ZDNET’s key takeaways
- Entire is a GitHub rival built around coding agents.
- The platform is designed to remove coding bottlenecks.
- AI-written code has changed what developers spend time on.
AI has sped up the pace of coding, putting new demands on infrastructure and changing how developers work. Entire, a developer platform from former GitHub CEO Thomas Dohmke, aims to be the solution to those changes.
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The platform, available for use today, is built on Entire’s decentralized Git network, announced in July, which is designed to withstand the sharp increase in AI coding activity that has caused mounting GitHub outages.
What makes Entire different
The first change Entire makes to the standard coding experience is that it replaces pull requests with what it calls Trails.
“Unlike the classic Pull Request model of reviewing code changes with a diff, Trails offer developers a heads-up display similar to an airline pilot, with customized evals that are scored by agents on every code push,” the company said.
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“All data is available not only on Entire’s web interface, but also through the open- a loop, while the human developer validates the progress. When all evals are satisfied, they can merge the code without needing to look at the code itself.”
Entire said that the agents running this process are customizable, and each trail shows how agents are spending tokens.
“With Trails, Entire treats the idea, the agent trace, and the outcome of a developer’s sessions as first-class artifacts for review alongside the code,” Dohmke said. “It uses multiple agents to generate evals that verify the quality and success of AI-generated code to let developers confidently merge without reviewing every line in the diff.”
The company also claims it offers Git hosting “up to 89x faster than GitHub” and six times faster than Cursor origin, thanks to that aforementioned decentralized network. The platform lets developers create, migrate, and host private repositories in multiple locations across the world, currently starting with the US, India, Australia, and the EU.
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Another differentiating factor is Continuous Memory, which keeps developer session logs next to code in the repo as a data source. Agents can use them to search for answers, which, Entire said, helps them use up to 71% fewer tokens. This is similar to other memory features in AI chatbots that reduce time spent gathering context by using past conversations or other agent interactions, where provided, to speed query response time. Entire said Continuous Memory is made up of a codebase graph and an “agentic brain” that uses previous sessions and prompts as lessons so agents don’t repeat mistakes.
Entire is available to try today for all developers.
