Stats for nerds
0recommend
7mentions
3people
2013–2026years
100% neutral
Mentions per year
What Hacker News says
- Set showdead=true and you can see his fanatical arguments in defense of Musk's Nazi salutes, and attacks on other people who disagreed with him, which rightly got his multiple comments flagged dead.
His evidence-free Nazi-salute-defending arguments were in such bad faith, ignoring and refusing to address other people's valid counter arguments, the exact same way he is now ignoring and refusing to engage with mine, and sealioning questions whose very premises so wrong they don't deserve the answers I gave him anyway, that his old dead boot licking comments don't deserved to be linked or repeated, since they're a distraction from the useful things I actually linked to. The only thing his comments prove is that he's not arguing in good faith, and can only dispense drive-by evidence-free attacks.
You can easily find them yourself if you really don't believe me, but it's a waste of your time, because his comments have no value. Have you looked at any of those valuable links I did post yet, or did you just get stuck in the worthless rabbithole of his posting history?
I believe your time would be much better spent reading the links I already provided than his Musk cheerleading and oligarch water carrying and Nazi salute whitewashing. But if you really care about his arguments, why don't you ask him now to explain what he said about Musk, privately? Or doesn't he publish under his own name with his own email address so you can ask him, like I do?
I'm more than happy to answer your personal email about my own arguments and projects, and continue this discussion in private: don@donhopkins.com
His own ungrounded vitriolic words attacking Jens Mönig's and Brian Harvey's and Seymour Papert's life work like "it's because this is BS and just a toy, it's got no connection to the real world" and "like almost as if the ecosystem simply serves to show off Snap and thus misses the mark" are dismissive, insulting, and provably wrong.
So I posted several links and evidence refuting him and defending my friends. Yet then he repeated the same pattern as his full throated defense of Musk's Nazi salutes, by ignoring all the evidence, and reiterating his unsubstantiated false accusations, without providing any objective evidence or coherent arguments or even any citations himself.
If you'd like to learn about cool free open source educational projects, please look at the many links I already posted, and what I posted to the Logo discussion. At least spend as much time on those interesting relevent links as you already have wasted reading his uninteresing irrelevent posting history, please.
He didn't post any links or evidence or even coherent arguments to support his mistaken and aggressive accusations, while I refuted "this is BS" by linking to and quoting the Wikipedia page about Seymour Papert's Constructionist Education philosophy, "just a toy" by naming and linking to many Snap! projects and demos, and "no connection to the real world" by citing several physical real world connections like TurtleStitch.
Yet he dismissed and ignored all of that, and questioned the motives of sincere professional educators and developers of free open source software as merely and illegitimately promoting Snap!, because he isn't arguing in good faith, just attacking, and he didn't bother looking at any of the links, so he still doesn't know what he's talking about.
If reading his oligarch-syncophantic posting history (which I was trying to save you from by identifying where he's actually coming from) isn't interesting enough for you, and you still thirst to learn about cool projects, then please look at the links I already posted, read my other recent posts about Logo, and check out some of the other educational projects I've been developing over several decades, which were deeply inspired by Seymour Papert's Constructionism, that I've written a lot about.
But if like him you're so offput and triggered by the very mention of diversity, constructionist education, free speech for anyone but Musk, and criticism of an unloving parent so terrible and unaccepting that he publically and vitriolically disparages, abuses, and disowns his own trans daughter in front of millions of people that you have to leap to his defense, then there's nothing I can do for you, sir.
But I certainly hope you're not like that, so here are some links in good faith:
Logo Adventure for C64 Terrapin Logo:
https://donhopkins.medium.com/logo-adventure-for-c64-terrapi...
Micropolis: Constructionist Educational Open Source SimCity:
https://donhopkins.medium.com/har-2009-lightning-talk-transc...
This is Micropolis (open source SimCity) in WebAssembly:
This is The Sims 1 character animation system (which I original wrote in C++ 30 years ago) rewritten in TypeScript and WebGL:
Micropolis and other open source code, documentation, and designs for new educational software:
https://github.com/SimHacker/MicropolisCore/
MOOLLM Constructionism skill:
https://github.com/SimHacker/moollm/blob/main/skills/constru...
