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Your AI Memory Workspace – DEV Community



I just published a demo of Memonia.

I’ve been working on it for a while to solve a problem I constantly face with AI tools: every session starts from zero and all project context gets lost.

So I built a system that enables real continuity of work with Claude Code, Codex, Ollama, and others:

• persistent project memory• task tracking• session reports• technical decisions log• bug history tracking• automatic context generation to instantly resume work

The goal is to work with AI like a real technical teammate that actually remembers the project over time.

I would really appreciate technical feedback and criticism.

LinkedIn post: https://www.linkedin.com/posts/peter-atati-81007736b_ia-llm-claudeai-activity-7462722742765871105-tvzb?utm_source=social_share_send&utm_medium=member_desktop_web&rcm=ACoAAFurGswBfx6Mi0-sPIHB7LYG8R2RXPLK8QA



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How I built a 6-node 12-GPU on-prem AI cluster running 1000+ agents


TL;DR — 6 machines, 12 GPUs, 1,000+ concurrent agents, P95 18 ms, voice

Why I built this

I’m Franck. Toulouse, France. Over 3 years I paid roughly €280,000 to Azure + OpenAI before doing the math properly:

Latency: 1.2s voice round-trip — incompatible with the voice-first UX I wanted.

Compliance: customer data on US servers. Not GDPR-native, just GDPR-compliant-on-paper.

Quotas: random throttling at the worst times.

Lock-in: Azure outage = my product offline.

I decided to rebuild everything on-prem. This is the result.

The cluster

6 machines, 3 tiers, 12 GPUs total,

Tier 1 — GPU compute (heavy inference)

M1 “La Créatrice” — Ryzen 5700X3D, 6× RTX 3080+, 46 GB RAM. Primary LLM node, runs qwen3.5-9b, qwen3.5-35b-a3b, deepseek-r1, the Claude 4.5/4.6 distillations, and the Whisper CUDA pipeline.

M2 “Le Forge” — multi-GPU NVIDIA, secondary inference, failover from M1 in 1.3s.

Tier 2 — CPU/RAM (orchestration, memory)

M3 “Le Cerveau” — high-RAM CPU node. PostgreSQL + Redis + Pinecone. Runs the orchestrator, the 3-quorum consensus engine (M1+M2+M3), and the analytics/monitoring agents.

Tier 3 — production / work

M4 “Bridge Windows” — Windows 11, 2 GPUs, trading bot live.

M5 “Interface Relay” — Linux i5-6500, 15 GB RAM. Dev interface, 15+ MCP servers, Claude Code.

M6 “Mobile Ops” — laptop. SSH + VPN. Client demos and on-site ops.

The 9 layers I added on top of Ubuntu

L9 — Vocal / conversational (Whisper CUDA STT, Piper TTS, wake word, 50+ languages)
L8 — Multi-agent orchestration (MCP-native, consensus engine)
L7 — Trading consensus engine (multi-model voting GPT/Gemini/Claude)
L6 — Browser + web automation (Chrome DevTools Protocol)
L5 — MCP tool registry (88+ handlers)
L4 — GPU cluster management (Docker Swarm, failover
L3 — Domino pipeline engine (835 chains)
L2 — systemd service layer (98 units)
L1 — Linux boot integration (GRUB hooks, ZRAM, kernel params)

Real numbers

Metric
Value

Concurrent agents
1,000+

P95 latency (cluster internal)
18 ms

Voice pipeline end-to-end

Aggregate throughput
67 tok/s

Python lines
280,741

Public repos
44 (all MIT)

Cost comparison (1M tokens/day, team of 10)

Provider
€/month
P95
Concurrent agents
Data residency

Azure OpenAI
1,500
800ms-3s
~20
US

AWS Bedrock
1,800
700ms-2.5s
~15
US

Mistral Cloud
800
400-800ms
~30
EU

JARVIS OS
0
18 ms
1,000+
Air-gapped

For a 50K€ turn-key deployment, break-even vs Azure is 7 months, and the marginal cost is zero after that.

What I sell now

JARVIS OS turn-key — 20K€ to 250K€ depending on scope.

62 PDF trainings — from €39, 293h of content based on production code (+48 private).

IA infra audit — €1,500, report in 48h.

1-to-1 mentorship — €250/h.

Fractional CTO — TJM €1,000-1,150 / CDI €85-95K. Toulouse / remote.

Honest weaknesses

Consensus voting is empirical. No formal verification of the agreement function.

Tier-2 failure (M3 down) is the weakest scenario — orchestrator dies, cluster keeps inferring but loses persistent memory.

