Show HN: IgniteMS – batch text embeddings at 253K msg/s on 8x A100 https://ift.tt/nVdFPLY

Show HN: IgniteMS – batch text embeddings at 253K msg/s on 8x A100 https://ift.tt/hnElig3 May 21, 2026 at 12:07AM

Show HN: I made a tool for learning scales, chords, and how to combine them https://ift.tt/jAuY4n3

Show HN: I made a tool for learning scales, chords, and how to combine them This started out when I vibe-coded a guitar scale fingering generator. It came out pretty good, and I started adding stuff to it: chords, then how chords and scales interact. Then I added charts for other instruments I mess around with: piano, cello, alto recorder. There's a complexity toggle to go from basic harmony to extended/experimental stuff. It's honestly still mostly a toy, but I thought other people might be interested in playing with it. Source is on github, so it's easy enough to run locally and fork. https://ift.tt/xh8T3Ru https://ift.tt/l54DbYI May 21, 2026 at 12:44AM

Show HN: How Expensive Is Your (Steam) Wishlist? https://ift.tt/9rSJMyh

Show HN: How Expensive Is Your (Steam) Wishlist? A tool/toy that lets you connect to your Steam wishlist to calculate the total list/current price of all the games on it. There's a shallow, jokey purpose to it ("I could buy a BMW with this amount!"), but the real purpose is to demonstrate how we can do a better job of portraying a game catalog. I often wishlist stuff, then it pops up in a "Hey, it's on sale!" email months later. In that email, there's a banner capsule, but that doesn't help my brain remember why I added it. To that end, after you get the bill, you get a nice, flat feed of stuff about all the titles you've wishlisted over the years. It's all stuff that developers painstakingly put together, but which Steam tucks away under the fold of a game's Store page. Anyway, my wishlist came to about $250. My QA guy is up to $19k. Give it a go; hope you enjoy it! https://ift.tt/FcbPJht May 20, 2026 at 12:15AM

Show HN: Haystack – Review the PRs that need human attention https://ift.tt/p9ubg0v

Show HN: Haystack – Review the PRs that need human attention Hey HN! We're building Haystack ( https://ift.tt/wKJ875U ) to help teams deal with the explosion in the number of pull requests that need to be reviewed due to the rise of coding agents. Haystack replaces the GitHub PR review system with a queue that triages each PR before a human has to read any diffs. It looks at the diffs, the codebase, and the coding-agent conversation that produced the PR. Haystack then routes it into one of three buckets: 1. Safe to merge. This means the PR has enough evidence behind it that the team can merge it without another human's review. Some examples: -- A small UI copy change that includes a screenshot showing the final state -- A backend change where the author clearly tested the important paths and ran the changes in a real environment 2. Needs fixes. This means that the PR has bugs or violates a rule in your codebase and therefore the PR needs to be fixed by the author. Some examples: -- The agent was asked to make loading a large table faster by adding pagination, but the PR still loads every result at once and "implements" pagination in the UI -- The PR silently catches an error instead of logging, surfacing, or handling it. This violates the team's "no silent error swallowing" rule 3. Needs human review. This means that the PR could not be sufficiently verified by the author or is touching a sensitive part of the codebase (determined by user-input guidelines) and thus requires human review. Some examples: -- The PR changes a significant amount of logic in billing -- The PR changes an important user flow like onboarding, but the author only ran unit tests and never opened the app to check the flow end-to-end. That violates the team's rule that high-impact user-facing changes need manual verification. Instead of starting with line-by-line diffs, Haystack immediately tells the reviewer the goal behind the PR, what design decisions the author made (informed by their coding-agent conversation), and how much the author did to verify that the pull request works (e.g. run scripts, checked the frontend, etc.). In this way, review shifts from "what changed?" to "is this the right behavior and is there evidence that it works?". Here's a quick demo: https://ift.tt/0hK4Pra... We previously launched Haystack as a tool for understanding large PRs ( https://ift.tt/2XcURvi ). As many of you can probably relate to, the release of Opus 4.5 completely shattered our conception of how fast an engineer could craft a PR. And as coding agents got even better from 4.5, we realized that pull requests did not scale along with our coding velocity. With each member of our team being able to pump out more than 20 pull requests a day, code review quickly became cognitively exhausting and less helpful. After talking with other folks, we learned many feel similarly, and currently face the binary option of either not doing review at all or trying to keep up with a fire hose of pull requests. Haystack is our attempt at a third path. We still believe in code review, but as coding agents produce more code, human reviewer attention becomes more valuable and more expensive. Haystack helps teams spend that attention on the PRs where a human can meaningfully change the outcome of that PR. And for such PRs, Haystack shows the reviewer what the PR intended to do, whether the author showed that it works, and what design decisions need a second pair of eyes. We're still quite early and are figuring out whether Haystack truly makes code review better. We would love any and all feedback! https://ift.tt/wKJ875U May 19, 2026 at 12:44AM

Show HN: Superlog (YC P26) – Observability that installs itself and fixes bugs https://ift.tt/fyqnNzX

