20 Hacktoberfest Ideas That Wins You TheWeekend Challenge

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I have been doing open-source for 10 years now. From side projects to small and popular libraries and to hackathon projects. That's why I when I saw the DEV's Hacktoberfest Weekend Challenge. I knew I had to jump on it and share some ideas with what you can build with Valyu as part of your toolkit for building an awesome project! You have until October 5 to make it useful to somebody you actually know, with open-source AI at its core.

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Hello DEV! 👋 This Is My First Post

Hey everyone! 👋 This is officially my first post on DEV.to! I recently discovered the amazing world of open source and decided to participate in Hacktoberfest this year. 🌱 I'm still figuring things out, learning how GitHub, repositories, issues, and pull requests work, and honestly... I'm excited to see where this takes me.

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ProgrammingDEV Community ·

8h40 com todos os dados e nenhum aviso

00:34. Uma manutenção automática do provedor de nuvem reciclou os dois servidores do banco de produção, com 6 minutos de intervalo entre eles. O failover funcionou em 4 segundos. A segunda reciclagem derrubou o novo primário no meio da ressincronização, e a réplica travou. Ficamos 8h40 operando sem cópia de segurança do banco.

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ProgrammingDEV Community ·

The provider that failed was the feature: building a model-agnostic AI arbiter (Kimi K2 · Qwen · EIP-712)

A real failure story from building EscrowLens, a passkey-native escrow protocol on Monad for the Metropolis hackathon — and why an AI provider hitting its quota ceiling mid-verification was the best thing that happened to the architecture. Live app: https://escrowlens.vercel.app · Code (MIT): https://github.com/timmyspurs12/escrowlens The setup: an AI that can advise but never move money That division of labor creates an uncomfortable question every judge, user, and sane engineer should ask: what happens when the model behind the arbiter is wrong, down, rate-limited, or — worse — its account r

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ProgrammingDEV Community ·

I showed 30 AI models 336 monitoring dashboards. Most of them can't tell time.

This is a submission for the Kaggle Benchmarking Challenge Most incidents start with a graph. Not a log line, but a panel: a latency line that jumped, a CPU series pinned at the ceiling, a gap where a metric stopped. The first thirty seconds of on-call are spent reading a picture. AI copilots for on-call usually get the evidence as text. Real incidents hand you a screenshot.

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