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I've written an 11 post sequence on (my favorite flavor of) non-standard approaches to AI existential safety: mishka-discord.dreamwidth.org/tag/ai+existential+safety
It's time to think what to do next, and to mark some notable events in this sense.
Scott Alexander awarded the grants (www.astralcodexten.com/p/acx-grants-results-2025) and, in particular, awarded a small grant to autogenerate novels about AI going well. They are asking for plot ideas: www.hyperstitionai.com/
Of course, one could also generate on one's own or hand-write some fiction of various sizes describing AI going well :-) Hyperstitional value might come not only from quantity, but also from quality of the text.
Do ignore the comment in the ACX saying "Presumably, these novels would need to be just shoddy enough to bias the next round of AI training, without biasing any humans who read them". The novels should be good, biasing humans in that direction would 1) counter the bias produced by all AI disaster fiction, 2) have great hyperstitional value.
***
What else?
There has been a tour de force post on the dos and don'ts for agentic coding with GPT-5-Codex by Peter Steinberger, "Just Talk To It - the no-bs Way of Agentic Engineering", steipete.me/posts/just-talk-to-it (his github is equally impressive: github.com/steipete). A must read for anyone who is systematically engaged in agentic coding, but to use it at this level requires having a very high professional qualification and a lot of energy.
Reported by Simon Willison, simonwillison.net/2025/Oct/14/agentic-engineering/
My forecast that the leading labs are going to have "Narrow AGI" internally by April 2026 if not earlier remains in place (and I am now more certain after reading this).
"Narrow AGI" an AGI-level artificial software engineer, an AGI-level artificial mathematician, an AGI-level artificial AI researcher (and probably a single entity combining these three application areas, because a strong AI researcher has to be a decent software engineer and a decent mathematician).
Achieving a "Narrow AGI" is a sufficient but not a necessary condition for enabling non-saturating recursive self-improvement.
***
Grigory Sapunov posted an overview of "Hierarchical Reasoning Model", gonzoml.substack.com/p/hierarchical-reasoning-model
This is a well known small model which achieved good results on ARC-AGI tests, see also arcprize.org/blog/hrm-analysis for details and clarifications.
Recently, Tiny Recursive Model achieved much better results with an even smaller and more straightforward model:
alexiajm.github.io/2025/09/29/tiny_recursive_models.html (and x.com/jm_alexia/status/1975560628657164426)
***
There are also recent super-tempting papers in theory of machine learning (no idea yet if they are good).
Siyuan Guo, Bernhard Schölkopf (Max Planck Institute for Intelligent Systems), "Physics of Learning: A Lagrangian perspective to different learning paradigms", arxiv.org/abs/2509.21049
David Layden et al., "Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models", arxiv.org/abs/2510.08462
***
Simon Willison overviewed the new $4K "desktop AI supercomputer", NVIDIA DGX Spark, simonwillison.net/2025/Oct/14/nvidia-dgx-spark/
The caveat is that software is less developed and less stable for Arm64, so he is saying to perhaps wait a few weeks:
>It’s a bit too early for me to provide a confident recommendation concerning this machine. As indicated above, I’ve had a tough time figuring out how best to put it to use, largely through my own inexperience with CUDA, ARM64 and Ubuntu GPU machines in general.
>
>The ecosystem improvements in just the past 24 hours have been very reassuring though. I expect it will be clear within a few weeks how well supported this machine is going to be.
It's time to think what to do next, and to mark some notable events in this sense.
Scott Alexander awarded the grants (www.astralcodexten.com/p/acx-grants-results-2025) and, in particular, awarded a small grant to autogenerate novels about AI going well. They are asking for plot ideas: www.hyperstitionai.com/
Of course, one could also generate on one's own or hand-write some fiction of various sizes describing AI going well :-) Hyperstitional value might come not only from quantity, but also from quality of the text.
Do ignore the comment in the ACX saying "Presumably, these novels would need to be just shoddy enough to bias the next round of AI training, without biasing any humans who read them". The novels should be good, biasing humans in that direction would 1) counter the bias produced by all AI disaster fiction, 2) have great hyperstitional value.
***
What else?
There has been a tour de force post on the dos and don'ts for agentic coding with GPT-5-Codex by Peter Steinberger, "Just Talk To It - the no-bs Way of Agentic Engineering", steipete.me/posts/just-talk-to-it (his github is equally impressive: github.com/steipete). A must read for anyone who is systematically engaged in agentic coding, but to use it at this level requires having a very high professional qualification and a lot of energy.
Reported by Simon Willison, simonwillison.net/2025/Oct/14/agentic-engineering/
My forecast that the leading labs are going to have "Narrow AGI" internally by April 2026 if not earlier remains in place (and I am now more certain after reading this).
"Narrow AGI" an AGI-level artificial software engineer, an AGI-level artificial mathematician, an AGI-level artificial AI researcher (and probably a single entity combining these three application areas, because a strong AI researcher has to be a decent software engineer and a decent mathematician).
Achieving a "Narrow AGI" is a sufficient but not a necessary condition for enabling non-saturating recursive self-improvement.
***
Grigory Sapunov posted an overview of "Hierarchical Reasoning Model", gonzoml.substack.com/p/hierarchical-reasoning-model
This is a well known small model which achieved good results on ARC-AGI tests, see also arcprize.org/blog/hrm-analysis for details and clarifications.
Recently, Tiny Recursive Model achieved much better results with an even smaller and more straightforward model:
alexiajm.github.io/2025/09/29/tiny_recursive_models.html (and x.com/jm_alexia/status/1975560628657164426)
***
There are also recent super-tempting papers in theory of machine learning (no idea yet if they are good).
Siyuan Guo, Bernhard Schölkopf (Max Planck Institute for Intelligent Systems), "Physics of Learning: A Lagrangian perspective to different learning paradigms", arxiv.org/abs/2509.21049
David Layden et al., "Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models", arxiv.org/abs/2510.08462
***
Simon Willison overviewed the new $4K "desktop AI supercomputer", NVIDIA DGX Spark, simonwillison.net/2025/Oct/14/nvidia-dgx-spark/
The caveat is that software is less developed and less stable for Arm64, so he is saying to perhaps wait a few weeks:
>It’s a bit too early for me to provide a confident recommendation concerning this machine. As indicated above, I’ve had a tough time figuring out how best to put it to use, largely through my own inexperience with CUDA, ARM64 and Ubuntu GPU machines in general.
>
>The ecosystem improvements in just the past 24 hours have been very reassuring though. I expect it will be clear within a few weeks how well supported this machine is going to be.
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