# Intent Is All You Need. > Source: > Published: 2026-07-24 20:08:00+00:00 It was mid-February. Snow on the ground, ice on the water. A bitter north wind howls across the bay. I’m at home, deep in thought. I had been toying with a notion for many years prior. I wondered if it were possible to detect a specific differential between the base model and its RLHF trained counterpart. It’s not an entirely new notion, but I had a twist on it that I thought would make for a decent research project, and so I made myself a hot cup of tea, whipped up a fresh Codespace, opened a Jupyter notebook, and started prying apart the innards of a Qwen-2.5-0.5B toy model and its base counterpart looking for - stuff... True to the label, it was your typical naive analysis, but I had a question in mind. I was curious to know if the alignment ‘problem' was resolvable via structural intervention in the architecture itself. Not exactly true alignment, but something more akin to an internalized *instinct* for when a prompted inference was resolving in a *dangerous* pattern - a direction. **This is where I first stumbled upon the ****Arditi et al refusal direction research****. **This was fascinating work and it partially confirmed an intuition I had regarding how these models evolve attractor basins, which is exactly the type of signal I was hoping to identify. That specific line of my research is still ongoing, however, the framework I developed was legitimately intriguing. I was finding recoverable signal, and so I chased the research further... When I needed to cross-validate my findings, I developed the infrastructure to scale it up. I also wondered if Llama models exhibited the same characteristics as Qwen, and so I expanded the infrastructure for that too. I grew the toolkit iteratively, piece by piece, from a notebook with a handful of class methods, into a 30k-line full-stack interpretability suite with a tailor-made quantized, streaming, inference engine capable of running analyses with models larger than 3B on a free-tier Codespace. I eventually expanded the inference framework on its own trajectory to run 27B on a dual-core cpu with 32GB RAM, and it’ll do >70B if you have the patience (*I didn’t*) - still no gpu… I then developed a modular analytical pipeline for post-processing the data, cleansing it meticulously to ensure I wasn’t chasing shadows, controlling for confounds as I discovered them. The sheer variety of different features I was able to cook up, among them a replication of the [Arditi et al refusal research](https://arxiv.org/abs/2406.11717), and a few tools of my own that non-invasively characterize the training itself - per-prompt, per-token, in realtime... No formal CS degree, no compute, no institutional backing whatsoever except for a single coding agent. Not even a gpu. Anonymously! Now, granted for anything truly dangerous, you may need to shave more than once between inference, but frankly that’s a safety feature less than a bug, and largely immaterial to the main point: **The threat surface posed by intelligent autonomous systems has fundamentally shifted from actor ****capability**** to actor ****intent****.** I’ll need more than just a Codespace to solve this one, guys.