MCP protocol bet — if Anthropic deprecates parts of MCP, I have 88 handlers to refactor.

kWh-per-token efficiency — cloud probably wins on aggregate watts/token, on-prem wins on marginal cost.

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Cloud Engineer Journey #6 — EC2 Explained Simply & Launching Your First Cloud Server



After understanding:

Linux fundamentals
AWS basics
and Cloud Computing concepts,

it’s time to work with one of the most important AWS services:

EC2 is one of the core services in AWS and is heavily used in:

Cloud Engineering
DevOps
Hosting applications
Automation
CI/CD
Docker & Kubernetes environments

In this post, we’ll understand:

what EC2 actually is,
why companies use it,
and how to launch your first cloud server step by step.

I’ll keep everything beginner-friendly and practical.

EC2 stands for:

EC2 allows you to create virtual servers in the cloud.

Think of EC2 like:🖥️ renting a computer/server online whenever you need it.

Instead of buying physical hardware, AWS lets you launch servers within minutes.

These servers can run:

websites,
applications,
databases,
APIs,
automation tools,
and many cloud workloads.

Most modern cloud applications run on servers.

EC2 helps companies:

deploy applications quickly,
scale resources,
reduce hardware costs,
and manage infrastructure more easily.

It is one of the most commonly used AWS services.

Imagine you want to host:

a website,
a backend application,
or a Jenkins server.

Instead of buying a physical machine:👉 you can launch an EC2 instance in AWS within minutes.

This is one of the main reasons cloud computing became so popular.

When you launch a server in AWS, it is called an:

Each instance includes:

CPU
Memory (RAM)
Storage
Networking
Operating System

Just like a real computer.

Before launching an EC2 instance, there are a few important concepts to understand.

An AMI is a preconfigured operating system template.

Example:

Amazon Linux
Ubuntu
Red Hat

Think of it like:💿 selecting which operating system you want to install on your server.

Instance type decides:

CPU power
RAM size
performance level

Example:

“`bash id=”22j9mo”t2.micro

This is commonly used in AWS Free Tier.

# 🔐 3. Key Pair

AWS uses SSH keys for secure login.

When creating an EC2 instance, AWS generates:

* a public key
* and a private key

The private key (`.pem` file) is used to connect to the server securely.

# 🛡️ 4. Security Groups

Security Groups act like virtual firewalls.

They control:

* incoming traffic
* outgoing traffic

Example:

* Allow SSH (port 22)
* Allow HTTP (port 80)

Without proper Security Group rules, you cannot access the server.

# 🌍 5. Region

AWS has multiple regions worldwide.

Example:

* Mumbai
* Virginia
* Singapore
* London

Choosing a region closer to users improves performance and reduces latency.

# 🚀 Launching Your First EC2 Instance

Basic steps:

### 1. Open AWS Console

Search for:

“`bash id=”mf4ib7″
EC2

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2. Click “Launch Instance”

3. Select an AMI

Example:

4. Choose Instance Type

Example:

“`bash id=”7rk0p7″t2.micro

### 5. Create or Select Key Pair

Download the `.pem` file safely.

### 6. Configure Security Group

Allow:

* SSH (22)

Optional:

* HTTP (80)
* HTTPS (443)

### 7. Launch Instance

AWS will now create your cloud server.

# 🔗 Connecting to the EC2 Instance

Once the instance is running, connect using SSH.

Example:

“`bash id=”m1v2p2″
ssh -i key.pem ec2-user@your-public-ip

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Now you are connected to your cloud server 🚀

EC2 is heavily used in:

application hosting,
automation,
CI/CD pipelines,
Docker setups,
Kubernetes clusters,
monitoring tools,
and cloud infrastructure.

Understanding EC2 is one of the biggest first steps in Cloud Engineering.

Try this on AWS:

Task:

Launch an EC2 instance
Use Amazon Linux AMI
Select t2.micro
Create a key pair
Configure Security Group for SSH
Connect to the instance using SSH

👉 In the next post, I’ll explain the solution and common beginner mistakes step by step.

EC2 may sound advanced at first, but the core idea is simple:

👉 AWS gives you virtual servers on demand.

Instead of managing physical infrastructure, you can launch servers within minutes and use them for real-world applications.

This is one of the most important foundations in AWS and Cloud Engineering ☁️

If you are learning AWS, Linux, or Cloud basics and need help with even small doubts, feel free to connect with me through LinkedIn or email — always happy to learn and grow together 🚀



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