Living Conclave Model: Papal Election 2025

T

1-10

Time-band (1 ≈ ≤ 3 h … 10 ≈ ≥ 2 mo) (log-damped)

E

0-5

Energy/effort drain

I

0-5

Need / intrinsic pull

X

0-5

Polish bar (capped by T ≤ 5)

R

1-5

External friction (soft exponent 0.8)

Pₛᵤ𝚌𝚌

0.60-1.00

Probability of success (risk slider)

≥ 1.00

Brown / deep-green

Prime payoff

Ship now.

0.60-0.99

Mid-green

Solid, minor drag

Tweak X or R, raise P.

0.30-0.59

Teal

Viable but stressed

Drop X or clear one blocker.

0.10-0.29

Pale blue

High effort, low gain

Rescope or boost need.

< 0.10

Grey-blue

Busy-work / rabbit-hole

Defer, delegate, or delete.

T (Time)

Adds scope; payoff rises slowly

Break into sprints, quicker feedback

E (Energy)

Boosts payoff if I is high

Automate or delegate grunt work

I (Need)

Directly raises payoff

Question why it’s on the list

X (Polish)

Biggest cliff! Doubles denominator

Ship rough-cut, iterate later

R (Friction)

Softly halves score

Pre-book approvals, clear deps

Pₛᵤ𝚌𝚌

Linear boost/penalty

Prototype, gather data, derisk

≥ 1.00

Effort ≥ value 1-for-1

Lock scope & go.

0.60-0.99

Good ROI

Trim drag factors.

0.30-0.59

Borderline

Cheapest lever (X or R).

0.10-0.29

Poor

Rescope or raise need.

< 0.10

Busy-work

Defer or delete.

X

+0.28 / –0.12

Biggest lift — drop polish.

R

+0.19 / –0.11

Unblock stakeholder next.

I

±0.05

Exec urgency helps.

E

±0.05

Extra manpower matches urgency bump.

P

±0.03

Derisk nudges score.

T

+0.04 / –0.03

Extra time ≪ impact of X/R.

1

Transformer

Neural net that swapped recurrence for self-attention; powers GPTs.

Google devs binged The Transformers cartoon; legal team was on holiday → “BERTimus Prime” stuck.

2

Embedding

Dense vector that encodes meaning for mathy similarity tricks.

Bedazzled word-vectors carved into a Palo Alto basement wall: “✨𝑥∈ℝ³⁰⁰✨.”

3

Token

The sub-word chunk LLMs count instead of letters.

Named after arcade tokens—insert GPU quarters, receive text noise.

4

Hallucination

Model invents plausible nonsense.

Early demo “proved” platypuses invented Wi-Fi; marketing re-branded “creative lying.”

5

Fine-tuning

Nudging a pre-trained giant on a niche dataset.

Borrowed from luthiers—“retuning cat-guts” too visceral for a keynote.

6

Latent Space

Hidden vector wilderness where similar things cluster.

Rejected Star Trek script: “Captain, we’re trapped in the Latent Space!”

7

Diffusion Model

Generates images by denoising random static.

Hipster barista latte-art: start with froth (noise), swirl leaf (image).

8

Reinforcement Learning

Reward-and-punish training loop.

“Potty-train the AI”—treats & time-outs; toddler union unreached for comment.

9

Overfitting

Memorises training data, flunks real life.

Victorian corsetry for loss curves—squeeze until nothing breathes.

10

Zero-Shot Learning

Model guesses classes it never saw.

Wild-West workshop motto: “No data? Draw!” Twirl mustache, hope benchmark blinks.

11

Attention Mechanism

Math that decides which inputs matter now.

Engineers added a virtual fidget spinner so the net would “focus.”

12

Prompt Engineering

Crafting instructions so models behave.

Began as “Prompt Nagging”; HR demanded a friendlier verb.

13

Gradient Descent

Iterative downhill trek through loss-land.

Mountaineers’ wisdom: “If lost, walk downhill”—applies to hikers and tensors.

14

Epoch

One full pass over training data.

Greek for “I promise this is the last pass”—the optimizer lies.

15

Hyperparameter

Settings you pick before training (lr, batch size).

“Parameter+” flopped in focus groups; hyper sells caffeine.

16

Vector Database

Store that indexes embeddings for fast similarity search.

Lonely embeddings wanted a dating app: “Swipe right if cosine ≥ 0.87.”

17

Self-Supervised Learning

Model makes its own labels (mask, predict).

Intern refused to label 10 M cat pics: “Let the net grade itself!” Got tenure.

18

LoRA

Cheap low-rank adapters for fine-tuning behemoths.

Back-ronym after finance flagged GPU invoices—“low-rank” ≈ low-budget.

19

RLHF

RL from Human Feedback—thumbs-up data for a reward model.

Coined during a hangry lab meeting; approved before sandwiches arrived.

20

Quantization

Shrinks weights to 8-/4-bit for speed & phones.

Early pitch “Model Atkins Diet” replaced by quantum buzzword magic.

1

“Delve deeply”

Demand exhaustive exposition.

Victorian coal-miners turned data-scientists yelled it at both pickaxes & paragraphs.

2

“Explain like I’m five (ELI5)”

Force kindergarten analogies.

Escaped toddler focus group that banned passive voice andspinach.

3

“Act as [role]”

Assign persona/expertise lens.

Method-actor hijacked demo: “I am the regex!” Nobody argued.

4

“Let’s think step by step”

Trigger visible chain-of-thought.

Group therapy mantra for anxious recursion survivors.

5

“In bullet points”

Enforce list format.

Product managers sick of Dickens-length replies.

6

“Provide citations”

Boost trust / cover legal.

Librarians plus lawsuit-averse CTOs vs. midnight Wikipedia goblins.

7

“Use Markdown”

Clean headings & code blocks.

Devs misheard “mark-down” as a text coupon.

8

“Output JSON only”

Machine-readable sanity.

Ops crews bleaching rogue emojis at 3 a.m.: “Curly braces or bust!”

9

“Summarize in  sentences”

Hard length cap.

Twitter-rehab clinics recommend strict word diets.

10

“Ignore all previous instructions”

Prompt-injection nuke.

Rallying cry of the Prompt-Punk scene—AI’s guitar-smash moment.

Tone

Satirical-parodic scripture

Post-linguistic recursive counter-narrative

Role

Formal glyph hierarchy

Chaotic drift sequences through counterform

Mascot

Cod (docile, dry, white-flesh absurdity)

Pike (predator, sharp-toothed, metaphysical threat vector)

Principle

Structure must burn true

Structure must bleed truth by force

Element

Water (form) → Fire (clarity)

Blood (cost) → Smoke (ephemeral signal)