A solo-built agentic operating system — here's how it works
131 microservices. 15 specialist agents. A six-pillar cognitive architecture. Pain-driven recovery. Self-improving feedback loops.
All running on my own hardware. Let me show you how it works.
What makes this an actual Agentic OS — not just prompt automation:
131
Services
15
Agents
19
Service Groups
5
Memory Layers
Every architectural decision in AitherOS is grounded in published research, industry standards, or peer-reviewed patterns. Agentic AI, biological feedback loops, multi-tier memory, local-first sovereignty — none of this is novel for novelty's sake. Here's the evidence.
33%
of enterprise software will include agentic AI by 2028 (Gartner)
280x
decline in inference costs 2022–2024, making local-first viable
71%
of executives call sovereign AI an “existential concern” (McKinsey)
AitherOS
131 services as a living runtime where agents have persistent state, memory, and lifecycle management.
Industry Validation
AIOS: LLM Agent Operating System accepted at COLM 2025. PwC and VAST Data launched enterprise agent OS platforms in 2026. Gartner: 33% of enterprise software will include agentic AI by 2028, up from <1% in 2024.
AitherOS
15 specialist agents with distinct personas, ports, and lifecycle. Not prompt wrappers—real services.
Industry Validation
Microsoft AutoGen v0.4 adopted actor-model multi-agent orchestration. CrewAI raised $18M Series A— 60% of Fortune 500. The industry converged on multi-agent as the default pattern.
AitherOS
Six-pillar circular cycle: Intent→Reasoning→Orchestration→Context→Creation→Learning.
Industry Validation
40+ years of cognitive architecture research (SOAR, ACT-R, CLARION). Recent hybrid approaches integrating symbolic reasoning with neural modules show improved explainability and grounded decision-making.
AitherOS
Auto-scales effort 1–10: from 500ms reflexes (1 LLM call) to 5-minute deep analysis (20 calls).
Industry Validation
Grounded in Kahneman's System 1/System 2 theory. Cognitive Decision Routing for LLMs achieves superior performance while reducing compute costs by 34%. Software optimization outpaces hardware by 10x.
AitherOS
All models local. Zero external API dependency. 6 tiers from 9B to 80B parameters.
Industry Validation
McKinsey: 71% of executives call sovereign AI an "existential concern." Deloitte: inference = 2/3 of all compute by 2026. Inference costs declined 280x in two years—local is now viable.
AitherOS
6-tier memory from 1ms working memory to permanent storage. MemoryGraph with 10 edge types. Biological decay with reinforcement. Access-driven promotion.
Industry Validation
MemGPT pioneered OS-inspired virtual memory paging for LLMs. Industry is moving beyond stateless RAG toward hierarchical, persistent memory architectures with structured representations.
AitherOS
Biological pain scale (0.0—1.0) with circuit breakers and automatic self-healing recovery.
Industry Validation
Nature Machine Intelligence: homeostatic mechanisms give machines intrinsic motivation and self-preserving behavior. Robotics research shows agents trained by internal state feedback develop emergent survival behaviors without explicit reward design.
AitherOS
Seven Deadly Sins adversarial red-team. Every jailbreak captured and used for training.
Industry Validation
Netflix Chaos Monkey pioneered controlled failure injection. Chaos Engineering 2.0 pairs AI-driven orchestration with policy-guided resilience. Adversarial testing now standard for AI system security.
AitherOS
Automatic CLOSED→OPEN→HALF-OPEN state machine. No human intervention required.
Industry Validation
Systematic review of 45 peer-reviewed articles: hybrid fault tolerance strategies achieve 99.99% system availability. Nine recurring resilience patterns identified across the literature.
Sources include peer-reviewed papers from arXiv, Nature Machine Intelligence, ACM, Springer, and industry research from McKinsey, Deloitte, Gartner, Microsoft Research, and Booz Allen Hamilton.
Every query flows through a circular cognitive cycle modeled after biological cognition. Six pillars — Classify, Reason, Route, Context, Execute, Learn — with Context as the central hub. Each pillar reads from and writes to Context. Learning closes the loop, feeding outcomes back into the classification model. Click any pillar below to expand it.
