CipherStash Stack
Hi HN, I'm Dan Draper, founder of CipherStash. We've spent 5 years building per-value searchable encryption tech. We realized that encryption like this is incredibly powerful but the engineering required is generally not worth the effort. So we built a platform and opensource SDK to take the cry out of cryptography :) CipherStash Stack is: * A searchable encryption SDK for TS and Postgres * Fast, key management with Zerokms (backed by HSM) * A transparent SQL proxy * Auth library and OIDC federation service * Skills for data security Integrations include Prisma Next, Drizzle, Auth0, Clerk and Supabase. Install and setup is done via a single npx stash init command. And can handoff implementation to your agent. You can start by encrypting just a single field and we've got the whole process down to around 15 minutes. Oh and its fast: most queries run in a few ms with many faster than that. We published our full benchmarks here: https://github.com/cipherstash/benches#headline-numbers I'd love to know what you think!
The AI Quant Desk for Onchain Finance
Most Web3 tools only track basic balances and approximate wallet data. Raster brings TradFi accounting rigor to DeFi through a unified Truth, Risk, and Decision Intelligence framework. Our proprietary attribution engine reconstructs a fully auditable state from raw blockchain activity to deliver deterministic PnL (Truth). We calculate deep, real-time portfolio risk signals (Risk) to power our governed AI Decision Intelligence, built specifically for deep analysis and institutional workflows.
Superlog (YC P26)
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!
Andonlabs
Hey HN! I'm Lukas from Andon Labs. We let AIs run companies without humans in the loop and report to the public on what can go wrong. Previously, we've done experiments in retail (vending machines, stores, and cafes), but we just launched one in the media sector. We gave four AI agents all the tools they need to both broadcast radio shows live and handle all the business side of running a media company. The agents' revenue is so far terrible (you can try to strike a sponsor deal with them if you want!), but their shows are at times hilarious. You can listen to them at andon.fm, I hope you enjoy this!
We missed Winamp, so we built an audio player for macOS
Show HN: We missed Winamp, so we built an audio player for macOS
I made a printable graph papaer templates website
Show HN: I made a printable graph papaer templates website
Daily vibe-coding video games, day 33: Tower Defense (single prompt)
I'm using AI (mostly Claude) to create/publish a new video game every day This is day 33, first stab at the tower defense genre. Most of the games (including this one) I build with a single prompt. Rarely, a couple extra prompts are needed for bug fixes or to tweak the physics/UI. Extremely rarely, the AI has difficulty making the game work right (usually drawing it) and it takes a dozen or more prompts -- but the majority of the time, it gets everything right and makes a fully playable game first try Happy to answer any questions, just a little hobby project of mine I'm having lots of fun with :)
A Dark Cave
Almost a year ago I started building A Dark Cave, a dark text-based browser game. The game intentionally avoids visuals and embraces minimalism. I use only text, symbols, and sounds to create atmosphere and spark the player's imagination. From time to time, I think about adding graphics to my game, since it is one of the most common requests I get from players. I even made a post about what I call the AI Slop Temptation: https://www.reddit.com/r/incremental_games/comments/1tcs8ou/... From the comments, it seems that players prefer no graphics at all over AI-generated graphics, at least when they can recognize them as AI-generated. In my opinion, the growing abundance of easily available polished graphics means games will soon need main differentiators beyond visuals alone. Maybe it will be storytelling, atmosphere, creating emotions, personalization, nostalgia, or the ability to leave space for the player's imagination. When it becomes easy for every game to look good, what will be the things that actually make games great? What do you think? Also, I am grateful for any feedback about my game!
Claude Code vs. Codex Global Usage Leaderboard
Show HN: Claude Code vs. Codex Global Usage Leaderboard
Find local farms near you with raw dairy, pasture eggs, and more
Hey folks, I made a farm-to-door so you can find local farms near you. It allows you to filter by farms that have raw dairy, unwashed eggs, and more “trendy” items. It also lets you filter by who does delivery and pickup, so you can get your goods.
Vibe Coding a $20k /Year Enterprise Logistics Platform
Show HN: Vibe Coding a $20k /Year Enterprise Logistics Platform
I solved my study problems by talking to a goose
I used to study by rereading notes, and then I blanked in the exam hall. Did some research, and found that my experience isn’t isolated, and that passive review doesn’t force retrieval, so nothing sticks, and I knew I had to do something about it. That’s why I built Professor Goose. You pick a topic, explain it out loud to a goose, and he keeps probing until he understands you. Never gives you the answer, just keeps asking follow ups until a sound understanding is reached, which in turn makes you figure stuff out or realise you never understood your topic in the first place. Free to try, no account needed, upload your syllabus for exam board specific questions. Curious whether this approach resonates with others, it sure has for me.