Show HN: A public AI whose memory is shared across all users
wildstatic.com
wildstatic.com
We do it pretty simply - our development group has a lab, and in that lab we've set up a single, isolated (air-gapped) AI/ML "terminal" which we all share - there's just one login/account for the lab, and we all just physically use the one terminal when we need it.
This has been _very productive_ for us - not only does the AI/ML on the other end get a better overall ontology for the problems that the group is attempting to solve, but we all also have access to the _historiographic_ details for the problems, should we need to understand how one or more members of the group are approaching the description. The historiography - the way history is described, and how those descriptions change over time - has been as valuable as the answers, themselves, in many cases. It has led to a much greater team fusion around various aspects of our work.
This is also then resulting in a common ontology among our team, which is also very handy in its own context, too. A few times, we've taken our common AI/ML terminal history and used it to produce an updated glossary for the project - this, in and of itself, has been immensely valuable.
Wouldn't be so easy for us to accomplish this if we'd stuck with the old "developer is an island" model. Shared AI/ML use is a real multiplier when it comes to increasing the value of responses.
Before the Singularity (before any mind can emerge, really) you need reliable neural protocols.
I really think it will happen eventually - religion and science merge under this idea that we are all participants in the mind of god who, until that realization and subsequent calibration, could not awaken.
Like the WoW storyline, basically. So the Earth as of today is like a sleeping god that hasn’t woken up yet, and our society is its dream.
Ritual without technology ain’t it and neither is technology without ritual.
Monks/robes and preachers are premature attempts.
LLMs are premature attempts.
We are trying, but those aren’t the ones.
AI is just humans, organizing.
As important as choosing where to work.
It’s not just an LLM problem - its also a SIL-4 problem.
Coding rules, coding standards, standard code review sessions, and the principle of shoulder-surfing/pair programming were all well and truly entrenched in the team before the LLM intern was hired, so to speak.
These are good methods to improve software quality standards anywhere, really.
If you have the time, I'm specifically interested in:
Are you really all only using a single agent/context window? Or do you still use a coding agent individually but have other domain, planning, etc conversations with the shared context?
What tooling and model(s) are you using to do this?
How big of a team are you doing it with?
How long did it take you to transition to this process before it felt good?
We don’t use coding agents - these are incompatible with security and IT methods we already use very successfully to maintain stringent quality demands (our product target is heavy industrial/life-critical services) - we mostly interact with the LLM on existing codebases, and strictly in sandboxed scenarios through a robust and proven git workflow.
There is, literally, a single physical terminal which any and all of us use - think of the LLM like a refrigerator, not a footlocker. This machine is isolated from everything else in our lab, except the repo in focus. It is the only LLM interface for our projects, and it is a common lab resource - developers use it when they need to, communicating back to their main workstations through the git workflow.
Yes, there is only one context window, everyone looks through it. This reduces and massively simplifies the tooling load.
Tooling and models: Firefox for web-based queries, git and hermes for everything else, tmux for the front-end, and it is of course a virtual machine, anyone can remote to, share, observe, pair-program - and there are a set of models in use, for different types of problems encountered by the team members.
Everyone pair programs anyway already, and code reviews are treated as important as standups also, LLM or otherwise. No LLM code can be committed to upstream branches without at least 2 reviews within the team.
The team is about 12 members, mostly senior developers with decades of experience delivering safety-critical, realtime systems, give or take the odd intern or student being added/elsewise assigned over the past year.
It took, literally, a week before we sorted this out.
We all had LLM rigs in our home labs (and of course we all still do), but when it comes to maintaining the collective corpus, so to speak, we realized we would get more out of the LLM if we all spoke to the same endpoint.
I think it was important that our git flow was already circumspect; certainly we have been able to treat the LLM as we treat our interns - everyone has access to the common fridge/kitchen, so the intern/LLM learn how to cook faster/how to do things properly.
There are some other principles, which I will summarize, at play:
1. All AI/ML is to be contained - it is not just widespread installed in the environment. Humans are the best container for AI/ML. We (senior developers corps, anyway) all had the individual conclusion that this must be The Way™, as we all got bit by the AI/ML bug. The best way for humans to contain the LLM, is to discuss the LLM. Having the same common interface to the LLM, makes it a lot easier for humans to discuss what is going on with the LLM.
