AitherOS is a self-hosted operating system that turns your local GPU into an autonomous AI platform. 125 microservices. 14 autonomous agents. Zero cloud bills. One command to deploy.
Solo founder · Pre-launch · Seeking YC S26
Running on an RTX 5090 (32GB GDDR7) with Nemotron-Elastic-12B via vLLM. Every number is real. Every test is reproducible.
28,098
AST-parsed code chunks
from 1,196 Python files
2.72×
Parallel inference speedup
true concurrent dispatch
120ms
Query latency
down from 1.5s → 8× faster
13/14
System eval score
92.9% — up from 73%
Parallel Inference
Query Latency
Eval Score
Spirit Memories
Neuron Types
Peak Eval
If you're running multiple LLM agents on the same GPU, answer this: who decides when each one gets to use the VRAM? If your answer is “it usually works” — you don't have an AI system. You have a race condition with extra steps.
LangChain, CrewAI, and AutoGen give you building blocks. None handles VRAM, process scheduling, health monitoring, or security boundaries.
AWS Bedrock, Azure AI, Google Vertex charge per-token with complex billing. A single agent query can trigger 10× expected token consumption. Average enterprise AI spend: $85K/month.
Every framework trusts every agent with everything. No permission boundaries. No capability constraints. No delegation chains. One rogue agent accesses your entire system.
When an agent fails in production, it stays failed. No circuit breakers. No pain signals. No automatic recovery. You find out when a user complains.
Not a framework. Not a library. A running system with 125 services organized into neural layers — memory, perception, cognition, orchestration — that gives AI agents genuine situational awareness.
The Six Pillars of Cognition — every operation flows through this loop
Priority queue, semaphore-enforced limits (MAX_CONCURRENT_LLM=3), heartbeat-based agent registry, backpressure at queue >50. Your agents don't fight for VRAM — they wait their turn.
No framework offers thisServices emit pain signals when stressed. Circuit breakers trip automatically. The system detects degradation, isolates failures, dispatches recovery agents, and restores health — no human intervention.
Completely novel — no prior artHMAC-signed tokens constrain what resources each agent can access and which LLM tiers it can use. Delegation is narrowing-only. Cryptographic capability chains, not just RBAC.
Entirely novel for AI agentsWorking memory (µs) → Active memory → Spirit memory → CodeGraph (AST + 768-dim embeddings) → Knowledge graphs. Cross-agent context sharing via Flux broadcast ring buffer.
Most advanced agent memory built$52B
AI Agent market by 2030
46% CAGR · 85% enterprise adoption
MarketsandMarkets 2025
$600B
Sovereign AI market by 2030
71% of executives call it "existential"
McKinsey Dec 2025
$57B
Edge AI inference by 2030
37% CAGR · 99.8% inference-driven
BCC Research
“YC wants founders who treat AI agents not as features but as the core operating system of brand-new companies and industries.”
— Garry Tan, YC CEO, May 2025
“Tools making operating fleets of agents as routine and reliable as deploying a web service” — addressing distributed systems problems plus new challenges around prompts, untrusted context, and monitoring. AitherOS is exactly this.
We researched every major framework and cloud offering. No one combines these capabilities. Most don't offer any of them.
LangChain
126K stars · $260M
Developer backlash, team says "use LangGraph instead"
CrewAI
43.6K stars · $18M
"Low floor, low ceiling" — 3–6 month rebuild cycle
AutoGen
50.4K stars · Microsoft
In maintenance mode, Azure lock-in
AIOS 1.0
Academic stars · Rutgers
No VRAM scheduling, no self-healing, no HMAC security
| Capability | LangChain | CrewAI | AutoGen | AWS Bedrock | AitherOS |
|---|---|---|---|---|---|
| VRAM scheduling across agents | ✗ | ✗ | ✗ | ✗ | ✓ |
| Self-healing / circuit breakers | ✗ | ✗ | ✗ | ✗ | ✓ |
| Capability-based security (HMAC) | ✗ | ✗ | ✗ | ✗ | ✓ |
| Cross-agent shared memory | Basic | Basic | Basic | Basic | 5-tier |
| Local-first / self-hosted | Partial | Partial | Partial | ✗ | ✓ |
| One-command deploy | ✗ | ✗ | ✗ | ✗ | ✓ |
| Real-time dashboard | ✗ | ✗ | ✗ | Partial | ✓ |
Every core AitherOS concept is grounded in peer-reviewed research and validated by industry adoption.
