Transcript
00:00:00This is Ruda, an open-source AI coding tool
00:00:03that turns your agents into something closer
00:00:05to a delivery system.
00:00:07Not another paste your repo context and pray it works,
00:00:11a delivery system with backlogs, dev, review,
00:00:14evidence and gates.
00:00:16Because AI coding tools made us faster,
00:00:19but they also gave us a new job, managing the AI.
00:00:22Ruda is free, local first and built
00:00:25around a Kanban board for AI agent.
00:00:27Let's see if this actually holds up.
00:00:30(logo dings)
00:00:34Most AI dev tools hit the same three walls.
00:00:36First is chat hell.
00:00:38Everything important lives inside a conversation.
00:00:42The plan, that failed attempt, the fix,
00:00:44that weird work around, it's all trapped us
00:00:46just scrolling back.
00:00:47We do it all the time.
00:00:48Then there's no traceability.
00:00:50The AI changes code, but you don't always know
00:00:53what it tried, why it chose that approach,
00:00:56or what evidence it actually used to back it.
00:00:58Then finally, there's no real quality gates.
00:01:01We still have to ask things like, did it run the tests?
00:01:04Did it check the diff?
00:01:06Did it follow the actual acceptance criteria?
00:01:08And that's the thing here, because writing code
00:01:10is not the same thing as delivering software.
00:01:13Ruda's idea is kind of simple here.
00:01:16Stop treating AI coding like a chat session.
00:01:19Treat it like a delivery pipeline with tasks,
00:01:22agents, review stages, evidence and gates.
00:01:25Basically, CI or CD thinking for AI assisted software work.
00:01:30Now, watch how that changes the workflow.
00:01:32If you enjoy coding tools to speed up your workflow,
00:01:34be sure to subscribe.
00:01:35We have videos coming out all the time.
00:01:37This is the Ruda desktop app.
00:01:39It can be self-hosted with Docker just by pulling the repo
00:01:42and running Docker Compose up for all you self-hosters.
00:01:45Though I opted for the desktop after some issues
00:01:48trying to sync my Git repo, so it's a plug and play there.
00:01:52I'm gonna create a workspace, attach a real repo I have
00:01:56and choose Kanban.
00:01:58Then it opens this up and I'm gonna give it
00:02:00one or two small tasks.
00:02:02Nothing dramatic guys, just I don't wanna build
00:02:05the whole app, just the kind of task you would actually
00:02:07hand to an AI tool during normal development.
00:02:11Normally, this is where I would open a blank chat
00:02:14and try and write a really good prompt.
00:02:16But here, after I add the task, it automatically goes through
00:02:19and adds it to the Kanban board.
00:02:22The task is now not floating around in my chat.
00:02:25It now actually has a place to go.
00:02:27And it's gonna start right here in the backlog.
00:02:30As the AI progresses, it moves it into development
00:02:34and the right agent is gonna pick up on them.
00:02:36Now you can see the handoff and all this
00:02:38is being done automatically.
00:02:39So it's going through the different stages here.
00:02:41Now that sounds small, but it matters.
00:02:44I can still check the flow, the output
00:02:46and what the AI is doing.
00:02:48Within a task, you could also even chat with it
00:02:51while it's running the process.
00:02:54So instead of one giant conversation pretending
00:02:56to be a workflow, this workflow is now visible.
00:02:59I get evidence, I got traces.
00:03:01I can see what actually changed, what was checked
00:03:03and where the task is within the process of development.
00:03:07This uses open code and any other AI agent you really want.
00:03:11You can connect your AI API and just choose the one you want.
00:03:14For all this, I synced up my anthropic key to use Claude.
00:03:18Now let's break down what Ruta actually is.
00:03:21It's not trying to be another AI chat box.
00:03:23That's the key thing to understand here.
00:03:25The main idea really is the Kanban board.
00:03:28It's the coordination layer.
00:03:30Think of it like the project board
00:03:32your AI agents have to work through.
00:03:35A task starts in one lane, it moves to another
00:03:37and it goes through the lanes.
00:03:39Different agents can handle different stages
00:03:42if you link them up with your AI keys.
00:03:44So instead of one agent trying to do everything,
00:03:47plan the work, write the code, review, test, explain,
00:03:50Ruta gives the work structure.
00:03:52You create a workspace, you connect a repo,
00:03:55you define a task.
00:03:56The agents work inside of that structure.
