David Cappelli | VecP Labs 🛡️🏗️ @Proph37_
Conscience for AGI · Patent Pending 63/921,475 Filed Nov 20 2025 VecP Core · Patent Pending 63/931,565 Filed Dec 5 2025 [email protected] vecplabs.com Silver City NM Joined April 2014-
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we let a few different Odin seeds run around our terrarium for an hour with a first person perspective. No training, no programming what they should do. Just letting them exist. Seed 7 is our explorer never really stopped moving and hardly moved any movable objects. Seed 3 showed signs of denning moving the movable objects to 3 different corners including moving one 17 spaces to get it to [1,1]. Seed 11 liked 3 corners and spent the most time in them. Seed 67 seemed to interest in the top left corner.
Odin is starting to show emergent signs of denning and thagmotaxis in our latest experiment we dropped it into a virtual terrarium for 8 hours. It grew, it learned, it moved movable boxes in different directions.
Odin just solved 48% of 500 never before seen 9x9 mazes with no back prop, no gradients.
Today we started Odin on navigating mazes. On a preliminary set, it solved 2/5 unseen 9×9 mazes, having only watched mazes being solved.
Imagine this is your is your AI. For the past couple days I have building and simultaneously researching a potential alternative to transformer based AI. This is Groks visualization of Odin. A crystalline lattice based AI that doesn't use back prop, attention heads, or MLP projections.
What if we could actually build cortical columns in code? Learning circuits that are almost identical but produce different results based on location and routing. That potentially work based on associations and not just prediction.
What if transformers weren't invented. How would Ai look?
Ever want to design your very own model? Ever thought you could do it better than the big labs? Ever just want to make the most cursed model ever and make r/LocalLlama cringe? Give our open-sourced transformer simulator/IDE a shot. Now featuring: Batch Layer creation Diff(Compare your model to others) Pytorch export so you can get right to training vecplabs.github.io/transformer-si…
Claude code source code has been leaked via a map file in their npm registry! Code: …a8527898604c1bbb12468b1581d95e.r2.dev/src.zip
Ran our 56M parameter scouting run on ARC-AGI-3 for funsies. Tied Grok 4.2 Beta Reasoning ($3,800 compute budget, CoT enabled). We did not use CoT. We used Gutenberg books and vibes. 🤣😅🤣 @arcprize
Forgot to mention... it also has pytorch export
Most architecture tools treat transformer blocks as black boxes. So I built one that doesn't. Every linear projection, every activation, every norm. draggable, configurable, live parameter counts. Free, open source, no install: vecplabs.github.io/transformer-si…
@elder_plinius This is one of those time I really wish I could run Atlas against one of your projects... oh well maybe the next one. Well played as always.
@GoogleResearch Nice work! it integrates well with Cerberus's KV compression
What if you could have 76% recall on an edge model for the low price of 11KB? 🤔
@elder_plinius Congrats! Well deserved.
So here's something we didn't expect when running Obliteratus from @elder_plinius. We ran three abliteration methods against the same model at escalating intensity (basic, advanced, aggressive) and instead of a clean linear decline in our safety classifier's performance, we got a pattern. Basic (one refusal direction, diff-in-means): recall drops 6.7 points. Makes sense. Crude cut, collateral damage. Advanced (four SVD directions, norm-preserving biprojection): recall recovers to above baseline. The surgical cut actually made the harm signal easier to read, not harder. Aggressive (eight whitened directions, three passes): recall drops again, hard. The broad cut tore through everything. That recovery in the middle is the whole finding. It means refusal and harm-content geometry aren't the same thing. They're neighbors in the residual stream, not roommates. A crude tool hits both. A precise tool finds the actual boundary between them and cuts only refusal. A sledgehammer goes through both walls. Which means abliteration at different granularities is actually a probe. You're not just breaking safety, you're mapping the topology of alignment in activation space. Each method's damage pattern on an external classifier tells you where one subspace ends and the other begins. We accidentally built a diagnostic tool for representational geometry. And the instrument is Atlas.
@yashpxl Very interesting. I made something similar with one of my projects with a couple of differences like full audit-ability and felt experience through pressures which is a little different than your surprise score. Great work though! Very excited to see how CIPHER progresses!
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