Rapid Prototyping Suggests Low Implementation Friction For Improved Review Ux

Issue 87 Edition 2026-03-28 7 min read

General

Sources: 1 • Confidence: High • Updated: 2026-03-29 03:25

Key takeaways

After the 107-file pull request, the author built a v0.1 prototype tool in about 30 minutes that analyzes a git branch diff and outputs grouped files, specialist findings, and a fast local diff viewer.
A pull request the author reviewed had 107 changed files and over 114,000 new lines of code, adding two new models that produce outputs for 53 app prompts.
The author predicts the ratio of time spent producing code versus reviewing code will continue moving toward review being the dominant constraint as agentic coding adoption grows.
Because of SOC2 and HIPAA obligations, logic.inc requires production code to be reviewed by at least two humans even when agents wrote the code.
The corpus asserts that LLMs can enable automatic semantic grouping of changed files by intent and feature role rather than filename order.

After the 107-file pull request, the author built a v0.1 prototype tool in about 30 minutes that analyzes a git branch diff and outputs grouped files, specialist findings, and a fast local diff viewer.
The author describes the prototype workflow as two commands (fetching a PR diff and running analyses) after which it serves grouped review results for faster human review.
The author states a minimal requirement is a review tool that can render large diffs locally without the browser failing.
The author reports the prototype helped by separating unimportant formatting changes and flagging areas of concern that might otherwise be missed in very large diffs.

A pull request the author reviewed had 107 changed files and over 114,000 new lines of code, adding two new models that produce outputs for 53 app prompts.
The standard code review UI failed to render the large diffs inline and the browser could not handle the full review in the usual interface.
The author reports the standard review workflow is tolerable around 15 files but breaks down at roughly 107 files.

The author predicts the ratio of time spent producing code versus reviewing code will continue moving toward review being the dominant constraint as agentic coding adoption grows.
The author states that over the last 1–2 years code production changed materially while the code review process did not.
The corpus asserts that accelerating code production shifts the delivery bottleneck toward review time in line with Amdahl’s law framing.

Because of SOC2 and HIPAA obligations, logic.inc requires production code to be reviewed by at least two humans even when agents wrote the code.
The author argues that using AI to review AI code is insufficient because compliance, knowledge sharing, and quality judgment still require humans in the loop.
The author serves as the first reviewer for agent-written code and sends it to teammates only after being satisfied.

The corpus asserts that LLMs can enable automatic semantic grouping of changed files by intent and feature role rather than filename order.
The corpus asserts that alphabetical file ordering in common review UIs increases cognitive load by forcing reviewers to rebuild context across unrelated files.
The author proposes using parallel specialist agents (e.g., security, best practices, consistency) to surface issues in large diffs as an aid to human review.

The author predicts the ratio of time spent producing code versus reviewing code will continue moving toward review being the dominant constraint as agentic coding adoption grows.

How common are extremely large pull requests (by files changed/lines changed) in teams adopting agentic coding, and how quickly is the distribution shifting?
What is the causal relationship between pull request size and post-merge defects, incident rates, or compliance findings in this setting?
What are the operational limits of existing review platforms (render timeouts, maximum diff size, browser memory constraints), and can configuration or alternative clients mitigate them?
Does semantic grouping by intent measurably reduce review time and improve issue detection compared to standard ordering across multiple reviewers and repositories?
How should specialist agent findings be calibrated (false positives/negatives) and presented so that they increase scrutiny without overwhelming reviewers?

Code review tooling could see rising urgency as agentic coding increases pull request scale and review becomes the bottleneck, especially where web-based diff viewers fail at large diffs.
Compliance-heavy software teams may demand review augmentation tools that improve throughput while preserving mandatory multi-human approvals under SOC2 and HIPAA constraints.
LLM-enabled review UX features like intent-based file grouping and specialist findings may become differentiators if they reduce reviewer time and improve issue detection versus standard alphabetical diff ordering.

Repeatable evidence across teams that pull request size and reviewer time are increasing as agentic coding adoption grows, with review taking a larger share of cycle time.
Demonstrations that standard review interfaces hit practical limits on large diffs, and that local or alternative viewers reliably mitigate render timeouts or browser constraints.
Controlled measurements showing semantic grouping and specialist findings reduce review time or increase defect detection compared to standard ordering, without overwhelming reviewers with false positives.

Data showing pull request size does not materially increase with agentic coding, or teams adapt by changing processes so review does not become the dominant constraint.
Platform improvements or configuration changes make web-based review scale smoothly to very large diffs, eliminating the described failure mode.
Evaluations find intent-based grouping and specialist analyses do not improve review outcomes, or their false positive rates and workflow overhead reduce reviewer effectiveness.