MOOLLM Adventure skill:
https://github.com/SimHacker/moollm/tree/main/skills/adventu...
Adventure Compiler:
https://github.com/SimHacker/moollm/blob/main/skills/adventu...
Gary Drescher's Schema Mechanism skill from "Made Up Minds" (based on his PhD thesis and book about how children learn, with his advisor Marvin Minsky at the MIT AI Lab):
https://github.com/SimHacker/moollm/blob/main/skills/schema-...
MOOLLM Play Learn Lift skill:
https://github.com/SimHacker/moollm/tree/main/skills/play-le...
- Check out "cursor-mirror", this extended Anthropic Skill I've developed as a part of MOOLLM, which will tell you all about how cursor assembles its context:
cursor-mirror skill: https://github.com/SimHacker/moollm/tree/main/skills/cursor-...
cursor-mirror
See yourself think. Introspection tools for Cursor IDE — 47 read-only commands to inspect conversations, tool calls, context assembly, and agent reasoning from Cursor's internal SQLite databases.
By Don Hopkins, Leela AI — Part of MOOLLM
The Problem
LLM agents are black boxes. You prompt, they respond, you have no idea what happened inside. Context assembly? Opaque. Tool selection? Hidden. Reasoning? Buried in thinking blocks you can't access.
Cursor stores everything in SQLite. This tool opens those databases.
The Science
"You can't think about thinking without thinking about thinking about something." — Seymour Papert, Mindstorms: Children, Computers, and Powerful Ideas (Basic Books, 1980), p. 137
Papert's insight: metacognition requires concrete artifacts. Abstract introspection is empty. You need something to inspect.
This connects to three traditions:
Constructionism (Papert, 1980) — Learning happens through building inspectable artifacts. The Logo turtle wasn't about drawing; it was about making geometry visible so children could debug their mental models. cursor-mirror makes agent behavior visible so you can debug your mental model of how Cursor works.
Society of Mind (Minsky, 1986) — Intelligence emerges from interacting agents. Minsky's "K-lines" are activation patterns that recall mental states. cursor-mirror lets you see these patterns: which tools activated, what context was assembled, how the agent reasoned.
Schema Mechanism (Drescher, 1991) — Made-Up Minds describes how agents learn causal models through Context → Action → Result schemas. cursor-mirror provides the data for schema refinement: what context was assembled, what action was taken, what result occurred.
What You Can Inspect:
Conversation Structure
Context Assembly
Tool Execution
Server Configuration
MCP Servers
Image Archaeology
Python Sister Script CLI Tool: cursor_mirror.py
cursor_mirror.py: https://github.com/SimHacker/moollm/blob/main/skills/cursor-...
Here is the design and exploration and hacking session in which I iteratively designed and developed it, using MOOLLM's Constructionist "PLAY-LEARN-LIFT" methodology:
cursor-chat-reflection.md: https://github.com/SimHacker/moollm/blob/main/examples/adven...
Look at the "Scene 19 — Context Assembly Deep Dive" section and messageRequestContext schema, and "Scene 23 — Orchestration Deep Dive" section!
PR-CURSOR-MIRROR-GENESIS.md: https://github.com/SimHacker/moollm/blob/main/designs/PR-CUR...
play-learn-lift skill: https://github.com/SimHacker/moollm/tree/main/skills/play-le...
MOOLLM Anthropic compatible extended meta skill skill: https://github.com/SimHacker/moollm/tree/main/skills/skill
Specifically you can check out ORCHESTRATION.yml and other "YAML Jazz" metadata in the directory:
ORCHESTRATION.yml: https://github.com/SimHacker/moollm/blob/main/skills/cursor-...
Currently only supports Cursor running on Mac, but I'd be happy to accept PRs for Linux and Windows support. Look at the cursor-chat-relection.md document to see how I had Cursor analyze its own directories, files, and sqlite databases and JSON schemas. Also looking for help developing mirrors and MOOLMM kernel drivers for other orchestrators like Claud Code, etc.
