feat: bump dependencies and readme (#8)

* feat: bump dependencies and readme

* add citation cff

* small tweaks

* small tweaks to readme

CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Ismail"
+  given-names: "Pelaseyd"
+title: "Superagent PokerEval"
+date-released: 2024-11-25
+url: "https://github.com/superagent-ai/poker-eval"

README.md

@@ -8,7 +8,7 @@
 
  A comprehensive tool for assessing AI agents performance in simulated poker environments. Written in Typescript.
 
-[Getting Started](#getting-started) | [Examples](#examples) | [FAQ](#faq) | [Development](#development) | [Contributing](#contributing)
+[Getting Started](#getting-started) | [Why Poker?](#why-poker) | [Leaderboard](#leaderboard) | [Examples](#examples)
 
 </div>
 
@@ -24,24 +24,27 @@ npm i @superagent-ai/poker-eval
 // index.ts
 
 import { PokerGame } from "@superagent-ai/poker-eval";
+import { Player, PlayerAction } from "@superagent-ai/poker-eval/dist/types"
 
 // See example agent: https://github.com/superagent-ai/poker-eval/blob/main/examples/ai-sdk/agent.ts
 import { generateAction } from "./agent";
-import { Player, PlayerAction } from "../../src/types";
+
 
 async function executeGameSimulation(numHands: number): Promise<void> {
   // Setup AI players
   const players: Player[] = [
     {
       name: "GPT 1",
       action: async (state): Promise<PlayerAction> => {
+        // Use any model, framework or code to generate a response
         const action = await generateAction(state);
         return action;
       },
     },
     {
       name: "GPT 2",
       action: async (state): Promise<PlayerAction> => {
+        // Use any model, framework or code to generate a response
         const action = await generateAction(state);
         return action;
       },
@@ -82,10 +85,41 @@ In this example, the dataset shows the position of the player, their hole cards,
 
 BB/100, or Big Blinds per 100 hands, is a common metric used in poker to measure a player's win rate. It represents the average number of big blinds a player wins or loses over 100 hands. To calculate BB/100, use the formula:
 
-BB/100 = (Total bb_profit / Number of hands) * 100
+`BB/100 = (Total bb_profit / Number of hands) * 100`
 
 This formula provides a standardized measure of performance, allowing for comparison across different sessions or players by normalizing the win rate to a per-100-hands basis.
 
 
+## Why Poker? 
+Poker combines elements of strategy, psychology, risk assessment, and partial information - perfect for testing an Agent's decision-making skills in complex, uncertain environments. Poker provides measurable KPIs like EV, BB/100, All-In adj BB/100 and VPIP. These KPIs are widely recognized standards, not created by a single company, making them ideal for objectively evaluating an Agent's decision-making skills.
+
+We've specificalyy chosen No Limit Texas Holdem cash games and are officially calling the eval `NLTH`.
+
+## Leaderboard
+Coming soon...
+
+## Examples
+We've created some examples using populat agent frameworks you can use as inspiration (feel free to contribute): 
 
+- [Vercel AI SDK](https://github.com/superagent-ai/poker-eval/tree/main/examples/ai-sdk)
+- [OpeaAI]() Coming soon
+- [Mastra]() Coming soon
+- [LlamaIndex]() Coming soon
+- [Langchain]() Coming soon
 
+## Citations
+```json
+{
+  "cff-version": "1.2.0",
+  "message": "If you use this software, please cite it as below.",
+  "authors": [
+    {
+      "family-names": "Ismail",
+      "given-names": "Pelaseyd"
+    }
+  ],
+  "title": "Superagent PokerEval",
+  "date-released": "2024-11-25",
+  "url": "https://github.com/superagent-ai/poker-eval"
+}
+```

examples/ai-sdk/package.json

@@ -10,7 +10,7 @@
   "license": "ISC",
   "dependencies": {
     "@ai-sdk/openai": "^1.0.4",
-    "@superagent-ai/poker-eval": "^1.0.0",
+    "@superagent-ai/poker-eval": "^1.0.1",
     "ai": "^4.0.3",
     "dotenv": "^16.4.5",
     "zod": "^3.23.8"