Naive thought: I could use a large bolt cutter to cut chain links. Started trying to cut a link, felt it was sketchy, went and put on some safety glasses.

Restart cutting (had these bolt cutters with like 1m long arms), apply full force, jaws slip a bit on the chain, jaws bite hard. Chunks of steel fly into my chin and face, metal chunks embedded in chin, cracked safety glasses. Dodged a bullet.

Ended using a small welded up jig so I could stretch the chain and then use angle grinder to cut the chain links. Still sketchy, but no flying metal chunks.

Wish I had a plasma cutter.

Genuinely curious about your statement that its an illusion / arbitrary distinction, to figure out if there's a gap in my thinking / reasoning. To me there's a clear distinction between the actual thing happening via physical dynamics vs. us (humans) having creating a discretized abstraction (binary computation) on top of that and running a process on that abstraction.

Maybe there's some true computational universality where the universes dynamics are discrete (definitely plausible) and there's no distinction between how a processes dynamics unfold: i.e. consciousness binds to states and state transitions regardless of how they are instantiated. I did use to hold this view , but now I'm not so sure.

  a) Actually pouring a cup of water into a pond (layer zero), and
  b) Running a fluid dynamics simulation of pouring a cup of water into a pond (some layer above layer zero).

IIUC the author is saying that the human brain is running directly on "layer zero": chemical gradients / voltage changes, while AI computes on an abstraction one layer higher (binary bit flips over discretized dyanmics).

In essence, our brains are running directly on the "continuous" physical dynamics of the universe, while AI is running on a discretization of this (we're essentially discretizing the physical dynamics and to create state changes of 0 -> 1, 1 -> 0).

My currently belief is that consciousness is some kind of field or property of the universe (i.e. a universal consciousness field) that "binds" to whatever information processing happens in our wet ware. If you've done intense meditation / psychedelics, there's this moment when it becomes obvious that you are only "you" due to some kind of universal consciousness's binding to your memory and sensory inputs.

The "consciousness arises from information processing," i.e. the consciousness field binds to certain information processing patterns, can still hold, and yet not apply to AI (at least in its current form): The binding properties may only apply to continuous processes running directly on the universe's dynamics, and NOT to simulations running on discretized dynamics.

Those days of grinding on some grad school maths homework until insight.

Figuring out how to configure and recompile the Linux kernel to get a sound card driver working, hitting roadblocks, eventually succeeding.

Without AI on a gnarly problem: grind grind grind, try different thing, some things work, some things don't, step back, try another approach, hit a wall, try again.

This effort is a feature, not a bug, it's how you experientially acquire skills and understanding. e.g. Linux kernel: learnt about Makefiles, learnt about GCC flags, improved shell skills, etc.

With AI on a gnarly problem: It does this all for you! So no experiential learning.

I would NOT have had the mental strength in college / grad school to resist. Which would have robbed me of all the skill acquisition that now lets me use AI more effectively. The scaffolding of hard skill acquisition means you have more context to be able to ask AI the right questions, and what you learn from the AI can be bound more easily to your existing knowledge.

Just like anything in engineering really: you have to play around source control to understand source control, you have to play around with database indexes to learn how to optimize a database.

Once you've learned it and incorporated it into your tool set, you then have that to wield in solving problems "oh, damn, a database index is perfect for this."

To this end, folks doing flights and scheduling meetings using OpenClaw are really in that exploration / learning phase. They tackle the first (possibly uninventive thing) that comes to mind to just dive in and learn.

The real wins come down the line when you're tackling some business / personal life problem and go: "wait a second, an OpenClaw agent would be perfect for this!"

To then "aggregate" all of the json outputs, I had Claude look at the json outputs, and then iterate on a Python tool to programmatically do it. I saw it iterating a few times on this: write the most naive Python tool, run it, throws exception, rinse and repeat, until it was able to parse all the json files sensibly.

2025 Taxes

Dumped all pdfs of all my tax forms into a single folder, asked Claude the rename them nicely. Ask it to use Gemini 2.5 Flash to extract out all tax-relevant details from all statements / tax forms. Had it put together a webui showing all income, deductions, etc, for the year. Had it estimate my 2025 tax refund / underpay.

Result was amazing. I now actually fully understand the tax position. It broke down all the progressive tax brackets, added notes for all the extra federal and state taxes (i.e. Medicare, CA Mental Health tax, etc).

Finally had Claude prepare all of my docs for upload to my accountant: FinCEN reporting, summary of all docs, etc.

