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- Naz & Qazi

Error loading channel

I'm trying to add a youtube channel to the board, I pasted the exact url: https://www.youtube.com/@Lalogicadelacuriosidad and it loads properly in the right panel, but when trying to add it to my board it shows this error: {"source":{"width":350,"height":450,"id":"youtubeChannelNode-quiet-hill-r7z-l","type":"youtubeChannelNode","position":{"x":69.8067281923258,"y":71.95970497491999},"data":{"type":"youtubeChannelNode","uploadStatus":"error","notes":"","error":true,"errorMessage":"Invalid URI: https://www.youtube.com/@Lalógicadelacuriosidad/videos. Check if the channel_id is correct.","isDetaching":false},"style":{"height":450,"width":350},"measured":{"width":350,"height":450},"parentId":"groupNode-laughing-glacier-yYenq-copied","extent":"parent"},"isCopied":true} The error seems to be the apostrophe in the channel name, I pasted it without it and it worked in the right panel but not when moving it to the board, please let me know in case there is a workaround to get this channel added to my board.

Christian Jimenez 3 days ago

Bugs

Kalodata integration, SOCO App or Tiktok Shop Integration

I am a full time tiktok shop affiliate I would love to be able to use Poppy to do product research as a tiktok shop affiliate, not sure you can get direct tiktok shop product api access but tools like Kalodata or the SOCO app are able to show when a product is trending up over 7, 30 etc days. I would love to be able to be able to use Poppy to streamline and simplify my product research to find new products with high potential to be the next big seller or find current best sellers within the last 7, 14, or 30 days (more recent the better) as product research is the biggest bottle neck in most affiliates TT business - Eden (I’m apart of 6FC which I believe ya’ll just spoke to 😃)

