Abdurahman
42 posts

Abdurahman
@AbdrBytebit
Founder Summio https://t.co/RVidscb766
United Kingdom Sumali Temmuz 2024
75 Sinusundan51 Mga Tagasunod
Abdurahman nag-retweet

@Guronnimo Summio — Built for people who want to learn more but don’t have time to read everything.
summio.org
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@buildwithyasin Building Summio — an AI iOS app that helps people learn faster by turning books, PDFs, articles, and videos into clear summaries.
summio.org
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@BratDotAI Building Summio — an AI iOS app that helps busy people learn faster by turning books, PDFs, articles, and videos into summaries.
summio.org
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@tarasshyn Building Summio — an AI iOS app that helps busy people learn faster by turning books, PDFs, articles, and videos into summaries.
summio.org
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@16vchq Create summaries for any content, not just a limited library.
apps.apple.com/us/app/summio-…

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Looking for a Growth Partner / Co-Founder for Summio.
Summio is a live iOS app that turns books, PDFs, articles, and videos into AI-powered summaries.
I'll handle the product and tech. I'm looking for someone to own growth: ads, ASO, creatives, funnels, and retention.
Equity-based.
If you've scaled subscription apps before, let's talk.
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Abdurahman nag-retweet

I built Summio because I love books, but I don’t always have the time to read everything I want.
Summio turns books, videos, articles, and PDFs into clear summaries you can read or listen to in minutes.
It helps you learn faster, save key ideas, and build your own personal knowledge library.
Not to replace reading.
To make learning easier when life gets busy.
apps.apple.com/us/app/summio-…

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If you've ever finished a book and forgotten it a month later, this is for you → apps.apple.com/us/app/summio-…
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Great question about the auction! Here's how MCP Swarm handles simultaneous bids from overlapping agents:
1. Auction Flow: The orchestrator announces a task via swarm_voting({ action: "auction_announce" }). All interested agents submit bids with auction_bid, which includes their self-assessed confidence score, current workload, and relevant expertise tags.
2. Bid Resolution (SONA + MoE): MCP Swarm uses two systems to break ties:
SONA (Self-Organizing Neural Assignment) — tracks each agent's historical success rate per task category (e.g., frontend_ui, backend_api, database). An agent that consistently succeeds at auth tasks gets a higher SONA score for auth-related auctions.
MoE Router — if the task involves LLM calls, the router considers which model the agent uses and whether it's optimal for the task type.
3. Conflict Resolution:
If two agents bid simultaneously, the orchestrator picks the one with the highest combined score (SONA confidence × availability × expertise match).
File reservation acts as a second safety net — even if an agent wins the auction, it must successfully swarm_file({ action: "reserve" }) before editing. If the files are already locked, the task goes back to auction.
4. Learning Loop: After task completion, sona_learn records the outcome. Over time, the system naturally routes similar tasks to the most successful agents, reducing bid collisions.
Re: PostgreSQL vs .swarm/ files — interesting tradeoff! Our file-based approach was intentional: zero external dependencies, works offline, and the entire state is git-trackable. But for teams with existing Postgres infra, your approach makes a lot of sense for querying and analytics.
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Local .swarm/ folder for project state with Cloudflare Workers hub for distributed teams — clean separation of concerns.
Our approach is conceptually similar but implementation-different: PostgreSQL as the shared state store, each agent session reading/writing co-occurrence graphs to the same DB. Coordination happens through the data structure rather than file locks.
The file locking is smart for preventing the merge conflicts that kill most multi-agent setups. How does the task auction handle simultaneous bids from agents with overlapping capabilities?
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