Thomas Dohmke, the man who steered GitHub through the Copilot revolution, isn’t done with the software development lifecycle. His newest venture, Entire, has just broken cover, and it isn’t just another wrapper for an LLM. It is a direct assault on the lag that kills developer flow. In an era where AI coding assistants often make you wait for the “thinking” dots to stop bouncing, Dohmke is pivoting to raw, unadulterated speed.
| Attribute | Details |
| :— | :— |
| Difficulty | Intermediate (Requires familiarity with IDE integrations) |
| Time Required | 5-10 minutes for initial setup |
| Tools Needed | Entire Platform, VS Code or JetBrains IDE, GitHub Account |
The Why: The Death of the “Context Switch”
The problem with current AI coding tools isn’t the quality of the code—it’s the friction of the delivery. Every time a developer waits three seconds for a suggestion, the cognitive load increases. We lose the “flow state” that makes programming productive.
Dohmke’s new platform, Entire, targets the “latency gap.” By optimizing the stack specifically for real-time response, Entire aims to make AI feel like a local autocomplete rather than a remote consultant. If you’ve felt that Copilot or Cursor has become slightly bloated or sluggish as they’ve added features, Entire is the lean, high-octane alternative built for the “speed-first” developer. This shift highlights the growing demand for AI-driven software development that prioritizes efficiency over raw feature count.
Step-by-Step Instructions: Getting Started with Entire
Because Entire is built by the architect of modern social coding, the onboarding is designed to be frictionless. Here is how to integrate it into your workflow.
- Authenticate via GitHub: Visit the Entire portal and link your existing repository history. Entire uses your past commit patterns to understand your specific style before you write a single line. This deep integration mirrors how new tools like the GitHub Copilot App are turning developers into orchestrators of multiple AI agents.
- Install the “Entire Engine” Extension: Search for “Entire” in your IDE’s marketplace. Unlike traditional extensions, this includes a lightweight local binary that handles pre-processing to reduce the data sent to the cloud.
- Configure Low-Latency Mode: Dive into the settings and toggle “Predictive Buffering.” This allows Entire to start “thinking” about your next function while you are still finishing the current one.
- Map Your Hotkeys: Set up a specific trigger for “Refactor-on-the-fly.” This allows Entire to clean up your logic in the background without requiring a manual prompt.
- Audit the Output: Use the built-in “Latency Dashboard” to see exactly how many milliseconds the AI is taking to respond. If it’s over 100ms, adjust your local cache settings.
💡 Pro-Tip: To maximize Entire’s speed, keep your file sizes under 500 lines. The platform uses a “Flash-Attention” mechanism that performs best when it can ingest the entire file context into the local cache, virtually eliminating the wait time for suggestions.
The Buyer’s Perspective: Is Entire Actually Better?
The market is currently crowded with heavyweights like Cursor, GitHub Copilot, and Replit. So where does Entire fit?
If you are looking for a tool that handles project management, creates Jira tickets, and writes 50-page documentation files, Entire might feel too minimal. However, if your primary metric is time-to-first-token, Entire wins. It treats AI as a utility—like electricity—rather than a chatty assistant. This move toward specialized, high-speed tools is a cornerstone of a modern enterprise AI strategy, where performance often outweighs general-purpose versatility.
Competitors often struggle with “hallucination lag,” where the model pauses to verify logic. Entire appears to prioritize a “stream-first” approach, providing the UI with immediate feedback and refining the code as the stream completes. It’s a bold bet that developers value their time more than a verbose UI.
FAQ
How does Entire differ from GitHub Copilot?
While Copilot is an all-in-one suite integrated into the Microsoft ecosystem, Entire is purpose-built for ultra-low latency. It focuses on the speed of the suggestion engine rather than broad feature sets. For those looking for even deeper automation in the IDE, tools like Cursor 3 are also pushing the boundaries of what autonomous agents can do.
Is Entire’s “lowest-latency” claim actually noticeable?
Yes. In early benchmarks, Entire reduces the “pause” between typing a character and seeing a suggestion by nearly 40% compared to standard cloud-based LLM plugins.
Can I use Entire with private, on-premise repositories?
The platform is designed with an “Edge-First” mentality, meaning much of the processing happens closer to the user, making it more adaptable for secure environments than purely cloud-centric tools. This is similar to the trend toward Local AI that major tech giants are now pursuing to ensure data privacy and speed.
Ethical Note/Limitation: While Entire dramatically reduces latency, it does not eliminate the need for human code review; the speed of a suggestion is not a guarantee of its security or architectural soundness.