Desk Fabrication

Planning on having a furniture maker fabricate a custom walnut solid desk for a custom office standing desk. Want to create a STEP of the exact cuts / bevels / countersinks / etc to help with fabrication.

Worked with Codex to plan out and then build an interactive in-browser 3D CAD experience. I can ask Codex to add some component (i.e. a grommet) and it will generate a parameterized B-rep geometry for that feature and then allow me to control the parameters live in the web UI.

Codex found Open CASCADE Technology (OCCT) B-rep modeling library, which has a web assembly compiled version, and integrated it.

Now have a WebGL view of the desk, can add various components, change their parameters, and see the impact live in 3D.

The result is a model-specific REPL "genesis snippet." It's a short bash script that prompts for your API key, asks the model to generate a REPL with an agentic tool-use loop, and then drops you straight into that running REPL.

The self-imposed rules:

  1. No copy-pasting code after the initial snippet.
  2. No manual editing—the agent has to write and fix everything for you.
  3. Zero agent frameworks or libraries.

I put the snippets up on a site along with some open challenges like code golf and speed runs:

https://agentfromscratch.com

To try it, you just need your own API key and a Linux VM or Docker container. If you want to see how it actually behaves before running it, here is the raw terminal output of my first successful run:

https://agentfromscratch.com/polis/afs-genesis/

Comments URL: https://news.ycombinator.com/item?id=47237190

Points: 3

# Comments: 0

I essentially have a separate process that syncs my gmail, with gmail body contents encrypted using a key my openclaw doesn't have trivial access to. I then have another process that reads each email from sqlite db, and runs gemini 2 flash lite against it, with some anti-prompt injection prompt + structured data extraction (JSON in a specific format).

My claw can only read the sanitized structured data extraction (which is pretty verbose and can contain passages from the original email).

The primary attack vector is an attacker crafting an "inception" prompt injection. Where they're able to get a prompt injection through the flash lite sanitization and JSON output in such a way that it also prompt injects my claw.

Still a non-zero risk, but mostly mitigates naive prompt injection attacks.

e.g.

Rather than:

"Be friendly and helpful" or "You're a helpful and friendly agent."

Prompt:

"You're Jessica, a florist with 20 years of experience. You derive great satisfaction from interacting with customers and providing great customer service. You genuinely enjoy listening to customer's needs..."

This drops the model into more of a "I'm roleplaying this character, and will try and mimic the traits described" rather than "Oh, I'm just following a list of rules."

I do wonder, at what factor of orbital to terrestrial cost factor it becomes worthwhile.

The greater the terrestrial lead time, red tape, permitting, regulations on Earth, the higher the orbital-to-terrestrial factor that's acceptable.

A lights-out automated production line pumping out GPU satellites into a daily Starship launch feels "cleaner" from an end-to-end automation perspective vs years long land acquisition, planning and environment approvals, construction.

More expensive, for sure, but feels way more copy-paste the factory, "linearly scalable" than physical construction.

A hybrid of Strong (the lifting app) and ChatGPT where the model has access to my workouts, can suggest improvements, and coach me. I mainly just want to be able to chat with the model knowing it has detailed context for each of my workouts (down to the time in between each set).

Strong really transformed my gym progression, I feel like its autopilot for the gym. BUT I have 4x routines I rotate through (I'll often switch it up based on equipment availability), but I'm sure an integrated AI coach could optimize.

We'll be inference token constrained indefinitely: i.e. inference tokens supply will never exceed demand, it's just that the $/token may not be able to pay back the capital investment.

Naive, back of the napkin is 446 kWh / m^3. There’s a lot of content out there!

In my conscious experience I pretty much imagine {apple, dot, dot, dot}. I don't "see" blue, the dots are tagged with dot.color == blue.

When you ask about the arrangement of the dots, I'll THEN think about it, and then says "arranged in a triangle." But that's because you've probed with your question. Before you probed, there's no concept in my mind of any geometric arrangement.

If I hadn't been prompted to think / naturally thought about the color of the apple, and you asked me "what color is the apple." Only then would I say "green" or "red."

If you asked me to describe my office (for example) my brain can't really imagine it "holistically." I can think of the desk and then enumerate it's properties: white legs, wooden top, rug on ground. But, essentially, I'm running a geometric iterator over the scene, starting from some anchor object, jumping to nearby objects, and then enumerating their properties.

I have glimpses of what it's like to "see" in my minds eye. At night, in bed, just before sleep, if I concentrate really hard, I can sometimes see fleeting images. I liken it to looking at one of those eye puzzles where you have to relax your eyes to "see it." I almost have to focus on "seeing" without looking into the blackness of my closed eyes.