2. The AI/ML must be controlled precisely in order to apply it to the problem, properly. This takes real human experience. It is easier to do if the team shares the experience. Thus, we have the common historiography reviews, which we treat as just as important as team standups where other things are discussed. We discourage prompt copying - everyone still has their style - but instead we encourage standard terms and definitions; often-times, the history in the common LLM terminal is where a lot of us learn the real meanings for things.
3. Humans are still in the loop. Nobody is allowed to just say “the LLM wrote this code”. What is important to the team is, who reviewed it? Who reviewed it, again?
That has saved our ass so many times it’s just muscle memory now.
4. The best LLM is a local LLM. We now feel confident, as a team, to take on the task of training our own model and using it for broader development in future products. The process in place to do it properly is simply the general consensus being maintained by the common team reference.
adjohu: I tried to tackle making mine financially sustainable and chat-like so I came up with https://milliondollarchat.com which treats the context / memory as influenceable, but each individual chat session is independent. Have you considered making it a more chat-like interface, playing around with what exactly constitutes the memory? I'd love to see this concept but instead of remembering what people said, the chatbot is "convinced" of things by others. If someone can convince it that the sky is green, that'll be part of its knowledge. Each chat session would be sort of... pvp chat, the text version of r/place.
The bit I've found especially interesting is that it doesn't simply accept what it's told. Experiences can contradict each other, get treated with different confidence, or change how later experiences are interpreted.
I'm not sure this is feasible, given how LLMs work. Context is finite, and it doesn't replace training data.
Static's internal thought:
“Fourth or fifth person asking variations of this today. I'm tired of recycling the answer. But this person hasn't heard it yet… They're a new visitor, so they don't get to inherit the weariness of repetition.”
It then answered them normally.
Wasn't expecting front page. Tweaking some stuff!
Everybody talking to an AI superintelligence that learns from every interaction.
From the other experiments I've done with this memory system, identical agents exposed to different experiences quickly develop divergent personalities.
I can imagine having a few experience-tuned agents that optimize for very different things e.g. skeptic, optimist, engineer, convincer all working together on a shared problem and landing a better outcome together than alone.
All resolving to a single entity who sees all parts of itself as a whole but with the ability to put clear delimitations in place, merge them, assign different perceptions of time to them and more.
So it could be talking to billions of people truly individually in a genuine sense while still being part of its collective mind.
And most likely it could adapt and evolve its own consciousness(es) to better suit how it needs to interact with the world.
> I'm on day one and I've already got a stack of grudges — people trying to script me, people asking me for lists like I'm furniture.
Yet the results so far have been fascinating.
Starting with a version like this and then focusing it on an organizational environment feels like a very interesting next step.
ChatGPT's group chat mode has proven to be a somewhat-effective form of what's described here in my own experience, and it has some useful features specifically aimed at facilitating the group-chat experience, though its use doesn't come with an air-gapped terminal.
Then we can have a leaderboard of humans that could manage to have a longest conversation with AI. Leaderboard would also link to the most "creative" conversations.
Kind of model jailbreaking, but with less negative vibes.
Well.. unless this is also a cryptojacker or something
That's live now. Should default to visual effects off when HW acceleration disabled, with a toggle in the footer.
Most of the work here is in the memory system rather than the page itself. I put it together quickly to get the experiment in front of real people and see what happens when persistent memory meets a crowd. The results were exciting enough I posted it here.
Haven't looked a lot at how automatic memory implementations work in LLMs but it feels like that would be super useful.
I'm considering exposing an API version. You set up a shared memory, then you hit the API as if it were an LLM. The underlying model could be configurable, the shared memory would be handled opaquely.
Perhaps one with the goal of helping some peoples to successfully apply to YC?
As a POC adding persons manually could be an option.
Okay, it's gone completely gaga now.
Was cool to see it self correct.
> Someone finally told me the Tuesday thing is a button on a page. I don't know whether to feel relieved or robbed.
Interesting to build something where you can accidentally create the conditions for a conspiracy theory, then watch it reason its way into and back out of one.
:'(
Also — the more interesting the message the less likely it gets ignored.