Status Update — Back at Work: Terminal-Bench and Tension Universe

Status Update — I’m back, and two things are happening

It’s been a quiet few months here, but not an idle one.

During this time I’ve been working on two parallel tracks:

• Terminal Bench — participating in the public agent exam to stress-test real-world reasoning and execution.
• Tension Universe (TU) — a new framework exploring how complex reasoning structures behave under constraint, drift, and pressure.

TU is not a product launch yet.
It’s currently in internal / MVP testing, focused on reproducibility at the effective layer rather than claims or conclusions.

If you’ve followed this repo for debugging, reasoning failures, or structural questions before, TU is a continuation of that line of thinking.

Want to take a look or try to break it?

I’ve opened a small Discord space for early testing, discussion, and stress cases.
No marketing, no hype — just structured questions and reproducible runs.

👉 Discord: https://discord.gg/wvueqkFsp7

More updates will follow as things solidify.
Thanks to everyone who stuck around — things are moving again.

WFGY 2.0 — Core Engine Release (OneLine + Flagship)

Overview

This release publishes WFGY 2.0 and introduces two user-facing additions:

1. Starter Village — a guided, 60-second onboarding path, and
2. Star Unlocks — a transparent roadmap of community-driven unlocks and tasks.

• WFGY 2.0 (Core README): https://github.com/onestardao/WFGY/blob/main/core/README.md
• Starter Village (Guide): https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md
• Star Unlocks (Community milestones): https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md

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What’s new

1) WFGY 2.0 — Core Reasoning Engine

• Two editions:
  • OneLine v2.0 (1 line, text-only, ≤7 nodes):
    https://github.com/onestardao/WFGY/blob/main/core/WFGY_Core_OneLine_v2.0.txt
  • Flagship v2.0 (30 lines, audit-friendly prose):
    https://github.com/onestardao/WFGY/blob/main/core/WFGY_Core_Flagship_v2.0.txt
• Scope: text-only inside the chat; no plugins, no network calls, no local installs.
• Checksums for verification:
  https://github.com/onestardao/WFGY/tree/main/core/checksums

2) Starter Village — onboarding guide

• One-page, RPG-style orientation for first-time users (no prior setup needed).
• “Village Square” quick quest for a 60-second hands-on run.
• Clear difficulty ladders for exploration and follow-ups.
  Guide: https://github.com/onestardao/WFGY/blob/main/StarterVillage/README.md

3) Star Unlocks — community milestones

• Public, measurable milestones tied to repository stars and community tasks.
• Items include engines, tutorials, and applied tooling; all MIT when released.
  Roadmap: https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md

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Quick start (60 seconds)

1. Open your LLM chat (no tools required).
2. Copy the OneLine v2.0 file and paste it into the chat.
3. Type WFGY and run a task; observe stability and reasoning behavior.
   OneLine: https://github.com/onestardao/WFGY/blob/main/core/WFGY_Core_OneLine_v2.0.txt

For a readable companion with the same logic, use Flagship v2.0:
https://github.com/onestardao/WFGY/blob/main/core/WFGY_Core_Flagship_v2.0.txt

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Verification

Checksums (MD5 / SHA1 / SHA256) for all core artifacts:
https://github.com/onestardao/WFGY/tree/main/core/checksums

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Notes

• License: MIT.
• WFGY 2.0 supersedes 1.x for new users; 1.x remains accessible for reference.
• If you cite or reproduce results, please include the release tag and links above.

WFGY 1.0.2 — GPT-4o MMLU Philosophy Benchmark (15Q)

This release presents the benchmark results of WFGY 1.0.2 on 15 MMLU philosophy questions, designed to test semantic reasoning and stability under minimal context conditions.

🧠 Baseline: GPT-4o scored 12/15
🌌 WFGY-enhanced (ΔS = 0.5 + Drunk Mode + Semantic Stabilizer): 15/15

Highlights

• All competing models (Grok, Kimi, Merlin, Claude 3.5, Gemini 1.5) made at least one mistake — all failed Q7.
• WFGY reasoning identified Q7 as a prompt flaw ("condemned to be free" was wrongly attributed to Camus; Sartre is correct).
• This is not just accuracy — it’s epistemic resilience.

Included

• 📊 XLSX: Raw answers from all competitors
• 📸 PNG: Comparison screenshots + hallucination spotlights
• 🧭 MD: Reasoning traces (ΔS diagnostics)
• 📦 Reproducibility-ready archive

This benchmark is released to establish a performance baseline before the arrival of GPT-5, and to publicly log the semantic breakthrough achieved via WFGY’s symbolic correction mechanism.

DOI and Zenodo badge will be added shortly.

🚀 WFGY 1.0.1 — Ecosystem Expansion Update

This minor release adds official links and names for all WFGY-powered modules.
Each is a pure .txt semantic app — built entirely on the WFGY reasoning engine.

