Daggr allows developers to define workflows programmatically in Python while automatically generating a visual canvas that exposes intermediate states, inputs, and outputs for each step in the pipeline. Daggr simplifies applied AI development by organizing workflows as directed graphs, allowing for independent inspection and re-execution of each node. This method enhances debugging and speeds up iteration by tackling the issue of slow and unclear experimentation,
Using a pre-built template strategy: The Atlassian team realized that AI was often messing up core elements and not completely understanding complex commands. So they created a sort of "design system" for their AI led prototyping. Here they feed a page with pre-coded elements which AI doesn't change, but lets the tool work on other elements which are open to interpretation in a way.
The pace of change in the burgeoning generative AI world is blisteringly fast. It's often hard to keep up with everything, even if it's your full-time job. Readers tell me that one area they find particularly confusing is the wide array of poorly-named AI models. What in the heck is the difference between GPT-5.1, Opus 4.5, Gemini 3, etc.? And why would you use one over the other?
"There's long stretches of time between game releases, and you lose touch with the players, people lose touch with what you're up to--and they generally cost a lot more, the longer they take," he said. Part of what Relic will look to do in the future is "get to market more frequently," and that doesn't mean the company wants to lower the quality bar and simply ship things faster for the sake of it. Already, the studio has improved how fast it can prototype new ideas, Dowdeswell said, noting that staffers can now get a prototype up and running in four weeks.