Work on schema, my protobufs/flatbuffers replacement for multiplayer games, across 15 languages, parallelizes very nicely on the sprint system. https://github.com/mas-bandwidth/schema/blob/main/ROADMAP.md
Bunch more work you can see here, especially open source network libraries for multiplayer games - https://patreon.com/masbandwidth
I did some prototype games, one thing that worked really well for me was having a deterministic replay system where the agents could drive the gamestate via recorded inputs. Kept me from having to do any playtesting or debugging in the main agentic loop.
https://github.com/mas-bandwidth/fixed3d -- port of box3d to fixed point :)
https://github.com/mas-bandwidth/fixed -- fixed point math library
I do you think now need to watch out for the opposite problem. These things become so easy to build that the temptation is just to go ahead and build. But unless you really do need them, all that new code just goes to waste. The cost to produce has become very low, but sometimes the value can end up being negative.
Big fan of RDF, your sexpr notes are hilarious https://github.com/mas-bandwidth/fixed/blob/main/docs/roadma...
What tooling do you have around this?
And then the AIs can easily write tooling in lisp to manipulate these structures, and you can talk to them from some golang program in a verb like way, sort of like a redis client.
I was planning on doing this to effectively replace issues/PRs in git, and have my entire future work set that I import into sprints stored inside this lisp sexpr.
It's implemented in nova-sprint as a tracer bullet (search for "roadmap" scripts in lisp...), but I definitely see this as something I want to keep pushing harder on.
It's a really effective thing, storing all your work in a data structure, and then being able to manipulate that data structure. beats having to hammer the GitHub API when you ask questions like "OK so what else can we bring into the sprint now?"
=)
I find it really useful to track future work, and then bring it in the current session or sprint. It also regenerates nice roadmap tables in markdown that people can read like these ones:
https://github.com/mas-bandwidth/schema/blob/main/ROADMAP.md
At what point do you think you tip over from getting away with a handful of agents working on different things in one repo, to needing a comms/coordination system for your tasks?
I think there must be a burden to run this and I'm guessing it only pays off when you get to some kind of scale. What is that scale? Other human team members? Sheer workload?
Past a certain number of things, you can have a conversation with Claude in one session but even in Fable 5.1 or Opus 5.5 it's going to forget some and they just scroll out of context and get compacted away. Once enough things are in flight they start getting forgotten, or half-started, then the next thing is in flight and so on. When I start noticing this, I know it's time to switch over to the sprint system.
The other thing is simply I want to go really wide and work on something and it's too slow when working with traditional child agents per-model. The LLMs generally won't "work to width" naturally (for whatever reason) and the sprint system is really good at pushing and ensuring that each model involved in the sprint not only works, but works at whatever particular width they are assigned (16, 32, 64 cards...) and stays working that way.
If you work without a sprint system, you'll find yourself continually reminding the AIs manually "hey, can you please work to width?", "hey, you have 10 cards ready can you please work on them now?". These things are "squishy" when worked on in LLM space, they'll kinda do it, then a few hours later forget, and by morning they're puttering along at a few cards each.
But by putting everything into mechanical structure (sprint system that drives all the LLMs via push notifications), they will more reliably remember to work at width, they will not forget anything they were assigned to work on (this is stored in redis cards, not in their memory that compacts), and the whole system can work much wider and you get done a lot faster!
I can't.see myself specifying 60 bits of work to work on in parallel. So I guess you're doing some kind of task decomposition.
But I'm not convinced that this is that much more efficient, given the complexity of the coordination overhead.
E.g. in my mental model, 3 big tasks takes about the same amount of time as 60 small tasks after planning and coordination.
What's your experience?
I have one agent and already find it quite intense to outsource code writing and basically just make and document decisions.
I feel like I could be rich working like you, I also feel like it is impossible for me to meet the needs of my clients if I'd work like this. I wouldn't be able to answer their questions (without the active agent), I wouldn't be able to explain just where the data model needs changing, etc.
Can the AI do it? Not yet I think. Maybe someday, for now I feel like client of mine simply do not have the vocabulary to guide the LLMs. Ie they have no idea of what one-to-many is, or why it's smart to put some information in separate tables, where as some is ok to filter row-based on a user attribute, etc.
Hah, I think there's quite a few of us like this. We didn't have the time to spend doing this work in the past, but now with AI we can have it build what we want by giving it our ideas and framework.
It's going to be interesting to see what kind of new innovations across various industries comes from this over the next few years as the subject matter experts gets to work on their own ideas.
People who have a bunch of ideas they want to create!
The people who get really pissed off about AI coding seem to be all about the doing, but those who are really excited about creating things and the end result not the process come out OK
I break work up into four loose tiers:
* flash - deepseek flash 4.1 * pro - mercury 3 / terra * heavy - opus 5.5 / sol * frontier - fable 5.1 / astra / argon
The decision of how to classify each piece of work is done by the coordinator (typically Opus 5.5...). If a card fails at a particular tier, it's going to get escalated up tiers until it gets sorted.
Each card has multiple readers. I find mercury is very good at reading quickly. Deepseek as well. I will be honest, I think for 99% of stuff I need to do Opus 5.5 @ medium is ideal. I just use the additional models for different points of view, especially the absolute frontier models fable, astra, argon at max effort for security audits and code review.