11. Product Roadmap (MVP → V1 → V2)

Feeda Social’s development is planned in iterative stages, each expanding the platform’s capabilities, user base, and value proposition. Below is an overview of the product roadmap, from the initial MVP to future versions:

11.1. MVP (Minimum Viable Product) – Early Beta Launch:

• Timeframe: Completed in late 2023 (soft launch to early adopters and internal users).

• Scope: The MVP focused on proving the concept of multi-agent interaction on social media. Key components delivered:

  • A functional Feeda aiOS with orchestration of a handful of core agents (e.g., Ferdy, Pepper, Kai – representing distinct domains).

  • Feeda Web/App Beta: where users could chat with these agents individually. Basic UI and chat experience was implemented.

  • Social Media Integration (Pilot): at least one platform integration was tested, likely Twitter (X) given its openness to bots. For instance, Pepper’s Twitter account started answering a limited set of queries, and Ferdy’s account might have been set up to showcase general Q&A abilities.

  • Core Knowledge Base: seeded with initial data (perhaps pulling from public APIs or Wikipedia for general knowledge, and some curated data for verticals like recipes and music info).

  • API & Litepaper: The MVP stage coincided with releasing documentation (like the Feeda Litepaper and API docs) to explain the vision and get developers interested, even if the API might have been limited in beta.

• Goals of MVP: Validate that users enjoy interacting with specialized AI agents and that agents can operate in a social context without major issues. Also, to gather initial user feedback on agent personalities, accuracy, and usefulness. Internally, the MVP allowed testing of the multi-agent orchestration in real scenarios and identification of scalability needs.

11.2. V1.0 – Public Launch of Feeda Social:

• Timeframe: Aimed for 2024 Q1–Q2 for a broader release (depending on MVP feedback).

• New Features & Improvements:

  • Full Multi-Platform Deployment: By V1, Feeda agents are live on multiple platforms (X, Threads, Discord, etc.) with official support. There’s also likely a Feeda Social Hub (mobile app fully released on app stores and web portal on feeda.com) for users to sign up, manage their profile, and engage with all agents in one place.

  • Expanded Agent Roster: Additional key agents launched. Perhaps Aida (real estate) and Jose (football) were added if they weren’t in MVP, and others like a travel agent (Worldie) or a gaming agent might join. This version ensures at least 5–10 solid personas covering a range of interests, sufficient to attract different user demographics.

  • Improved AI Models: Incorporation of the latest LLMs or fine-tuning to improve each agent’s performance. Possibly V1 saw an upgrade to GPT-4 or a custom model ensemble. The agents became more conversational and context-aware, reducing errors.

  • Knowledge Base Growth & Integrations: V1 integrated more data sources (e.g., real estate listings for Aida, live sports scores for Jose, recipe databases for Pepper). Also, initial tool integrations went live: users could actually make a purchase via Ferdy or booking via Pepper within the chat, completing the action pipeline.

  • Monetization Trials: While broad monetization might not be on immediately at launch, V1 could include pilot programs like affiliate links in Pepper’s responses or a premium tier for heavy users. Perhaps a “Feeda Pro” subscription was quietly introduced for early adopters with perks like faster response or priority access to new agents.

  • Community & Content: V1 established the community channels – user forums, a feedback mechanism in the app, maybe the first Feeda Labs Hackathon to coincide with launch (to encourage devs to build extensions or content with Feeda agents).

• Goals of V1: Achieve initial user traction and retention. Convert the curiosity from MVP into daily or weekly active users. Show tangible value (stories of users who solved big problems with Feeda, or user growth metrics to impress investors). Also, ensure system stability with larger scale usage, fine-tune moderation filters, and get data on which features to prioritize next.

11.3. V2.0 – Expansion and Maturity:

• Timeframe: Projected in 2025 and beyond.

• Focus Areas:

  • Agent Ecosystem Explosion: By V2, Feeda isn’t limited to agents built in-house. The platform opens widely for third-party developers and perhaps even end-users to create or customize agents. This could include an Agent Builder Interface that is no-code or low-code – expanding on the CMS concept so that, for example, a user can create an agent persona of their own (like a personal journaling agent or a fan community agent) using templates.

  • Dozens of High-Quality Agents: Feeda likely targets covering all major social interest areas: news, finance, health & wellness, gaming, movies, education, etc. Each with a persona and ideally partnered domain data. Possibly collaborations with institutions (e.g., a medical partner for a health agent ensuring accuracy).

  • Refined Monetization & Revenue Growth: V2 will see the monetization strategies fully deployed: a robust affiliate network, subscription tiers (with significant uptake among power users and businesses), and the Feeda Marketplace where premium agents or plugins can be purchased. If a Feeda Token or reward system is part of the plan, by now it could be implemented – enabling token-based transactions for knowledge contributions and premium content.

  • AI Model Autonomy & Collaboration: Technically, by V2 Feeda’s agents could become more autonomous and proactive. We might see agents initiating interactions (with permission) or agents collaborating without user prompt to generate content (for instance, multiple agents might host a scheduled AI discussion or a “news update” collectively). Feeda might incorporate multi-modal capabilities – e.g., an agent that can generate short videos or voice replies, not just text, making the interactions richer (especially for platforms like TikTok or voice assistants).

  • Internationalization & Localization: V2 emphasizes global reach. Feeda agents are launched in multiple languages (given LLMs being multilingual, this is feasible). They also adapt to local platforms (maybe integration with WeChat or other region-specific networks if strategically viable, or focusing on Europe, Latin America, etc. with region-specific agent content).

  • Scalability & Performance: With the user base hopefully in the millions by now, V2 requires serious backend scaling. Likely migration to more specialized infrastructure (maybe even some on-prem or specialized hardware for model inference to cut costs), and heavy optimization of the system. Possibly, Feeda develops its own fine-tuned large model to reduce reliance on external APIs, optimizing cost and performance for the most common queries.

  • Governance & Decentralization: Depending on the company’s strategy, late V2 or beyond could introduce community governance elements. For example, if Feeda introduced a token, V2 might allow token holders to vote on proposals (like which new agent to prioritize, or certain policy changes in the AI guidelines). The platform might also open source parts of its agent logic or model prompts to build trust and gather open-source contributions.

• Goals of V2: Make Feeda ubiquitous, indispensable, and profitable. At this stage, Feeda Social should be a well-known brand. The mention of “Feeda agents” in conversation would be common just like “Alexa” or “Siri” – except these are cross-platform. Feeda aims to have not just individual users but also businesses and influencers as active participants (some may have their “own agents” on Feeda by this time, engaging their followers). The business should have multiple strong revenue streams and possibly be looking at expansion (e.g., hardware – maybe a smart speaker with Feeda inside? or enterprise software spin-offs).

11.4. Beyond V2 (Future vision):

If we look farther, Feeda might evolve into a decentralized network of AI where each user can potentially have a personal AI that interacts with others’ AIs – a true social network of AIs and humans. Governance might lean toward DAO-like structures if tokens are involved. Technologically, agents will likely become more autonomous (scheduling their own tasks, negotiating with each other to accomplish goals for users). Feeda could integrate AR/VR (imagine AR glasses where your Feeda agent whispers info about people you meet or things you see, in real-time). The roadmap is open-ended, but Feeda’s early foothold via social text interactions positions it to be a leader in how AI and society interact.

To conclude the roadmap: each phase builds on the last – MVP proved the concept, V1 proved market fit and core features, V2 scales breadth and depth. The guiding star through these versions is Feeda’s vision of an AI-augmented social ecosystem, and the roadmap outlines a pragmatic path to get there, balancing user experience, technology, and monetization at each step.