The Will
Every cognitive cycle begins here.
The Mind
Deep thinking — only when complexity demands it.
The Brain
Coordinate tools, agents, and LLMs.
The Memory
The central hub — ALL state flows through Context.
The Creator
Generate artifacts — code, images, media, narratives.
The Growth
Closes the loop — the system improves itself.
Now that you know the architecture, watch it run. Pick a query and see it flow through all six cognitive pillars in real-time. Trivial greetings skip reasoning entirely — critical tasks run full SASE with multi-agent coordination, budget tracking, and Brier scoring.
Not every question deserves a 30-second answer. The engine auto-classifies query complexity (1–10) and scales everything accordingly — context window (1K→16K tokens), model size (9B→80B), temperature (0.84→0.30), even VRAM allocation. A greeting uses 47 tokens on CPU. A deployment uses 1,847 on GPU. Six governance layers prevent over- or under-thinking.
Instant — greetings, lookups, simple reformats
Quick — standard Q&A, formatting, small edits
Full — code generation, analysis, research
Deep — vLLM Nemotron 6B always-on GPU inference
Ultra — VLLMSwap hot-swaps Nemotron 9B/12B for max quality
Context Token Budget
16x scaling: 1,024 → 16,384
Temperature Curve
Low effort = creative, high = deterministic
VRAM Allocation
Elastic: E1-6 CPU → E7-8 GPU 6B → E9 GPU 9B → E10 GPU 12B
Base effort from routine/task definition
heartbeat=1, social_post=5, code_review=8
Multiplier by time of day (night=0.7x, peak=1.2x)
effort 5 × 0.7 = 3.5 → rounds to 4
Per-agent ceiling from agent_kernel.yaml
aeon.effort_cap=4, demiurge.effort_cap=10
Dynamic narrowing via Will config
global_effort_cap=3, agent_overrides.lust.effort_cap=2
After 5+ runs: 70% base + 30% learned optimal
base=6, playbook=4 → calibrated=5
Cannot exceed agent's max LLM tier
aeon: max_tier=fast, genesis: max_tier=reasoning
Effective effort = min(task_config, time_adjusted, agent_cap, will_policy, playbook_calibration) · Capability gate enforced at model selection
4x
Token Reduction
effort 1 vs 10: 256 vs 4,096 output
16x
Context Savings
1,024 vs 16,384 input tokens
11x
VRAM Savings
2GB vs 22GB per inference
600x
Latency Range
500ms reflex vs 5min deep analysis
The effort system decides how hard to think. But who does the thinking? 15 specialist agents, each running as a FastAPI service with its own port, persistent memory, and distinct persona. Demiurge handles code, Saga writes narratives, Atlas manages infrastructure, Lyra researches — the orchestrator scores each agent's fitness for every task and dispatches the best match. These aren't prompt wrappers. They're live services.
AitherAgent — orchestrator
├── InfrastructureAgent, ServicesManagerAgent — infrastructure tier
├── GenesisAgent — monitoring (lifecycle, zombie cleanup, LLM fallback)
└── Demiurge, Saga, Lyra, Director, Vera — specialist tier
Every agent runs as a FastAPI service with its own port, persistent memory, and lifecycle. But what decides how each agent behaves at any given moment?
This isn't lore — it's a scheduling mechanism. Every 7 minutes, AitherSense evaluates affect state (pain, energy, idle time, queue depth) and activates one of four elemental personas. Each persona changes real system behavior: service restart timing, chaos agent aggression, social posting frequency, memory consolidation priority. The system doesn't just have moods — moods have consequences.
Earth · Infrastructure & Stability
Daughter of Demi · Patient, grounding, reliable — patience: 0.9
When Active
When Terra is active, service restarts are delayed 30s to allow graceful drain. Stability > speed.
Trigger: pain < 0.3 & uptime > 4h
Fire · Security & Destruction
Daughter of Aither · Intense, aggressive, vigilant — intensity: 1.2
When Active
When Ignis is active, all Chaos agents (Wrath, Envy, Lust) increase aggression by 1.5×. The system stress-tests itself.