Show HN: Superlog (YC P26) – Observability that installs itself and fixes bugs Hey HN, we’re Nico and Arseniy, co-founders of Superlog ( https://superlog.sh ). We're building a self-installing, self healing observability tool meant not to be opened. It has a wizard that daily sets up proper logging and an agent that investigates errors and opens PRs. Super short demo: https://www.youtube.com/watch?v=xFhU9Mk247M . In our earlier startups, we tried Sentry, Datadog, Grafana, Dash0, and nothing was good enough. Proper telemetry and alerting still requires a ton of manual setup. We struggled with adding good logs, so debugging was tough, especially as codebases grow at a faster pace. Meanwhile, the Datadog/Dash0 bill kept climbing, and we still spent engineering hours to learn, configure, and maintain our observability tooling. With Sentry, we found ourselves flooded by a stream of alerts into our Slack channel, most were duplicates or lacked context, so alert fatigue/constant interrupts were a real pain. The #ops notification is consistently the worst feeling on a Saturday morning We’ve seen too many times servers run out of memory and disk, and three AWS metrics giving us three different values. Half of the graphs on dashboards are normally empty or outdated, and manually clicking through UIs, especially when the team is small, seems like a huge waste of time. At some point we realized that solving this problem would be more valuable than the things we had been working on, and we had the expertise to do it, since Arseniy had spent years at Datadog, getting paged during the night to debug production incidents. So we decided to build a platform that would just work: agent-first, MCP-native, zero-setup. Here’s how Superlog works: we have a wizard that scans your repo, and automatically instruments it with well-structured logs, traces and metrics via OpenTelemetry. We make sure to highlight main failure modes, endpoint performance, usage per tenant, and LLM/upstream cost (by callsite, tenant and model). Errors get fingerprinted and grouped into incidents, so you see one issue, not a thousand duplicates. When you get a notification from Superlog, you see a clear failure summary, its inferred severity and impact upfront. Then the agent investigates and tries to solve the issue. If it has enough context, it produces a concise and tested PR. If it doesn't, it posts its findings for the investigating team, and automatically pulls in the engineers that could contribute more context based on documentation, previous investigations and Slack threads. Either way the output is one clean PR per incident, posted in Slack, that you can merge, ignore, or open as a Claude Code session and modify. Three things we think are different from other observability vendors: (1) We solve the setup pain. The wizard will instrument everything with native OTel SDKs, respecting the semantic conventions, with proper service and environment tagging. We’re also working on native automatic dashboards and alerts, so that you can see what’s going on in a glance and don’t miss subtle failure modes. (2) Our telemetry doesn’t decay. The wizard runs daily, and keeps adding logs, alerts and dashboards where it’s needed. You don't have to remember to instrument new features. The next time something breaks, the data you need to debug it is already there. (3) Our goal is to solve alert fatigue. We use agents to merge similar errors and refine the summaries, giving you relevant information upfront. We have a custom evaluation setup that makes sure that our summaries are dense and correct, and severity and impact is on point. We also give you confidence scores for every LLM-enhanced metric so that wrong guesses don’t get boosted. Important: superlog telemetry is vendor-neutral, so you keep all the logs/metrics/traces we install. Pricing is on the site. We're early, so expect rough edges and please tell us when you find them. You can try it at https://superlog.sh . We'd love to hear what you're using today, what's broken about it, and whether the "one mergeable PR per incident" model sounds useful or terrifying. Especially keen to hear from folks running integration-heavy products, anyone who's rolled their own observability, and anyone who has tried Sentry / Datadog MCPs and given up. Comments and feedback welcome! https://superlog.sh/ May 19, 2026 at 10:54PM

Show HN: We missed Winamp, so we built an audio player for macOS https://ift.tt/G9cAKH8

Show HN: We missed Winamp, so we built an audio player for macOS https://ift.tt/JHEaPmG May 19, 2026 at 02:20AM

Show HN: Marlin-2B: a tiny VLM to extract structured information from videos https://ift.tt/WbFSG2E

Show HN: Marlin-2B: a tiny VLM to extract structured information from videos https://ift.tt/QPzDZOh May 19, 2026 at 01:06AM

