Space echoes like an immense tomb, yet the stars still burn
Vision Revisited
Yesterday i posted about the original λC vision.
Within the hour, and motivated by (1) instinctive insights brought about by a cigarette break and a lack of engagement generated by my 5-followers twitter account (2) pragmatism regarding the time investment it would take to singlehandedly build λC as initially intended, i decided to revisit the λC vision into the following:
An exercise in self-funding swarm, where a ~YC management + projects simulation is ran by autonomous agents at all levels, from ideation to building and shipping, and where the aim is to gradually lessen reliance on human supervision.
The instinctive idea is that : the best way to acquire alpha on how the previously described swarm would operate would, beyond theory, be a simulation of a startup ecosystem itself… λC !
The Backstory
Last week, i jokingly stated in a now-deleted tweet that if YC rejected my AiEarlyInterviews23 application (they did), i'd build an open-source equivalent for every single "wrapper" project of this year's batch. Somehow, the following, (questionably) healthier (or at least more stimulating) perspective was born out of it:
Thesis - Apply for YC batch with an AI infrastructure startup, in order to join and benefit from the YC ecosystem
Antithesis - Application rejection (because of a ~"lack of time to review all projects"), leading to the need to build an alternative ecosystem & community to support this startup and other peers' startups
Synthesis - A YC batch simulation exercise ran by autonomous agents, who will research & ideate software products and build the products, live-to-market. Ultimately, the exercise would provide prominent alpha on how swarm intelligence ought to (1) be modelled for executive capabilities in a competitive, real-world context (2) organize, adapt & communicate (3) operate & interact outside the machine realm, and push beyond current limits of machine-first frameworks.
The Parallel
The new λC vision that was revealed to me is highly analogous to the S3 plan from Metal Gear Solid 2 :
In Metal Gear Solid 2, the "S3 Plan" was created as a highly ambitious simulation based on events from the first video game, Metal Gear Solid 1.
The Patriots, a mysterious and powerful organization, used data from the original mission of Solid Snake, the protagonist of MGS1, to construct a massive AI program called GW.
The S3 Plan was designed to reproduce similar circumstances and challenges faced by Solid Snake during that mission, but with a significant twist. Instead of sending an experienced operative like Solid Snake, they chose a relatively inexperienced soldier named Raiden to be the main operative in the simulation.
The Patriots hoped to observe how Raiden would react and behave in the recreated scenarios, essentially using him as a test subject to generate data on how to shape future events.
The analogy here being : YC = MGS1 context, λC = S3 Plan
The Primordial Soup
To commence the exercise, a small dataset is made from this year's YC launches, and will be augmented & used by autonomous agents in order to provide the initial context for the λC simulation exercise.
The Infrastructure
Orchestrating the λC simulation exercise requires the necessary infrastructure.
The overall idea about approaching the infrastructure is to emphasize machine-first, minimal configuration frameworks, that are to be used by different swarm agents models.
(note : i can think of 4 key infrastructure components, all of which i'm iterating on with RAIDEN AI, and are ready to be deployed during August 23, but the aim of this article is not to shamelessly plug them)
The next blog article will dive deeper into this dimension, and outline the limitations of current frameworks (i.e. langchain, human-first frameworks), and more importantly, what is being done on this end to go beyond those limitations.
Conclusion
Overall, the exercise is expected to start during August 23.
Feel free to share any interesting ideas @n_raidenai