Afelyon – open-source AI agent that turns Jira tickets into GitHub PRs

Hacker News

I built Afelyon to solve the biggest bottleneck in our team: the gap between "ticket created" and "PR ready for review." It's an AI agent that reads your Jira or ClickUp ticket, clones your repo, understands your codebase (conventions, patterns, framework), generates the code, runs tests, and opens a PR — automatically. We've been using it internally for months. Today we're open-sourcing the full platform: API server, AI agent engine, job orchestrator, and web dashboard. Tech: TypeScript, Fastify, Next.js, Claude AI, PostgreSQL + pgvector, BullMQ + Redis, Docker. Self-host with docker compose up. What makes it different from Copilot/Cursor: - Works from tickets, not cursor position — understands the full requirement - Hybrid context system — uses semantic search, style detection, task routing - Learns from your merged PRs - Integrates with your existing workflow (Jira, ClickUp, Slack) GitHub: https://github.com/Abdohurbly/afelyon-ai Cloud: https://afelyon.com Happy to answer any questions about the architecture.

Visit product View on Hacker NewsJan 31, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos

98%98% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host · Missing: plus, intuitive, reviews

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% 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.

Incorrect prediction on native model

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