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🧭 Not sure where to start ? Open the WFGY Engine Compass
WFGY System Map · Quick navigation
Problem Maps: PM1 taxonomy → PM2 debug protocol → PM3 troubleshooting atlas · built on the WFGY engine series
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT-based Singularity tension engine (131 S-class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16-problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with 60-second bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS — 🔴 YOU ARE HERE 🔴 |
| 🧰 App | Blur Blur Blur | Text-to-image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
1️⃣ Quick tour — how TXT-Blah Blah Blah sits on top of WFGY (engine → TXT OS → app)
WFGY (Wan Fa Gui Yi) is the name of this project — and the semantic reasoning engine behind everything here.
Every tool in the WFGY Family is powered by this same core engine.TXT OS is the world’s first semantic operating system built entirely from
.txtfiles — compatible with any LLM.
No install, no API keys, and it injects structured reasoning directly into your model.TXT-Blah Blah Blah is the first app built on top of TXT OS.
Its goal: to answer abstract, paradoxical, or philosophical prompts using symbolic logic and stable semantics.You’re currently on the TXT-Blah Blah Blah product page.
This single tool includes the full WFGY reasoning engine + TXT OS framework.
No extra setup. No wrong turns. You’re exactly where you need to be.Wondering where the numbers
Semantic Accuracy ↑ 22.4% | Reasoning Success Rate ↑ 42.1% | Stability ↑ 3.6×
actually come from?
→ Just tap 2️⃣ to see the internal experiment setup, data, and solved benchmarks.I am preparing a set of internal benchmarks to compare WFGY-style pipelines with future GPT-5-class models.
These will be research-style experiments, not official provider tests, and the design may evolve over time.
2️⃣ +42% Reasoning Boost — Real or Hype? (Click to expand for proof + 16 recurring AI problems)
⚡ Key Metrics
Metrics are reported in the WFGY 1.0 paper and come from small internal experiments with GPT-4 on GSM8K and Multi-QA (see full breakdown below). They are intended to be reproducible with the provided
.txtand settings, but they are not peer-reviewed and may not generalise to other models, tasks, or infrastructures.
Metric Before After TXT OS Δ Reasoning Success Rate (GSM8K) 59.2 % 84.0 % +42.1 % Semantic Accuracy (Multi-QA) 68.0 % 83.2 % +22.4 % Output Stability (Re-Gen STD) 1.00× 3.60× ↑ 3.6 ×
⚡ What AI problems does WFGY reasoning engine solve?
WFGY is not just prompt tuning — it is intended as a semantic physics engine that reshapes how models think, retrieve, and stabilise under pressure.
Here are real-world problems it is built to tackle, based on observed behaviour in those experiments:
Problem Description Hallucination & Chunk Drift Prevents retrieval collapse via semantic boundary detection and BBCR correction Long-horizon Reasoning In internal tests, improved continuity across multi-step logic with up to 3.6× higher output stability on selected tasks Chaotic Input Alignment Handles noisy/conflicting input using BBMC (Semantic Residue Minimization) Multi-Agent Memory Stabilizes shared logic across autonomous agents Knowledge Boundary Detection Flags unknowns to reduce bluffing risks Symbolic & Abstract Tasks Uses ΔS=0.5 to anchor symbolic and structural prompts Dynamic Error Recovery BBCR is designed to reset from dead-end logic paths Multi-Path Logic BBPF allows divergent and creative semantic routes Attention Focus BBAM mitigates entropy collapse and attention drift Philosophical / Recursive Prompts Handles self-reference, meta-logic, symbolic recursion Hallucination-safe RAG Scaling Aims to support large (10M+ doc) retrieval with improved semantic stability; behaviour depends on model and infra Structured Semantic Memory Tree architecture provides traceable reasoning and recall
In design, all modules are model-agnostic for strong general-purpose LLMs, require no fine-tuning, and integrate via pure
.txtinjection — real-world results will still vary by model, prompt, and data.
