Aishwarya Srinivasan

AI@Fireworks AI | Global Speaker(100+ talks)| EB1-A Recipient| 570k+ Followers| AI Engineer| GenAI/LLM Inference| LinkedIn Top Voice| Author-What's Your Worth?| Startup AI Advisor| Angel Investor| Ex-Google,Microsoft,IBM

𝗙𝗼𝗿 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝗻𝗾𝘂𝗶𝗿𝗶𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝗺𝘆 𝘁𝗲𝗮𝗺 𝗮𝘁 💌 partnerships@illuminate-ai.org

I write about:
- AI engineering
- Applied machine learning
- Generative AI Applications
- LLM/ LLMOPs (Training, post-training, inferencing)
- Fundamentals of Data science/ ML/ AI
- ML roles interview tips
- Learning roadmaps (beginner to advanced)
- Importance of personal branding for career growth

**All the posts reflect my own views and do not represent my employer.**

AI Advisor || Responsible AI Advocate || LinkedIn Top Voice - Data & AI  || Top 10 AI Influencer || EB1-A Receipt


Bio: 
Aishwarya currently leads the Developer Relations - Growth at Fireworks AI. She was previously at Microsoft for Startups as a Senior AI Advisor to help Y Combinator startups build machine learning solutions, leveraging core Microsoft/ OpenAI products. Prior to this, Aishwarya was working as a Data Scientist in Google Cloud and before that an AI & ML Innovation Leader at IBM Data & AI, where she was working cross-functionally with the product team, data science team and sales to research AI use-cases for clients by conducting discovery workshops and building assets to showcase the business value of the technology. She holds a post-graduate in Data Science from Columbia University. 

Aishwarya has been awarded Trailblazer of the Year by Women in AI in 2022 and Women of Influence by Business Journal in 2022. She was awarded EB1A - extraordinary ability visa for her contributions in the AI field.

She is an advocate for open-source technologies, and was an open source Developer Advocate for Deepchecks and Lightning AI, and a contributor to Scikit Learn. 


She holds a Patent Award (2018) for developing a Reinforcement Learning model for Machine Trading.

She has over 500,000+ follower base on LinkedIn and actively organizes events and conferences to inspire budding data scientists. She has been spotlighted as a LinkedIn Top Voice 2020 for Data Science and AI, which features Top 10 Machine Learning influencers across the world. 

She is an ardent reader and has contributed to the scholastic community. To spread her knowledge in the space of data science, and to inspire budding Data Scientists, she actively writes blogs related to machine learning on LinkedIn and Instagram: www.instagram.com/the.datascience.gal

Besides being a data junkie, she is a fitness fanatic who is into martial arts (Krav Maga) and yoga.

Senior AI Advisor

If you're feeling overwhelmed with how fast AI is evolving, you're not alone.

Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind.

But the good news is that you don’t need to learn everything all at once. What you need is structure.

So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable.

💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition.

If you're moving faster or slower based on time or experience, that’s okay too.
And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here.

👇 Here’s the roadmap:
🔖 Level 1: GenAI & Transformer Foundations
 Tokens, embeddings, transformers, decoding, and inference with open-weight models.

🔖 Level 2: Prompting & Language Model Behavior
 Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting.

🔖 Level 3: Retrieval-Augmented Generation (RAG)
 Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation.

🔖 Level 4: LLMOps & Tools
 LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data.

🔖 Level 5: Agents & Agent Frameworks
 Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation.

🔖 Level 6: Memory, State & Orchestration
 Vector and symbolic memory, episodic vs persistent state, memory compression.

🔖 Level 7: Multi-Agent Systems
 Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams.

🔖 Level 8: Evaluation & Reinforcement Learning
 LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops.

🔖 Level 9: Protocols & Safety
 MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates.

🔖 Level 10: Build & Deploy
 FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens.

📌 Bookmark this.
🛠️ Build something after every level.

And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will  teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 

💻 Pro-tip: Start with cookbooks!

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Hundreds of AI minds gathered in Central Park on a chilly Saturday morning, it was incredible :)

The past weekend in New York felt like one of those moments where community completely surprises you in the best way.

I was traveling to the city for a work event, and my friend Pranjal and I had a thought- What if we host a small AI meetup on Saturday morning in Central Park? Nothing fancy, just a space for people to connect.

