Al

Alba – Earn and bid on unique software using idle AI credits

Hacker News

Alba – Earn and bid on unique software using idle AI credits

I have been using Claude Code lately, and I hated seeing my daily/weekly API quotas go to waste during downtime. So I built ALBA, a system designed to turn that idle power into digital assets. How it works: ALBA is an autonomous software factory that runs through Claude Code. You join as a worker node, and the system orchestrates Claude Code to build, test, and deploy functional micro-MVPs like tools, landing pages, and small SaaS. The Credit and Auction Model: Contribute: Run a command through Claude Code to power an AI agent task. Earn: You receive ALBA Credits proportional to the tokens and compute you provided. Bid: Use those credits in our Auction House to bid on unique software built by the collective. To make the discovery process more intuitive, I added an "Explore" feature that works like a vertical feed (similar to Shorts). You can quickly cycle through live demos of everything the factory has produced so far. It helps users visualize the "Asset" they are about to bid on. Exclusive Ownership: This is the "burn" logic I am most excited about: if you win an auction, you get the source code, and the demo environment is hard-deleted from our servers forever. You own a unique piece of software with zero duplicates. Tech Stack: - Frontend: Next.js (Vercel) - Backend: Java Spring Boot (GCP Cloud Run) - Database: Supabase/Firestore Agent Orchestration: via Claude Code CLI I would love to hear your thoughts on this "Token-to-Asset" arbitrage and the viability of credit-based auctions for exclusive code.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, exclusive, users · Missing: plus, platform, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: visualize, users · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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