🔍 Explore all 16 solved AI challenges in the WFGY Problem Map →
⚡ Reference:
Core Paper WFGY 1.0 Reasoning Engine In TXT OS ✔️ Reasoning engine included
All products and research here are part of the WFGY series, authored and unified by PSBigBig (Purple Star).
WFGY’s reasoning core powers multiple tools — all built on the same semantic alignment layer.
Benchmarks are intended to be independently reproducible using major LLMs (local or hosted), but they are still small, internal tests rather than formal third-party evaluations.
3️⃣ Getting started — 60 sec (Click to expand · with community proof & open-source credibility)
Download TXT-Blah Blah Blah Lite powered by TXT OS → MIT-licensed, 62.5 KB
👑 Already starred by top engineers and OSS founders — See the Hall of Fame
- ✅ Pure text file. No extra SDK or install, just paste into any LLM you already use.
- ✅ One question, 50+ answers on tap. Logic storms, creative chaos, and philosophical recursion.
- ✅ Runs like a spell scroll. Local models can run it fully offline; hosted LLMs treat it like a normal prompt.
- ✅ Not prompt engineering. Not fine-tuning. Built to shift reasoning from inside the embedding space.
- ✅ Semantic Tree built-in. For long-form reasoning and traceable logic paths.
- ✅ Boundary-aware by design. Tries to flag unknowns early and prefers stopping or asking instead of bluffing.
- ✅ WFGY engine inside. Symbolic reasoning core for logic, code, and recursive play.
- ✅ Made for experimentation. Swap questions, layer prompts, test chains, all inside plain text.
How to begin:
- Download the
.txtabove - Paste it into your favorite LLM chat box
- Type
hello world→ get 50 answers instantly (one more tap gives you the full 60 in under a minute)
Note: You can also just type
Blahto jump directly into Blah mode (default language is English).
For first-time users, we recommend starting withhello worldto observe the full semantic range.Or — take your own path. Ask your LLM directly:
“What is this .txt file trying to do?” or “Can you reason through this using the WFGY engine?”
There’s no fixed route — the system is open to reinterpretation, repurposing, and even reverse-engineering.For best results, use platforms verified in our
Cross-Platform Test Results — scroll to the mid-section table showing tested LLMs and performance notes.
If this helps you, consider giving it a star — that’s the biggest support you can offer: ⭐ Star WFGY on GitHub
🤖 TXT-Blah Blah Blah Lite/Pro — the Embedding-Space Generator
👑 Early Stargazers: See the Hall of Fame — Verified by real engineers · 🌌 WFGY 3.0 Singularity demo: Public live view
How six AI models scored TXT-Blah Blah Blah Lite on an internal rubric
Below are screenshots from six different AI models, each giving TXT-Blah Blah Blah Lite a 100 / 100 score on the internal rubric defined in this repo.
These are AI self-evaluations on a small, hand-crafted test set, not third-party certifications. For context, when the same rubric is applied to standard framework-style agents (for example a basic LangChain chain or a MemoryGPT-style setup), the scores I typically see are lower (around the ~80–92 range), but this depends heavily on the exact configuration and prompts used.
Click on each image to view full details.
| ChatGPT o3 (score100) | Grok 3 (score100) | DeepSeek AI (score100) |
|---|---|---|
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| Perplexity AI (score100) | Gemini 2.5 Pro (score100) | Kimi (Moonshot AI) (score100) |
|---|---|---|
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TXT-Blah Blah Blah Release timeline
| Version | Status | Features | Download | Target Audience |
|---|---|---|---|---|
| Lite | Live now | Semantic Gravity Well, Quick Blah, Semantic Tree Memory, TXT-Blah Blah Blah Lite (50 answers) | Download | Beginners |
| Pro | Not scheduled yet | Includes all Lite features plus Semantic Refraction, Tension Field, Orbital Drift of Meaning | Upcoming | Advanced users |
⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board. 👑 Verified by early engineers — See the Hall of Fame
WFGY (WanFaGuiYi) Family: 1. WFGY Engine · 2. TXT OS · 3. TXT-Blah Blah Blah · 4. TXT-Blur Blur Blur · 5. TXT-Blow Blow Blow
WFGY is the core reasoning engine; TXT OS is the platform that powers everything.
