Agbo, Daniel Onuoha

Organiser @gdg_futo || Robotics Developer || Vice Chair @ieeefuto
I Design • Code • Experience @shielddeviser || shieldstring violinist.

[Agbo, Daniel Onuoha @Shieldstring]

Build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free.

Bookmark & watch this today before someone takes it down and read this article below https://t.co/KKoREK962o

[Ram Singh Verma @RamSingh_369]

Learn AI for free directly from top companies

1 - Anthropic:
https://t.co/Q2Z6GQ4FLG

2 - Google:
https://t.co/dfcoYu9F5t

3 - Meta:
https://t.co/KehbL6SMqe

4 - NVIDIA:
https://t.co/yJJeBybAiq

5 - Microsoft:
https://t.co/o6zEI08e0V

6 - OpenAI:
https://t.co/Ldf6JOVnWH

7 - IBM:
https://t.co/YRr7afbzih

8 - AWS:
https://t.co/vDgMAY0FPI

9 - DeepLearning AI:
https://t.co/KGHQ6IxLW7

10 - Hugging Face:
https://t.co/P75G9cA8BL

Comment "AI" for more resources.

Like, Retweet & Bookmark for future updates.

[Tech with Mak @techNmak]

The full AI engineering curriculum is now free.

It's called AI Engineering from Scratch. 20 phases, 428 lessons, roughly 320 hours end to end. Free. MIT license. Runs on your own laptop.

The design principle that makes it different from everything else => every algorithm gets built from raw math before a single framework loads. Backprop by hand. Tokenizer by hand. Attention by hand. Agent loop by hand. Then you implement the same thing in PyTorch or sklearn. By the time the production library appears, you already know what it's doing underneath.

Every lesson ends with something you keep:
→ Prompt templates for any AI assistant
→ Skill files for Claude, Cursor, Codex, OpenClaw, Hermes 
→ Agent definitions you wrote the loop for yourself 
→ MCP servers built from scratch in Phase 13

428 lessons means 428 artifacts by the end. Tools you built and actually understand.

The full 20 phases:
→ Phase 0 - Setup & Tooling (12 lessons) 
→ Phase 1 - Math Foundations (22 lessons) 
→ Phase 2 - ML Fundamentals (18 lessons) 
→ Phase 3 - Deep Learning Core (13 lessons) 
→ Phase 4 - Computer Vision (28 lessons) 
→ Phase 5 - NLP (29 lessons) 
→ Phase 6 - Speech & Audio (17 lessons) 
→ Phase 7 - Transformers Deep Dive (14 lessons) 
→ Phase 8 - Generative AI (14 lessons) 
→ Phase 9 - Reinforcement Learning (12 lessons) 
→ Phase 10 - LLMs from Scratch (22 lessons) 
→ Phase 11 - LLM Engineering (15 lessons) 
→ Phase 12 - Multimodal AI (25 lessons) 
→ Phase 13 - Tools & Protocols (23 lessons) 
→ Phase 14 - Agent Engineering (42 lessons) 
→ Phase 15 - Autonomous Systems (22 lessons) 
→ Phase 16 - Multi-Agent & Swarms (25 lessons) 
→ Phase 17 - Infrastructure & Production (28 lessons) 
→ Phase 18 - Ethics, Safety & Alignment (30 lessons) 
→ Phase 19 - Capstone Projects (17 projects, 20-40 hours each)

Python, TypeScript, Rust, Julia throughout.

GitHub Repo: https://t.co/E2Rg09gnrR

[Agbo, Daniel Onuoha @Shieldstring]

Yesterday’s AI Builder’s Pitch Party at FUTO AI Experience Center (powered by SATH Foundation) was unmatched. ⚡

We didn't just build another community; we built a central HUB for all innovators.

Congrats to the Team NaijaVoice, and thanks judges & mentors!

#FUTOAI #SATH

[Agbo, Daniel Onuoha @Shieldstring]

Want to understand Agent Swarms? This 40-minute masterclass from the founder of a $20B Chinese AI company is the clearest breakdown I’ve ever seen.

[Agbo, Daniel Onuoha @Shieldstring]

@RaconteurR2D2 @heynavtoor We’re about to witness another level/category of depression

[Agbo, Daniel Onuoha @Shieldstring]

@lifeonbeta1224 @heynavtoor A good question to reflect on

[Agbo, Daniel Onuoha @Shieldstring]

@lukas_m_ziegler Awesome

[Agbo, Daniel Onuoha @Shieldstring]

A deep-dive into designing, implementing, and validating a multi-layer adaptive algorithm for real-time network fault detection in IoT production environments — achieving 98.7% detection accuracy with sub-200ms latency.

#ai #iot #ml #networking https://t.co/7tqfJZ08YB

[Agbo, Daniel Onuoha @Shieldstring]

Google Cloud AI engineer just showed how they go from idea to deployed app at Google in 30-minutes using Claude.

26-minutes. free. by Google AI team.

one person + Claude + Google Cloud = a full engineering org running on a laptop.

#google #cloud #ai

[Stitch by Google @stitchbygoogle]

Today, we’re open-sourcing the draft specification for DESIGN.md, so it can be used across any tool or platform. We’re also adding new capabilities.

DESIGN.md lets you easily export and import your design rules from project to project. Instead of guessing intent, agents know exactly what a color is for and can even validate their choices against WCAG accessibility rules.

Watch David East break down this shared visual language in action👇. New capabilities and links in 🧵

[Anuj @anujcodes_21]

Claude FULL COURSE 1 HOUR (Build & Automate Anything)

[Agbo, Daniel Onuoha @Shieldstring]

#New week New #vibes

[Google for Developers @googledevs]

Accelerate ML workflows using @NVIDIA CUDA-X libraries on @GoogleCloud with a single extension-loading command. ✨

Learn more in a new pathway through the #GoogleDeveloperProgram → https://t.co/BYAUPx442l

[Agbo, Daniel Onuoha @Shieldstring]

April just dropped. 🗓️

This month, whose idea am I turning into a product?

DM me 'BUILD' — let's ship your idea this April."

#BuildWithDaniel #shieldstring #FullStack #April2026 #DevLife

[Agbo, Daniel Onuoha @Shieldstring]

Want to try it yourself?

The Colab notebook from the session is free and open:
🔗 https://t.co/Mu1EUbiwtO…

⚠️ Heads up: image generation has API costs — don't run it on War and Peace without checking pricing first!

#GeminiAI #BuildWithAI #AIWorkflows #NanoBanana 🍌

[Agbo, Daniel Onuoha @Shieldstring]

I just watched @GeminiApp illustrate an entire book in seconds—zero designers required. 🤯

The secret? A 2-step "Agentic Workflow" that handles the art direction AND the execution.

Here is the exact 5-step pipeline (and the @GoogleColab code to clone it) 🧵👇

[Agbo, Daniel Onuoha @Shieldstring]

The real unlock here?

Chaining AI tools together as a pipeline:

🧠 Text model = the thinking Art Director
🎨 Image model = the executing illustrator

Individually, they’re tools; together, they’re a team. This is a simple agentic workflow doing weeks of human work in minutes.