Charles Pan knew something had broken when his team's pull request queue started looking like a freeway at rush hour—bumper to bumper, nobody moving.
The culprit? Productivity. Or more precisely, the kind of productivity that becomes its own problem. Engineering teams using AI coding assistants have watched their code output increase roughly 200% year-over-year, according to Anthropic, and the bottleneck has quietly migrated from writing code to reviewing it. Pull requests stack up faster than humans can parse them. GitHub's diff view, elegant as it was for the previous era, wasn't built for this volume.
Pan and his co-founder Dean Stratakos—both Stanford CS graduates who cut their teeth at quantitative trading firm Five Rings—think they've found a workaround. Stop treating pull requests like raw patches, they argue, and start treating them like chapters in a book.
That philosophy underpins Stage, their startup, which emerged from Y Combinator's Spring 2026 batch and launched publicly on April 30. The platform does something deceptively simple: it takes the chaotic sprawl of a GitHub PR and reorganizes it into structured, logical sections that engineers can read sequentially, the way you'd follow a narrative.
Reading Code Like Prose
The core transformation happens automatically. Instead of scrolling through a linear dump of file changes—often ordered alphabetically or by commit timestamp, neither of which reflects how the code actually works—Stage groups related diffs into what it calls "chapters." Each chapter gets an AI-generated summary explaining what changed and why, plus a "Review Focus" list spotlighting the lines that need closer scrutiny.
The platform syncs bidirectionally with GitHub, so comments and approvals flow back and forth without friction. It preserves existing checks, required reviews, and merge rules. Stage isn't replacing your workflow; it's layering on top of it, offering a different lens.
There's also "Stagent," an assistant that fields questions like "what should I review first?" or "what's risky in this chapter?" and responds with citations to specific files and line numbers. The goal, as the company frames it, is to put humans back in control rather than letting AI output overwhelm the process.
Whether that framing resonates depends partly on how you view the broader shift. AI coding assistants have sparked debate in engineering circles, with perspectives ranging from enthusiastic adoption to concerns about code quality and maintainability. Stage is betting on the idea that even engineers who embrace AI productivity gains need better tools to keep up.
Born from Frustration
Pan and Stratakos built Stage because they were tired of the existing interface. Pan had been an early engineer at Yuzu Health, a healthcare startup, while Stratakos led AI initiatives at Five Rings and built an in-house coding agent. Both describe the same experience: reviewing PRs felt less like thoughtful examination and more like sifting through noise, hunting for the narrative buried in the diff.
It's a familiar complaint in engineering circles, though perhaps more acute now. The team is listed as just the two of them on Stage's Y Combinator profile as of May, though LinkedIn suggests the company may have between 2-10 employees. Lean, certainly, but they've already secured at least one public endorsement. In a May 13 case study, Yuzu Health's engineering manager and CTO described how Stage helped reduce their PR backlog and improve review quality as the team scaled from five to fifteen engineers in a year.
That kind of testimonial matters in a crowded market. And make no mistake: the market is crowded.
A Packed Field
Anthropic launched its own code review tool on March 9—a multi-agent system that runs approximately $15 to $25 per typical PR in token costs, based on configuration settings. Google folded automated code validation into its Gemini Conductor CLI extension in February. OpenAI rolled out Codex Security on March 6 to handle security-focused reviews. Established players like Qodo (which released a 2.0 version in February) and venture-backed Graphite are already positioned in this space, alongside a wave of newer entrants: Distik, Foldo, DeepSource.
Stage's differentiator is the chapters metaphor and the emphasis on readability over automation. While competitors lean into fully automated review or security scanning, Stage keeps the human in the loop, structuring information rather than replacing judgment. It's a subtle distinction, but one that seems to resonate. The company's initial Show HN post on April 16 climbed to around 125-130 points with over 100 comments—solid engagement for a developer tool. Community feedback asked for more context on the "why" and "how" of a codebase's evolution, and some users suggested integrating ticket context from tools like Linear.
Pan and Stratakos also released an open-source CLI tool under an MIT license, which has picked up around 200 GitHub stars as of late May. The CLI lets developers create chapters for local changes before they even open a PR—useful if your AI assistant just generated 500 lines of code and you want to understand it before pushing. A separate Show HN post for the CLI in early May garnered 46 points and 32 comments.
Security and the Trust Problem
Code review tools face an inherent trust problem: they need access to your source code, which is often the crown jewels. Stage's answer is not to store it. According to the company's security documentation (last updated April 26), code is processed in memory and then discarded. The platform routes analysis requests through Google Gemini, Anthropic Claude, and OpenAI via an API gateway, and Stage says those providers don't train models on customer data.
User data—account details, PR identifiers, AI-generated summaries—is encrypted at rest with AES-256 and in transit with TLS. The infrastructure runs on Vercel (backed by AWS) and uses Neon for Postgres; both have SOC 2 Type II certifications. Authentication is GitHub OAuth only, with minimal scopes requested.
It's a reasonable security posture for an early-stage startup, though enterprises will likely want more—dedicated instances, audit logs, data residency guarantees. That'll come later, presumably, if Stage survives long enough to sell into larger organizations.
The Business Model
Pricing is straightforward: $30 per seat per month, with a 14-day free trial that doesn't require a credit card. Stage's Y Combinator Launch post in late May offered a promotional code—"LAUNCH"—for 25% off for three months. As of mid-May, the homepage claimed over 14,600 pull requests reviewed, though that number updates dynamically, so it's unclear how meaningful the figure is.
Thirty dollars a month is cheap enough that individual engineers might expense it without much friction, but expensive enough that Stage will need volume to build a sustainable business. The unit economics work if the product becomes habitual—if engineers start reflexively opening Stage instead of GitHub for reviews. That's a behavior change, which is always harder to pull off than it sounds.
What Comes Next
Stage is midway through its YC batch and moving quickly. The company posted its "Introducing Stage" blog on April 30, launched the CLI around May 10, published the Yuzu case study on May 13, and went live on YC's Launch platform in late May. The homepage offers a one-click demo on any public GitHub PR, which lowers the friction for curious engineers to try the tool before committing.
The founders haven't disclosed funding amounts beyond whatever Y Combinator invests at the seed stage. Whether Stage scales by building out the team or by keeping the product tight and focused on a specific workflow wedge is an open question. For now, the bet is simple: engineers drowning in AI-generated diffs will pay $30 a month to read them like a book instead of a patch file.
It's a bet that hinges on a particular view of the future—one where AI assistants keep getting better at writing code, but humans remain essential to reviewing it. If that holds, Stage has found a genuine pain point. If not—if the next wave of AI tools can review code as fluently as they write it—then the window might close faster than Pan and Stratakos expect.
Either way, the company is addressing a problem that didn't exist two years ago and is now acute enough that multiple well-funded startups are racing to solve it. That's the strange rhythm of this moment in software: the tools that make us productive create new bottlenecks almost immediately, and someone else builds a tool to fix that, and the cycle continues. Stage is the latest turn of the wheel.
