OrbitAI: AI Agent & RAG Integration Hub

I don't disagree with all your points. That said, what we have built has proven useful for us as we have built pipelines for customers and think it might be useful for others.Probably the main point I disagree with you is that RAG is just ETL. If that was the case, all of the AI apps people are building would be AMAZING because we solved the ETL problem years ago. Yet, app after app being released have issues like hallucinations and incorrect data. IMO the second you insert a non-deterministic entity in the middle of an ETL pipeline, it is no longer just ETL. To try to add value here, our focu

Hey HN. We built Frequency, AI agent orchestration that autonomously detects, defines, and runs any repeatable workflow in your repo for you, continuously and at scale.We used it to automate our app development repo, and rapidly ship 30 production apps (and counting) at Cashew Crate (https://cashewcrate.com) from scratch, autonomously. These are real, fully functional apps with working payments, accounts, analytics, and marketing integrations. Now, all we need to do is run Frequency and we see new apps appear everyday, without any prompting or babysitting.While we needed to intervene to set up

You hit the nail on the head. A couple years ago I started prototyping a similar node based AI workflow builder which worked well but everyone I showed it to was either; confused at how to use it (even for super basic stuff), or liked it but poked holes by asking "can it do x, can it do y" which it couldn't. There is such a small niche of people technical enough to use something like this that wouldn't just opt in for coding it themselves (especially with AI assisted coding nowadays)I do still use a part of it on one of my projects for building multi-step image processing pipelines for AI prop

I've been pondering this recently. There have been loads of trivial automation targets throughout the economy for years but they haven't really been fixed, despite being a small Python script (things like converting one csv file to another). Why do we suppose LLMs are suddenly going to fix them? I guess the overhead is possibly lower now, but I suspect there are other barriers to automation that most techies can't comprehend.

There's this magnetic appeal to use a visual graph editor as the UI paradigm for your app.. it seems to fit so many problems well.Yet if you complicate the problem just a notch, the graph becomes big fast and that renders like complex spaghetti you're afraid to touch in the UI.The other problem is that the UI paradigm becomes the driver - you try to think of the problem in terms of fitting it into the UI graph editor, which is often not a natural way of thinking about said problem.All of this to say is that I've always thought that graph/flow editors looked cool, except I've never found good u

as a person who spent years building flow.ai before flowise.ai, i'd say drag n drop ui was dead on arrival it works for a set of very hand picked cusomers , the rest were in a weird spot between people who can code( and think accordingly ) and don't.

This will come with higher risks, no transparency, and higher costs, of course. That seems like a recipe for even higher frustration than normal software incurs.

My solution for that is to use skills and add a omnibus shell script in each particular skill folder that does all the deterministic steps and then write the skill markdown to only use the script, usually passing a command line step parameter to the script. I also usually add a note in the skill markdown that it should monitor how it calls the script and suggest any improvements when the skill is used.As an example I have a `today` skill that has a today.sh script that can check my email, bug tracker, calendars, custom timesheet system and other systems via the script and then the skill assem