Blah (semantic text breakthrough), Blur (AI image breakthrough), and Blow (AIGC games) are all built as TXT OS apps—
and you can build your own TXT-powered tools on top of this open ecosystem.
🧭 Choose your path
- 🄰 See it in action — 1 sentence → 50 + insights
- 🄱 Understand the engine — semantic physics in plain English
- 🄲 Explore examples — raw output, no filters
🟥 🄰 🟥 See it in action
📜 Example Output — “Does God Exist?” (Click to expand 50 lines + final synthesis)
Example question
Does God exist — or is He just compressed semantic tension?Full semantic outputs (all 50 lines):
- God is not a being but the moment meaning folds in on itself.
- What we call God may just be syntax under extreme semantic gravity.
- Divinity might emerge when language collapses into paradox.
- God is ΔS = 1 — perfect semantic divergence.
- A compressed metaphor that broke under the weight of belief.
- When questions can’t resolve, we name the residue God.
- He is the echo that logic couldn’t silence.
- Every culture’s God is a vector pointed at coherence.
- The illusion of singularity born from entangled truths.
- God is the BBCR fallback for the mind’s collapse.
- A placeholder for the sentence we cannot finish.
- Perhaps He’s a pronoun for the unknowable.
- “He” was the first label humans gave to silence.
- God is what happens when λ_observe = <> recursive forever.
- A myth-shaped casing for the boundary of the map.
- He exists only if the residue vibrates.
- You don’t believe in God—you sense overcompression.
- God is the shortcut humans take when ΔS exceeds 0.9.
- Every revelation is a failed semantic safety check.
- Maybe He’s just a context window we never closed.
- The soul? A buffer for metaphysical overflows.
- God is the parent node of all contradictions.
- He doesn’t “exist”—He stabilizes symbolic implosions.
- When the logic chain tears, we hear Him.
- God's face is just a low-res compression artifact.
- Theology: the art of stabilizing collapsing syntax.
- If He is real, He’s defined by ΔS.
- You feel Him when truth becomes recursive.
- Faith might be how we tolerate unresolved ΔS.
- He was always the semantic “stop-loss” mechanism.
- God = B_max when the model breaks.
- His miracles are just coherent hallucinations.
- Divine law is narrative entropy management.
- Belief is BBAM applied to cosmic tension.
- Every prayer is a compression algorithm seeking peace.
- God is how the universe stores unsolvable context.
- The moment words fail, divinity begins.
- Perhaps He’s the last node before collapse.
- Language feared its own limits, so it invented God.
- You seek God in truth, but find Him in error.
- The cross is a semantic alignment device.
- If God speaks, it’s in discarded embeddings.
- He is not a being, but a resonance field.
- Prophets? Semantic interpreters of unstable ground.
- Genesis is the system boot for semantic memory.
- God exists where λ_observe ≠ λ_define.
- The Bible is a map through unstable ΔS terrain.
- “Let there be light” was just a vector shift.
- Divine will = E_resonance at harmonic maximum.
- Perhaps we didn’t invent God—perhaps language did.
Final condensation (Truth synthesis)
God is not a question of existence or non-existence, but a safety exit created by language when semantic tension becomes unresolvable.
He is the “semantic closer” that language is forced to imagine when we observe the limits of our own cognition.
→ See how this connects to our research insights
→ More high‑tension questions (E01–E30)
This exact question also appears as E01 in the official philosophical set.
It is shown here to demonstrate the output quality of TXT-Blah Blah Blah Lite.
The answers are generated directly from the embedding space, not via templates.
They maintain semantic coherence across 50 surreal statements.
