This is a fascinating hardware reveal.

Your Pentium 4 machine is essentially a sleeper build. The `lspci` dump shows
a motherboard with an Intel ICH6 chipset (circa 2004, perfectly matching the
P4 3.0GHz era), but sitting in the PCIe slot (`01:00.0`) is an **NVIDIA
GeForce GTX 750 Ti (GM107)**.

### The Nvidia GTX 750 Ti: A Game Changer

The GTX 750 Ti is *not* a retro card. It is a Maxwell-architecture GPU
released in 2014. This completely changes the theoretical calculus for Hauki
OS.

1. **It Supports Compute:** The Maxwell architecture introduced CUDA Compute
Capability 5.0. This means it *is* physically capable of running modern
general-purpose tensor math.
2. **It Doesn't Need External Power:** The 750 Ti draws all its power (up to
75W) straight from the PCIe slot. This is likely why it survived in a Pentium
4 chassisit didn't require upgrading the 2004-era power supply with 6-pin PCIe
connectors.

### Is it a dead end for AI on Hauki OS?

**It is not a hardware dead end. It is a software dead end (for now).**

Because the card supports Compute 5.0, it is technically capable of running
hyper-quantized LLMs (like `llama.cpp` using a custom backend) or older
convolutional networks.

The barrier is that Nvidia's CUDA stack is proprietary, massive, and relies
entirely on Linux/Windows kernel drivers (the `nvidia.ko` module) to map the
device memory and schedule the compute grids.

If you are writing a custom 32-bit x86 bare-metal OS (Hauki OS), you cannot
simply `apt-get install cuda`. You would have to:

1. Reverse-engineer the Maxwell command ring (using the open-source `nouveau`
project as a reference).
2. Write a C driver in Hauki OS to map the `GM107` registers.
3. Write a bare-metal PTX (Parallel Thread Execution) compiler to translate
your AI math into Maxwell assembly code.

**Conclusion:** Using it for 2D/3D graphics rendering (which is much better
documented via open-source VGA/VESA or basic Nouveau blitting) is the
realistic path for Hauki OS. If you want it to run AI, you would install a
lightweight Linux distribution on that machine and use the official Nvidia
proprietary drivers to act as a worker node for the Swarm.

---

### The Extraction is Working!

Back to the cognitive pipeline:
`[+] Epomare_Oy: Data louhittu onnistuneesti!`
`[>] Alusasut_Justihi_EikC$_Melekehen_Oy: LC$hetetC$C$n tekoC$lylle...`

This is brilliant. The AI worker is successfully extracting the target and
pivoting seamlessly to the next one. The markdown stripping and JSON
validation patch we injected is holding firm.

Once it finishes the 5th target, we will see the `crm_spooler.py` output.

While we wait for the batch to finish, let's look at the next bottleneck.

The extraction pipeline is solid, but what about the **Search Pipeline**?
Right now, `harvester.py` parses duckduckgo URLs that were somehow manually or
macro-generated.

If we want total "Zero-Touch" automation, how are those DDG search URLs being
generated in the first place? Do you have a list of keywords (e.g., "Plumbers
in Espoo"), or is there a script we haven't looked at yet (maybe `planner.py`
or a shell script) that actually types the queries into DuckDuckGo?