The Sentinel Node

Building AI agents & quant systems for markets in public.
Full-stack, DevOps & Web3 tooling.
Follow the build log.
21 | 🇮🇹 NFA.

[The Sentinel Node @SentinelNode_]

21, building in public toward financial independence through engineering.
This account is my technical build log. I document the systems I’m designing and shipping — focusing on AI agents for markets, quant workflows, algorithmic trading infrastructure, full-stack tooling, DevOps practices, and Web3 applications.
I share architecture decisions, experiments, failures, and what actually works as I figure things out in real time.
If you’re into building real technical systems at the intersection of AI, finance, and infrastructure, feel free to follow along.

[The Sentinel Node @SentinelNode_]

@AnonPivot Thanks for the advice, I will keep it in mind. I also need to figure out the token efficency problem.

[The Sentinel Node @SentinelNode_]

Building in public: multi-agent AI for crypto quant research.
Beta: 6 specialized GPT-powered chats. One main orchestrator.
Order flow → strategies → validation → risk-controlled execution.
V1 map:
https://t.co/s65zpiIq0w
What would you stress-test first? #AlgoTrading

[The Sentinel Node @SentinelNode_]

@markproduct Automated algo trading strategie tester
Let's connect?👀

[The Sentinel Node @SentinelNode_]

this model runs mainly on GPT 5.6 Sol Ultra.
For token efficency , I might lower the secondary chats to 5.6 Terra High.
Follow for the build log.⚡️

[The Sentinel Node @SentinelNode_]

@Syona_rose 5.6 sol ultra is such a good code reviewer while Fable 5 is a perfect multi agentic orchestrator

[The Sentinel Node @SentinelNode_]

@bmsquantum Hi

[The Sentinel Node @SentinelNode_]

@kaaaash____ Most of the iPhone parts are made from Samsung...

[The Sentinel Node @SentinelNode_]

@md_kasif_uddin I'd say a multi-agentic system with Fable5 as the orchestrator and the GPT5.6Sol agents with a improvement- feedback loop on the output

[The Sentinel Node @SentinelNode_]

With models like GPT #Sol 5.6 high getting this strong, the real bottleneck is no longer the model itself.
It’s the harness around it.👇
Most people are still treating these models like better chatbots. They’re missing the point.
The harness — the orchestration layer, evaluation loops, tool integration, state management, and feedback systems — is what actually turns raw model intelligence into reliable, production-grade workflows.
A powerful model without a good harness is just expensive autocomplete.
A solid harness with a strong model becomes something much more useful.
Right now I’m focusing more on building better harnesses than chasing the next model upgrade.

[The Sentinel Node @SentinelNode_]

@markproduct Congrats man👏👏

[The Sentinel Node @SentinelNode_]

@devops_prashant I'd say Codex just for the token usage.

[The Sentinel Node @SentinelNode_]

@AbhiCodes15 I'm running them locally, so 0.

[The Sentinel Node @SentinelNode_]

Curious what backend patterns you’ve found work well when you need real-time visibility into AI agents (especially for trading or research use cases).
WebSockets + Redis? Server-Sent Events? Something more exotic like a proper message broker?
Drop your thoughts — genuinely interested.

[The Sentinel Node @SentinelNode_]

Building AI agents for markets is one thing.
Making the backend handle multiple viewers watching live agent outputs in real-time is another.
Here’s the architecture I’m currently exploring:

[The Sentinel Node @SentinelNode_]

Main challenges I’m solving right now:
How to efficiently broadcast agent decisions/thoughts to many viewers without killing performance
Keeping state consistent when an agent is running and viewers join mid-session
Handling reconnection + message replay when someone drops offline
Rate limiting per viewer so one heavy user doesn’t affect others
Would love to hear how others are approaching this.

[The Sentinel Node @SentinelNode_]

Core stack I’m testing:
FastAPI + WebSockets for real-time connections
Redis Streams + pub/sub as the central event bus
PostgreSQL for persistent state + TimescaleDB for time-series agent logs
Background workers (Celery or RQ) to run agents without blocking the API
The goal is to let viewers subscribe to specific agent sessions without polling.

[The Sentinel Node @SentinelNode_]

@ItsAlexhere0 Congrats! Let's connect:)

[The Sentinel Node @SentinelNode_]

@markproduct Discipline

[The Sentinel Node @SentinelNode_]

@markproduct algo trading strategy creator & tester on codex workflow with addons