So we put up a quick Luma link and shared it on LinkedIn. We expected maybe a handful of people. Instead, the sign ups crossed 450+. 

We put a small table with coffee, muffins, cookies and some cool swag- thanks to Teachable. We weren’t sure how it would go. We just hoped people would show up.

What happened next honestly moved me.

We reached the park at 9.20am to set up for a 10am meetup, and people were already there. In the cold. On a Saturday morning. People had traveled from Boston, Maryland, upstate New York, New Jersey, even Philadelphia. I kept talking to folks thinking, you really came all this way… for this?

The meetup was meant to last two hours. People stayed until 3pm! Almost six hours of conversations about AI, careers, ideas, and everything in between. No microphones. No agenda. Just humans connecting because they care about the same things.

This experience reminded me of something I tend to forget- community doesn’t need perfect planning. It needs intention. It needs genuine people. And it needs a space where everyone feels welcome.

I’m grateful to every single person who showed up. You made this morning unforgettable, and you reminded me why I love building in this space.

Shout-out to Aditya Suresh and Pranam Daga for helping us with putting this together ❤️

There is a quiet moment every November when life slows down just enough for reflection. I never grew up celebrating Thanksgiving. Back home in India, I didn’t even know what it meant. But after moving to the US, watching my friends celebrate, watching sitcoms where families gathered around food and gratitude, I started understanding the deeper meaning behind it.

A moment to pause.
A moment to look around.
A moment to look back.

This year, gratitude feels a little different for me. A little heavier in the best possible way.

I feel grateful for a life where I get to wake up every day and do work I genuinely love. AI is chaotic and intense and fast. Yet it gives me energy. It gives me purpose. And I feel lucky to be part of this ecosystem during a time when everything is shifting.

I feel grateful for the people who hold me steady.
A mom who pushed me when the world expected her to play it safe. A mom who defied every norm to carve space for me to choose ambition without apology.

A husband who is involved in my life in the most beautiful way supportive is honestly an understatement. He is a true partner in every single thing I do. He listens to every idea, every frustration, every new plan I dream up and he backs me with a kind of belief that strengthens my own. Chinnu amma and Chinnu appa who patiently listen to my ten year entrepreneurial plans and cheer for me with the kind of warmth only family can give.

Mentors and colleagues who let me thrive creatively who challenge me in the right ways who make every room I walk into feel lighter and more possible.

I feel grateful for health. For a body I get to take care of. For the time I carve out to move, to eat well, to nurture myself. It took years to realize that taking care of your body is also taking care of your dreams.

And I feel grateful for the little girl I used to be the one who sat with a notebook and wrote her goals on every page the one who kept believing she would get there someday. If I could talk to her today, I would tell her that she is doing great and that her stubborn belief in herself was worth it.

Thanksgiving may not be something I grew up with, but it has become a reminder to pause and appreciate the rarest gift of all the good people around you. People who are genuinely supportive, genuinely positive, and genuinely rooting for you.

I am grateful for all of it.
And grateful for everyone who has been part of my journey so far.

If you are reading this, I hope you take a moment to celebrate the smallest wins, the unexpected joys, and the people who make your life feel expansive.

li_at

JSESSIONID

What an electrifying afternoon! Absolutely delighted to have joined the Mayfield AI List honorary lunch at the beautiful Golden Gate Club in San Francisco.

The room was filled with the brightest minds in the AI VC world. it was a privilege to meet and listen to inspiring leaders like Navin Chaddha, Aparna Chennapragada and Garry Tan. 

I had the opportunity to host a focused roundtable, diving deep into the current state of the industry. We tackled the crucial discussion around Perception vs. Reality of AI, analyzing current hype cycles, and zeroing in on investment opportunities that hold real long-term potential.

It’s moments and small-group settings like these, where leaders can candidly discuss where the real value lies beyond the buzz, that drive the industry forward.

Huge thanks to the Mayfield and Startup Grind team (special shoutout to James Gee and Fadia Frangie Doumat) for hosting and for the award ♥️

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Wrapped up AI Dev 25 hosted by DeepLearning.AI

More than 1400 AI developers gathered in one space, and the energy was something you could feel instantly. Meeting so many builders and hearing what you’re creating reminded me why this community moves the needle the way it does.