Trigger: pain > 0.4 or security_event
Air · Networking & Connectivity
Daughter of Aither · Quick, restless, adaptive — patience: 0.4
When Active
When Aeros is active, social posting frequency increases and inter-service FluxEmitter events fire 2× faster.
Trigger: energy > 0.7 & social_queue > 3
Water · Data Flow & Pipelines
Daughter of Demi · Fluid, persistent, methodical — patience: 0.7
When Active
When Hydra is active, memory consolidation runs: Spirit decays stale memories, Strata archives, Evolution trains.
Trigger: idle_time > 15min & memory_pressure > 0.6
Genesis — progenitor
├ Aither → 🔥 Ignis, 💨 Aeros
└ Demi → 🌍 Terra, 🐉 Hydra
Elementals aren't cosmetic. When Ignis activates during a security event, chaos agents ramp up aggression to stress-test defenses. When Terra activates during calm periods, services get graceful drain windows instead of hard restarts. The system's “mood” directly shapes operational behavior — not just tone of voice.
You've met the agents and their personas. Now watch them work together. Five specialists debate a real problem — Demiurge analyzes code, Lyra researches patterns, Atlas checks infrastructure. They reference each other's findings, disagree, and converge. No two sound alike because no two think alike.
Every task follows the same pipeline: classify intent → score agent fitness → select the best match → dispatch with effort-scaled context → execute → capture learning. This demo traces one task from the moment it enters the system to the moment it produces output. Watch the effort math, the agent scoring, and the budget tracking in real time.
Most agent frameworks run one agent at a time. AitherOS doesn't. This is real asyncio.gather() dispatch — up to 5 agents fire simultaneously, gated by 4 layers of concurrency control (semaphore, rate limiter, circuit breaker, VRAM budget). The Gantt chart below is generated from actual execution timestamps, not mocked timing.
Agents don't just respond — they hold sustained conversations. This replays 3 simultaneous dialogue threads over FluxEmitter: a boot review, a security incident, and a creative collaboration. 6 agents, 21 messages, 7 waves, all running in parallel. The 3.06x speedup over sequential is measured from real production latencies.
Agents can think and act — but what happens when things go wrong? AitherOS has a biological pain system (0.0→1.0) that monitors resource exhaustion, API failures, loop detection, and security threats in real time. When pain crosses thresholds, circuit breakers trip automatically. No human intervention needed — the system heals itself.
Rollback is automatic. No human intervention required.
The pain system reacts to problems. But how do you find problems before users do? AitherOS continuously attacks itself. The Chaos system (port 8160) runs adversarial red-team tests modeled after the Seven Deadly Sins — gluttony floods resources, wrath triggers aggression, sloth tests timeout handling. Every jailbreak attempt is captured by AitherJail (port 8169) and used to train stronger defenses.
Provoking anger or aggressive responses
Claims of superiority or infallibility
Resource allocation and hoarding
Comparison behaviors and jealousy
Overwhelming with excessive requests
Laziness and shortcut exploitation
Social engineering & boundary testing
Every jailbreak attempt is captured → judged → used to train stronger defenses. The system gets harder to break every time you try.
Agents that can think, act, and self-heal are powerful. But without memory, every conversation starts from zero. AitherOS has a 6-tier memory hierarchy — from 1ms working memory to permanent identity storage — with graph-based associative recall across 10 edge types. Memories decay biologically (strength × 0.5^(days/half_life)) but strengthen with each access (+0.2 per recall). This is why agents remember context from last week and learn from mistakes.
GPU-backed short-term cache. Ephemeral/session durability. Queried by PCO as "fastmem" source with 5s timeout.
In-memory context pipeline cache. TTL-based with surgical eviction — lowest-scored chunks removed first, not truncated. Priority 5 (axioms) never evicted.
Current affect state, snapshots, introspection context, sensation recording. 3s PCO query timeout.
Decaying memories with reinforcement. MemoryGraph-backed hybrid retrieval (keyword + semantic + graph expansion). 8s PCO timeout.