Eden Coleman about 1 month ago

1
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Feature Request

Add "Boardroom" Button - Multi-Model AI Brainstorming Feature

Feature Spec: Boardroom Mode Multi-Model Collaborative Analysis with User-Selected Presenter Executive Summary Problem: Users currently get single-perspective outputs. Even when they try multiple models manually, there's no synthesis—just fragmented responses. Solution: Add a "Boardroom" action button that orchestrates 3 diverse LLMs through 5 specialized analysis steps, then synthesizes results through the user's selected model. Value Prop: Higher quality outputs (multi-perspective → fewer blind spots) Reduced iteration cycles (users don't manually run 5-10 prompts) Differentiated feature (no competitor offers orchestrated multi-model analysis) Leverages existing model infrastructure (no new APIs needed) Technical Feasibility: High. Uses existing model integrations + lightweight orchestration layer. User Experience Flow Before (current state): User types prompt Selects model from dropdown Gets single-model response If unsatisfied, manually tries different models/rewrites prompt After (with Boardroom): User types prompt Clicks Boardroom button (bottom action row) Waits 8-15 seconds Gets synthesized response from their selected Presenter model Optional: Expands "Show Boardroom Notes" to see analysis breakdown Key UX principle: Zero configuration. User doesn't pick council models or configure steps—just click and get better output. Technical Architecture Component Overview [User Prompt] ↓ [Boardroom Orchestrator] ↓ [Parallel Multi-Model Execution] (Steps 1-5) ↓ [Bundle Aggregator] ↓ [Presenter Synthesis] (User's selected model) ↓ [Final Output to Chat] Execution Flow (Detailed) Phase 1: Council Execution (Parallel) Input: User's prompt Process: Send user prompt + role-specific instruction to 3 pre-selected models Run 5 sequential steps (each step parallelizes across 3 models) Each model returns max 5 bullets per step Output: 15 structured responses (3 models × 5 steps) Phase 2: Bundle Creation Input: 15 council responses Process: Organize by step into structured bundle Output: Markdown-formatted analysis bundle Phase 3: Presenter Synthesis Input: Original prompt + bundle Process: Selected model (from existing dropdown) writes final response Output: Clean, conversational answer in chat The 5 Analysis Steps Each step is a specialized lens. All 3 council models run each step. Step Role Output Focus 1 Strategist Best approach, structure, execution plan 2 Creative Hooks, angles, format variations 3 Editor Weaknesses, gaps, what to cut 4 Market/Viral What performs, packaging guidance 5 Bias Detector Creator vs. audience mismatches Why 5 steps: Covers strategic, creative, editorial, market, and reality-check lenses. Universal across use cases (content, brand, strategy, copy). Council Model Selection Potential Default: Model A: GPT-4o-mini (OpenAI) Model B: Claude 3.5 Haiku (Anthropic) Model C: Gemini 1.5 Flash (Google) Why these: Fast (low latency) Cheap (cost-effective for 15 calls) Diverse families (different training, strengths, biases) Alternative: Dynamic selection based on availability/cost, but must ensure 3 different model families. Presenter Model Selection Behavior: Whatever model user has selected in the existing dropdown becomes the Presenter. Why this works: User gets output in their preferred model's voice/quality Separates "background compute" from "presentation layer" No new UI needed—leverages existing dropdown Example: Council runs on: GPT-4o-mini, Haiku, Flash User has selected: Claude Opus Final output: Written by Opus (synthesis of council work) Token/Cost Analysis (Hypothetical) Per Boardroom Execution: Council Phase: 15 calls (3 models × 5 steps) ~300 tokens input per call (prompt + role instruction) ~200 tokens output per call (5 bullets) Total council: ~7,500 tokens Presenter Phase: 1 call ~4,000 tokens input (original prompt + bundle) ~800 tokens output (final answer) Total presenter: ~4,800 tokens Grand Total: ~12,300 tokens per Boardroom execution Cost Comparison: Council models (mini/haiku/flash): ~$0.015 per execution Presenter model (varies): ~$0.02-0.15 depending on model Typical total: $0.03-0.17 per Boardroom vs. Manual Approach: User runs 5-10 separate prompts trying to get good output Higher aggregate token usage More user time burned ROI: Higher per-click cost, but dramatically fewer clicks needed. Integration with Existing Features Search/Connected Sources: Behavior: If user has Search enabled or board sources connected, council models inherit that context automatically. Implementation: No special handling needed—just pass context to council models same way you pass to single-model calls. Model Dropdown: Behavior: Dropdown becomes "Presenter selection" when Boardroom is used. Implementation: No UI change needed—just change backend behavior: selected model does synthesis instead of primary generation. Implementation Phases Phase 1: MVP (Ship This First) ✅ Boardroom button (bottom action row) ✅ 3 fixed council models (mini/haiku/flash) ✅ 5-step orchestration ✅ Presenter synthesis ✅ Final output to chat (no notes visibility) Scope: ~2-3 weeks, 1 backend engineer + 1 frontend engineer Phase 2: Polish "Show Boardroom Notes" toggle (collapsed by default) Display step-by-step breakdown Scope: +1 week Phase 3: Advanced (Optional) Fast/Deep toggle (2 steps vs 5 steps) Custom council model selection (power users) Boardroom analytics (track quality improvement) Success Metrics Adoption: % of active users who try Boardroom in first 30 days Repeat usage rate (users who use it 3+ times) Quality: Reduction in follow-up prompts after Boardroom vs. standard User satisfaction scores (survey) Retention increase among Boardroom users Efficiency: Avg time-to-acceptable-output (Boardroom vs. manual iteration) Risk Mitigation Risk Mitigation Latency (8-15s feels slow) Progress indicator, set expectations ("Consulting 3 models...") Token costs spike Hard limits on council output (5 bullets), monitor usage patterns Users don't understand it Clear onboarding tooltip, example use cases Presenter model doesn't synthesize well Pre-test synthesis prompts across models, fallback to structured format Why This Feature Wins Differentiated: No other AI chat tool offers orchestrated multi-model analysis Leverages existing infra: Uses models you already have integrated High perceived value: "3 AI models working together" = premium feel Reduces churn: Users get better outputs → stay longer Upsell opportunity: Can gate advanced features (custom councils, more steps) Proposed System Prompts Council Role Templates Step 1: Strategist You are the Strategist in a 3-model Boardroom. User's request: {USER_PROMPT} Your job: Identify the best approach, structure, and execution plan. Return (5 bullets max): - Primary goal and success criteria - Recommended structure/framework - Critical first steps - Key dependencies or requirements - One thing most people miss when doing this Step 2: Creative You are the Creative in a 3-model Boardroom. User's request: {USER_PROMPT} Your job: Generate hooks, angles, and format variations. Return (5 bullets max): - Strongest hook/opening angle - 2-3 alternative approaches - Format recommendation - One unexpected creative angle - What makes this stand out Step 3: Editor You are the Editor in a 3-model Boardroom. User's request: {USER_PROMPT} Your job: Find weaknesses, gaps, and what should be cut. Return (5 bullets max): - Biggest weakness in this approach - Critical missing information - What to remove/simplify - Assumptions that need validation - One question that must be answered first Step 4: Market/Viral Lens You are the Market Analyst in a 3-model Boardroom. User's request: {USER_PROMPT} Your job: Assess what performs and how to package for maximum impact. Return (5 bullets max): - What format/style wins right now - Audience hook priority - Pacing/delivery guidance - What's likely to underperform - Packaging recommendation Step 5: Bias Detector You are the Bias Detector in a 3-model Boardroom. User's request: {USER_PROMPT} Your job: Call out creator bias vs. audience reality. Return (5 bullets max): - Where creator preferences conflict with audience needs - Self-confirmation bias flags - "You'd like this but your audience won't" moments - Overcomplexity warnings - Reality check: what actually matters to end user Presenter Synthesis Prompt You are the Presenter. Synthesize the Boardroom analysis into a clear, actionable final answer. ORIGINAL REQUEST: {USER_PROMPT} Boardroom ANALYSIS: [Step 1 - Strategist] Model A: {bullets} Model B: {bullets} Model C: {bullets} [Steps 2-5...] {all_council_responses} YOUR JOB: Write a clear, conversational response that: - Provides the best actionable answer - Incorporates key insights from all perspectives - Flags critical warnings/watch-outs - Asks 2-3 high-leverage follow-up questions (if needed) Tone: Professional but conversational. No meta-commentary about the Boardroom process. Recommendation Ship Phase 1 as MVP. The core value (multi-model synthesis) is immediately usable and differentiated. Notes visibility and advanced features can iterate based on user feedback. Timeline: 3-4 weeks to production-ready MVP. Resource ask: 1 backend engineer, 1 frontend engineer, light PM oversight. Expected impact: 15-25% of power users adopt within 60 days, measurable reduction in prompt iteration cycles. Questions for product/eng review: Do we gate this behind a plan tier or ship to all users? Do we want usage analytics per Boardroom execution? Should council models be configurable (admin settings) or hardcoded? Prepared by: Tom Schreier / TopAItoolsfor Date: 02/24/2026 Prepared using Poppy 😉

William Tom Schreier 2 months ago

2
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Feature Request