00:03:59It also uses agent protocols like MCP and ACP.
00:04:03So you can add those in or use those where you need.
00:04:06It's more like infrastructure
00:04:08for coordinating software agents.
00:04:10And Ruta is not just asking an LLM,
00:04:12"Hey, Claude, does this look good?"
00:04:14It's trying to add checks, fitness functions,
00:04:17evidence, review gates.
00:04:19That changes the question, right?
00:04:22How are we using this?
00:04:23How can this speed up our workflow?
00:04:25Now, a lot of devs go through the same AI coding curve.
00:04:29You ask for a function, it spits one out.
00:04:32You ask for tests, it spits those out too.
00:04:34You paste an error, what does it do?
00:04:36It gives you a fix.
00:04:38Well, hopefully it gives you a fix.
00:04:41But then you start using it on a real code base.
00:04:43And that's when it all turns into actual maintenance.
00:04:46The context gets messy,
00:04:48the agent forgets what it already tried.
00:04:51It changes files you didn't even ask it to touch.
00:04:53You have to keep checking its work.
00:04:55And what's happening here is little by little,
00:04:58something annoying is happening.
00:05:00You didn't get rid of the work.
00:05:02You are now just the manager for the AI.
00:05:05It's a different kind of work.
00:05:06We're tracking the tasks, we're reviewing the diff,
00:05:09we're checking the tests, all that stuff.
00:05:12So it's just a new job.
00:05:13Ruta gives you a visible board for all of this.
00:05:17So where does this even fit compared to tools
00:05:19we're already using, right?
00:05:20Tools like Cursor and Clod, they're chat first,
00:05:23which is not a bad thing, right?
00:05:25They're great when you want a really strong code assistant
00:05:28that's really integrated and close to our code.
00:05:31But the center of gravity is still the conversation,
00:05:34the prompts we're giving it.
00:05:36Ruta's center is a bit different.
00:05:38It's the task moving through a delivery system,
00:05:41backlog to do testing review.
00:05:44Now compare that to agent frameworks like CrewAI
00:05:46or LaneGraph, those are more flexible.
00:05:49But that flexibility means you often have
00:05:51to build the workflow yourself.
00:05:52Who plans, who implements, where does the evidence go?
00:05:55All that stuff.
00:05:56Ruta is free, local first, and it's pluggable.
00:05:59Local repos, local workflow, there's no mandatory account
00:06:02just to try the basic idea.
00:06:05This is not where I say drop everything,
00:06:08replace it with Ruta.
00:06:09No, come on, I'm not gonna do that.
00:06:10That's not true at all.
00:06:12And this is only good for, well, certain things.
00:06:15I had a good time with it.
00:06:17There are some things that I liked right away.
00:06:19A visible board, clear task states,
00:06:22and traceable handoff, that's great.
00:06:24That's more professional than a 300 message chat thread.
00:06:27The local first approach is great.
00:06:30A lot of us are tired of AI tools becoming
00:06:32a subscription model with no boundaries around our code.
00:06:35Being able to keep this close to our local workflow
00:06:38is a real advantage.
00:06:40But I mean, yeah, technically you could do
00:06:42all this on your own, but this does not help
00:06:44keep things more organized, so in my mind,
00:06:47using Ruta actually speeds up the flow.
00:06:49The Kanban and protocol model has a learning curve.
00:06:52And if you just wanna open a chat box
00:06:53and ask a question, paste an answer,
00:06:56this is too much, right?
00:06:57That's not what this is at all.
00:06:59The desktop app is the best way to use it,
00:07:01but it's not gonna feel as great
00:07:02as the biggest commercial AI.
00:07:05I mean, Cursor feels good.
00:07:06It's that interface, right?
00:07:07Clawed code.
00:07:08And there are fewer ready-made agents
00:07:10that you might get from closed tools.
00:07:12But that's actually kind of why I like the direction.
00:07:15It's not pretending the hard parts
00:07:17of software delivery disappeared.
00:07:19It's just trying to organize them.
00:07:20AI is not going away, but the chat-first workflow
00:07:22is starting to show its limits.
00:07:24And the next step is not just smarter models,
00:07:27it's better coordination, it's better traces,
00:07:29it's better gates.
00:07:30If you enjoy coding tools like this,
00:07:32be sure to subscribe to the Better Stack channel.
00:07:34We'll see you in another video.
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