Show HN I scraped 743 large employers' careers pages to find their ATS https://ift.tt/isy2JHz

Show HN I scraped 743 large employers' careers pages to find their ATS https://ift.tt/Meb1zIr June 7, 2026 at 11:15PM

Show HN: I Derived a Pancake https://ift.tt/y2cPf5W

Show HN: I Derived a Pancake After 25 years of making other people's pancake recipes - always yearning for more tang, more fluff, and more predictability - I decided to derive the pancake recipe from the chemistry. You mark checkboxes for what you have on hand (ricotta, sour cream, kefir, buttermilk, yogurt, cottage cheese, lemon, cream of tartar, etc.) and it computes the best recipe based on targets for acid, fat, salt, sugar, and CO2. My particular favorite are the yeast-raised lemon ricotta kefir pancakes - the best I've ever had. The math is done in a small pure-ESM library: ingredient composition to component masses and acid moles, a stoichiometry layer, and a bisection solver for the target deficits. I'm not a chemist, so if something is off, tell me and I will fix it! https://ift.tt/RcC2iYs June 5, 2026 at 12:12PM

Show HN: Nightwatch, The open-source, read-only AI SRE https://ift.tt/cOQVJXg

Show HN: Nightwatch, The open-source, read-only AI SRE nightwatch is a local-first, read-only layer on top of your monitoring. it groups alert storm into incidents, flags noisy checks and has an agent that can investigate for you live systems. You can e.g. jump from the incident into the agent directly. the reason for this weekend project is that we had a kubernetes upgrade that went wrong, and at some point a rollback wasn't possible anymore, so it had to be fixed live during the night while several problems came together. We run a lot of different systems, on-prem and several Kubernetes clusters, and in a situation like that you spend most of the time just figuring out what is actually broken and where. So i thought that it would be pretty cool to have eyes in the dark in each system that can talk to your "brain". so the idea is to put a baby owl into each environment. Each owl runs where the systems live, keeps that environment's credentials local, and only dials outbound to a central brain, so there is no inbound hole into prod. It exposes a set of read-only skills, and the agent uses them to gather evidence and form a root-cause hypothesis, so the on-call engineer starts with a head start instead of from zero. read-only for now, i don't trust it near prod yet and honestly neither should you. llocal-first for easy self-hosting and to keep credentials on your side. the clustering and recommendations run fully offline with no llm at all. the agent needs a tool-calling llm, you can point it at a remote one, or self-host one (ollama etc.) if you want to stay fully offline. for non selfhosters: before every remote llm call, nightwatch strips real secrets (unrestorable) and swaps identifiers like ips, hostnames and paths for reversible placeholders, so the model only sees masked data while real values are restored only in the proposed commands and tool calls Would love if you try it in your Systems https://ift.tt/kFlmA0t June 8, 2026 at 01:54AM

Show HN: Dap-mux – Connect your editor and REPL to the same debug session https://ift.tt/7V4d6hF

Show HN: Dap-mux – Connect your editor and REPL to the same debug session I have been coding over four decades, in many languages, on many projects (including Firefox, Final Cut Pro, the Newton, and Fullwrite Professional if you can remember that far back; all these using my "dead-name"). I wrote something small and simple to scratch an itch. It's the UNIX philosophy: small "one-trick ponies", each *really* good at their one trick, then the user can hook them together to solve actual problems. I'm a CLI guy, and for almost everything, I already have this. But not for debugging. The itch I scratched was the connector that enables this philosophy for debugging. That thing is dap-mux. A DAP multiplexer turning a one-to-one protocol into a cooperating session of as many tools as you need to get it done! How it started: Helix and Python for me (and sometimes IPython), with the rest of my team using PyCharm (which I have long loved!). My team's problem is that they want the PyCharm debugger, and so must be satisfied with the JetBrains editor. *My* problem was I could use a full-blown debugger *or* I could have IPython *or* I could have Helix (or sometimes an unsatisfying combination of Helix and the debugger). That was my "itch". DAP (Debug Adapter Protocol) is the tantalizing answer, except it isn't. DAP is what editors (that don't want to write their own debuggers) are starting to adopt. The problem with DAP is it's one-to-one. One editor connects to one debugger. Done. Not a solution to my problem. And then suddenly, it *was* the solution. I realized that a DAP multiplexer would let you connect any DAP-aware editor to any debugger for any language, and simultaneously to a REPL, another session of your editor (or a different editor)! With the side benefit that now, like screen or tmux, since each process is its own thing: sessions are durable. Just restart whatever crashed and you're back where you were! There were hard parts: sequencing, late joiners, state management. Different end-points working on different actions in different sequences but with the same message ids. I solved these problems something like how NAT works. Instead of translating network addresses, though, I'm translating the sequence numbers of each client into something global and ordered, then correctly routing replies back to the end-point awaiting them, while mapping the sequence numbers for those replies back into the space of that end-point. Knowing the current state of the debugger, and replaying that as a message sequence to late joiners lets you start/connect the clients in any order. I chose Python: asyncio fits the I/O-router pattern perfectly, and it lets the IPython extension run in-process rather than over IPC. There are problems not yet solved: for instance, I think configuration in the clients and/or the startup sequence is too complicated. But it functions! I got what I wanted! The combination I use every day: Python + debugpy + Helix + IPython, all connected simultaneously. Step with `%n` or `%s`, evaluate expressions with `%eval`, watch Helix track the current line in real time. Rust with codelldb is the second confirmed combination — I debugged a Dijkstra implementation with Helix and a third-party DAP observer tool both connected to the same codelldb session. A community member, Sean Perry, has already built [dap-observer]( https://ift.tt/kt1qRuQ ), which renders the current frame's variables as a navigable terminal tree. *This* was my exact dream! Small, focused, connectable tools all playing together! There's so much left to try: other editors, other debug adapters, Windows, other languages. None of this has been touched yet. The most helpful thing now is people testing it with their own setup and reporting what they find. It's time to play! `uv tool install 'dap-mux[ipython]'` for Python + IPython. `uv tool install dap-mux` for headless use with any language and adapter. No need for any part of the Python ecosystem. https://ift.tt/9p7Qd3s June 7, 2026 at 02:43AM