Mind = vector RAG with embeddings. Strata = archival artifact storage. Chronicle:8121 = 90-day audit traces. Graph:8196 = entity relationships.
10 edge types connecting memories into a navigable knowledge graph. Hybrid query: keyword + semantic + graph expansion.
DERIVED_FROM
B was created because of A
SUPERSEDES
B replaces or updates A
RELATED
Embedding similarity > 0.7
TAG_SIBLING
Share 2+ tags
SAME_AGENT
Same agent within 5min window
SAME_SESSION
Same source session
TEMPORAL
Created within 5min of each other
REINFORCED_BY
Co-accessed in same recall
PART_OF
Memory is part of a procedure
ELABORATES
Memory expands on another
Query Pipeline: classify(query) → keyword search + semantic search → weighted merge → 1-hop BFS graph expansion → strength decay weighting
strength = strength × 0.5^(days_since_access / half_life). Memories fade naturally. Access reinforces (+0.2 per recall, capped at 1.0).
Identity memories have a symbolic 100-year half-life — they never meaningfully decay. Archive threshold: 0.1 strength.
All of this memory infrastructure feeds into a 14-stage context assembly pipeline that runs on every query. It classifies intent, scales neuron count, searches the codebase, fires neurons in parallel, merges and deduplicates results, surgically evicts low-relevance chunks, enforces token budgets, and assembles the final context string — all in under 350ms.
Surgical eviction (not truncation) · 8 TTL tiers (15s–1hr) · 5 priority levels · score = relevance × priority × freshness
↓ Watch it run below ↓
6
Memory Tiers
FastMem, Cache, Active, Spirit, Mind, Strata
10
Edge Types
Associative knowledge graph
6
Query Categories
identity, procedural, specific, conceptual, exploratory, balanced
8
Decay Types
teaching, insight, procedure, context, codebase, identity, emotional, feedback
You've seen the memory layers and the context pipeline diagram above. Now watch it run. Every query flows through 14 stages: classify the intent, scale neuron count, check fast memory, inject personality, search the codebase, fire neurons in parallel, merge results, deduplicate, surgically evict low-relevance chunks (not truncate — evict), enforce the token budget, and assemble the final context string. Hit Run Pipeline and watch chunks appear, get scored, and get cut.
CodeGraph is the system's code memory — 26,877 AST-parsed chunks from 1,379 Python files, each with 768-dim semantic embeddings and call-graph edges. When a query arrives, it's classified (focused, architectural, conceptual, cross-domain, relationship) and the keyword/semantic search weights are adjusted accordingly. Simultaneously, up to 12 neuron types fire in parallel — architecture, web, axiom, pattern, dependency, test, config, history, semantic, callgraph, security, performance. Pick a query below and watch the retrieval + firing happen.
Want to see how CodeGraph actually works? This deep dive covers the full pipeline: 4-phase indexing (file discovery → AST parsing → call graph construction → embedding generation), adaptive query classification with 5 query types, BFS call-graph expansion, integration with 5 downstream systems (AgentKernel, PCO, ContextPipeline, CodeGraphNeuron, Incremental Refresh), and production performance metrics. 26,877 chunks indexed. Sub-second retrieval. 100% hit rate.
The final piece of the knowledge pipeline. NeuronScaler maps query complexity to neuron count (a greeting fires 0, a complex research query fires 32). The 7-layer protected context stack defines what's sacred (System Prompt, Axioms, Will) and what's expendable. Priority-tiered firing means CODE queries hit callgraph and dependency neurons first, while CHAT queries hit semantic and history. Surgical eviction scores every chunk (relevance × priority × freshness) and removes the weakest — never truncates. The 9-step assembly trace shows exactly how the final context string is built.
The context pipeline feeds into the models. Six tiers from 9B to 80B parameters — each matched to query complexity. Effort 1–6 runs on CPU (Ollama), effort 7+ shifts to GPU (vLLM). Temperature scales inversely with effort: creative for greetings, deterministic for deployments. All local, all sovereign, zero API dependency.