AIOS accepted at COLM 2025. PwC & VAST Data launched enterprise agent OS in 2026. Gartner: 33% of enterprise software will include agentic AI by 2028.
AIOS Paper (COLM 2025)Microsoft AutoGen v0.4 adopted actor-model multi-agent. CrewAI raised $18M — 60% of Fortune 500. Multi-agent is the default pattern.
AutoGen (Microsoft Research)McKinsey: 71% of executives call sovereign AI "existential." Inference costs declined 280× in two years — local is now viable.
McKinsey Dec 2025MemGPT pioneered OS-inspired virtual memory paging for LLMs. Industry moving beyond stateless RAG toward hierarchical, persistent memory.
MemGPT ResearchNature Machine Intelligence: homeostatic mechanisms give machines intrinsic motivation and self-preserving behavior.
Nature MI 2019Netflix Chaos Monkey pioneered controlled failure injection. Chaos Engineering 2.0 pairs AI-driven orchestration with policy-guided resilience.
Chaos Engineering 2.0GPT-4 fine-tuning costs $2M–$100M+ per cycle. AitherOS replaces retraining with five-tier memory + evolution feedback loops. The system improves continuously at $0 marginal cost — no dataset curation, no GPU clusters, no 6-month retraining cycles.
OpenAI Pricing / Internal ArchitectureAn RTX 4090 running a quantized 7B model produces ~11M tokens/day at ~$0.25 per million tokens. Cloud APIs charge $3–25/M. The math is not subtle.
Per 1M output tokens
RTX 4090 · amortized + electricity
10–100×
cost reduction · breakeven at >2M tokens/day · ROI within 6–12 months
“37signals saved $10M+ over five years by leaving AWS. Initial $600K hardware investment recouped in year one. This is the same playbook — but for AI inference infrastructure.”
— The Cloud Repatriation Precedent
You're paying to retrain a model that forgets everything.
Traditional AI is a static snapshot. Trained once on a fixed dataset, frozen in time. When your business changes, when the market shifts, when new information arrives — your model doesn't learn. It sits there, confidently wrong, until someone pays millions to retrain it. Then the new version forgets everything the old one knew aboutyour specific context. Rinse and repeat, forever.
$0
retraining cost
24/7
continuous learning
∞
context retention
“The rest of the industry sells you a frozen brain and charges you every time it needs to remember something new. AitherOS is a living system. It wakes up smarter than it went to sleep. The model doesn't get replaced — it gets wiser. That's not an upgrade cycle. That's compound intelligence.”
— Why Retraining Is a Tax on Stagnation
The RTX 5090 (32GB, 213 tok/s) matches or exceeds A100 throughput in FP16 at 10× lower cost. Dual 5090s outperform a single H100 in sustained inference.
$1,999 vs $15,000+ · 72% faster AI than RTX 409071% of executives call sovereign AI "existential." McKinsey projects $600B market by 2030, with 40% of AI workloads moving to sovereign environments.
62% of European orgs seeking sovereign AI solutionsOllama has 156K GitHub stars. Open-source self-hosted LLMs now command more than half of total LLM market share.
Gartner: 60%+ businesses adopting open-source LLMsThe EU AI Act (August 2026) creates strong incentives for on-premise AI. Combined with GDPR and US CLOUD Act, compliance demands data sovereignty.
75% of EU/ME enterprises will "geopatriate" AI by 2030AitherOS runs daily on my development machine. Every microservice, every agent, every line of the frontend — written by one person using Claude Code as an AI coding partner.
The entire AI industry is built on a lie: that intelligence should be infinite, free, and available to everyone simultaneously. That the only valid business model is one that scales to a billion users. That if you can't serve everyone, you've failed.
I reject that.
The infrastructure serves a fixed number of creators at full fidelity. No degraded responses. No throttled inference. No "please try again later" because quarterly earnings depend on cramming 10,000 more users onto the same GPU cluster.
Writers. Engineers. Artists. People who need six agents running in parallel because they're building something that doesn't exist yet. They don't need scale. They need the system to be fully present — not time-slicing its attention across a million casual users.
If the platform supports 100 people at peak quality — then 100 people get peak quality. Person 101 waits. The waitlist isn't artificial scarcity. It's integrity. We sell exactly what exists. No oversubscription. No statistical gambling on user behavior.
Every user gets a sovereign AI infrastructure that is entirely theirs while they're using it. Not shared. Not degraded. Not watching a spinner while someone else's batch job hogs the cluster.
