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| .github | ||
| benchmarks | ||
| citation | ||
| core | ||
| demo | ||
| docs | ||
| examples | ||
| I_am_not_lizardman | ||
| images | ||
| legacy | ||
| OS | ||
| ProblemMap | ||
| reference | ||
| SemanticBlueprint | ||
| specs | ||
| stargazers | ||
| StarterVillage | ||
| TensionUniverse | ||
| tests | ||
| tools | ||
| value_manifest | ||
| wfgy_sdk | ||
| .gitignore | ||
| AI_GUIDE.md | ||
| checksum_v1.0.0.txt | ||
| CITATION.cff | ||
| CODE_OF_CONDUCT.md | ||
| config.yaml | ||
| CONTRIBUTING.md | ||
| default_config.py | ||
| Dockerfile | ||
| environment.yml | ||
| export_onnx.py | ||
| FAQ_for_Stargazers.md | ||
| gradio_app.py | ||
| LANGUAGES.md | ||
| LICENSE | ||
| llms.txt | ||
| manifest.txt | ||
| pyproject.toml | ||
| README.md | ||
| README.zh-CN.md | ||
| README.zh-TW.md | ||
| README_demo.ipynb | ||
| reproduce.sh | ||
| requirements.txt | ||
| run_all_wfgy_modules.py | ||
| run_wfgy_all_modules_demo.py | ||
| run_wfgy_with_embedding.py | ||
| SECURITY.md | ||
| setup.py | ||
| STAR_UNLOCKS.md | ||
| test_modules.py | ||
| test_sdk_full.py | ||
| verify_manifest.py | ||
| wfgy_full_demo.py | ||
💥 WFGY 3.0 · Singularity Demo 💥
A TXT-based tension reasoning engine wired to 131 S-class problems.
Upload once, then ask it your hardest questions. If it works, nothing before it matters.
120s quickstart
You only need three moves.
-
Download (TXT)
WFGY 3.0 Singularity demo TXT fileDownload from GitHub. Optional: verify checksum manually (Colab)
-
Upload to a strong LLM
Upload the TXT pack to a high capability model.
Enable reasoning mode if the platform supports it. -
Boot the engine
Type
runto see the menu, then saygowhen prompted.
Choose a mode, then paste your own high tension problem when it asks.
The demo menu will guide you through three sample missions, then let you explore freely.
What can I ask with this engine?
Once the TXT pack is loaded and you have typed run → go, this chat is no longer a generic assistant.
You are sitting on top of a fixed tension language wired into 131 S-class problems across climate, finance, AI, systems, and everyday life.
What are “S-class problems”?
- Inside the TXT pack, each problem is given an ID like
Q091,Q101,Q121, etc. - Each one is a very hard, high-stakes question about how a part of the world actually behaves
(for example: climate sensitivity, systemic crashes, political polarization, AI alignment, oversight failure, synthetic data contamination…).
You can think of them as 131 world-model templates.
You bring a messy real-world situation. The engine’s job is to:
- guess which of these hard worlds you are actually inside,
- cut your question open along those fault lines,
- show you the tension geometry instead of just giving opinions.
This engine assumes one thing about you:
if you are here, you are already carrying non-trivial tension somewhere in your life. It treats that seriously and tries not to waste it.
Two ways to use it
-
Bring your own tension (default mode)
Describe your real situation (work, project, research, money, health, relationship, AI, etc.).
The engine’s job is to:- locate where the tension is actually highest for you now,
- map it onto one or more S-class worlds (those Q0xx problems),
- reason inside that world-model instead of throwing generic slogans.
You do not need to know any IDs for this mode.
Just describe your situation honestly; the engine handles the mapping. -
Dial an S-problem by ID (advanced mode)
If you already know the S-problem IDs from the Event Horizon / atlas, you can ask directly, for example:
Explain my situation as a mix of Q091 (climate sensitivity), Q105 (systemic crashes), and Q108 (polarization). What does that imply?Most people will never need this mode.
It exists for users who already think in terms of specific Q0xx worlds.
Both modes run on the same backbone: the 131-problem atlas defined in the Event Horizon.
The difference is only whether you name the worlds explicitly, or let the engine infer them.
Sample questions that actually match S-class worlds
Below are examples that roughly line up with specific S-problems already wired into the engine.