Show HN: I ported Xonotic (arena FPS) to WebAssembly with full P2P multiplayer https://ift.tt/Aq6NPeg

Show HN: I ported Xonotic (arena FPS) to WebAssembly with full P2P multiplayer https://dpgame.xonotic.workers.dev/ June 7, 2026 at 12:59AM

Show HN: Resonate – Low-latency, high-resolution spectral analysis https://ift.tt/l7J9esp

Show HN: Resonate – Low-latency, high-resolution spectral analysis Last April I shared about my Resonate project here ( https://ift.tt/YEM61nI ) A lot has happened since: the work I presented in much more detail at last June's International Computer Music Conference (ICMC) got best paper award. I also gave a talk at the Audio Developer Conference in Bristol last November, the video is on YouTube). This year's work, which I recently presented at this year's ICMC, starts with known techniques from the phase vocoder literature to build self-tuning filter banks that extract very efficiently the frequency components that are actually present in the input signal. Overview on the project website, more details in the papers, including applications to super-resolution spectrograms and re-synthesis experiments. As many people have pointed out, none of the techniques I have used are new (some of them even have different names across different fields), but I haven't seen them applied together in this way, and to me the results are incredibly satisfying and sometimes look magical. See for example this demo: https://youtu.be/LasdoIJJkw8 Of course the best way to experience in person is through the free demo app: https://ift.tt/K76V0AW Looking forward to feedback from the community! https://ift.tt/mcW0VBS June 6, 2026 at 11:39PM

Show HN: I nerfed our coding agents on purpose https://ift.tt/QYc7o41

Show HN: I nerfed our coding agents on purpose Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across the board, but we felt it in Codex more acutely. We’re a startup filled with people who work around the clock and are obsessed with building — naturally our daily bill alone was striking. Luckily we’re going after a big mission and speed matters significantly more than marginal token spend on the edges. Still, it got us thinking about how it was ludicrous that while our product has a side effect of decreasing token spend and speeding up agentic workflows by many orders of magnitude, we were using these top tier models for all types of internal coding tasks without any of those optimizations. The waste felt pretty ridiculous — the most glaring culprit was that we were seemingly using the max intelligence model on max reasoning for every task even when the task clearly didn’t require it. As a company who spends a lot of time on cached intelligence, it was also easy for us to see how there was plenty of other low hanging fruit as well. So, on a recent weekend, I quickly built a tool to optimize our usage. At its core is a very fast classifier that classifies your requests to the least intelligence required for the task and includes some nice token optimizations on top. The result is roughly the same quality for multiples lower token spend. But even more exciting for us, is that the properly bin packed intelligence and reasoning levels meant our speed also went up considerably. This wasn’t negligible. We’ve observed up to 3x savings and hours per day per person in saved time that we would have otherwise been waiting on tool turns and coding agent responses. For us, that means improved engineering velocity and significantly higher usage for the same spend. It also means more usage before getting throttled. As I told friends about this, they also wanted to start using it to maximize the usage they could get out of their coding agent plans. There are now engineers across many of the most cutting edge AI companies using this tool to optimize their token utilization in this way. Not just to save money, but to maximize output. Turns out that the best way to avoid getting nerfed by Claude is to intentionally nerf yourself selectively. We decided to release it for the rest of the builder community to use as well. You can now turn on Nerfguard for yourself and start getting more usage today. June 6, 2026 at 04:49AM