Relvy
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 visualizations that help engineers verify and build trust with the AI. From there on, Relvy can be configured to automatically respond to alerts from Slack Some example runbook steps that Relvy automates: - Check so-and-so dashboard, see if the errors are isolated to a specific shard. - Check if there’s a throughput surge on the APM page, and if so, is it from a few IPs? - Check recent commits to see if anything changed for this endpoint. You can also configure AWS CLI commands that Relvy can run to automate mitigation actions, with human approval. A little bit about us - We did YC back in fall 2024. We started our journey experimenting with continuous log monitoring with small language models - that was too slow. We then invested deeply into solving root cause analysis effectively, and our product today is the result of about a year of work with our early customers. Give us a try today. Happy to hear feedback, or about how you are tackling on-call burden at your company. Appreciate any comments or suggestions!
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Wirespan, a daily power grid optimization puzzle I made for my son
This was a lot of fun to build! I have an autistic son who is extremely interested in power lines, electrical transmission towers, and how the grid works. I decided to build a little game for him. When I finally showed it to him with bated breath, he rolled his eyes and said, “Is it 3D? I’ll only like it if it’s 3D!” So, I decided to polish it up and release it to the world! You get a 9x9 map with one power plant, three houses, and terrain obstacles, and the goal is to connect the power plant to the houses with an optimal path. Beneath the coat of paint, it’s a graph theory optimization puzzle, very nearly a Steiner-tree puzzle. Any two nodes within Chebyshev distance 2 are wired together automatically. But the wires travel in Euclidian space, so reach is measured on a square and wire on a circle. The skill is balancing that asymmetry to find the shortest route to light up the whole map. Because the original goal of this project was to provide joy to my son, I’ve done my best to make it accessible to everyone. It’s localized into 18 languages so far, has a high-contrast mode meeting AAA standards, reduced motion mode, etc. I do collect usage telemetry to help me improve the game, but I’ve done my very best to take privacy very seriously, collecting anonymous stats about the gameplay itself, with a random id the game generates rather than anything about the player or device. I also generate the privacy policy directly from the analytics code, as a contract to ensure nothing is ever collected without being fully disclosed. I had a really great time with this project, and hope to continue to improve it! I’d love any feedback I can get! Please let me know if you have any questions, and I’ll stick around to answer them! (Reposting because my first attempt landed during a super quiet hour and didn't really get seen)
I built a lite LPU that can do inference on Karpathy's MicroGPT
We had no guide or course that teaches chip design at our university. We had taken a digital logic course, but were disappointed with the fact that the most complex project we did was building a full adder in Quartus using logic blocks, not even in RTL!5 Therefore, we decided to challenge ourselves to dive deep into machine learning (ML) hardware and learn as much as we could on our own. We wanted to prove that basic math (like y = mx + b) and basic logic circuits are enough to help anyone understand how modern AI hardware works. Our goal was to design our own version of the LPU from scratch and run a simple Transformer-style model on it, proving that with minimal Machine Learning and computer design knowledge, it’s totally possible. We were also driven by a simple question: What makes the LPU architecture so compelling that even Nvidia licensed it? Keep in mind, this article is not intended to serve as a tutorial for “how to build an LPU from scratch,” and our architecture is not a 1:1 LPU. It serves as an educational resource for how someone with minimal hardware experience can approach this field, and our journey in building what we think an LPU would look like.