The amazing thing here is that the Brokk AI can access your code like an IDE, can ask for usages or gather the summary of a file before deciding to get the implementation of a method!
It mimics like a Dev is navigating the codebase.
And this is more reliable and token-efficient than the usual grep/rg approach
The "Read" file list sounds a lot like Copilot Edit mode, where you manually specify the list of files that are added to the context. Similarly, Copilot has an Ask (Chat) mode that doesn't change the code. One of the downsides of all these new IDEs is that it is difficult, even for the developers of those tools, to have enough time to test out coding in each of their competitors. Also, the switching cost of changing IDEs is pretty high, even if they are forks of the same code base, which makes it hard for the users to really test out all the options. In the long run, I expect that the "larger" IDE providers will purchase the smaller ones. IOW, if you wait long enough, all the good bits will be in Copilot (or maybe Cursor with their new funding).
idk, everyone else seems to want to take the 40 year old IDE paradigm we're all used to (really! that's how old Turbo Pascal 3 is!) and graft AI onto it. I think we need a fundamentally different design to truly take advantage of the change from "I'm mostly reading and writing code at human speeds" to "I'm mostly supervising the AI which is better at generating syntax than I am."
of course the downside to going against the crowd is that the crowd is usually right, we'll see how it goes!
Really cool project! I tried it a couple of weeks ago with an Anthropic API key and will give it another shot.
Could you share a bit more about how you handle code summarization? Is it mostly about retaining method signatures so the LLM gets a high-level sense of the project? In Java, could this work with dependencies too, like source JARs?
More generally, how’s it been working with Java for this kind of project? Does limited GPU access ever get in the way of summarization or analysis (Jlama)?
I'd be interested to try this out. I'm especially keen on AI tools that implement a native RAG workflow. I've given Cursor documentation links, populated my codebase with relevant READMEs and diagram files that I'm hoping might provide useful context, and yet when I ask it to assist on some refactoring task it often spends 10-20 minutes simply grepping for various symbol names and reading through file matches before attempting to generate a response. This doesn't seem like an efficient way for an LLM to navigate a medium-sized codebase. And for an IDE with first-class LLM tooling, it is a bit surprising that it doesn't seem to provide powerful vector-based querying capabilities out of the box — if implemented well, a Google-like search interface to one's codebase could be useful to humans as well as to LLMs.
What does this flow look like in Brokk? Do models still need to resort to using obsolete terminal-based CLI tools in order to find stuff?
Likely not an important note but the name sounds close enough to grok that I assumed this was a spin off of some xAI product. I had to look around to see if it was actually associated (it looks like it isn't) but it may be something to be aware of.
No offense, but that video is brutally boring. Even at 1.5x speed I couldn’t get past 10 min. You should transcribe the audio and use an LLM to write a punchy sales pitch.
[+] [-] lutzleonhardt|10 months ago|reply
[+] [-] unknown|10 months ago|reply
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[+] [-] danjl|10 months ago|reply
[+] [-] jbellis|10 months ago|reply
idk, everyone else seems to want to take the 40 year old IDE paradigm we're all used to (really! that's how old Turbo Pascal 3 is!) and graft AI onto it. I think we need a fundamentally different design to truly take advantage of the change from "I'm mostly reading and writing code at human speeds" to "I'm mostly supervising the AI which is better at generating syntax than I am."
of course the downside to going against the crowd is that the crowd is usually right, we'll see how it goes!
[+] [-] tschellenbach|10 months ago|reply
cool to see more AI tools address this
[+] [-] ElijahLynn|10 months ago|reply
[+] [-] jbellis|10 months ago|reply
I made an intro video with a live demo here: https://www.youtube.com/watch?v=Pw92v-uN5xI
[+] [-] bchapuis|10 months ago|reply
Could you share a bit more about how you handle code summarization? Is it mostly about retaining method signatures so the LLM gets a high-level sense of the project? In Java, could this work with dependencies too, like source JARs?
More generally, how’s it been working with Java for this kind of project? Does limited GPU access ever get in the way of summarization or analysis (Jlama)?
[+] [-] neoncontrails|10 months ago|reply
What does this flow look like in Brokk? Do models still need to resort to using obsolete terminal-based CLI tools in order to find stuff?
[+] [-] saratogacx|10 months ago|reply
[+] [-] soco|10 months ago|reply
[+] [-] corysama|10 months ago|reply
[+] [-] silverlake|10 months ago|reply
[+] [-] esafak|10 months ago|reply
[+] [-] insin|10 months ago|reply