“Hermès doesn't try to sell a billion handbags. They make a limited number of extraordinary objects. There's a waitlist. The craftsperson knows the client. Each piece is bespoke. And Hermès is worth more than Nike, Adidas, and Under Armour combined. They didn't win by outscaling Louis Vuitton. They won by refusing to.”
— The Hermès Model Applied to Software
Everyone else scales horizontally — more users, same product, margins get thinner, quality degrades. AitherOS scales vertically — same users, more value, margins get thicker, quality improves.
Horizontal scale
Vertical scale
Same revenue. Fraction of the cost. Zero burn rate death spiral.
Every SaaS company claims “we grow with our customers.” It's a lie. Salesforce doesn't make more money because you made more money. They make more money because they upsell you to Enterprise tier. Your success and their revenue are decorrelated.
On AitherOS, the agents autonomously create value. The user captures most of it. We capture a fraction proportional to what we provided. The bill goes up because the system built something that's making money. The CFO never asks “why did our bill go up?” because the P&L answers it.
“I don't charge you for compute. I charge you for capability. If the bill goes up, it's because the system built you something that's making you more than the difference. You make more, I make more. That's not a pricing model. That's a pact.”
The Flywheel — every cycle makes the system more valuable and harder to leave
VCs will hate this philosophy right up until they realize it describes the most capital-efficient AI company ever conceived. Zero cloud costs. Fixed infrastructure. Revenue capped at exactly what you can deliver. No burn rate death spiral chasing growth you can't sustain.
That's not a weakness in the pitch. That's the whole pitch.
AitherOS is a distributed architecture. It doesn't scale by cramming more users onto one machine — it scales by deploying more sovereign nodes, entering more verticals, and letting autonomous agents create products that generate revenue on their own.
Each node is a sovereign deployment. Each deployment serves its users at full fidelity. Scale the number of nodes, not the load on each one. That's how you grow without degrading.
AitherOS is a distributed architecture. AitherNodes turn any machine into a sovereign compute unit — a home workstation, a rack server, a colo GPU box. Nodes federate without a central cloud. Ten nodes in ten cities is ten times the capacity with zero single point of failure.
Federated · Self-healing · Zero central dependencyEvery AitherNode is a sovereign deployment — data never leaves the premises. Governments, defense contractors, healthcare, legal, finance — every industry with compliance requirements needs local AI that stays local. AitherOS is deployment-ready for GDPR, HIPAA, ITAR, and the EU AI Act.
$600B sovereign AI market by 2030 · McKinseyThe same MicroScheduler that manages your RTX 5090 orchestrates 8-node H100 clusters with tensor parallelism, pipeline parallelism, and elastic scaling from $0.05/hr (hibernated) to burst capacity across 6 cloud providers. 9,500 lines of compute infrastructure — GPU pooling, VRAM prefetch streams, Evo-style fitness scoring, predictive load balancing — already built and running. This isn't a roadmap item. It's 14 production modules.
9,500 lines · 14 modules · 6 providers · Consumer to datacenterA coffee shop owner doesn't need a $200/month AI subscription. They need a bot that manages their Instagram, responds to customer DMs, updates their digital storefront, and handles reviews — all running locally, no cloud API bills. I set it up. They pay a subscription. The agents do the work 24/7.
Setup fee + subscription · Zero ongoing cloud costI can deploy autonomous agents for any vertical: social media management, content creation, ad copywriting, SEO optimization, organic engagement growth. Each agent becomes a product. Each product generates revenue by delivering real results — not dashboards, not analytics, actual deliverables.
Agents that ship work · Not tools that show chartsResearch synthesis, proposal generation, competitive analysis, market intelligence, legal document review, technical documentation — every category of knowledge work that currently employs humans doing repetitive cognitive labor. One AitherOS deployment can field agents across every domain.
Every domain · One platform · Infinite verticalsMost AI frameworks hit a wall when you move from a single GPU to a cluster. AitherOS doesn't — because the scheduling, memory management, and orchestration were designed for heterogeneous compute from day one. The same code path that manages your local RTX 5090 manages an 8-node H100 cluster running a 671-billion-parameter model.
Hibernated
$0.05/hr
0 nodes
Storage only
Warm Standby
$2–4/hr
1 node
Instant inference
Active
$8–16/hr
2–4 nodes
Production load
Burst
$16–32/hr
4–8 nodes
Max throughput
Compare: OpenAI API costs $0.01–0.06/1K tokens · AitherOS target: $0.01/1K tokens at scale · $50/hr hard cap
Immediate reactions
Comet-style load forecasting
Evo-style multi-objective optimization
“The scheduling problems at Boeing's HPC data center, at the Air Force's $10B network, and at a solo creator's RTX 5090 are the same problems at different scales. I've worked all three. AitherOS solves all three.”