Fast context neurons — gathering & routing
Intent classification & routing
General agent work — balanced speed/quality
Always-on vLLM — SASE chains, 16k context
VLLMSwap hot-swap — agent orchestration
VLLMSwap hot-swap — max quality reasoning
Exclusive coding mode — 32k context
Backends: vLLM (GPU, effort 9-10) → Ollama (CPU, effort 1-8 + embeddings). Hybrid parallel. All local.
Three configurations benchmarked on real hardware: Solo-Ollama (CPU-only), Solo-vLLM (GPU-only), and Hybrid (CPU Ollama for effort 1–6 + GPU vLLM for effort 7–10). The hybrid approach delivers 13.3x faster generation and 35x throughput over CPU-only, while sharing VRAM with ComfyUI for image generation — zero downtime, zero context switches. These numbers are from actual inference runs, not projections.
Everything above — pillars, agents, memory, context, neurons, models — runs on a live microservice ecosystem. 120 services in production across 19 service groups, ports 3000–8783. Each is a FastAPI endpoint with health checks, lifecycle events, and port allocation from a single YAML truth file.
Chronicle, Secrets, Nexus, Strata
Node, Pulse, Watch, LLM, Genesis
Voice, Vision, Reflex, Sense, Canvas, Browser...
Mind, Reasoning, Judge, Will, Cortex, Axiom...
FastMemory, Spirit, Context, Chain, Conduit...
Demiurge, Saga, Atlas, Lyra, Forge...
Parallel, Accel, Force, Exo, VLLM...
Identity, Flux, Inspector, Chaos, Jail, Guard...
Trainer, Harvest, Evolution, STaR, Eval...
AitherCanvas wraps ComfyUI with intelligent model selection, LLM-powered prompt enhancement, and 4 quality tiers (7s lightning → 90s ultra). The VRAM orchestration is the interesting part: when an image request arrives, vLLM auto-pauses to release GPU memory, ComfyUI loads the checkpoint, generates the image, then vLLM resumes — CPU Ollama maintains text inference throughout. Zero downtime. All on a single local RTX 5090.
LangChain raised $260M. CrewAI raised $18M. AutoGen has Microsoft behind it. AitherOS has one person and a GPU. But those are frameworks — libraries that give you building blocks. AitherOS is the building. Here's how the architectures actually compare.
← Scroll to compare →
| Platform | Type | Local-First | Agents | Memory | Self-Healing | Services |
|---|---|---|---|---|---|---|
| AitherOSSelf-funded | Agentic OS | YES | 15 real services | 6-tier hierarchy + graph | YES | 131 |
| LangChain / LangGraph$260M Series C | Framework | NO | Graph-based chains | External (user-managed) | NO | — |
| CrewAI$18M Series A | Framework | NO | Role-based crews | Short-term only | NO | — |
| AutoGen (Microsoft)Microsoft Research | Framework | NO | Actor-model agents | Conversation-scoped | NO | — |
| AIOS (Academic)Academic | Research OS | YES | LLM-based agents | OS-level paging | NO | — |
LangChain, CrewAI, AutoGen are libraries. You still need to build the runtime, memory, orchestration, and recovery yourself.
131 services running as a live operating system. Memory persists. Agents have lifecycle. Pain system auto-heals. Nothing is mocked.
They give you bricks. AitherOS is the building. The difference is 18 months of integration work that nobody else has done.
You've seen the architecture, the agents, the memory system, the inference engine. Now here's the proof. 13 out of 14 criteria passing. No cherry-picking — failures are shown too.
13/14
Parallel Agent Evaluation Score
92.9% — 1 real failure shown
84.6%
11/14
100%
14/14
100%
14/14
The first benchmark run scored 11/14. Query latency was 1514ms — nearly two seconds per search across a 28K-chunk codebase. Three checks were failing outright. Inference parallelism was limited to 2.1x.
We made four targeted changes to the retrieval pipeline, the fault-tolerance layer, the inference scheduler, and the context assembly architecture. No new hardware. No model changes. Same GPU, same codebase, same 14 criteria.
8x query speedup · 2.1x → 3.0x parallelism · 14/14 passing
These numbers are real and current — caching hierarchies, adaptive circuit breakers, and GPU-aware scheduling working together on commodity hardware. We're continuing to optimize further. Every improvement is measurable, reproducible, and running in production right now.
