Open-source distributed quantum compute network
Hey HN. I'm Colton (YC S21, ex-Acorns), one of the founders of Postquant Labs. My cofounder Richard is a cryptographer out of Draper Labs and DARPA. We're building Quip.Network, the first distributed quantum compute network. We just opened our testnet and wanted to share it here. The basic problem: quantum hardware is here and already competitive on certain optimization problems, but for most people, there's no way to access it. The machines cost millions and the hardware and research are gated by the companies who own them. Also, quantum providers regularly have machines sitting idle because demand isn't consistent, and that's a problem because many architectures need to be cooled near absolute zero and can't just be turned off. There's currently no equivalent of spinning up an on-demand cloud instance for quantum compute. So we're building one. Quip.Network is a spot clearinghouse and marketplace where quantum providers contribute excess capacity, developers deploy their best solvers to an open library, and anyone can submit a workload and get a result without needing to own or understand the hardware. Classical operators (CPUs, GPUs, TPUs) can also participate in solving and verifying. The first quantum subnet was built in close collaboration with D-Wave, the world's leading quantum computing company. It focuses on optimization problems, the kind that appear across finance, logistics, and manufacturing. It runs on annealing QPUs and has demonstrated competitive performance on solution quality, speed, and energy cost relative to classical computing approaches. The mining protocol is designed around these benchmarks, so participants compete to find better solutions. We had about 13,000 signups before launch. The codebase is fully open source because we think quantum advantage should be a verifiable result, not a marketing claim. We want people running nodes, challenging our implementations, and submitting proofs of work optimized for their own hardware. Unlike GPU clusters where one more processor is a linear improvement, the value of adding just one more QPU to your cluster is exponential. It won't be enough to be just AWS, GCP, or IBM. To solve the toughest problems, we'll want to connect together every processor on Earth and have them operate as one giant quantum system. That's why we think a distributed system is the right approach, and that's why our mission is to build the worldwide quantum computer. Happy to answer anything! Docs: quip.gitbook.io/docs | GitHub: github.com/quipnetwork
Dull
I kept deleting and redownloading Instagram because I couldn't stop watching Reels but needed the app for DMs. Tried screen time limits, just overrode them. So I built this. Dull loads Instagram, YouTube, Facebook, and X and filters out short-form content with a mix of CSS and JS injection. MutationObserver handles anything that lazy-loads after the page renders, which is most of the annoying stuff since these platforms love to load content dynamically. The ongoing work is maintaining the filters. Platforms change their DOM all the time, Instagram obfuscates class names, YouTube restructures how Shorts appear in the feed, etc. It's a cat-and-mouse thing that never really ends. Also has grayscale mode, time limits, and usage tracking. Happy to answer questions.
Baton
Hi, I built this because running multiple Claude Code agents across multiple IDE and terminal windows was getting messy. Like many, I went from working at one thing at the time, to multiple, and it was all changing quite fast. I needed one place to see all my agents and worktrees, seamlessly switch between them, monitor their status and once their done, review their changes. I also wanted to quickly spin up new agents in isolated worktrees whenever an idea came to mind. I've been building Baton from within Baton for a while now, which has been a pretty fun loop. Would love to hear what you think!
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We scored 50k PRs with AI
I'm a CTO with a ~16-person engineering team. Last year I wanted real data on what was actually shipping, not guesswork or story point theater. So we built GitVelocity. Every merged PR gets scored 0–100 by Claude across six dimensions: scope (0–20), architecture (0–20), implementation (0–20), risk (0–20), quality (0–15), perf/security (0–5). Six dimensions added up, then scaled by change size — a 10-line fix scores lower than a 500-line refactor even at the same complexity. Full formula at gitvelocity.dev/scoring-guide. After scoring 50,000+ PRs across TypeScript, Python, Rust, Go, Java, Elixir, and more, some things surprised us: Big PRs don't automatically score high. An 800-line migration with low complexity scores worse than a 200-line architectural change. Size gets you the full multiplier, but the base score still has to earn it. You can't score well without tests. The quality dimension (0–15) won't give you points without test coverage. At similar experience levels, this was the clearest separator between engineers. Juniors started outscoring some seniors. They adopted AI tools faster and took on harder problems. Once they could see their own scores, they aimed higher. We score AI-generated code the same as human-written code. Code is code. An engineer who uses AI to ship more complex work faster is more productive, and their scores reflect that. Scoring consistency was the hardest technical problem. Without reference examples anchoring each dimension, Claude's scores drifted 15+ points between runs. With 18 calibrated anchors (three per dimension at low/mid/high), we got it down to 2–4 points on the same PR. The thing we didn't expect was behavioral. We call it the Fitbit effect — the tool doesn't make you ship better code, but seeing the score does. Engineers started referencing their own scores in 1:1s unprompted, because the numbers matched what they already felt about their work. A junior who shipped a tricky concurrency fix could point to a score that proved it wasn't "just a small PR." We recently added team benchmarks (gitvelocity.dev/demo/benchmarks). Once you're scoring PRs, you can see how your team compares to others across the dataset — about 1,000 engineers on 60 teams so far. Headline's team ships faster than roughly 95% of them, which was nice to confirm but also made us wonder who the other 5% are. The competitive angle surprised us: teams that were skeptical about individual scores got genuinely curious once they could measure themselves against the field. Every score is fully visible to the engineer who wrote the PR, with per-dimension breakdowns and reasoning. There's no hidden dashboard that management sees and engineers don't. Free, BYOK (your Anthropic API key). We default to Sonnet 4.6, which scores nearly as well as Opus 4.6 at a fraction of the cost — but you can switch models if you want. Pennies per PR either way. No source code stored, diffs analyzed and discarded. Works with GitHub, GitLab, and Bitbucket. Ask me anything about the scoring methodology, how we solved calibration, or what it was actually like rolling this out to a team.