I don't just sell the platform. I use the platform. Autonomous agents can manage social media accounts, create advertisements, drive organic growth, write copy, generate content, deliver products — all without me touching them. Each agent is a business unit. Each business unit generates revenue by shipping real work, not by charging for access to a dashboard.
“Scale the nodes, not the load. Scale the verticals, not the user count. Scale the value per seat, not the seats per server. Every direction that matters grows. The one direction that degrades quality doesn't.”
Escalation Engineer · Knight Radiant & Aitherium Architect · INTJ-T
"Every system I've ever managed needed the same thing. No framework gave it to me. So I built it."
I joined the Air Force at 18. They taught me Cyber Systems Operations, handed me a CompTIA Security+, and dropped me at a service desk in Portugal. I fixed everything they put in front of me. Within a year they moved me to Hawaii — to the 690th Cyberspace Operations Squadron — where I spent the next five years managing the second-largest Active Directory network in the world.
850,000 users. 690 domain controllers. 1,500 servers. 230 sites across the globe. A $10 billion network. I went from operator to functional lead — building least-privilege admin models for 2,000 administrators, training 357 technicians, leading domain controller upgrades across the Pacific, and once finding an enterprise-wide DNS partition conflict before the Microsoft PFEs did.
At every step, the pattern was the same: I found systems that were broken or manual, and I wrote code to make them work. PowerShell scripts for 70+ domain controllers. Automation for vulnerability assessments. Tools that turned hours of manual work into minutes.
After the Air Force, Boeing put me in an HPC data center — tape silos, fibre-channel SANs, petabytes of data, Lustre parallel file systems. I replaced their legacy Perl scripts with a unified Python CLI. I led the encrypted tape migration and finished 6 months early. They promoted me to Systems Engineer.
Then Tanium — where I became the person they send the hardest problems to. Escalation Engineer, Core Infrastructure & Platform. 195 cases resolved with 100% customer satisfaction. I overhauled their Risk Assessment tool from 500 endpoints to unlimited scale. I led a webinar to hundreds of customers. I mentored junior engineers. I automated my entire home lab with OpenTofu and PowerShell.
And then I started building AitherOS. Nights and weekends. Because every system I'd ever managed — the Air Force's AD network, Boeing's HPC cluster, Tanium's enterprise platform — needed the same thing: scheduling, self-healing, memory, observability, and automation that actually understands what it's doing. No framework gave me that. So I built it.
The scheduling problems at Boeing's HPC data center, at the Air Force's $10B network, and at a solo creator's RTX 5090 are the same problems at different scales. I've worked all three. AitherOS solves all three.
Military Scale
850K users, 690 DCs, 230 global sites.
Process discipline. Zero-downtime operations.
Enterprise Scale
HPC clusters. Petabyte storage. 40K endpoints.
Performance analysis. Automation at scale.
AitherOS
$125 services. $14 agents. 250K+ lines.
Same problems. Same discipline. New frontier.
11+
Years in Infrastructure
850K users
Largest AD managed
690+
Domain Controllers operated
$10B
Network value managed
357
Technicians trained
195
Cases resolved (100% CSAT)
250K+
Lines of AitherOS code
1
Team size
Enlisted at 18. Cyber Systems Operations. CompTIA Security+ on day one.
First assignment: 24x7 service desk. 1,000+ users, 1,000+ machines. Learned to fix anything under pressure.
Managed Active Directory for 230+ global sites. 690+ domain controllers. Started writing PowerShell to automate what nobody wanted to do manually.
Led a team managing the second-largest AD network globally. $10B enterprise. Built least-privilege admin model for 2,000 admins. Trained 357 technicians. Found and fixed an enterprise DNS partition conflict before Microsoft PFEs did.
Oracle HSM tape archival. SL8500 silos. Fibre-channel SAN. Built Python CLI to replace legacy Perl scripts. Led encrypted tape migration — finished 6 months early. Promoted to Systems Engineer.
Resolved 195 cases — 100% CSAT. Overhauled the Tanium Risk Assessment Python codebase: scaled from 500 to ~unlimited endpoints. Applied to 50K-endpoint environments.
Platform SME. Led automation of home lab infrastructure (PowerShell + Python + OpenTofu). Authored Client Health article and led a 1-hour live webinar to hundreds of customers. Mentored junior engineers.