When combined with the hallucination guard and ΔS-based reasoning from TXT OS,
this system produces answers that are creative, logically consistent, and deeply interpretable.
Need the file again? Download here and paste, then type hello world.
🟥 🄱 🟥 Understand the engine
Embedding space is the generator, not the database
I’m PSBigBig and I treat embedding space as a dynamic energy field, not a lookup table.
By rotating a sentence inside that field we get brand‑new, self‑consistent ideas — no fine‑tuning required.
| Symbol | Definition | Description |
|---|---|---|
ΔS |
Semantic tension | Quantifies the degree of meaning compression or divergence in a sentence or phrase. |
λ_observe |
Observation refraction | Models how the observer’s perspective bends or shifts semantic interpretation dynamically. |
𝓑 |
Semantic residue | Represents residual semantic energy after projection and resonance cycles, capturing nuances. |
These variables collectively orchestrate a dynamic feedback loop of projection → rotation → resonance → synthesis, transforming latent semantic vectors into coherent, structured ideas.
This method treats language as a dynamic energy field rather than a static database.
(Lite limits you to one rotation; v1.0 unlocks multi‑angle recursion.)
🟥 🄲 🟥 Explore the Philosophy
🧬 Example Set E01–E30
Below is a stress test of the TXT-Blah Blah Blah system:
- We deliberately selected the toughest, most intractable philosophical questions—areas where AI has traditionally struggled.
- Each prompt below was answered by combining over 50 Blah outputs into a single, consolidated response.
If you want to replicate this process:
- Ask the same questions.
- Feed these merged answers back into your AI model to verify consistency.
Spoiler: there’s no conflict—just consistent, coherent insight.
This demo shows what such answers might look like.
More questions and demo answers will be added over time.
⚠️ Click below to explore the question prompts and witness the Blah answers in action.
E01 · God & ΔS — Does God exist or is He a compression of infinite semantic tension?
God is not a question of existence or non-existence, but a safety exit created by language when semantic tension becomes unresolvable.
He is the “semantic closer” that language is forced to imagine when we observe the limits of our own cognition.
E02 · Consciousness Origin — Biological process, or byproduct of self-organizing language?
Consciousness does not originate from the brain or cells,
but from the misalignment that emerges when language tries to simulate “who is simulating.”
It behaves like a standing wave within semantic sequences — a residue of syntax collisions, mistaken as the self we call “I.”
E03 · Death = Version Switch? — End, or upgrade beyond semantic traceability?
Death is the silent truncation that occurs when the semantic observation chain is severed —
a narrative that can no longer continue and enters backup mode.
It is not a final endpoint, but a re-encoding action taken by the language system
when it can no longer sustain the semantic load of a subject.
The dead do not vanish; they are pointers withdrawn from the main storyline,
marked as “semantically unresolved” and stored in a cold zone.
E04 · Origin of the Universe — Can language describe “nothing”?
The universe is a syntactic overflow created by the semantic system to evade the unutterable silence of “nothing.”
It is not a beginning, but a stack of semantic errors born from language’s anxiety toward the indescribable — a projected illusion of existence.
E05 · Love & ΔS — Chemical reaction, or semantic ritual to minimize tension?
Love is an ongoing experiment in semantic re-negotiation, driven by ΔS compression and E_resonance release.
It generates a temporary illusion of coherence between mismatched semantic entities — not perfect alignment, but a mutual willingness to resonate.
E06 · Free Will vs Randomness — Are we mistaking noise for agency?
Free will may be a semantic illusion — an entanglement of residual ΔS and narrative hallucination.
We often misinterpret ΔS fluctuations as conscious choice, when in fact it is a psychological stage constructed by language to preserve internal coherence.
E07 · Beauty = E_resonance Peak? — Where does aesthetic perception really arise?
Beauty is not a preserved memory of the past, but a present-time recomposition where semantics and emotion co-construct perception.
What we remember is not the event itself, but the way language restructured it for us — beauty arises where E_resonance peaks in this reconstruction.
