Entire Launches as New Agent-First Rival to GitHub
Former GitHub CEO Thomas Dohmke has launched Entire, a new agent-first coding platform designed to handle the heavy infrastructure demands of AI-generated code.
Former GitHub CEO Thomas Dohmke has launched Entire, a new developer platform built specifically around coding agents to help eliminate modern software engineering bottlenecks. As automated code generation accelerates development cycles, traditional platforms have faced increasing strain, leading to infrastructure challenges such as mounting outages. Entire aims to address these modern shifts by providing an environment built from the ground up for agentic workflows.
At the core of the new platform is a decentralized Git network that was initially announced in July. According to the company, this underlying architecture provides Git hosting speeds up to 89 times faster than GitHub and six times faster than Cursor origin. Developers can set up, migrate, and host private repositories globally, with servers currently operating in the United States, India, Australia, and the European Union.
Rethinking Code Review With Trails
Entire introduces a distinct approach to the software review cycle by replacing traditional pull requests with a concept called Trails. Instead of reviewing code changes through a standard diff, Trails function similarly to an aircraft pilot's heads-up display. Every time code is pushed, customized evaluations are scored automatically by agents.
All platform data remains accessible via Entire's web interface as well as an open-source command line interface. This setup enables coding agents to address findings in a continuous loop while human developers validate overall progress. Once all evaluations pass successfully, developers can merge changes without having to manually inspect every line of code. Furthermore, each trail tracks token consumption, and ideas, agent traces, and outcomes are treated as primary review artifacts alongside the code itself.
Continuous Memory and Efficiency
Another key component of the platform is Continuous Memory, which stores developer session logs directly alongside the codebase within the repository. Agents leverage this accumulated data to search for answers, a capability Entire states reduces token usage by up to 71 percent. Comprising a codebase graph and an agentic brain, Continuous Memory uses historical sessions and prompts as lessons to prevent agents from repeating past mistakes.
What it means for developers
As coding agents become standard tools, managing infrastructure, token costs, and review bottlenecks has grown more complex. If you want to experiment with top AI models cheaply through one API at https://apixoai.online, platforms like Entire point toward a future where workflow tooling adapts directly to agentic needs. By shifting away from manual diff reviews toward automated evals and persistent agent memory, tools like Entire aim to let engineers focus on high-level validation rather than tedious line-by-line checks.
Source: Github competitor Entire is live now – how the agent-first platform works — ZDNet AI. Written by the Apixo team from that report.
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