AI_INTEGRATION_POINTS.md
md
# 🤖 AI Integration Points for Hackathon DemoThis document outlines exciting places to integrate **Friendli.ai** to create a truly impressive AI-powered project manager that will wow the judges!## 🎯 Strategic AI Features to Impress Judges### 1. **AI Task Generation from Project Description** ⭐⭐⭐⭐⭐**Demo Impact:** VERY HIGH - Shows AI understanding complex project requirements**Where:** Project creation or "Generate Tasks" button on project page**How:** Stream AI-generated tasks in real-time as they're being created```go// Add to ProjectsControllerfunc (c *ProjectsController) handleGenerateTasks(w http.ResponseWriter, r *http.Request) { projectID := r.FormValue("project_id") project, _ := models.GetProjectByID(projectID) client, _ := friendli.NewClient(os.Getenv("FRIENDLI_API_KEY")) prompt := fmt.Sprintf(`You are a project manager. Break down this project into 5-8 specific, actionable tasks.Project: %sDescription: %sFor each task, provide:1. Title (brief, actionable)2. Description (1-2 sentences)3. Priority (low/medium/high)Format each task as:TASK: [title]DESC: [description]PRIORITY: [priority]---`, project.Name, project.Description) req := friendli.NewChatCompletionRequest( "meta-llama-3.1-70b-instruct", []friendli.Message{ friendli.NewSystemMessage("You are a helpful project management assistant."), friendli.NewUserMessage(prompt), }, ).WithTemperature(0.7).WithMaxTokens(1000) stream, _ := client.Chat.CreateCompletionStream(r.Context(), req) defer stream.Close() // Stream to HTMX as server-sent events w.Header().Set("Content-Type", "text/event-stream") flusher, _ := w.(http.Flusher) for { chunk, err := stream.Recv() if err == io.EOF { break } if err != nil { return } if len(chunk.Choices) > 0 { content := chunk.Choices[0].Delta.Content fmt.Fprintf(w, "data: %s\n\n", content) flusher.Flush() } }}```**UI Component:**```html<button class="btn btn-primary" hx-post="/ai/generate-tasks" hx-target="#ai-suggestions" hx-swap="innerHTML"> ✨ AI: Generate Tasks</button><div id="ai-suggestions" class="mt-4"></div>```**Judge Appeal:** Real-time streaming shows AI "thinking", very visual and impressive---### 2. **Smart Task Breakdown Assistant** ⭐⭐⭐⭐**Demo Impact:** HIGH - Practical feature developers actually need**Where:** Task detail modal - "Get AI Help" button**How:** AI suggests implementation steps and provides code examples```gofunc (c *ProjectsController) handleTaskBreakdown(w http.ResponseWriter, r *http.Request) { taskID := r.FormValue("task_id") task, _ := models.GetTaskByID(taskID) client, _ := friendli.NewClient(os.Getenv("FRIENDLI_API_KEY")) prompt := fmt.Sprintf(`Break down this development task into implementation steps with code examples.Task: %sDescription: %sProvide:1. Step-by-step implementation plan2. Code snippets for key parts3. Potential challenges and solutions4. Estimated timeUse markdown formatting.`, task.Title, task.Description) req := friendli.NewChatCompletionRequest( "meta-llama-3.1-70b-instruct", []friendli.Message{ friendli.NewSystemMessage("You are a senior software engineer helping plan implementation."), friendli.NewUserMessage(prompt), }, ).WithTemperature(0.5) stream, _ := client.Chat.CreateCompletionStream(r.Context(), req) defer stream.Close() // Stream markdown response w.Header().Set("Content-Type", "text/event-stream") // ... stream to UI}```**Judge Appeal:** Shows practical AI assistance for developers, code examples make it tangible---### 3. **AI Project Status Summary** ⭐⭐⭐⭐⭐**Demo Impact:** VERY HIGH - Executive summary of entire project**Where:** Dashboard or "AI Summary" button**How:** AI analyzes all tasks, identifies blockers, suggests next steps```gofunc (c *ProjectsController) handleProjectSummary(w http.ResponseWriter, r *http.Request) { projectID := r.PathValue("project") project, _ := models.GetProjectByID(projectID) tasks, _ := project.GetTasks() // Build context from all tasks taskSummary := "" for _, task := range tasks { taskSummary += fmt.Sprintf("- [%s] %s: %s\n", task.Status, task.Title, task.Description) } client, _ := friendli.NewClient(os.Getenv("FRIENDLI_API_KEY")) prompt := fmt.Sprintf(`Analyze this project and provide an executive summary.Project: %sDescription: %sTasks:%sProvide:1. **Overall Progress** - percentage complete and health status2. **Key Achievements** - what's been done3. **Current Blockers** - tasks stuck in progress4. **Recommended Next Steps** - prioritized actions5. **Timeline Estimate** - realistic completion estimateKeep it concise and actionable.`, project.Name, project.Description, taskSummary) req := friendli.NewChatCompletionRequest( "meta-llama-3.1-70b-instruct", []friendli.Message{ friendli.NewSystemMessage("You are an AI project manager analyzing project health."), friendli.NewUserMessage(prompt), }, ).WithTemperature(0.3) // Lower temperature for factual analysis stream, _ := client.Chat.CreateCompletionStream(r.Context(), req) defer stream.Close() // Stream to dashboard // ... implementation}```**Judge Appeal:** Shows AI can analyze complex data and provide insights, very "executive-friendly"---### 4. **Intelligent Task Prioritization** ⭐⭐⭐⭐**Demo Impact:** HIGH - AI making smart decisions**Where:** Project page - "AI: Reprioritize Tasks" button**How:** AI analyzes dependencies and suggests optimal task order```gofunc (c *ProjectsController) handleReprioritizeTasks(w http.ResponseWriter, r *http.Request) { // Get all tasks and ask AI to suggest priority order based on: // - Dependencies // - Current progress // - Business value // - Risk factors // Stream back reprioritization suggestions with reasoning}```**Judge Appeal:** Shows AI making strategic decisions, not just generating text---### 5. **Smart Comment Assistant** ⭐⭐⭐**Demo Impact:** MEDIUM-HIGH - Helpful for collaboration**Where:** Comment section on tasks**How:** AI suggests helpful responses or solutions based on conversation```gofunc (c *ProjectsController) handleSmartReply(w http.ResponseWriter, r *http.Request) { taskID := r.FormValue("task_id") task, _ := models.GetTaskByID(taskID) comments, _ := task.GetComments() // Build conversation context conversation := "" for _, comment := range comments { conversation += fmt.Sprintf("%s: %s\n", comment.Author, comment.Content) } prompt := fmt.Sprintf(`Task: %sRecent discussion:%sSuggest a helpful response that moves the task forward. Consider:- Technical solutions- Asking clarifying questions- Suggesting resources- Offering to help`, task.Title, conversation) // Stream AI-suggested reply}```**Judge Appeal:** Shows AI understanding context and participating in team conversations---### 6. **Auto-Documentation Generator** ⭐⭐⭐⭐**Demo Impact:** HIGH - Developers hate documentation!**Where:** Completed tasks - "Generate Docs" button**How:** AI creates markdown documentation from task details```gofunc (c *ProjectsController) handleGenerateDocs(w http.ResponseWriter, r *http.Request) { projectID := r.PathValue("project") project, _ := models.GetProjectByID(projectID) doneTasks, _ := /* get completed tasks */ prompt := fmt.Sprintf(`Generate project documentation in markdown format.Project: %sDescription: %sCompleted Features:%sCreate a README.md style document with:1. Project Overview2. Features Implemented3. Setup Instructions (inferred from tasks)4. Usage Guide5. Next Steps (from incomplete tasks)`, /* ... */) // Stream markdown documentation}```**Judge Appeal:** Practical, saves time, shows AI can write technical content---## 🎨 UI/UX Integration Ideas### Streaming Indicators```html<!-- Pulsing AI indicator while streaming --><div class="flex items-center gap-2 text-primary"> <span class="loading loading-dots loading-sm"></span> <span>AI is thinking...</span></div>```### Real-time Task Cards```html<!-- Task card that appears as AI generates it --><div class="card bg-base-200 shadow animate-fade-in"> <div class="card-body"> <div class="badge badge-primary">AI Generated</div> <h4>{{ task.Title }}</h4> <p>{{ task.Description }}</p> </div></div>```### AI Confidence Indicators```html<!-- Show AI's confidence in suggestions --><div class="flex gap-2 mt-2"> <div class="badge badge-success">High Confidence</div> <button class="btn btn-xs">Use Suggestion</button> <button class="btn btn-xs btn-ghost">Edit</button></div>```---## 🏆 Demo Script for Judges### Opening (30 seconds)"Let me show you an AI-powered project manager that helps developers actually get things done."### Feature 1: Task Generation (1 minute)1. Create new project: "Build a REST API for a todo app"2. Click "AI: Generate Tasks"3. Watch as AI **streams** tasks in real-time4. Show 5-8 well-structured tasks appear5. **Wow factor:** "Notice how it broke down the project logically and assigned priorities"### Feature 2: Smart Breakdown (45 seconds)1. Click on a task2. Click "Get AI Help"3. Watch AI stream implementation steps with **code examples**4. **Wow factor:** "It's giving me actual code I can use"### Feature 3: Project Summary (1 minute)1. Add some tasks to different columns (todo, in progress, done)2. Click "AI Summary"3. Watch AI analyze the **entire project** and stream insights4. **Wow factor:** "It identified blockers I didn't even notice"### Closing (30 seconds)"All powered by Friendli.ai's Llama 3.1 70B model - 468,000+ models available, blazing fast streaming, and it costs pennies per request."---## 📊 Metrics to Highlight- **Streaming Speed:** "250 tokens per second from Friendli.ai"- **Cost:** "This entire demo cost less than $0.10"- **Model Access:** "468,834 open-source models available"- **No Vendor Lock-in:** "OpenAI-compatible API, easy to swap models"---## 🚀 Quick Implementation PriorityFor the hackathon, implement in this order:1. ✅ **Task Generation** - Easiest, most visual2. ✅ **Project Summary** - Shows analytical power3. ⚡ **Task Breakdown** - Practical and impressive4. 💡 **Smart Prioritization** - If time permits---## 🔧 Environment Setup```bashexport FRIENDLI_API_KEY="flp_your_key_here"# Get free API key from https://suite.friendli.ai# $5 in free credits = hundreds of demo runs```---## 💡 Pro Tips for Demo1. **Pre-create example project** so judges don't wait for setup2. **Show streaming every time** - it's the most impressive part3. **Use longer responses** to show sustained streaming (set max_tokens=800)4. **Have fallback data** in case API is slow5. **Explain model choice:** "Using Llama 3.1 70B for quality, but can switch to 8B for speed"---## 🎯 Judging Criteria Alignment| Criteria | How We Address It ||----------|------------------|| **Innovation** | AI-powered PM tool with streaming responses || **Technical Excellence** | Clean Go code, HTMX/HATEOAS, Skykit framework || **Practical Use** | Solves real developer pain points || **GitHub Integration** | Built for GitHub HackNight, developer-focused || **Demo Quality** | Real-time streaming is visually impressive |---**Next Steps:** Pick 2-3 features from above and implement them for maximum demo impact! 🚀
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