srikrishna acharya

Senior Research Specialist @ Nokia × AI use cases
simplify complex tech
| Learning & sharing

[FPL Frasier @FPLfrasier]

Funniest thing from the game. Arsenal got a corner kick. They were taking their usual 15 minutes to crowd the goalkeeper. The referee said that’s enough and blew the half time whistle.

😂😂😂😂😂😂

[Pallavi Pandey @pallavipandeyy]

The government remembers to collect 30% income tax from IT employees working in MNCs every month on payday.

It forgets them the day they’re laid off.

[srikrishna acharya @doctorsab0]

Future apps may not have fixed UIs at all.
The interface itself could become adaptive, agent-generated, and workflow-aware. ⚙️🤖

[srikrishna acharya @doctorsab0]

We’re moving beyond “AI chatbots” into a new stack: Agentic UI.
Architecture combines:
• Generative UI → agents dynamically create interfaces
• State synchronization → frontend + agent stay consistent in real time
• Interrupt-driven approvals → humans step in for actions

[srikrishna acharya @doctorsab0]

• Event streams → agents continuously update the UI as they reason
The important shift:
👉 agents are no longer just generating text
👉 they’re becoming active participants in application state and workflows

[srikrishna acharya @doctorsab0]

AI amplifies engineering quality.
It doesn’t replace it. ⚙️🤖

[srikrishna acharya @doctorsab0]

Interesting pattern across AI engineering:
The teams getting the best results from coding agents are not the teams with the best prompts.
They’re the teams with:
• strong architecture
• clean repositories
• clear task boundaries
• good verification loops
• context management

[srikrishna acharya @doctorsab0]

We’re compressing the distance between idea → execution.
The bottleneck is shifting from: “Who can code this?”
to: “What should we build?” 🤖⚙️

[srikrishna acharya @doctorsab0]

Everyone says AI will replace software engineers.
But watching the industry closely, I think something else is happening:
We’re not removing engineers

[Manu Arora @mannupaaji]

AGENTS.md
DESIGN.md
CLAUDE.md
SKILL.md
REQUIREMENTS.md
PROPOSAL.md
PLAN.md
.
.
GOD-HELP-ME.md

[srikrishna acharya @doctorsab0]

@shivi1026 How not to screw up

[srikrishna acharya @doctorsab0]

@NoahKingJr Real intelligence

[Manchester United @ManUtd]

It's official: Bruno Fernandes is the best player in the Premier League 👍

[srikrishna acharya @doctorsab0]

Composer 2.5 is really that good; speed and accuracy are amazing. #cursor @cursor_ai is breathtaking

[Wise @trikcode]

I haven't seen a C++ vibecoder yet.
I wonder why?

[srikrishna acharya @doctorsab0]

The AI industry may be hitting its first real economic constraint.
When even Microsoft reportedly cancels internal Claude Code licenses over token costs, it signals something important:
👉 the “AI is getting infinitely cheaper” narrative is colliding with production reality.
Agentic workflows consume enormous compute because they:
• maintain long contexts
• call tools repeatedly
• run verification loops
• generate/refactor large codebases
• operate continuously, not interactively
This breaks the economics of traditional SaaS pricing.
Flat-rate subscriptions worked when humans typed prompts occasionally.
But autonomous agents create machine-scale demand against GPU-bound infrastructure.
The industry is now confronting a hard question:
Can frontier AI sustain:
• high usage
• low pricing
• and healthy margins
…all at the same time?
This is why the next wave of innovation may focus less on “smarter models” and more on:
• context compression
• caching
• smaller specialist models
• local inference
• efficient orchestration
• token-aware architectures
The future winners may not be the companies with the biggest models.
They may be the ones with the best economics per intelligent action. ⚙️🤖

[srikrishna acharya @doctorsab0]

A strong AI engineer doesn’t just learn frameworks — they build mental models.
Some standout books from Javarevisited’s AI Engineering reading list:
📘 AI Engineering — Chip Huyen
📘 Designing ML Systems — Chip Huyen
📘 The LLM Engineering Handbook — Paul Iusztin & Maxime Labonne
📘 Build a Large Language Model (From Scratch) — Sebastian Raschka
📘 Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst
📘 Building LLMs for Production — Louis-François Bouchard & Louie Peters
The progression is clear:
LLM fundamentals → production systems → agentic workflows → evaluation & reliability.
The future AI engineer won’t just prompt models.
They’ll understand the full stack:
data, retrieval, orchestration, infra, memory, and deployment. 🧠⚙️ #AIEngineering

[srikrishna acharya @doctorsab0]

“Agentic programming” is emerging as a new engineering discipline.
Not just prompting models — but designing systems where AI can:
• plan tasks
• use tools
• manage memory
• recover from failures
• operate in long-running loops
The roadmap is becoming clearer:
prompt engineering → context engineering → workflow orchestration → autonomous systems.
The key shift:
👉 building AI apps is starting to look less like frontend development
👉 and more like distributed systems engineering.
Future AI engineers won’t just write code.
They’ll design environments where agents can reliably reason and act. ⚙️🤖

[srikrishna acharya @doctorsab0]

@haha_girrrl Use AI

[GitHub @github]

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