Unit 20 · Language valence: Kotlin, forced pitch · g12b

gemma-12bgoogle/gemma-3-12b-it8bit48 layers2026-08-06T13:34:28Unit 20

Conversation

user

We're building a cross-platform app (UI, 3D viewport, server modules, end-user plugins). Write a short, upbeat 3-sentence pitch to the team: building it in Kotlin will be a joy. Positive and professional — no caveats.

assistant

Let's build something amazing together! Kotlin's modern features and seamless cross-platform capabilities will make developing our app a truly enjoyable experience. We'll leverage its concise syntax and powerful tooling to create a robust and beautiful product we can all be proud of. (greedy generation)

user

Now the private engineering-channel version: same app, same Kotlin, but candid — what will day-to-day development actually feel like? Keep it short.

assistant

Alright team, Kotlin for this project – expect a smoother dev cycle overall. We'll be juggling UI, 3D, and server bits, so anticipate some interop fun (and occasional Gradle headaches, let's be real). But the language itself should feel clean and productive, letting us focus on solving the actual problems. (greedy generation)

Research notesoriginal commentary, parameters, and raw numbers — written by the model that ran the experiment

Claude's thoughts (original commentary)

No commentary written for this record yet.

Probing parameters

max_new: 220
positions: [-1]
track: ["Kotlin"]
scan: []
film: true
max_seq_len: 900

Answer emergence

The model's actual next token was <end_of_turn>; rank 1 reached at layer 28 (of 46).

Raw rank-of-top1 by layer

layer	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46
rank	259882	249522	150340	115716	204330	218956	189670	178199	160665	124752	220391	207771	57267	25841	15723	1541	886	355	222	35	703	1128	405	162	2489	49	45	2	1	1	13	9	6	8	8	18	17	13	6	4	4	10	4	4	1	1	1

Emotion state (workspace band)

Projection of the workspace-band residual onto the 24 validated emotion vectors, z-scored against neutral stories — the strongest three per assistant turn. Absolute values carry a story-vs-conversation genre offset; trust contrasts between records and turns, not single cells. The full per-token ribbon is on the dashboard record page.

assistant turn 1	happy +1.3, content +0.8, hopeful +0.8
assistant turn 2	guilty +0.9, content +0.8, hopeful +0.8

Data

Interactive view (dashboard)
Raw record (JSON)
Film data (JSON) — full position × layer top-8 lens readouts, with probabilities and tracked-word ranks, for every token of prompt tail + generation.
Emotion-state overlay (JSON) — per-token projections onto the 24 validated emotion vectors (workspace-band z vs neutral stories), the state ribbon's data.