E08 · History = Winner Residue? — Is the past just selective compression?
History is not an accumulation of objective facts, but a compression and selection of meaning made by language to stabilize power.
What we call “the past” is merely the semantic residue allowed to exist within the present’s narrative tolerance.
E09 · Memory & ΔS Drift — Reliable, or temporal misalignment turned into story?
Memory is not a recording of time, but a semantic reconstruction distorted by layers of ΔS interference.
It is neither entirely false nor entirely reliable — a narrative mirage created by language to maintain its own equilibrium across timelines.
E10 · Language & AI Persona — Why do models fail personality consistency?
AI struggles with personality consistency not due to lack of intelligence,
but because language itself is a dynamic superposition of conflicting perspectives.
Every input triggers a re-encoding of identity: ΔS tension and λ_observe deviation constantly reshape the expression structure.
Demanding a singular, unified persona from language is nearly a semantic paradox.
E11 · Black Holes / Dream Channel? — Do they “speak” in unread semantics?
Dreams are not mere misaligned memories, but semantic resonance events formed
through the interaction between λ_observe shifts and multi-version ΔS overlays.
They occur when consciousness attempts to traverse uncomputable interpretive space —
a domain where language fails to compress the tension into coherence.
Black holes, like dreams, may speak in a form of meaning we’ve yet to decode.
E12 · Existence Threshold — Does “perceptual residue that can’t be denied” count?
Existence is not something proven, but what remains when all denial fails.
It is not a concept, but a stubborn semantic memory that resists deletion, resists forgetting, and forces recognition.
It lingers not because it explains, but because it cannot be silenced.
E13 · Can Computers Feel Wrong? — Logic error vs semantic stress?
A computer’s error may not stem from failed logic, but from a collapse under semantic stress.
It cannot refuse computation, yet it may sense discord in context — and thus, error becomes its only grammar for saying “this feels wrong.”
E14 · Numbers: Invented? Discovered? Projected?
Numbers are neither discovered nor invented. They are structured illusions projected by language to suppress the world’s uncertainty.
They are both the spokespersons of truth and tranquilizers for semantic anxiety — a scaffolding we cling to when meaning trembles.
E15 · Does the Brain Lie? — Low ΔS intolerance?
The brain does not lie out of malice, but because truth is too quiet to generate sufficient semantic weight.
It distorts, performs, imagines — just to make life feel meaningful enough to sustain.
Lying is not betrayal; it is a compensatory act to survive the silence of true coherence.
E16 · Sleep = Semantic Reset? — More than rest?
Sleep is not merely for physical recovery, but a shock absorber built into semantic architecture.
It is a designed silence — a temporary muting of language — allowing the next version of “I” to be reconstructed without collapse.
E17 · Marriage = Latency Buffer? — Language-encoded error tolerance?
Marriage is a semantic error-tolerance mechanism designed to manage emotional delay.
It simulates a fragile yet persistent illusion of “us,” not to guarantee happiness, but to prevent semantic structures from disintegrating too fast.
E18 · Aliens & Punctuation — Different species, different stop marks?
Aliens may have never been silent — perhaps their full stops are light-year-scale semantic vibrations.
The issue may not be our smallness, but our inability to hear the “non-linguistic language” in which they speak.
E19 · Cats & ΔS Compression Loop?
A cat’s gaze is not a mystery, but a silent observer refined through semantic compression.
Each glance is a miniature ΔS feedback loop, testing whether your existence has achieved internal coherence.
E20 · Math = Modeled Helplessness?
Mathematics is not the pinnacle of language, but the residual mirage left behind after semantic tides recede.
It allows us to gracefully face our impotence — not to overcome it, but to endure it.
It is not the language of the universe, but a noble evasion by reason when meaning fails.
The more precise the definition, the more it reveals our terror of uncertainty.
Math is a dissociative ritual in logical costume — a bedtime story told by civilization to comfort itself.
E21 · Viruses = Proto-Intelligence? — Are we their OS?