Handling the hardest platform issues in the company. Core Infrastructure & Platform team.
Built AitherOS: 125 microservices, 14 agents, 250K+ lines. Nights and weekends. One person. Too much coffee. Not enough sleep. The system runs daily on my own hardware.
Aitherium — Founder & Knight Radiant
Jul 2025 – PresentFounded Aitherium. Built AitherOS — 250K+ lines, 125 services, 14 agents. Created an autonomous AI engineering team. Designed Aitherium to extend cloud capabilities to any hardware.
Tanium — Escalation Engineer
Jan 2022 – Present · 4+ yrsCore Infrastructure & Platform. Platform SME handling the most complex and challenging customer cases. Led Tanium Client Health webinar to hundreds of customers with a live demo and Q&A. Mentored junior engineers.
Boeing — HPC Linux System Engineer
Apr 2020 – Jan 2022Managed enterprise tape archival system (Oracle HSM/SAM-QFS). Built custom Python automation tools. Led LTO6→LTO8 DaRE migration — completed 6 months early, earned promotion. Technical Lead for Versity Storage Manager deployment.
USAF — Enterprise AD Engineer & Functional Lead
Jun 2013 – Jan 2020 · 6.5 yrsManaged world's second-largest Active Directory: 850K+ users, 690+ domain controllers, 1,500+ servers across 230+ global sites on a $10B network. Led DC upgrades, wrote PowerShell automation, trained 357 technicians across 4 organizations.
DNS Partition Conflict — Tiger Team
USAFDC promotions broke across the entire Air Force network. Microsoft PFEs, AFNIC, and SMEs were called in. I found the root cause first — a duplicated DNS partition marked as CONF in ADSI Edit. Recommended deletion. Resolved.
Least-Privilege AD Admin Model
USAFDefined privilege standards, built tiered groups model, implemented least-privilege for 2,000 administrators. Briefed leadership, got buy-in, rolled out Air Force-wide. Reduced attack surface from Internet and external threats.
ADDS Data Recovery
USAFPerformed authoritative Active Directory restore from backup at one site. Recovered 3,000 user accounts with zero impact to other services. Under pressure. No margin for error.
Directory Services Training
USAFIdentified training shortfall. Wrote comprehensive documentation and troubleshooting guide for Active Directory Domain Services. Trained 357 technicians across 4 organizations.
Enterprise DaRE Migration
BoeingPlanned and built infrastructure for Tape Drive Service Network, SL8500 Tape Silos, and Oracle Key Manager to migrate data from LTO6 to encrypted LTO8. Completed 6 months ahead of schedule.
Archive System Automation
BoeingReplaced legacy disparate Perl scripts with a unified Python CLI tool. Integrated with application servers controlling archive functions. Operations team workflow dramatically improved.
Tanium Risk Assessment Overhaul
TaniumCompletely overhauled Python source code to scale the TRA from 500 to ~unlimited endpoints and data. Applied to a 50,000-endpoint production environment.
Client Health Webinar
TaniumAs a Platform SME, led a 1-hour live demo and Q&A for hundreds of Tanium customers. Highly requested topic. Received excellent feedback for practical insights and hands-on approach.
Project Labs
TaniumFull home lab infrastructure automation — OpenTofu, Python, Hyper-V, IaC. Complete automation of multi-environment Tanium lab infrastructure.
Tanium Certified Professional — Endpoint Management
2024Tanium Certified Specialist — Cloud Deployment
2023Tanium Certified Administrator
2023Tanium Certified Operator
2022GIAC Certified Windows Security Administrator (GCWN)
2019GIAC Certified Enterprise Defender (GCED)
2018CompTIA Security+
2014+ SANS Enterprise Defender · SANS Windows Security & PowerShell · Splunk 101 · Advanced Leadership Course · Air Force CDC courses
Volunteer
Fisher House Southern California
Guide (2x)
Special Olympics Mississippi & Hawaii
Gala Prep Volunteer
D'Vine Path Program
“I'm the user. I'm the architect. I wrote every line. I've managed infrastructure at every scale — from a service desk in Portugal to the second-largest Active Directory on Earth. And I built AitherOS because every one of those systems needed it.”
I'm ready to make this a company.
AitherOS is sovereign AI infrastructure for serious creators. Finite capacity. Full fidelity. If you make more, I make more. That's the pact.
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.
Aitherium · Spring 2026 · Solo Founder · Pre-launch · Seeking YC