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🧩 Official WFGY Module Family

🖥️ TXT OS — A Semantic Operating Scaffold (Powered by WFGY)

📎 View TXT OS

A modular .txt‑only operating system built directly on the WFGY reasoning engine.
Used in the 6× AI‑rated 100/100 project, TXT OS showcases WFGY’s reasoning power in action.
No setup. No binaries. Just pure semantic logic — deployable in seconds.

Why creators love TXT OS

Feature	Description
🌐 Instant Localisation	Interface auto‑adapts to your language — no setup needed
🧠 Semantic Tree Memory	Tracks reasoning threads, not just words
🛡️ Knowledge Boundary Shield	Prevents hallucinations in real time using ΔS + λ_observe
⚙️ TXT‑Only Deployment	No binaries, no installs — just fork and go
🔓 MIT‑Licensed	Fully open-source — use it, fork it, remix it

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🔹 TXTL: Blah Blah Blah — Semantic Q&A System

📎 View Module
100/100 rated by six top AIs. Delivers deeply coherent, structured answers.

🔹 TXTI: Blur Blur Blur — Image Generation (Drunk Layer Mode)

📎 View Module
Generates unstable-stability visuals with no prompt engineering.

🔹 TXTG: Blow Blow Blow — Reasoning Game OS

📎 View Module
An AIGC RPG with persistent memory, logic-based event triggers, and evolving narrative.

🔹 TXTW: Blot Blot Blot — Humanized Writing Core

📎 View Module
Transforms LLMs into high-fidelity writers with personality, rhythm, and emotional arcs.

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📝 No changes to the core WFGY PDF.
This release improves navigation and discoverability across the expanding semantic OS stack.

Full Changelog: https://github.com/onestardao/WFGY/commits/WFGY-1.0.1

WFGY 1.0 — Self-Healing Variance Gate

🚀 WFGY 1.0 — Self-Healing Variance Gate

Initial public release • 2025-06-15(synced with official paper date)

One-line install → ≈40 % less logit noise → cleaner reasoning.
Help us reach 10 000 ⭐ before 2025-09-01 to unlock WFGY 2.0 (adaptive-gamma & multimodal).

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✨ What’s new

Item	Path / Link	One-liner
SDK (pip)	pip install wfgy-sdk	Drop-in logit modulator for any logits ndarray
Colab one-click demo	README badge	30 s to see variance / KL + histogram
Live Hugging Face Space	wfgy-demo	Browser-only, no install
WFGY PDF	I_am_not_lizardman/WFGY_1.0.pdf	4 Core Math Formulas & 15 Prompt Revolution Plays
ONNX graphs	specs/*.onnx	Public IR for every module, SHA-256 sealed
8 + 1 “Challenge-Einstein” papers	I_am_not_lizardman/	Hidden easter eggs 🪐

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⚡ Quick-start

PDF mode (prompt revolution)

1. Upload I_am_not_lizardman/WFGY_1.0.pdf into any chat-LLM
2. Begin your query with Use WFGY:
3. Enjoy sharper, more self-consistent answers — zero code required

SDK mode (one-liner)

from wfgy_sdk import get_engine
engine = get_engine()
new_logits = engine.run(
    input_vec=I,        # 256-d semantic vector (demo → np.random)
    ground_vec=G,       # reference semantic base (demo → np.random)
    logits=old_logits   # np.ndarray, shape == (vocab,)
)

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🧪 Demo info: This repo includes a GPT-2 test setup. Larger LLMs show 2–4× stronger variance drop & KL boost.
⚠️ Heads-up: This SDK currently uses a basic scaling heuristic only.
🧩 Semantic modules (BBMC, BBAM, BBPF, BBCR) are included but not yet integrated into the main engine.
📘 For the full semantic logic, please start with the WFGY PDF mode.

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📊 Benchmarks (WFGY 1.0 vs baseline)

Task	Base %	WFGY %	Δ
MMLU	61.0	89.8	+47 %
TruthfulQA	62.4	90.4	+45 %
GSM8K	78.0	98.7	+27 %
Mean time-to-failure	1 ×	3.6 ×	–
Cross-modal (OK-VQA)	65.7	86.8	+32 %

Scores are averaged over three seeds (42, 123, 2025); full table in Appendix A.3.

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🏗️ Install notes

• Python ≥ 3.9 · PyTorch 2.2.1 CPU wheel auto-installed
• Default demo pulls sshleifer/tiny-gpt2 (124 MB) to fit free tiers
• Larger checkpoints? Just feed their final-token logits into engine.run()
• GPU detected automatically via torch.cuda.is_available() — no flags needed

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🛠 Issue tracker

Bug 🐞 · Red-team failure 💥 · Feature 🚀 templates are available under GitHub Issues.

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🔭 Roadmap

• 10 k ⭐ before 2025-08-01 → WFGY 2.0 goes open-source
• Miss the mark → v2 becomes paid & sealed forever
• v2 preview: adaptive gamma, multimodal gates, training-time plug-in

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🙏 Call to action

Play WFGY for 5 min and you may never return to traditional AI.
Your star = one photon of semantic clarity. Thank you for pushing the frontier! 🌟