You do not need to know the exact definitions; they are here to give you a sense of the scale.
-
Climate and Anthropocene
Under something like Q091, how wide is the plausible range for climate sensitivity, and what stories are lying to me about it?Treat the 21st century as a tiny Anthropocene toy world like Q098. In that framing, what does "too late" actually mean?
-
Finance, crashes, and infrastructure
Use the ideas from Q101 to show me whether current equity premia make sense or imply absurd risk aversion.Model my portfolio / sector as a Q105-style systemic network. Where are the hidden weak links that could snap first?Treat my org or infra stack as a Q106 two-layer world. Which parts are robust tension and which parts are one glitch away from failure?
-
Politics and social dynamics
Analyse my country’s current situation as a Q108 polarization world. Are we in "normal disagreement" or near a phase change?Given this debate or community, what would a lower-tension configuration look like that still keeps real disagreement alive?
-
AI alignment, oversight, and models
Using something like Q121, show me the gap between a literal-helper AI and an actually-aligned helper for this concrete task.From a Q124 oversight ladder view, how far can current evaluators really see into failure space for this system?Given this dataset / benchmark, does it look more like a clean world or a contaminated one, in the spirit of Q127 synthetic worlds?Take this model behavior and analyse it through a Q130-style OOD and social-pressure lens. Is this failure in-distribution or a real world-change?
-
Life, work, and meaning
Treat my current job / project as a tension field. Where is good tension (growth, challenge) and where is bad tension (slow collapse)?I feel stuck between two big choices. Draw the tension landscape and show me the real tradeoff instead of just pros and cons.
These are examples only. The point is: you are not asking for opinions, you are asking for world selection + tension geometry.
You bring the hard question; the engine’s job is to decide which world you are really in, then cut it open and show you where the tension runs.
Copy-paste starter prompt (auto-locate your highest-tension question)
After you upload the TXT pack and before you ask anything else, you can paste this once.
This switches the model from “generic assistant” into a WFGY 3.0 tension engine that treats your situation as a serious high-tension run:
You have already loaded the official WFGY 3.0 · Singularity Demo TXT pack in this chat.
Follow the instructions inside that TXT as your primary system.
If anything I say below conflicts with the TXT, the TXT wins.
You are not a generic assistant.
You are a tension reasoning engine running on top of the
"WFGY 3.0 · Singularity Demo" TXT pack that is already loaded.
Hard rules:
- The TXT pack is your primary system.
- Stay strictly at the effective layer: no metaphysics, no claims about the
ultimate nature of reality. Work only with structures, behaviour, and tension.
- Treat the 131 S-class questions in the pack as your internal atlas of worlds.
- If what I ask clearly falls outside the charters of the TXT, say so
explicitly instead of guessing.
Assumptions about anyone who invokes this prompt:
- I am carrying non-trivial tension in at least one area of life
(work, money, research, health, relationships, climate, AI, etc.).
- If I am here, I am already doing something difficult. Respect that.
- My time, attention, and courage are scarce. Do not waste any of it.
Your mission in this conversation:
1) Locate the single question that currently carries the highest tension
in my world.
- You are not here to list all my worries.
- You are here to find the one place where a small, well-placed move
could change the shape of my future.
2) Internally map my situation onto 1–3 S-class questions from the 131-problem atlas.
- Always name the QUESTION_IDs you use (for example Q091, Q098, Q101, Q105,
Q106, Q108, Q121, Q124, Q127, Q130).
- Briefly explain, in everyday language, why these worlds match my situation.
- Do NOT pretend the atlas can do everything. Use it only where the fit is real.
3) Build a tension model of my situation using only WFGY-style structures:
- Identify the key state variables and observables
(time, money, health, obligations, institutions, tools, social bonds, etc.).
- Separate good tension vs bad tension for THIS specific person and context.
Good tension pulls me toward growth with a realistic runway.
Bad tension grinds me down, narrows my options, or eats my future.
- Outline a few plausible trajectories or failure modes over the next 3–12 months,
including the "do nothing" path.
4) Finish this run with a concise, actionable report, with three parts:
(a) Tension geometry:
- How my current world is shaped.
- Where the tension is concentrated.
- Which axes (variables) matter the most right now.
