Command Center, the AI coding env for people who care about quality
Hi HN! We’re Jimmy and Ray. Jimmy is a Thiel Fellow with a Ph. D. from MIT who has worked on programming tools for 15 years; Ray became VP of Sales at a $2B company when he was 19 and has built side-businesses vibe-coding. Last year, we set to answer the question “If AI can write code 100x faster, then why aren’t you shipping 100x faster?” What we learned shocked us — even fairly nontechnical people and solo founders told us they were spending more than half of their development time reading the AI-written code. And much of the rest of the time was spent either de-slop-ping it, or wishing they had done so. As luck turns out, our last two products were a tool that quickly onboards people to large codebases ( https://x.com/0xjimmyk/status/1873357324229984677 ) and trainings that taught deep concepts of code quality to CEOs, YC founders, and engineers at top companies ( mirdin.com ), so we were extremely well-positioned to solve these problems. Command Center is an agentic coding environment focused on quality. With a few keypresses, you can start building 3 features at once and soon have 3 diffs ready, each consisting of 2000 changed lines across 50 files…. This is normally the point where you think “Crap, what now?” With Command Center, at this point you simply click “Refactor,” and watch the vibed slop turn into readable robustness. Then you click “Generate Walkthrough,” and then suddenly, to read a 2000 line diff, instead of scrolling up and down trying to make sense of it, you just press the right arrow key 200 times. See something you don’t like? Click on line 37, type “Do this and all other network fetches in the background Cmd+Enter,” and you have a few more agents getting your code into final shape. Click or type “Commit,” “Push,” “Create PR” — you just shipped a high quality, non-slop feature We’re striving to be the best at every step of the pipeline, but can just try Command Center in pieces wherever you feel your current workflow is weakest. We have users who do all their coding in Zed or the Codex app, and then jump over to Command Center for a walkthrough when it finishes running. There’s even a skill that will pop open a Command Center walkthrough from the environment of your choice. Or you can just keep Command Center running while you do your work elsewhere, and if your AI deletes anything, you have Command Center’s snapshots to the rescue. We launched quietly last year and have been refining since. The quality and usability have kept going up, and Command Center is now ready for a lot more attention. Since our quiet launch, we’ve seen at least a dozen other agentic coding environments appear….approximately all of which have the same feature set focused on the part which is already easy (generating the first version of the code) and with at best a shoddy answer to the hard part (everything that comes after). Command Center’s focus is making the hard parts easy. Here’s what our users have to say: “[The refactorings] give your LLM taste. I’ve never seen an LLM write code this good before.” — Doug Slater, Staff Engineer, Climavision “With Command Center walkthroughs, I can get through a 400-line diff in less than half the time.” — Prateek Kumar, Platfor Engineer, Sumo Logic This product is not for everyone. If you’re someone who preaches “the prompt is the source, the code is the compiler output,” then you probably won’t enjoy Command Center. But if you want to uphold traditional engineering discipline while also shipping 20 PRs a day, then this is the environment for you.
Social network where inviting someone makes you accountable for them
Chirpper is invite-only. When you vouch someone in, they join your TrustChain. Their behavior affects your TrustRank, and that propagates up the lineage. No moderators. The accountability is architectural, not policy-based. You can be pseudonymous, but you can't be unaccountable. Happy to get into the mechanics in comments.
Stillwind
We’ve spent the last couple of months building Stillwind Search, a search engine for electronic components that helps users find parts that fit even the most complex set of specifications. After talking to the people that actually build PCBs we found out that finding the exact part you are looking for, is consuming enormous amounts of times, is very tedious and then often doesn’t yield the best results. So we tried to cut down this search time by just requiring a (broad) description of the specifications and we find the correct part in minutes, not hours. This is possible through our own database of parts and their properties. We used LLMs to extract every parameter about a part into >1k schemas, collectively covering more than 130k properties. This depth of properties could no longer be visualized, so the database is queried interactively by an AI agent (Sonnet 4.6) to find the needle in the haystack of parts. Before results are shown, we use another model to verify the data (that’s why it might take a moment before the first results appear). We currently have almost all microcontrollers, sensors, and other advanced ICs on the market, as well as a wide selection of passives and generic parts. We are working on adding more parts and are more than happy to take suggestions. I know that folks on HN like technical details on how this works, so let me give a short overview: Frontend: SvelteKit + Cloudflare Workers + Hyperdrive Backend: PostgreSQL 18 (with io_uring) database, with extensions on NVMe drives hosted on a beefy server. We appreciate all feedback and are happy to answer any questions :) Btw: We are already working on a way that you can search combinations of parts, finding the optimal combination of parts.