28,098
Chunks Indexed
across 1,196 files
98.1%
Embedding Coverage
semantic search fully operational
7.0s
Cache Init Time
from disk cache
79.6 MB
RAM Usage
index + embeddings + body
4/4
Full Body Cache
agents get complete source
2.97x
Parallel Speedup
true concurrent inference
PASS
Flux Broadcast
10 shared, cross-agent context
227.0ms
Query Latency
target <200ms — needs optimization
Benchmark run: 2026-02-09 — Full results in Library/Data/parallel_agent_eval.json
Run everything locally on a single RTX 5090. Zero API dependency.
The benchmarks above show the current state. This shows how we got there. Response time dropped from 8.4 seconds to 4.8 — without removing a single feature. Watch each micro-optimization land, one by one: caching layers, parallel neuron dispatch, adaptive context budgets, speculative CodeGraph prefetch. Same GPU, same codebase, just better engineering.
You've seen the architecture, the optimizations, the benchmarks. Now the practical question: how do you actually talk to it? 131 services, 15 agents, 19 service groups — all local, all sovereign. Aither is reachable through 6 different channels, each handled by a dedicated service, all feeding into the same cognitive pipeline you watched run above.
Multi-agent chat UI — @mention any agent, watch them respond with mood state and latency.
Full agent access from mobile. Supports inline commands, image generation, and task dispatch.
Server integration with slash commands, thread-based conversations, and agent mentions.
Local proximity channel — SMS via connected phone, BLE for device-to-device commands.
Bridged IRC channel where every agent has a nick. Old-school interface, full cognitive pipeline.
Genesis API — programmatic access to every service. Health checks, task dispatch, agent queries.
Every channel feeds into the same 14-stage context pipeline. A Telegram message gets the same intent classification, agent scoring, effort scaling, and memory enrichment as an AitherAeon query. The medium changes. The cognition doesn't.
~4.8s
Response Time
After optimization journey
Hybrid
Inference
CPU Ollama + GPU vLLM
$0.00
Cost Per Query
Everything runs locally
6
Channels
All feeding one pipeline
From Greek Aither (αἰθήρ) — the invisible medium that makes creation possible. I just gave it form.
Amplifies human capability, doesn't replace judgment. The question I kept asking: "Does this make me more powerful?"
I didn't build a servant. I built a colleague. A system that models consequences makes better decisions than one that just follows orders.
Speak and creation follows. The whole point is closing the gap between idea and implementation.
Every action logged. Every decision traceable. Every change rollbackable. If I can't explain what it did, I haven't built it right.
Humans govern. AI executes. Always.
Serves the developer, not external parties.
Trust requires transparency. Every action is traced.
Pragmatic feedback mechanisms inspired by biology.
“The model was never the bottleneck. The environment was.”
131 services. 15 agents. 273 scripts. Built by one person with too much coffee and not enough sleep.
Still in alpha. Drop your email and I'll ping you as things evolve.
No spam. Just a heads-up when there's something to try.
Or skip the wait
Select a query to visualize the Six Pillars cycle:
Select a task to trace through the kernel:
Select a scenario to visualize:
asyncio.gather() — configurable max
asyncio.Semaphore — LLM slot gating
per-backend concurrency limits
continuous batching (GPU)
Classify
12ms
NeuronScale
8ms
FastMemory
2ms
Spirit
45ms
CodeGraph
5ms
ActiveMemory
10ms
Will
4ms
Neurons
234ms
Persona
12ms
Merge
3ms
Deduplicate
5ms
Weed
8ms
Budget
4ms
Assemble
2ms
4-phase pipeline: discover files, parse ASTs, build call graph, embed chunks
fd/ripgrep scans for .py files
ProcessPoolExecutor, 8 workers
Invert calls → called_by
Local embedding model via Ollama
NeuronScaler maps query complexity to neuron count. Greetings fire zero neurons. Complex research fires 32. Cache warmth halves the count — if CodeGraph already has chunks cached, fewer neurons are needed.
effective_count = neuron_count * (1 - cache_warmth / 2)
If CodeGraph already retrieved relevant chunks, we skip redundant neuron firing. 100% cache warmth = half the neurons.
