Hey HN! We're Charles and Dean, and we're building Stage: a code review tool that guides you through reading a PR step by step, instead of piecing together a giant diff.Here's a demo video: https://www.tella.tv/video/stage-demo-1pph. You can play around with some example PRs here: https://stagereview.app/explore.Teams are moving faster than ever with AI these days, but more and more engineers are merging changes that they don't really understand. The bottleneck isn't writing code anymore, it's reviewing it.We're two engineers who got frustrated with GitHub's UI for code review. As coding agents took off, we saw our PR backlog pile up faster than we could handle. Not only that, the PRs themselves were getting larger and harder to understand, and we found ourselves spending most of our time trying to build a mental model of what a PR was actually doing.We built Stage to make reviewing a PR feel more like reading chapters of a book, not an unorganized set of paragraphs. We use it every day now, not just to review each other's code but also our own, and at this point we can't really imagine going back to the old GitHub UI.What Stage does: when a PR is opened, Stage groups the changes into small, logical "chapters". These chapters get ordered in the way that makes most sense to read. For each chapter, Stage tells you what changed and specific things to double check. Once you review all the chapters, you're done reviewing the PR.You can sign in to Stage with your GitHub account and everything is synced seamlessly (commenting, approving etc.) so it fits into the workflows you're already used to.What we're not building: a code review bot like CodeRabbit or Greptile. These tools are great for catching bugs (and we use them ourselves!) but at the end of the day humans are responsible for what gets shipped. It's clear that reviewing code hasn't scaled the sam
FL score
out of 100
Verdict
high confidence
Competition
22
competitors found, emerging market, funded players
Trend
No signal yet
A human-centric code review tool that transforms complex GitHub PRs into guided, chapter-based reads, addressing the growing challenge of reviewing AI-generated code.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Stage – Putting humans back in control of code review”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Strong problem and clear gap in human-centric code review, especially with the rise of AI-generated code. High willingness to pay for a solution that addresses this specific workflow bottleneck. Buildability is moderate for a solo builder due to technical complexity, but a promising area.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
High market viability with a strong value proposition, especially with growing market trends. Differentiation is clear, but build complexity poses a challenge for lean teams.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Strong problem clarity, excellent creator fit, and clear monetization. The core technical challenge makes build simplicity moderate, but the niche and leverage are strong.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong target audience and compelling value proposition for a clear business model. Distribution is feasible. Key risks are the effectiveness of the 'chaptering' and overcoming adoption friction.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
High demand reality and specific pain point for engineers, with a narrow wedge and strong future fit in an AI-driven coding landscape.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
An engineering intelligence platform that provides DORA and SPACE metrics and workflow automation to optimize software delivery.
Pricing: Offers user-based pricing with various packages.
An engineering management platform that provides visibility into engineering organizations and aligns engineering decisions with business initiatives.
Pricing: Subscription-based, with pricing structures that may incorporate additional features related to business strategy alignment.
A software engineering intelligence platform that analyzes Git activity to help measure team performance and accelerate delivery.
Pricing: Typically subscription-based, with pricing tiers that scale with user count; offers flexible plans to pay per active contributor.
An engineering intelligence platform that focuses on core delivery metrics like DORA and PR analytics, with an emphasis on simplicity.
Pricing: $20/user/month.
An engineering productivity software that tracks DORA and SPACE metrics, monitors engineering hours, and runs developer experience surveys.
Pricing: Free tier for teams under 10 developers.
An engineering intelligence platform that combines SDLC analytics with AI-powered code review.
Pricing: Free tier for up to 10 developers, paid plans from $16/dev/month.
A complete DevOps platform that includes source code management, CI/CD, and code review capabilities.
Pricing: Starting Price: $29 per user per month.
A platform for version control and collaborative software development, widely used for hosting Git repositories and facilitating code review.
Pricing: Freemium model with paid tiers for organizations and enterprises.
A Git repository management solution for professional teams that offers code collaboration, automated testing, and code deployment.
Pricing: Freemium model.
A web-based code review and repository management tool for the Git version control system, offering strict control over changes.
Pricing: Free and open-source.
A code review tool that integrates with Jira and other workflows to help find bugs and improve code quality through peer review.
Pricing: Commercial, not explicitly detailed.
A complete suite of open-source web applications for developing software, including task management, code review, and repository hosting.
Pricing: Free and open-source.
What they charge
Recent news
Codacy Blog, April 03 2026
Software Advice, March 15 2026
GetApp, March 15 2026
GetApp, March 15 2026
SaaS Adviser USA, March 15 2026
Market signals
The market for code review and engineering intelligence tools is growing, with a significant shift towards AI-powered solutions. There's an increasing focus on improving developer experience, automating tedious tasks, and providing actionable insights beyond just surface-level metrics. Recent funding rounds indicate strong investor interest in platforms that unify various aspects of the software development lifecycle, from code quality and security to performance and business alignment.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI