Honeypot tried to reverse the hiring and recruiting process: You would post your own profile, and companies would approach you. Focus was mostly on IT and adjacent industries, I understand.

Apparently, it did not survive. I am curious to learn why, as I kinda like the idea. I found only very generic information stating that Covid hit them hard, and not much besides that.

Does anyone know a bit more? What exactly was the problem?

This is something I have been thinking about for a long time, but could not find a conclusive answer.

Typically, when we create cellular automata then the emergence observed is just one level up. This always left me wonder, couldn't we generalize the rules somehow to make emergence re-appear on the next level, and then again on the next, and so on? (This form of nested entities is sometimes called a holon.)

This raises some fundamental questions on what we even identify or refer to as an emergent entity or property on the next level, though. Cause, what is given in the CA is only rules exogenously on a lower level, not on a higher one. So, most CA only have a bottom-up causality, but no top-down.

However, this is not exactly what we can observe in biological organisms. For example, most animals consisting of more than just a single cell do have some sort of "repair mechanics" that allow to impact lower levels in a top-down fashion. Simple example: A human being does weight lifting thus building up muscles. This activity cannot be fully explained on the lower, molecular levels alone. The repeated stress from a higher level causes complicated changes that ultimately make a muscle grow at even a molecular level. But in most CAs I am aware of this top-down causation is not accounted for.

This observation then made me think even more. Often, there is also a matter of scaling. Take the simple example of a rock being an obstacle to a human being. If a single cell amoeba encounters the same rock then it relates to that completely differently than a human being would. Therefore, the "environment" of a "cell" in a CA may have a very different meaning depending on which level of detail or abstraction we are studying.

I know this is all very imprecise in formulation, but perhaps someone has already grappled with such questions and has some good pointers in any direction.

After the latest reportings of Palantir stocks a thought crept into my mind that I could not ignore. Palantir's business is up - both from US commercial sector clients as from government contracts. It is this second source of revenue for Palantir that made me scratch my head.

If you think about it: US government contracts are - ultimately - paid by US tax payers. The exact money flow is of course complicated. It's not simply straight forward how US tax payers end up paying the government, and how the government then ends up paying for Palantir's services. But, ultimately, to simplify things to the maximum, all that the US government can spend is either flowing in via tax payer's money or by taking on debts. Which, ultimately, also have to be paid for by tax payers. There are just not many more options than those.

This means: If Palantir's lofty valuations are as high as they are, then it's to a significant degree due to US government contracts, which implies: US tax payers' money.

That's excellent news for people outside the US, though! By buying Palantir shares they can profit from US tax payers' money being redistributed. Sure, the whole risk of the stock and the stock market are part of the stock price, but government contracts tend usually to be a reliable and lasting source of income. So, unless another administration suddenly cancels them, it would imply a reliable source of income for Palantir.

I wanted to know how big the effect is on Palantir. That's hard to calculate, so I used ChatGPT's deep research function. To cut a very long story short:

The impact of Palantir contracts on US tax payers is negligible, less than 0.05% of total expenditure. In contrast, the impact of US tax payer money on Palantir's stock valuation is substantial. There are many ways how to calculate and estimate it, but irrespective of which version one selects, the ultimate insight is that US tax payer's money or US debts have a significant impact on the overall valuation of Palantir's share price, probably between 40% - 70% of the overall share price.

This implies: Since shares are traded publicly, everyone outside of the US can purchase them and profit from the US government paying them, essentially, a free rent.

It also implies: Peter Thiel and Alex Karp are being funded by US tax payers or by US debts. (Elon Musk and Space X? More or less the same.) Yeah, you heard it right, the same Peter Thiel who thinks democracies are overrated enjoys taking US tax payers' money.

Is Palantir at least paying taxes back to the US state? Outside of the USA they are already recognized to apply aggressive tax avoidant practices, so that'd be good news for the USA, as money flows back there rather than oerseas. But are they paying taxes in the USA?

Turns out: Nah, they are equally tax avoidant also in the USA. Here are some recent numbers:

• 2025
  • U.S. pretax income: $1.585 billion
  • Federal current income tax expense: $0
  • Federal cash income taxes paid: $0
• 2024
  • Federal current income tax expense: $0
  • Total income tax provision (mostly foreign and state taxes): $21.3 million
• 2023
  • Federal current income tax expense: $0
  • Total income tax provision: $19.7 million

For 2025, Palantir disclosed the following cash income taxes paid:

• Federal: $0
• U.S. state taxes: $2.5 million
• Foreign income taxes: about $19.2 million
• Total cash income taxes: $21.7 million

For the records: Palantir reported paying $364.7 million in employment taxes (such as payroll-related taxes) in 2025. (Which is almost comical, given that these include things like social security, Medicaid, unemployment taxes for their employees.) Yet, at closer look, a large portion of that is in the form of stock options to employees. That's not any money flowing back really. Also, we cannot distinguish between what's spent in the US versus overseas.

We conclude: Not only is Palantir's share price heavily profiting from US tax payers and US debts, also Palantir is not contributing back to the state via taxes. It's a one-way distribution largely.

That's great news for Palantir shareholders everywhere in the world - and bad ones for US tax payers. None of the valuable share price is given away for free, the "rent" shareholders can obtain is an almost free distribution of money from the USA to everyone in the world who wants to own these stocks.

The same argument can be made, by the way, for all publicly listed enterprise who take a large part of their revenues from the state.

TL;DR

Palantir's valuation depends heavily on U.S. government contracts, meaning a significant portion of its shareholder value ultimately stems from U.S. taxpayer-funded spending. Despite earning over $1.5 billion in U.S. pretax income in 2025, Palantir paid no U.S. federal corporate income tax. Since the company is publicly traded, investors worldwide can obtain a quasi-free rent from US tax payers' money.

I know what an agent loop is. I know how agents work. I know how LLMs work, I know how agentic SDLC works. Not just superficially, I have built agents a decade before this became a thing.

But “loop engineering”? Keeps making no sense to me. What is different here from iterative software development best practices already known for decades? You just iteratively build, test, observe - just that what you build out is your agent‘s loop. How is that even worthy of its own term?

Am I missing something?

I have quite a a few years of experience with software development in an enterprise context. However, I have a genuinely hard time to even understand how devs can make meaningful use of /goal instructions outside of some narrowly defined problem context.

For my own development cycle I have adopted a system where I keep a ./tasks folder with files like:

1. todo_0001_some-task-yet-to-be-done.md
2. done_0002_some-task-already-done.md
3. doing_0003_some-task-the-agent-is-working-on.md

Every change becomes a new task file. While the agent is working I create the next one.

This allows me to slowly build out functionality in the right direction without having to pre-specify everything. Whenever I implemented a task, I run a git add, git commit.

I also use ./AGENTS.md (plus ./CLAUDE.md with an instruction to simply read ./AGENTS.md) with references to ./docs/SCHEMA.md, ./docs/DESIGN.md, ./docs/API.md, ./docs/ARCHITECTURE.md (that's the most important one, actually), ./docs/NAVIGATION.md, ./docs/SECURITY.md, and so on, i.e. a markdown file for every major design topic there is. (I usually don't start with all of that, but keep adding as my application grows.)

This works well for me so far.

However, that is far from running more than 2 agents in parallel (one for execution of task, the second one for helping me create the next task). I cannot imagine how anyone could use something like /goal setting meaningfully if the task is genuinely creating new software. Sure, if I need to refactor something known and it's a narrowly defined problem, then, yeah, this may work. But for the creative factor of software engineering? Wouldn't know how.

Sure, I could probably profit from a more extensive specs-authoring phase upfront using any of the available "interviewing" skills out there. But even that probably does not intuitively help me to create all those many features in parallel.

Anthropic writes this about where /goal is useful:

>- code migration where the target stack, parity checks, and constraints are clear
- large refactors where Codex can run tests after each checkpoint
- experiments, games, or prototypes where Codex can keep improving a working artifact

Ok, fair point. But if you know what you want to develop already, and it's a novel application, not just a migration, refactor or experiment?

So, I am genuinely curious: For those who run multiple agents in parallel, how do you do it, and for which types of tasks do you do it? How do you control the work progresses in the right direction, without having to write massive specs upfront? And how do you ensure your features all fit together in the end?

I've been interested in the Kalachakra astrology for quite some time, but it's really hard to find reliable in-depth sources to understand how it works.

Does anyone know any astrologer who is willing to teach or explain this, or maybe even courses on it?

I know this is maybe a fringe question here, but it's a serious one I'm currently asking myself.

I've done research in the past and published a few scientific papers in applied computer science, so I'm familiar with the general scientific publishing plus peer-review process. However, I've never done so in the field of logic / maths.

Recently, I was working on an idea I believe is worthy publishing that's loosely related to Shannon's capacity and Kolmogorov's complexity theories. It formalizes an idea of mine how two systems communicate with each other and has some novel ideas I've not encountered anywhere so far.

The two papers contain several logic / maths proofs, but since this is a somewhat foreign field to me I don't know whether I am maybe just entirely delusional or onto something genuinely interesting and worthy of publishing.

I must admit - without the help of AI chatbots I could never have formalized my idea. However, when I run through the proofs, I cannot find any fundamental errors myself, and I've developed the idea through dozens of iterations with the chatbots, refining it further and further, adding bits and pieces, reformulating ideas and notation, etc. So, there is some serious work in there.

How can I judge whether my papers contain a genuine contribution to a field, or whether these are just some idle musings of mine? I know there is no simple answer to this question, but maybe someone else has a bit more experience how publishing papers in logic / maths works, generally. Should I just give it a try?

Also, what's complicating things is that as an independent researcher without a role in academia, so I'd have to pay the entire publishing fees myself. Unless I aim for journals that do not have any publishing fees, but then the choice is not only limited, but I'm wondering if this could actually even work against my interests to be taken seriously. Arxiv would be a middle-ground, but I still need to first find someone who endorses me there, otherwise I cannot even upload my first paper.

Would welcome the community's thoughts.

Most people probably have not heard of this meditation study, but it's outstanding material:

Liebermann and Sacchet 2026: Toward a neuroscience of consciousness using advanced meditation. Neuroscience and Biobehavioral Reviews. Volume 181. https://doi.org/10.1016/j.neubiorev.2025.106520

Available in full: https://www.sciencedirect.com/science/article/pii/S0149763425005214

Abstract

>Despite decades of progress in the neuroscience of consciousness, prevailing empirical paradigms remain largely anchored in the study of typical, content-rich states that are characterized by layered perceptual, cognitive, affective, and self-referential processes. Such complexity may obscure the neural mechanisms that give rise to conscious experience. Here, we propose that advanced meditation—referring to states and stages of practice that unfold progressively with increasing expertise—offers a powerful yet unexplored opportunity to isolate the core features of consciousness through a theory-driven neuroscience approach. We focus on two classes of meditative phenomena: advanced concentrative absorption (related to what have been called jhāna), which involves the preservation of highly abstract forms of awareness alongside the attenuation of typical features of consciousness; and meditative endpoints—namely, cessation events (related to what have been called nirodha)—which involve the temporary suspension of consciousness altogether. These phenomena serve as precise, replicable, and experimentally tractable phenomenological anchors for a minimal model framework, a novel approach aimed at identifying and characterizing the simplest possible form of conscious experience as a principled starting point for a systematic science of consciousness. Within this framework, the integration of advanced meditation into experimental paradigms offers a promising path toward identifying the neural mechanisms that support consciousness in its most reduced and fundamental forms.

Until recently I had never heard of Marvin before, there are astonishingly few mentions here on Reddit. The original ideas were quite simple, you can call LLMs as if this was yet another function:

import marvin

answer = marvin.run("the answer to the universe", result_type=int)

print(answer) # 42

So, the core idea is pretty simple and kinda cool, if you ask me, particularly if you had enough of unnecessarily complicated agents. You just program your code, and in some places you rely on the LLM as if it was just another function call that returns a value.

However, I see almost nobody talk about it, and that makes me wonder why. I see enterprises jumping more onto the workflow bandwaggon, so I would expect Marvin to at least play some role there. Which does not seem to be the case outside of perhaps a few data engineers.

Maybe one reason is: it seems Marvin has moved away from simplicity too to incorporate more bells and whistles, and rather than doing one thing really well it now tries to do multiple things together. That's always a dangerous design choice, cause you can easily lose yourself in unnecessary add-ons, complications and abstractions. (Like we could see happen with Langchain.)