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- 🤖 GitHub just dropped a coding agent
🤖 GitHub just dropped a coding agent
Plus: X/Twitter Mastery guide to build an irresistible brand in less than 30 days
Today, we’re looking at GitHub’s boldest attempt yet to industrialize AI coding, a shift that could redefine how developers build with AI. We’re also looking at how India’s smallest towns are quietly powering the world’s biggest AIs with a new wave of “cloud farmers” labeling data, training models, and redefining where high-tech work happens.
In today’s post:
The Brutal Beauty of Coding Like It’s 1999
GitHub’s plan to make AI dev actually reliable
The hidden towns training your AI
Launch: Complete X Mastery guide
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RESEARCH
Assembly isn’t dead, it’s the ghost in the machine

Image Credits: Wired
RollerCoaster Tycoon wasn’t just a cult hit. It was hand-crafted in pure assembly language by one person: Chris Sawyer. That level of control is nearly extinct now and yet, it may be the key to the future of AI.
Here’s everything you need to know:
Assembly code speaks directly to the machine, skipping layers of abstraction.
In the '90s, that meant performance. Today, it means precision and control.
DeepSeek’s AI shocked the field by doing more with less, thanks to assembly.
Google, DeepMind, and others are quietly teaching AIs to code low-level.
Coding in assembly forces clarity: one command, one effect. No room for fluff.
Sawyer’s story reminds us: sometimes limitations spark the best design.
As AI eats software, knowing the machine’s native tongue may matter again.
Most of us will never touch assembly. But understanding it even just conceptually reminds us that software is not magic. It’s logic, laid bare. And maybe, as AI becomes more alien, that kind of grounding is exactly what we need.
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UPGRADE
From messy prompts to structured AI workflows

Image Credits: GitHub
If AI coding still feels like trial and error, GitHub wants to change that. They’ve just shared a framework that turns Copilot from a guessing machine into a repeatable engineering tool using something they call agentic primitives.
Here’s everything you need to know:
Most devs start with one-off prompts. That doesn't scale.
GitHub’s new 3-part system: prompt structure, agent primitives, and context engineering.
Agent primitives are reusable building blocks for repeatable AI tasks.
Markdown is key, it adds structure and clarity to AI instructions.
New Copilot CLI lets you run and debug workflows from the terminal.
APM (Agent Package Manager) brings versioning, dependency management, and packaging.
These tools move AI from your editor to CI/CD pipelines, production ready.
This shift mirrors early web dev: messy scripts turned into structured codebases with tooling. GitHub’s pushing for the same leap with AI from creative chaos to dependable infrastructure. If it works, AI won’t just assist developers. It’ll work like one.
INNOVATION
Who is AI’s backbone?

Image Credits: BBC
While the world watches Silicon Valley, rural India is quietly becoming the engine room of artificial intelligence. Towns like Virudhunagar are where today’s smartest machines are learning to think thanks to people like Mohan Kumar.
Here’s everything you need to know:
AI training is moving beyond metros into small Indian towns.
Workers like Kumar label data, transcribe audio, and fine-tune models.
Firms like Desicrew and NextWealth pioneered this “cloud farming” model.
60–70% of their workforce are women, many in their first salaried roles.
The work: transcription, object recognition, language modeling, content moderation.
These centers mirror urban tech hubs just with lower costs and deeper local impact.
India's scale, talent pool, and early lead give it a 5–7 year edge globally.
Everyone talks about the front end of AI, the ChatGPTs, the breakthroughs. But behind it all are thousands of quiet hours, labeled frames, and refined decisions. Rural India isn’t just catching up, it’s becoming essential infrastructure for the AI era.
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