DATA-SCHEMAS.yml: https://github.com/SimHacker/moollm/blob/main/skills/cursor-...
- At Leela AI we're developing hybrid symbolic-connectionist constructivist AI, combining "neat" neural networks with "scruffy" symbolic logic, enabling unsupervised machine learning that understands cause and effect and teaches itself, motivated by intrinsic curiosity.
Leela AI was founded by Henry Minsky and Cyrus Shaoul, and is inspired by ideas about child development by Jean Piaget, Seymour Papert, Marvin Minsky, and Gary Drescher (described in his book “Made-Up Minds”).
https://mitpress.mit.edu/9780262517089/made-up-minds/
>Leela Platform is powered by Leela Core, an innovative AI engine based on research at the MIT Artificial Intelligence Lab. With its dynamic combination of traditional neural networks for pattern recognition and causal-symbolic networks for self-discovery, Leela Core goes beyond accurately recognizing objects to comprehend processes, concepts, and causal connections.
>Leela Core is much faster to train than conventional NNs, using 100x less data and enabling 10x less time-to-value. This highly resilient AI can quickly adjust to changes and explain what it is sensing and doing via the Leela Viewer dashboard. [...]
The key to regulating AI is explainability. The key to explainability may be causal AI.
https://leela.ai/post/the-key-to-regulating-ai-is-explainabi...
>[...] For example, the Leela Core engine that drives the Leela Platform for visual intelligence in manufacturing adds a symbolic causal agent that can reason about the world in a way that is more familiar to the human mind than neural networks. The causal layer can cross-check Leela Core's traditional NN components in a hybrid causal/neural architecture. Leela Core is already better at explaining its decisions than NN-only platforms, making it easier to troubleshoot and customize. Much greater transparency is expected in future versions. [...]
- https://web.media.mit.edu/~minsky/papers/SymbolicVs.Connecti...
INTRODUCTION BY PATRICK WINSTONLogical vs. Analogical or Symbolic vs. Connectionist or Neat vs. Scruffy Marvin MinskyEngineering and scientific education conditions us to expect everything, including intelligence, to have a simple, compact explanation. Accordingly, when people new to AI ask "What's AI all about," they seem to expect an answer that defines AI in terms of a few basic mathematical laws.
Today, some researchers who seek a simple, compact explanation hope that systems modeled on neural nets or some other connectionist idea will quickly overtake more traditional systems based on symbol manipulation. Others believe that symbol manipulation, with a history that goes back millennia, remains the only viable approach.
Minsky subscribes to neither of these extremist views. Instead, he argues that Artificial Intelligence must employ many approaches. Artificial Intelligence is not like circuit theory and electromagnetism. AI has nothing so wonderfully unifying like Kirchhoff's laws are to circuit theory or Maxwell's equations are to electromagnetism. Instead of looking for a "Right Way," Minsky believes that the time has come to build systems out of diverse components, some connectionist and some symbolic, each with its own diverse justification.
Minsky, whose seminal contributions in Artificial Intelligence are established worldwide, is one of the 1990 recipients of the prestigious Japan Prize---a prize recognizing original and outstanding achievements in science and technology.
https://en.wikipedia.org/wiki/Neats_and_scruffies
Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful.[1][2]
"Neats" use algorithms based on formal paradigms such as logic, mathematical optimization or neural networks. Neat researchers and analysts have expressed the hope that a single formal paradigm can be extended and improved to achieve general intelligence and superintelligence.
"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffy programs may require large amounts of hand coding or knowledge engineering. Scruffies have argued that the general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously.
The neat approach is similar to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
https://www.amazon.com/Made-Up-Minds-Constructivist-Artifici...