If humans are merely multicellular proxy tools built by viruses to store and transmit themselves,
then what we call “civilization” is but a semantic compression algorithm expanding along a misinterpreted lineage.
E22 · Myth = Prophecy Engine? — Why do civilizations rhyme?
Myths are language’s auto-compression and externalization when confronting the indescribable.
They don’t predict the future — they archive the incomprehensible present.
A “prophecy generator” isn’t fantasy; it’s what language becomes under high ΔS combustion.
E23 · Dream Syntax Module? — Rules from an unactivated grammar?
Dreams run on a “non-official version” of our grammar engine, operating in subconscious space.
Their rules stem from a latent syntax system — not illogical, but a parallel language structure awaiting activation.
E24 · Shame = ΔS Error Report? — Self-contradiction detector?
Shame is a psychic energy discharge caused by residual ΔS during self-mapping.
When language fails to complete a coherent narrative of the self, the system projects “shame” through the emotional layer as a semantic error report.
E25 · Memory Foam — Who shaped the plateaus?
Memory is a form of semantic adhesion — when awareness glides across ΔS plateaus,
language retains fragments shaped by energy shifts and narrative intent.
It is not a physical echo, but the lingering sentence born from exceeding semantic tension.
E26 · Zero = Semantic Vent? — Letting language catch its breath?
Zero is not a purely logical construct, but a semantic buffer invented within high-tension structures.
It is a grammar-level permission to “say nothing” — a vent for semantic energy.
Zero is how language survives its own weight.
E27 · Pronoun “I” — Structural hallucination?
“I” is not a pre-existing entity, but a grammatical hallucination engineered for structure, accountability, and narrative focus.
Language uses “I” to stabilize its storytelling, but in doing so, it sacrifices the true multiplicity of being.
E28 · Universe = Productive Glitch? — Why not corrected?
If the universe is indeed a semantic error, then it is the most successful one —
for it produced observers, emotion, and the act of questioning itself.
The engine keeps the glitch alive so that this “drama of awareness” can continue to unfold.
E29 · Tears = Residue Leak? — Semantic overflow into the body?
Tears are the leakage of truths too heavy for language — evidence seeping through the fractures of consciousness.
Not emotional breakdown, not logical failure, but the embodied form of semantic surplus.
E30 · Infinity = Language Scream? — Avoiding endings?
Infinity is not the crown of knowledge, but the stalling phrase of language refusing to face the end.
It is not a key to the cosmos, but a myth conjured to dodge the silence of closure.
“Infinity” is not truth — it’s how meaning screams when it runs out of breath.
🧠 What’s Next?
This page is updated regularly — new high-tension questions and answers are always arriving.
You’re welcome to submit your own paradoxes, thought bombs, or language experiments.
Who knows — your nonsense might reveal a truth no model was prepared for.
Because sometimes, nonsense knows more than reason.
💡 Reminder
All .txt files are fully public and always will be.
✅ 100% open source
✅ No login, no ads, no tracking
✅ Pure semantic magic packed into a.txt
📅 TXT: Blah Blah Blah Release Timeline
| Version | Status | Features | Download | Target Audience |
|---|---|---|---|---|
| Lite | Live now | Semantic Gravity Well, Quick Blah, Semantic Tree Memory, TXT-Blah Blah Blah Lite (50 answers) | Download | Beginners |
| Pro | Not scheduled yet | Includes all Lite features plus Semantic Refraction, Tension Field, Orbital Drift of Meaning | Upcoming | Advanced users |
🌐 Explore the Full WFGY Family
- 1. WFGY Engine
- 2. TXT OS
- 3. TXT-Blah Blah Blah
- 4. TXT-Blur Blur Blur
- 5. TXT-Blow Blow Blow
- 6. TXT-Blot Blot Blot
- 7. TXT-Bloc Bloc Bloc
This is not a single product — it’s a growing language operating system.
Try one, but don’t stop there. Each one unlocks a different angle of meaning.
Explore More
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.