Brightdeck
Show HN: Brightdeck – an OOXML-compatible AI presentation maker
Philosophy for Kids
Sometimes my son asks me 'why' questions that could be answered well by a kid-friendly philosophy article. But I don't know where to find those, so I ask Claude or ChatGPT, and have a specific workflow for getting the type of output I want. I figured other people might find those AI-generated articles helpful, so I put them here: https://philosophy.ocaho.com/ There's a search box at the top.
machine0
Hi HN! Excited to launch machine0, a CLI that makes it easy to create, provision and snapshot persistent NixOS (& Ubuntu) VMs. You can think of machine0 as a modern VPS provider. VMs stay on unless switched-off (with 99.99% uptime), they have static IPs and HTTPS endpoints, 1-60 vCPU, up to 240GB RAM and optionally GPUs. The CLI provides commands to manage lifecycle, snapshots and also provision the VMs using Nix flakes or Ansible playbooks. VMs are priced by the minute of usage. What makes machine0 unique is that it has first class support for NixOS! In a nutshell, NixOS lets you define your entire OS as code (think Terraform but for your Linux). A flake declares your system state (packages, services, firewall rules, users...) and pins all dependencies via a lockfile. Given the same flake.nix and flake.lock, `nixos-rebuild switch` always produces the exact same system. The NixOS ecosystem is mature, and flakes are expressive: at the system level you can define packages, what's in /etc, firewall rules, users & groups etc. At the user level, you can define your shell, aliases, tmux and vim config. Having your entire environment defined as code makes it easy to audit what's installed and how things are set up. You can rollback by reverting the last commit. And agents can write the code for you and test it against disposable machine0 VMs. If you'd like to dive right in, these commands will get you started: npm install -g @machine0/cli machine0 new my-vm --image nixos-25-11 # create a new nixos VM machine0 provision my-vm ./flake#my-profile # provision it using a nix flake machine0 ssh my-vm # ssh in machine0 stop my-vm # stop the VM machine0 images new my-vm my-snapshot # create a snapshot machine0 new my-clone --image my-snapshot # create a new VM from the snapshot - Demo of installation + NixOS provisioning via Claude Code: https://www.youtube.com/watch?v=RT8N0_e3Vfg - Documentation: https://docs.machine0.io/introduction/overview - machine0 NixOS flakes: https://github.com/fdmtl/machine0-nixos If you're in the habit of using VMs, or want to know what the NixOS fuss is about, would love for you to give machine0 a try!
