Show HN: I scanned every YC Spring 2026 startup for what AI crawlers see https://ift.tt/JP9zwBn

Show HN: I scanned every YC Spring 2026 startup for what AI crawlers see Used 'potatometer.com' to scan and analyze all All 197 YC Spring 2026 startups on their SEO / GEO / AEO technical setup. I scanned the URL each startup lists in YC's directory. Most are readable by AI crawlers. Most don't tell a crawler what they are. Read more in the blog above. https://ift.tt/VL8XvbS June 23, 2026 at 06:40AM

Show HN: Durable Agent Sessions API (Preview) https://ift.tt/cs6n8Ih

Show HN: Durable Agent Sessions API (Preview) https://ift.tt/AEnCNts June 23, 2026 at 05:37AM

Show HN: Kitcat 2.0 – A Matplotlib back end for terminal plotting https://ift.tt/ZGenzWi

Show HN: Kitcat 2.0 – A Matplotlib back end for terminal plotting https://ift.tt/BPesDTv June 22, 2026 at 09:30PM

Show HN: Pure Effect – Reproduce production bugs on your laptop without a DB https://ift.tt/7QjbnyZ

Show HN: Pure Effect – Reproduce production bugs on your laptop without a DB Hi HN, I think it's safe to say that the majority of developers don't give a second thought to writing code with I/O tangled in business logic. It's all too common to see code like: const user = findUser(email); if (!user) await saveUser(user); Now, you may ask: what's the big deal? When we write code like this, two things happen: 1. It gets harder to debug production bugs. Unless you have the exact same database and remote API services to connect to, you may fail to reproduce the bug. 2. You have to use mocks and fakes in your tests, or use test containers, which only help somewhat, and they are slow! To solve these issues, I built Pure Effect, a tiny TypeScript/JavaScript effect library. The core idea is simple: if a function performs I/O, it isn't pure. But if it returns a description of the I/O it wants to perform, it is. So instead of await findUser(email), you return a Command object that says, "I would like to call this function, and when it finishes, here's what to do next." Your business logic becomes a pure function. Same input, same output, every time. The database never gets touched until the interpreter (runEffect) runs. When I first started the library, I didn't expect just how far that one idea would stretch. Once your pipelines are just data, a lot of wonderful things become possible: - No need for mocking libraries. You walk the tree in tests and assert on its structure: assert.equal(flow.cmd.name, 'cmdFindUser'). Nothing is executed. - Wrap any effect with Retry(effect, { attempts: 3, delay: 200, backoff: 2 }). The configuration is plain data, so you can assert on it in tests. - Every command's input and output flows through the interpreter, so you get a full execution trace for free. You can write a simple timeTravel() function that replays it locally without touching any I/O. Perfect for debugging complex production bugs. - An onBeforeCommand hook sits between your business logic and the interpreter. Since it sees every intended side effect before it fires, it can be used to enforce runtime guardrails. You can quarantine destructive calls before they happen for example. - You can review AI-generated code before it runs. Since Pure Effect pipelines are plain data, you can inspect what the generated code intends to do before it touches anything. There are just six primitives: Success, Failure, Command, Ask, Retry, and Parallel, plus effectPipe and runEffect. Zero dependencies. Under 1 KB minified and gzipped. How it compares to Effect-TS Effect-TS is the full-featured option in this space and has a large ecosystem. Pure Effect offers a different tradeoff. It covers the 80% case: testable pipelines, dependency injection, retry, and OpenTelemetry hooks, all in under 1 KB with zero dependencies and no new vocabulary to learn. Effect-TS is a framework you build around. Pure Effect, on the other hand, is a pattern you drop into existing code. I've been using Pure Effect in production since December. It's at v0.8.0, not 1.0 yet, but stable enough that I wanted to put it out there and hear what people think. GitHub: https://ift.tt/NaxgqJ7 I wrote five posts that document how Pure Effect evolved. They are tagged at https://ift.tt/JVM5GWh if you want the longer story. https://pure-effect.org June 21, 2026 at 09:36PM

Show HN: DebugBrief – turn debugging sessions into reports, no AI https://ift.tt/9gQY80R

Show HN: DebugBrief – turn debugging sessions into reports, no AI https://ift.tt/1IO7qZH June 21, 2026 at 11:57PM

Show HN: CleverCrow: give tokens to your favorite projects https://ift.tt/PrkhETB

Show HN: CleverCrow: give tokens to your favorite projects Howdy all. I'm Zack :wave:. I've been thinking about the problem of misguided AI pull requests and figured I'd throw a possible solution out there for feedback. Basically, CleverCrow lets supporters give tokens to a GitHub repo (or set of issues in that repo) for the maintainers to use to build/fix stuff. The fun implementation challenges have been around implementing the pooling dynamics and keeping the maintainers in charge while the backers are motivated to support their work. https://clevercrow.io June 21, 2026 at 11:06PM

Show HN: An ASCII 3D Rendering Engine https://ift.tt/e7sQkTL

Show HN: An ASCII 3D Rendering Engine https://glyphcss.com June 20, 2026 at 11:10PM