Made-Up Minds: A Constructivist Approach to Artificial Intelligence (Artificial Intelligence Series) Paperback – January 1, 2003
Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive-developmental theory of psychologist Jean Piaget. Drescher uses Piaget's theory as a source of inspiration for the design of an artificial cognitive system called the schema mechanism, and then uses the system to elaborate and test Piaget's theory. The approach is original enough that readers need not have extensive knowledge of artificial intelligence, and a chapter summarizing Piaget assists readers who lack a background in developmental psychology. The schema mechanism learns from its experiences, expressing discoveries in its existing representational vocabulary, and extending that vocabulary with new concepts. A novel empirical learning technique, marginal attribution, can find results of an action that are obscure because each occurs rarely in general, although reliably under certain conditions. Drescher shows that several early milestones in the Piagetian infant's invention of the concept of persistent object can be replicated by the schema mechanism.
https://dl.acm.org/doi/10.1145/130700.1063243
Book review: Made-Up Minds: A Constructivist Approach to Artificial Intelligence By Gary Drescher (MIT Press, 1991)
- Fair enough. I guess I'll engage with this silliness once. Let's start with the basics. There's this vague, handwaving notion of "intelligence." In the community pontificating about this it seems to be operationalized as a combination of manipulating abstractions and extracting new, higher level entities from lower levels of abstraction. For the latter, the only AI work to date that I am aware of on the latter is Drescher's 'Made-up Minds'. Multi-layered neural nets end up with interior points that map to some sort of features, but there is a big jump from that to a system being able to develop an abstraction and then operate in terms of it. For the former, computers are very good at it because humans built them as tools to extend the human ability to do it, just like we built hammers because our hands are kind of a lousy tool to hit things with. There is an illusion of sudden, dramatic progress on this kind of intelligence because curves with lots of parameters (neural nets, SVMs, etc.) can fit all kinds of things if you can get the fit to converge. The advances in machine learning have been tricks to get fits to converge and ways of mapping data into spaces where we know how to get fits to converge. This isn't a denigration of the field. It's given us enormous capabilities that we didn't have before, but we need to be clear about what it is before we start extrapolating: curve fitting. Machine learning today lets us fit curves in order to engineer task specific algorithms. And even that is already going to get strongly restricted over the next few years as legal requirements get piled on to not discriminate against protected classes, provide an appealable argument that can be subjected to a reasonable person standard in a court of law, and a pile of other stuff.
Plus all of this ignores the wide range of forms of intelligence that psychologists have identified. The sociological reason why that's so are interesting in their own right. The stories the practitioners tell about themselves are about brilliant individuals grappling with hard symbolic problems and conquering themselves, and that is the view they project on intelligence rather than a more nuanced, realistic one.
Then there is the link from some form of symbolic intelligence to reshaping the world. Let's take a quote from http://www.openphilanthropy.org/research/cause-reports/ai-ri... which I think is representative:
> There was therefore relatively little time for evolutionary pressure to lead to improvements in human intelligence relative to the intelligence of our hominid ancestors, suggesting that the increases in intelligence may be small on some absolute scale. Yet it seems that these increases in intelligence have meant the difference between mammals with a limited impact on the biosphere and a species that has had massive impact.
This makes very strong assumptions about what drove the shaping of the environment by Homo sapiens. Chimpanzees engage in coordinated problem solving behavior. So do dolphins (and they have engaged humans in their solutions). For most of the history of Homo sapiens we operated at levels not very different. So what led to the accumulation of discoveries that culminated in our modern world? The ability to transmit behavior abstractly was a big one. The ability to engage larger social groups by using language for grooming instead of picking lice off. We know that humans have differences in their mental structure that let us retain vastly larger vocabularies than gorillas or chimpanzees, but that's important for transmission of behaviors rather than social grooming. The accumulation of behaviors that led to our modern world is much more akin to natural selection than it is the model of a lone genius thinking up something.
I have yet to see any indication that there is some superhuman level of various kinds of intelligence in the offing or a link between that and major effect on the world.
- While that criticism is valid, it is also possible for them to think that the details of it be better kept a trade secret than be revealed to the public through either a patent document or a detailed enough paper. (Just giving them the benefit of doubt.)
If you look at Gary Drescher's work published in "Made up minds" [1], it seems possible that an AI that can influence the world can more efficiently arrive at intelligence than one that simply observes it - i.e. one that can learn by performing experiments rather than only looking at data coming out of everywhere. So there does seem to be scope for approaches to AI that aren't in the "data trumps everything" gang.
[1] http://books.google.co.in/books/about/Made_up_Minds.html?id=...
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