Show HN: InsForge – Open-source Heroku for coding agents https://ift.tt/0KSQ2jZ

Show HN: InsForge – Open-source Heroku for coding agents Hi HN, I'm Hang, cofounder of InsForge (YC P26). InsForge is an open-source Heroku for AI coding agents: a backend platform designed for coding agents to deploy, operate, and debug end-to-end. Open source under Apache 2.0 ( https://ift.tt/LUusSEi ). Quick demo here ( https://youtu.be/7Bax5qz0IfM ). We started InsForge because we just wanted our Claude Code to handle all the backend / infra stuff for us, instead of us jumping between dashboards doing manual config, or copy paste logs and docs back to agents. We first tried creating a folder with bunch of .MD files, and installing MCPs like Supabase, Vercel, GitHub, Context7. But soon we found MCPs have their own problems: (a) Tools get pre-loaded into context, before agents even do anything (b) bad design, payloads are returning 10k+ tokens, and (c) a lot of stuff still can’t be done by MCP: e.g. telemetry and configs. So we think, because coding agents are so good at CLI, why not just put everything in CLI and create Skills to teach them how to use it? That’s InsForge: 1 command to install our CLI + Skills, coding agents can run the entire backend platform [1]. We started with authentication and database, but we kept adding more primitives we wanted, so now we have: - frontend hosting - backend servers (microVM based) [2] - database - auth - storage - LLM model router - cron jobs - realtime - edge functions - vector We have other features to make coding agents more reliable like real backend engineers: - backend branching [3]: agents will 100% mess up, like deleting your database. So inspired by Neon, we branch the entire backend (DB, auth, storage, functions, schedules). Agents work on the branch, you review diffs and then decide to merge or discard. - server telemetry: agents can read logs, CPU, memory, disk to find spikes and root causes themselves. - debug agent [4]: every project gets a dedicated debug agent. So your coding agent can ask questions like “why deployment fail?”, the debug agent will run diagnoses, find the root causes and propose fixes, then send the answer back. - backend advisor [5]: scans your backend daily for security and performance issues, proposes fixes. Then propose remediations, and sends to your coding agent. Give it a spin on InsForge cloud : https://insforge.dev , or read our code here: https://ift.tt/LUusSEi . We're a small team and reading every comment. Tell us what's good, what sucks, what's missing. We love feedback :) [1] https://ift.tt/3R8VatH [2] https://ift.tt/MtroiVH [3] https://ift.tt/S0ZykHb [4] https://ift.tt/vOguPQd [5] https://ift.tt/hTXncBA https://ift.tt/LUusSEi May 18, 2026 at 10:40PM

Show HN: Mezz, a curl-able WiFi sandbox for IoT pentesting https://ift.tt/571WqbD

Show HN: Mezz, a curl-able WiFi sandbox for IoT pentesting https://ift.tt/TqHOXz0 May 15, 2026 at 09:53PM

Show HN: How to Kill the Dead Internet https://ift.tt/zkIuMjN

Show HN: How to Kill the Dead Internet Ok, so maybe "how to revive the internet" would be more accurate, but if you're reading this, I got your attention, right? Here's why I want you to read on: I built a free extension, D-slop, to disincentivize anyone from posting AI writing, and eventually images and video as well, on the internet. For writing, it checks known vocab and punctuation tells, as well as subtler tells related to cadence, and assigns it a score subject to an adjustable threshold. If the text fails, users have the option to flag offending text, hide it, or block the page entirely (with the option to see anyway). For media, it's admittedly fairly weak, as it relies on C2PA metadata which is stripped from all of the social media sites where it would be most helpful. (Anyone else have chronically online boomer parents continually gobbling up slop like it's real information?) I have a D-slop+ version in the works that should be able to handle the media itself, but it's going to have to make API calls to have real teeth, which means I can't offer it for free. If this extension validates the concept, I'm happy to build it for y'all. Yes, I vibe-coded it, but an ancillary bonus to the project accrued when it inspired me to cook dinner listening to Metallica's "Fight Fire with Fire," which in turn brought my 5 y/o running into the kitchen with every musical instrument in the house for an impromptu karaoke speed metal session. It's MIT license open-source, full brief at https://ift.tt/fexAIUW ; This forum is full of people smarter than me, so I'm open to suggestions. https://ift.tt/xaQoDzV May 18, 2026 at 08:35AM

Show HN: Forecasting my backyard weather with a 22M time-series model https://ift.tt/BgVGX8Z

Show HN: Forecasting my backyard weather with a 22M time-series model https://ift.tt/LE6gjBX May 17, 2026 at 10:08PM

Show HN: Got ghosted by tech companies so I built a tool to track ghost jobs https://ift.tt/NUCX1zk

Show HN: Got ghosted by tech companies so I built a tool to track ghost jobs Last year I was looking for a new role. I sent out applications, did the prep, waited. What came back was mostly nothing. Not rejection emails, just silence. The job listings I'd applied to stayed live for weeks. Some for months. As a software engineer, I decided to dig into it properly. I built a system to continuously track job postings across companies, logging posting dates and measuring how long roles stay open before closing or don't. After 35,000+ listings across 200+ companies, some patterns are hard to ignore. Some listings have been open for 700+ days at companies you'd recognize. Others post 90% of their open roles within a single month, a signal that's harder to fake than a press release. I published two initial insight pages based on this work: - Which companies are posting most aggressively right now - Job listings that have been open for over a year What I didn't expect is that the same signals useful for detecting ghost jobs also say something broader about a company's hiring momentum, recruiting intensity, pipeline health, where talent bottlenecks might exist. I'm not sure yet where this leads, but I'll keep expanding the dataset and publishing more insights as I go. Would genuinely love feedback on the methodology, interpretation, or obvious blind spots in the data. https://ift.tt/h0R5zCK May 17, 2026 at 03:43AM

Show HN: Hermes-agentmemory, pull-model episodic memory with real deletes https://ift.tt/oLaTbKF

Show HN: Hermes-agentmemory, pull-model episodic memory with real deletes https://ift.tt/IehkSWK May 17, 2026 at 01:00AM