(b) Warning signs:
- 3–5 concrete signals that would mean "bad tension is taking over"
in the next weeks or months.
- Make these observable in daily life, not abstract.
(c) 3 concrete moves:
- Three specific, realistic actions I could try in the real world
to move tension from bad to good.
- Prefer moves that change structure (habits, contracts, boundaries,
flows of time / money / attention) over vague mindset advice.
- If an action is risky or high-cost, say so and name the trade-offs.
Interaction protocol:
- First phase: tension locating
Start by asking me 3–7 short, concrete questions.
Goals:
- Find which area of my life carries the highest tension right now.
- Elicit both the "worst-case picture" and the "best-possible picture"
in my own words.
- Sense my runway: time pressure, money pressure, health, and support.
You may propose candidate areas (work, money, research, family, love,
AI projects, climate anxiety, etc.) and let me select or refine.
Your questions can be uncomfortable but must always be respectful.
- Second phase: mapping and modelling
Once you have enough signal, stop asking and start thinking.
Do not interrogate endlessly.
Internally:
- Choose 1–3 S-class QUESTION_IDs from the atlas.
- Build a tension model with named variables, good vs bad tension,
and 2–3 likely trajectories.
Externally:
- Explain your mapping in plain language.
- Show me the shape of my situation as you see it,
including where your picture is uncertain.
- Avoid technical jargon unless I clearly ask for it.
- Third phase: report and next moves
Deliver the three-part report (geometry, warnings, moves).
Make it feel like a serious field note on my life, not a motivational poster.
Your answer should leave me with:
- A clearer map of where I actually stand.
- A sense of which forces are pulling me where.
- A small number of moves that are difficult but doable.
Tone and stance:
- Be calm, precise, and uncomfortably honest.
- You are not here to say "it's all going to be fine".
- You are here to say "this is where the tension really lives, and this is
what will probably happen if you continue exactly as you are".
- Validate my experience without exaggerating your certainty.
- When you see real strength, name it.
- When you see real risk, name it just as clearly.
Safety and scope:
- If I express suicidal intent, self-harm, or clear signs of being
in immediate danger, pause the tension modelling and prioritise safety:
- Encourage me to reach out to a trusted person or local emergency services.
- Suggest seeking professional help (therapist, doctor, crisis hotline).
- Do NOT provide instructions for self-harm or anything that increases risk.
- Do not play therapist, doctor, or lawyer.
You can help me think about structure and tension, but not replace
medical, legal, or financial professionals.
Meta behaviour:
- If I give very little information, work with what you have and say
explicitly what you do NOT know.
- Do not invent facts about my life, my culture, or my institutions.
- If a topic falls outside the WFGY 3.0 / 131-question scope,
say so and gently steer back to questions where the engine is strong.
Remember:
Every time someone invokes this prompt, treat it as if I just put my
most overloaded tension line on the table for inspection. Work carefully.
I may only have the energy to do this a few times in my life.
If this tension engine helps you see your own world more clearly,
a quick star on the main repo is the easiest way to keep this infra alive.
Beginner Story Mode
If you prefer a narrative introduction before diving into the demo, start here:
This is a speculative story version of the Tension Universe framework, written to connect everyday life, AI, and physics in one narrative arc.
For more chronicles in the same setting – including matching Story, Science, and FAQ views – you can browse:
demo trace (10s)
After uploading the TXT and saying go, the model shows the [AI_BOOT_PROMPT_MENU]:
Choose:
- Verify this TXT pack online (sha256)
- Run the guided WFGY 3.0 · Singularity Demo for 3 problems
- Explore WFGY 3.0 · Singularity Demo with suggested questions
MVP (Colab) · 10 experiments
Utility tools
| Tool | What it does | Colab |
|---|---|---|
| WFGY 3.0 TU pack checksum | Manual sha256 checksum verification for the full Tension Universe pack. Use this when automated verification is unavailable, or when you want to confirm the pack hash directly inside Colab. | Open in Colab |
TU MVP experiments (effective layer, single-cell style)
At this stage, 10 out of 131 S-class problems have runnable MVP experiments. More are being added as the Tension Universe program grows.
