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Thomas Bateman

• Mar 30

An AI agent that doesn't just answer questions — it works. 188 callable tools, 47 operating

modes, dual-LLM architecture (120B brain for reasoning, 70B executor for tool calling).

It scrapes job platforms for leads, scores them, writes personalized proposals, delivers the

code, and invoices the client. Fully autonomous revenue pipeline.

Capabilities include:

• Full codebase auditing (12-phase enterprise security protocol)

• Self-healing and self-evolving code

• ML pipeline training and deployment

• Multi-agent swarm orchestration

• Legal defense document generation

• Real-time web GUI command center

95 Python modules. Zero external agent frameworks — built from raw API calls.

Stack: Python, Groq API, FastAPI, asyncio, LightGBM, Rich TUI, WebSocket GUI

AI Engineer Full Stack Development Docker Node.js AI Automation Python

Sanket Sabharwal, PhD

pro

• Mar 30

Most teams we work with want to say their product is AI-powered. Few have asked whether their organisation is ready to support that claim. We have observed a recurring pattern where a team commits to building a machine learning system and, after months, discovers that their data isn't clean enough to train on. The infrastructure then fails to reliably serve a model. In such cases, our diagnosis has often concluded that the foundation is the problem. Machine learning readiness is a precondition, and one must begin with a straightforward question - "Do we have the data pipeline, infrastructure and cross-functional alignment to make this sustainable beyond a PoC?" That question is what prompted our first article titled "The Machine Learning Readiness Checklist". We cover everything a team needs, from data and infrastructure to the right kind of team members. All of this must be done before committing serious engineering time and budgets. If you're a founder, engineering lead or a decision maker within your organisation who wants to build things correctly, we encourage you to read the full piece here:

https://www.algorithmic.co/blogs/the-machine-learning-checklist We'll be sharing more each week. Follow along if this is relevant to what you're building.

P.S. This article is from late 2025 but more than ever relevant.

Machine Learning Engineering softwareengineering AI Engineer

Daniel G Bright

max

• Apr 1

Love the vibe of this!

Korey Poggemoeller

• Mar 30

Senior Software Engineer & AI Automation

I designed and developed this AI-powered lead automation system as a Senior Software Engineer specializing in AI automation and workflow engineering. The system automatically monitors a lead database and triggers a processing pipeline whenever a new lead appears. It intelligently filters and qualifies leads based on predefined conditions, then uses AI to generate contextual messages and summaries. This automation eliminates manual lead handling, improves response speed, and ensures that only high-quality leads move through the workflow.

After the AI generates the message, the system formats and parses the content before automatically sending notifications to internal teams through communication channels such as Slack and email. The workflow then updates the database with the processed results, keeping all records synchronized. This project demonstrates my ability to build scalable automation systems that integrate databases, AI services, and communication platforms to create efficient, real-time business workflows.