Spotlight shows what your Claude Code/Codex are doing
Hola HN! Long time lurker, sometimes commentor, first time poster here. I’ve been working alongside my two co-founders and a few colleagues on a project I’m excited (and a little nervous) to share with you all! Like many of us, I’ve lived a tortured existence with AI coding (is it vibes?) over the past few months - I think November was a big moment with this. But, one thing I’ve noticed after building orchestration layer after orchestration layer is that the thing I always came back to was “what the hell is Claude Code actually doing?” Perhaps it’s because of the time Claude Code got confused and “rm -rf”’ed root, or the time it deployed a feature flag flip to prod instead of stage, or the time it got stuck in a cycle of doom logging in with playwright, etc etc - but it inspired us to build this tool; we call it Spotlight by Backplanes. Spotlight takes your Claude Code and Codex sessions and finds security issues, things that could be sped up, and where you’re burning your time and tokens. We also create fun little archetypes of what kind of builder you are. The way it works: you install a backplanes CLI daemon/TUI that takes your Claude and Codex sessions, scrubs them of their PII and secrets locally, sends them to us where we do a second level scrub using a locally hosted model, and store your sessions row level encrypted with customer keys. Today we create and store the customer keys in AWS secrets manager, which we can’t access directly. Doing this work hosted lets us stitch sessions across machines and multiple harnesses and even gives you the ability to give team-level patterns. Details are at https://backplanes.com/trust. You can see an example report at https://backplanes.com/features/session-reports, To play with this, it's a one-line CLI install (yes, there's a signup, I'm sorry), and it's free at backplanes.com. In the coming weeks we will be releasing a Powershell version along with native MacOS and Windows apps. Please let us know what you all think. Thanks guys and gals! Nick
Voice Age Verification
I miss the old web. As a kid I could type in "a/s/l" in AOL messenger and chat with someone my own age, without worrying about the dangers that lurk on the web today. After seeing what happened to Omegle, a question stuck: is there a simple way to do age verification that both keeps people safe and doesn't contribute to a surveillance state? After a year of hard work, that question resulted in AGEWARDEN. Each part of the service puts people first. No tracking, nothing stored (it's more difficult these days to NOT collect data :smh:). Please give it a try if you have a moment https://agewarden.ai/demo. Feedback is very much welcomed. GG
RandoFont
Inevitably when starting a new web project I want to pick the perfect font. This is just a browser that shows Google Fonts, randomly, and lets me tag favorites. I built this years ago and just used AI to give it a minor facelift.
Reviving my 2001 college band with AI
25 years ago, I was approached to join a band called Fading Maize at Ripon College in Wisconsin. We did what we could with what we had. We recorded 3 albums over the next 3 years and played at as many bars and coffee shops as we could. We built a website with Microsoft Frontpage. Then we all went our separate ways, got married, had kids, focused on other things. Earlier this year I had the idea to approach the lead singer who wrote all of the lyrics and melodies to the stuff we played back then and wanted to "reimagine" everything in 2026 using AI. That's the project I want to share here! The site has a before/after player where you can flip between the original dorm-room recording and the 2026 version mid-song without losing your place, so you can hear exactly what changed. The original 2001 website is preserved and browsable at https://www.fadingmaize.com/2001, rough edges intact. On the AI question, since it's the elephant: the songs, lyrics, and arrangements are the original human work from 2001-2003. AI gets a bad rap and I can totally see why, but our case was different. We wrote the lyrics, we created the melodies, we played the parts, it just didn't sound as good as we heard it in our own heads. Being fully transparent about our use of AI, sticking tightly to our original lyrics and melodies, but making full use of AI to give us the studio, session players, and production budget we never had seemed like the right balance of concerns. I'm super proud of how it turned out and the transparency we've used along the way. Happy to discuss the audio pipeline, the site (Next.js), or what it's like to A/B your 20-year-old self!
I built a free app for New Yorkers to save money on groceries
I built this because I see that grocery savings are achievable in NYC. People usually just go to the store they're used to going to, and it's rarely worth the effort of combing through card cashback, weekly coupons, CPG rebates. Most people leave real money on the table by not stacking them, and even more don't even know that these deals are out there.... so I built a way to automate it. You can use it for free, no login, currently NYC-only with ~690 stores. I built it so that you just search whatever you want (use commas if you want to search multiple items). Or - use the AI tool to help shop for you. If you're curious, it's powered by a trained LLama model. Honest limitations are coverage and freshness. Id love some feedback on where the data looks wrong or is stale. Question for the room - what to prioritize if you're working with messy, multi-source retail/pricing data? Is freshness or coverage the top priority if you cant get a uniform response from every source? curious on what to prioritize here.
We beat Gemini Embedding 2 by training only 16M params (open weights)
Show HN: We beat Gemini Embedding 2 by training only 16M params (open weights)