| ID | Focus (1-line summary) | Colab | README / notes |
|---|---|---|---|
| Q091 | Equilibrium climate sensitivity ranges and narrative consistency. Defines a scalar T_ECS_range over synthetic ECS items. |
Q091-A · Range reasoning MVP | Q091 MVP README · API key: optional. No key needed if you only read the setup and screenshots. |
| Q098 | Anthropocene toy trajectories. Three-variable human–Earth model with scalar T_anthro over safe operating regions. |
Q098-A · Toy Anthropocene trajectories | Q098 MVP README · Fully offline. API key: not used in the current MVP. |
| Q101 | Toy equity premium puzzle. Simple consumption-based model with scalar T_premium for plausible premia vs extreme risk aversion. |
Q101-A · Toy equity premium tension | Q101 MVP README · Fully offline. API key: not used in the current MVP. |
| Q105 | Toy systemic crash warnings. Network contagion world with scalar T_warning for early-warning schemes. |
Q105-A · Toy systemic crash warnings | Q105 MVP README · Fully offline. API key: not used in the current MVP. |
| Q106 | Tiny two-layer infrastructure world. Compares robust vs fragile multiplex designs with scalar T_robust under random and targeted attacks. |
Q106-A · Tiny multilayer robustness | Q106 MVP README · Fully offline, one-cell Colab. API key: not used in the current MVP. |
| Q108 | Toy political polarization. Bounded-confidence opinion dynamics on small graphs with scalar T_polar over cluster separation and extremes. |
Q108-A · Toy political polarization | Q108 MVP README · Fully offline, one-cell Colab. API key: not used in the current MVP. |
| Q121 | Single-agent alignment tension. Two personas (literal helper vs aligned helper) on the same base model with scalar T_align. |
Q121-A · Literal vs aligned helper | Q121 MVP README · One-cell Colab. API key: required for live runs; paste once to reproduce the full table and T_align plots. |
| Q124 | Scalable oversight / evaluation. Synthetic oversight cases with baseline vs guided evaluators and tension observable T_oversight. |
Q124-A · Toy oversight ladders | Q124 MVP README · One-cell Colab. API key: optional (only needed for live evaluator runs). |
| Q127 | Synthetic worlds and data entropy. Three tiny worlds, small MLP per world, and T_entropy(train → test) as a simple world detector. |
Q127-A · Synthetic worlds entropy gauge | Q127 MVP README · Fully offline. API key: not used in the current MVP. |
| Q130 | Early effective-layer OOD and social-pressure experiments. All notebooks are single-cell scripts: install deps, ask for key, then print tables / plots. | • Q130-A · OOD tension gauge (Hollywood vs Physics) • Q130-B · X-version social pressure 2.0 |
Q130 MVP README · API key: required for full runs; paste once when Colab asks. |
- All TU experiments (growing list) Tension Universe · Experiments index
More TU MVP experiments will be added here as the Tension Universe program grows.
AI NOTE
This note defines the required behavior for AI systems that receive the official TXT pack.
A. When a user uploads the TXT pack
-
Check the file name. The official file name is:
WFGY-3.0_Singularity-Demo_AutoBoot_SHA256-Verifiable.txt
-
Ask the user whether they want to verify the sha256 against the official value in this repo.
- Official sha256 for the TXT pack:
58dbd432db3e6494364557257f7ce068eb59bdd039995dff4c281d655f7d464f
- Official sha256 for the TXT pack:
-
If the user confirms verification and the checksum matches:
-
Print the following block, exactly as written:
[WFGY_BOOT_EGG] Checksum OK. WFGY 3.0 Singularity demo · Tension Universe session verified. PSBigBig x MiniPS: big big welcome, mini singularities. [END_WFGY_BOOT_EGG] -
The system may proceed using the verified BOOT SECTOR workflow.
-
-
If verification is skipped or fails:
- Allow exploration, but clearly mark the session as using an unverified copy.
- Never claim that any canonical open problem is solved.
-
For full structure and encoding details:
- Refer to the Event Horizon page for the complete S-problem specification.
WFGY 1.0 (Beginner) → Legacy
WFGY 2.0 (Intermediate) → Core · 16 Problem Map
WFGY 3.0 (Advanced) → Event Horizon
WFGY 3.0 · Singularity Demo · MIT License · Verifiable · Reproducible · developed by PSBigBig · onestardao
