Hey HN! We are Bharath, and Simranjit from Relvy AI (https://www.relvy.ai). Relvy automates on-call runbooks for software engineering teams. It is an AI agent equipped with tools that can analyze telemetry data and code at scale, helping teams debug and resolve production issues in minutes. Here’s a video: [[[https://www.youtube.com/watch?v=BXr4_XlWXc0]]]A lot of teams are using AI in some form to reduce their on-call burden. You may be pasting logs into Cursor, or using Claude Code with Datadog’s MCP server to help debug. What we’ve seen is that autonomous root cause analysis is a hard problem for AI. This shows up in benchmarks - Claude Opus 4.6 is currently at 36% accuracy on the OpenRCA dataset, in contrast to coding tasks.There are three main reasons for this: (1) Telemetry data volume can drown the model in noise; (2) Data interpretation / reasoning is enterprise context dependent; (3) On-call is a time-constrained, high-stakes problem, with little room for AI to explore during investigation time. Errors that send the user down the wrong path are not easily forgiven.At Relvy, we are tackling these problems by building specialized tools for telemetry data analysis. Our tools can detect anomalies and identify problem slices from dense time series data, do log pattern search, and reason about span trees, all without overwhelming the agent context.Anchoring the agent around runbooks leads to less agentic exploration and more deterministic steps that reflect the most useful steps that an experienced engineer would take. That results in faster analysis, and less cognitive load on engineers to review and understand what the AI did.How it works: Relvy is installed on a local machine via docker-compose (or via helm charts, or sign up on our cloud), connect your stack (observability and code), create your first runbook and have Relvy investigate a recent alert.Each investigation is presented as a notebook in our web UI, with data visualizat
FL score
out of 100
Verdict
high confidence
Competition
13
competitors found, emerging market, funded players
Trend
No signal yet
A complex AI-driven solution for automating on-call runbooks and debugging for software engineers, facing high technical hurdles.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Relvy (YC F24) – On-call runbooks, automated”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
The problem is real, severe, and specific, with a clear need for better automation in on-call debugging. However, the solution is highly complex and not suitable for a solo builder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
High-value problem in a growing market with a differentiated AI approach, but complex to build and execute.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem with good monetization potential, but extremely complex to build and unsuitable for a solo founder due to technical demands and lack of simplicity.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear target and value proposition, but high technical risk and complex distribution make it challenging for a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Strong demand for solving on-call pain, but high technical hurdles for AI-driven debugging make future-fit and initial surprises critical.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
PagerDuty provides a digital operations management platform that helps organizations identify issues, bring together the right people, and automate incident response.
Pricing: Free tier for up to 5 users; Professional plan starts at $21 per user per month; Business plan offers more features.
Rootly is an AI-native incident management platform that streamlines on-call scheduling, incident response, retrospectives, and status updates through intelligent automation and deep integrations.
Pricing: Tiered, value-based pricing.
FireHydrant is an incident management platform that streamlines the incident response process through automation and integrations.
Pricing: Yearly licensing: $9,600 per year for the entire platform; Incident management only is $6,000 per year (on-call is extra and priced based on number of engineers).
incident.io is a Slack-integrated incident response platform designed for rapid-growth companies, focusing on seamless integration and automated incident management.
Pricing: Starts from $16 per month, billed yearly. Custom pricing for enterprise-focused plans.
Opsgenie is a robust incident management solution integrated within the Atlassian ecosystem, offering alerting, on-call schedules, and streamlined workflows.
Pricing: Free for up to 5 users, then $9-$29 per user per month.
Jira Service Management is an ITSM platform that centralizes and manages ITSM workflows including ticketing, incident management, and request management, often adopted by companies with existing Atlassian infrastructure.
Pricing: Around $21 per agent per month.
xMatters provides a service reliability platform that automates workflows and ensures teams are alerted to and can resolve incidents quickly.
Pricing: Free for up to 10 users, then custom pricing.
Redwood offers a low-code runbook automation platform that automates routine IT operations tasks across on-premises, cloud-based, and containerized environments.
Pricing: Undisclosed.
Octopus Deploy provides runbook automation alongside deployments, allowing control over infrastructure and applications for tasks like routine maintenance and emergency recovery.
Pricing: Undisclosed.
Stackstorm is an open-source, event-driven automation platform that connects and automates various tools and services by reacting to real-time events.
Pricing: Open-source (free), enterprise version available with undisclosed pricing.
Azure Automation offers Runbook Studio, a feature that allows users to create, manage, and execute automated workflows for routine tasks across cloud and on-premises environments.
Pricing: Billing based on job run time minutes and watcher hours used in the month; free included units available.
Temperstack enhances existing monitoring tools with alert health, automation, incident management, and AI-runbooks, empowering developers to optimize alerts, triage, and auto-heal with human oversight.
Pricing: Undisclosed.
What they charge
Recent news
Hacker News, April 09 2026
TechTrends Now, April 09 2026
Relvy AI Blog, March 15 2026
Relvy AI Blog, March 9 2026
Relvy AI Blog, February 16 2026
Market signals
The global on-call scheduling software market was valued at USD 3.6 billion in 2024 and is projected to reach USD 39.7 billion by 2033, growing at a CAGR of 28.24%. This growth is driven by the increasing need for effective employee scheduling, the proliferation of remote and hybrid work models, and the rising demand for automation in various sectors, including healthcare and IT. Key trends include the incorporation of AI and machine learning for forecasting scheduling, a focus on mobile-first solutions, and the leveraging of real-time analytics.
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