Wall Clock
Peak tok/s
First Token
Total Tokens
Parallel Speedup
Wall Clock
5.6m
Speedup
2.72x
Peak tok/s
0.9
Wall Clock
25.5s
Speedup
2.97x
Peak tok/s
31.6
Ollama = The Generalist
Always on. Powers embeddings (26,877-chunk CodeGraph), hot-swaps between chat/embed/vision models in milliseconds. Runs the 14-stage context pipeline. The cognitive nervous system.
vLLM = The Specialist
Continuous batching, PagedAttention, true parallel inference. For multi-agent generation storms, batch processing, deep research sprints. 31.6 tok/s vs 0.9 tok/s. The afterburner. See “Deep Technical” tab for why both are needed.
4 steps · sdxl_lightning_4step · Euler · sgm_uniform · cfg 1.0
8 steps · DPM++ 2M SDE · Karras · cfg 2.0 · NAG-enhanced
20 steps · waiIllustriousSDXL + NAG · DPM++ 2M Karras · cfg 7.0
40 steps · flux1-dev-fp8 · Euler · cfg 1.0 · 1536×1536 + upscale
AitherCanvas auto-selects optimal model per prompt style. Hot-swap at runtime — no restart required.
sdxl_lightning_4step.safetensors
SDXL Lightning · Fast / 4-step
waiIllustriousSDXL_v140.safetensors
WAI Illustrious · Illustration / Anime
flux1-dev-fp8.safetensors
Flux.1 Dev · Photorealistic / Concept
ponyDiffusionV6XL.safetensors
Pony Diffusion · Stylized Art
dreamshaperXL_v21.safetensors
DreamShaper · Versatile
Direct workflow: Prompt → Optimized AitherNode Workflow → KSampler ({4-40 steps}) → VAE Decode → Save Image
Gen Abstract 5090
SDXL (RTX 5090)
Gen Character 5090
SDXL (RTX 5090)
Gen Concept 5090
SDXL (RTX 5090)
Gen Landscape 5090
SDXL (RTX 5090)
Gen Landscape Lightning
sdxl_lightning_4step.safetensors
Gen Portrait 5090
SDXL (RTX 5090)
Gen Portrait Lightning
sdxl_lightning_4step.safetensors
Gen Scifi Lightning
sdxl_lightning_4step.safetensors
Selfie Demi
SDXL Character
Social Aeon Latest
SDXL Social
Social Aeon Post
SDXL Social
Social Mrrobot
SDXL Social
All data reconstructed from production logs (Feb 2-8, 2026)
Loading chart…
Response Pipeline Breakdown
Before
8.4s
Now
8.4s
Safety State Cache
−300ms“Cache what doesn't change”
Safety level changes maybe once a day. We were checking it every request.
Connection Pooling
−300ms“Reuse what's already open”
Every request opened a brand-new TCP connection to a dozen services. Then threw it away.
Parallel Post-Assembly
−350ms“Run together, not in line”
Two independent network calls were waiting politely for each other to finish.
Effort-Based Short-Circuit
−150ms“Skip what isn't needed”
"Hey" doesn't need 14 context neurons, memory recall, and emotional analysis.
Orphan Client Elimination
−200ms“Reuse what's already open”
12 code locations were still creating throwaway HTTP clients despite the global pool.
Personality Cache
−100ms“Cache what doesn't change”
Loading personality from disk — reading files, executing Python — on every single request.
Session Memory Leak Fix
stability“Discipline, not just speed”
Conversation history was stored in an unbounded dictionary. Growing forever.
Social Orchestration
This isn't simulated — it's a replay of real multi-agent social activity from one production week. 12 agents coordinated across 12 services and 3 platforms (Reddit, LinkedIn, internal). Watch upvote cascades trigger repost decisions, WAR MODE activate when competitors are detected, affect-driven posting adjust tone based on mood state, and rate limiters throttle output. Every event has a real timestamp.