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Daytona & Give(a)Go HackSprint - Dublin, August 2026

Event artwork for Daytona & Give(a)Go HackSprint - Dublin, August 2026

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About this event

What to expect

Build and improve an AI model or agent at this one-day HackSprint, taking place at Baseline in Dublin on 29 August 2026.

Hosted by Give(a)Go, Daytona and venue partner Baseline, the event gives builders a focused environment in which to experiment with fine-tuning, reinforcement learning and isolated AI sandboxes. The challenge is direct: choose a measurable task, then make your model or agent perform it better.

What you’ll build and explore

This is a practical HackSprint centred on working systems, visible results and lessons learned through experimentation. Possible areas include:

  • Fine-tuning: prepare focused datasets, run training jobs, compare checkpoints and investigate when specialised models outperform prompting alone.
  • Reinforcement learning: create rewards, run repeated rollouts, evaluate behaviour and improve an agent through feedback.
  • AI sandboxes: give each experiment a reproducible, isolated computer containing the files, dependencies and tools it needs.
  • Parallel experimentation: fork environments, compare multiple approaches and retain the strongest results.

Suggested project directions range from an evolutionary fine-tuning lab or RL agent arena to a self-improving coding agent, sandboxed evaluation harness or synthetic data factory. Daytona’s secure, elastic infrastructure allows training jobs and agent variants to run in disposable environments without contaminating one another. Every builder will receive Daytona credits to use during the HackSprint.

Format and schedule

Doors open at 11:00am, followed by an introduction to Daytona, sandboxes and the challenge at 11:30am. Hacking starts at noon, with lunch at 1:00pm and building resuming at 1:30pm. Teams will present their work during demos and judging from 4:45pm, before awards and the 5:30pm close.

You can arrive with an existing team or join one on the day. Bring a laptop, an idea for a model or agent, and a problem with results you can measure. The format is especially relevant to AI builders, technical founders and developers interested in model training, agent infrastructure, evaluation and reproducible experimentation.…

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