Stage – Putting humans back in control of code review
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
- Addresses a high-severity, growing pain point exacerbated by AI in software development.
- Clear differentiation from existing engineering intelligence and AI code review bots.
- Strong founder-market fit with personal experience of the problem.
- Existing demo and early validation show proof of concept.
- High willingness to pay for tools that solve developer productivity bottlenecks.
Risks
- Technical complexity of reliably and intelligently grouping code changes into 'chapters' without false positives or negatives.
- Overcoming the inherent friction of introducing a new tool into established developer workflows, despite GitHub integration.
- Reliance on GitHub's API stability and potential for GitHub to implement similar features natively.
- Solo buildability for a complex AI/heuristic-driven system might be challenging without prior infrastructure.
Next steps
- Focus MVP on a specific language or framework where AI-generated code is most prevalent and complex.
- Gather user feedback on the 'chaptering' logic and ordering to refine the intelligence layer.
- Validate pricing model with target users (engineering managers/leads).
- Explore open-sourcing parts of the code parsing/grouping logic to build community and get contributions.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 20
- Willingness to pay
- 20
- Buildability
- 12
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
- Focus MVP on a specific language or framework where AI-generated code is most prevalent and complex.
- Gather user feedback on the 'chaptering' logic and ordering to refine the intelligence layer.
- Validate pricing model with target users (engineering managers/leads).
- Explore open-sourcing parts of the code parsing/grouping logic to build community and get contributions.
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.
- GerritVisit
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.
- PhabricatorVisit
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
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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
- Waydev: Users appreciate the detailed insights but note the learning curve.
- LinearB: Can be dashboard-heavy and its metrics might be seen as 'surface-level'.
- GitPrime: Well-regarded for its analytics depth but can be complex for new users.
- Jellyfish: Some find it integrates better with existing processes than others.
- GitHub: Side-by-side diffs and checkpointed diffs are handled very poorly, and the pull request workflow can become intractable as the team grows.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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