AI

The MemoryTool in Pydantic AI is a built-in tool that enables agents to persist information across conversations. Here's how it works:

Architecture

Pydantic AI Side: The MemoryTool class (builtin_tools.py:336-345) is a simple marker that tells Anthropic's API to enable the memory capability:

@dataclass(kw_only=True) class MemoryTool(AbstractBuiltinTool):

Implementation Retrospective

What Went Smoothly

Parallelization worked well - The foundation files (signal.ts, trigger.ts, handler.ts) and handlers (compaction.ts, struggling.ts) were implemented in parallel without issues. The spec's interface definitions were clear enough that agents could work independently.
Pattern discovery - The initial exploration agent provided excellent context on Bus, Instance.state, and namespace patterns. This made subsequent implementation consistent with the codebase.
TypeScript caught issues early - The type system immediately flagged that the SDK types also needed updating for the "lizard" trigger.

Issues Encountered

	#!/usr/bin/env bun
	/**
	* Prometheus MCP Server
	*
	* Enables AI agents to query Prometheus metrics for debugging and observability.
	* Provides tools to:
	* - Query instant metrics
	* - Query range metrics over time
	* - List available metrics
	* - Get service health status

	"""
	AI-powered code review workflow with multiple focus areas.

	Each focus runs an independent review pass with tool access to explore
	the codebase and gather context before making judgments.
	"""

	from plue import workflow, pull_request
	from plue.prompts import CodeReview
	from plue.tools import readfile, grep, glob, websearch

	#!/usr/bin/env bun
	/**
	* Prometheus MCP Server
	*
	* Enables AI agents to query Prometheus metrics for debugging and observability.
	* Provides tools to:
	* - Query instant metrics
	* - Query range metrics over time
	* - List available metrics
	* - Get service health status