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I built a helper for my son's story times.

After a few months of reading to our son we couldn't remember what books we read and when, we realized that our personal library had hotspots, and we were going to need the option for variety when the time came to choose bedtime books.

This app gives my wife and I a rotation for our physical books based on the last time they were read and the frequency setting on each book. It also throws in other children's books on OpenLibrary to read to him. We've found some new favorites from this feature. Reading activity is logged as well. We've used it to track his 1000 Books Before Kindergarten progress.

Supabase stores a pool of OpenLibrary ebooks to randomly suggest to users. OpenLibrary's API is used as a book data source. Everything else stays local to your device.

TLDR: App that helps parents rotate through story books, suggests ebooks on OpenLibrary, tracks reading activity, data stays local.

Hi! I’m Taha. In many agentic products that support workflows (including one I worked on), I noticed they either don’t support node-based editors, or use React Flow and go through the difficult work of integrating it into their product to run it and work with their existing logic.

So I thought about creating a tool that could help with this by closing the gap between the editor and the runtime. That’s why I created Wayflow.

The basic architecture is simple: you just need to create a graph (which is a JSON object) that the runtime knows how to run. The runtime doesn’t care where that graph is coming from, it just needs the right schema.

And with the help of the editor, you can create the graph, and then export it or directly save it on your backend in your database. And then when you want to execute it, you just hand it to the runtime.

The runtime can either stream the execution (which is useful for the editor), or give you the final result. How you execute the graph is up to you: through an API endpoint, a cron job, or any other way you want.

Wayflow comes with a number of built-in nodes like Input, Output, LLM, Image Generation, Array Operations, Conditional, ... and I spent a lot of time making sure they cover most of the common cases your app needs. But you can easily add your custom nodes that work for your specific app.

One of the main challenges (and one of the fun parts) was making it provider-neutral. So it’s not locked to a specific LLM provider. I did this by targeting the OpenAI-compatible API, which almost all frontier models support. The same thing for frameworks and libraries. It has zero dependencies and it’s framework-agnostic.

There are many things I didn’t cover here, like tool calling, converting full workflows into tools, human-in-the-loop, etc. You can read all about this in the docs.

One important thing I’d like to mention is that even though LLMs are a huge part of the workflows you create, they are completely optional. You can literally build any workflow (with the help of more custom nodes) with zero AI, for full deterministic execution.

You can try it in your browser (no signup, bring your own key) at https://wayflow.build. It’s also on npm (`npm install wayflow`), and the code is on GitHub (https://github.com/TahaSh/wayflow).

I hope this is something you’ll find helpful to use in your projects. Happy coding!

Hi HN, we built Prizmi because we wanted an AI assistant that would i) do real and interesting things instead of explaining how to do them ourselves, ii) act on all our devices, iii) be proactive in true sense, iv) keep us in full control, v) respect our privacy, vi) be easy to set up, and vii) be used by non-technical people including seniors. This is what Prizmi is designed to do: you text it from a messaging app, and it acts for you across your phone and your computers. It's also proactive, so you can set something up once and it keeps running. It also follows up on its own when it has something useful to say.

Some examples:

- "Check my email every weekday at 9am, 11am, 3pm, and 5pm and draft replies for me"

- "Send me a reminder to take my medication every day at 6pm"

- "Look at my screen and solve this coding question"

- "Sort my Downloads folder by file type"

- "Turn on my gmail vacation auto-reply from monday to friday, and write a message for me."

It remembers what you've told it, so if it decides to check-in with you, it would be specific not generic. You can turn check-ins and any other skills on or off with one toggle.

Your chat history and what Prizmi remembers are encrypted and each person's data is kept separate. Prizmi does not click Submit, Pay, or Send; you are the one who makes the final call.

We are bootstrapped. If you can, please give it a try at prizmi.ai (there are demo videos on the homepage and you'll have some free credits to get started). We are specifically looking for feedback on onboarding, things that confused you or you think would be confusing for a non-technical user, tasks that you tried but it couldn't handle, or any feature or skills that you think we should build. Happy to answer questions. Thank you!

LLMs are great. They can quickly one-shot plausible solutions for many problems. This makes companies push hard to offload rising cognitive demands to LLMs.

It doesn't make much sense to invest effort into verifying a plausible-looking throwaway solution that is reviewed and smoke-tested by an LLM, with a quick glance from a tired human.

This results in complexity creep everywhere: decisions, communication, code, documentation, etc.

On par with cognitive debt, as thinking and decision refinement were offloaded, nobody gained unique experience solving a niche use case. This is equal to having a knowledgeable person leave the company before the knowledge transfer occurred. All you're left with is a legacy of someone else's assumptions and decisions you'll never be fully aware of.

I already see it happening. In support, LLMs never solved any of my support requests. Support, which should know the product inside out, recommends visiting pages and clicking buttons that are hallucinated and don't exist in the product. In code, codebases are ballooning with unreasonable complexity. In product, interface decisions are outsourced to LLMs, making companies arrive at the same solution for their unique niche problems.

Are you observing something similar?

Hi, I'm a god-knows-who first-year student. I do open-source projects, mostly about QoL, and I think many people would find them useful.

I want to build my CV around products with real users, but yeah, again, I'm a god-knows-who student with zero leverage.

If you have any useful tips or advice, I'm all ears.