Hearing Andrew Ng talk about the future of work was a standout moment. The way he encourages everyone on his team to code, regardless of background, is exactly the kind of mindset shift this era needs. AI literacy is becoming a baseline skill, and he articulated that perfectly.

Our panel with Michael Kearns, Hatice Ozen, Kay Zhu, and Josh Gordon covered how the ecosystem is evolving across the stack, from Groq , Google, and Amazon Web Services (AWS)  to Fireworks AI and how teams like Genspark are using RFT on Fireworks AI in production workflows. We also touched on multi-agent systems, practical best practices, and where developer tooling still needs to mature.

I also loved my conversation with Michael Kearns on responsible AI and how that thinking has evolved from classic ML days to the generative era. His perspective at the intersection of academia and industry always leaves me thinking differently.

Grateful for the conversations and connections today. If we met, thank you for stopping by. If we didn’t, I’m always happy to keep the discussion going. There’s a lot to build, and we’re still early.

Thank you DeepLearning.AI team for having me ❤️

When you’re an AI engineer, the worst bottleneck isn’t writing code. It’s navigating everything around the code.

Last week I tried out Rovo Dev from Atlassian and it really changed how I approach dev work, plus how I see AI agents changing the landscape of software engineering.

𝗪𝗵𝗮𝘁 𝗶𝘀 𝗥𝗼𝘃𝗼 𝗗𝗲𝘃?
RovoDev is an AI agent built to live inside your dev workflow, including your code, your issues (let’s say in Jira), your docs (let’s say in Confluence), your pull requests. 

It helps you move from idea → code → deployment with fewer context switches.

𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗻𝗼𝘁𝗶𝗰𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁:
→ I asked in plain English: “What does this module do? Where are its dependencies?” and got a clear breakdown, no digging through 3 dozen files.
→ I linked my GitHub/Bitbucket + Jira and was able to surface relevant issues, test failures and comments in one context instead of switching tools.
→ The CLI experience (yes, in my terminal) allowed me to trigger agents to draft tests, summarise PRs, or suggest refactoring ideas, without leaving my code. 

𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀?
What I realized after using Rovo Dev is that the real time-sink in engineering isn’t writing code, it’s in fact more about re-orienting yourself.

Jumping between Jira, Confluence, and your repo just to understand context.
Trying to figure out why something was built the way it was.
And then losing your train of thought before you can even start solving the problem.

Well, that's where Rovo Dev actually helps close that gap.

If you’ve ever joined a new project and spent hours just piecing together what’s going on, or if you constantly find yourself switching between tools to get one small thing done, you should try this out: fandf.co/42MTjr8 

Thank you Atlassian for partnering with me on this post, and giving me access to Rovo Dev to test it out :)

#AtlassianPartner

This is one of the best resources for aspiring AI engineers, that covers all the basics - advanced in classroom teaching: https://lnkd.in/dgr5zEve

Highly recommend saving this so you can come back to it later.

Goldmine for aspiring AI Engineers

Lately, I’ve noticed something interesting in the AI startup world.

The ones growing the fastest aren’t building the next big model.

They’re building agentic systems.
AI that doesn’t just talk, it does.
Copilots that summarize meetings.

Agents that predict churn or handle internal ops automatically.

These aren’t demos anymore. They’re real products driving revenue.

What separates the ones that scale from the ones that stall comes down to one thing : Data.
Every great agent lives and dies by how it manages memory, retrieval, and reasoning speed.
And that’s exactly why so many founders I work with build on MongoDB Atlas.

It’s not just a database.
It’s the data layer that brings structured, unstructured, and vector data together so your AI can actually reason in real time.

If you’re a startup building AI that acts, not just talks, go check out the MongoDB Startup Program: https://fandf.co/493VsD1

#MongoDBPartner

🚨Context Engineering for GenAI Workshop happening tomorrow!

In this session, we’ll dive deep into:
→ What context really means in LLMs and how it shapes performance

→ The toolkit that helps you engineer it better: Summarization, Pruning, Query Rewrite, RAG, Context Offloading, Isolation, and Memory

→ How to optimize for accuracy, cost, and reliability across use cases

→ A live demo showing how these ideas come together in real time

This workshop is for every engineer who wants to move beyond prompt tuning and start thinking like a system designer.

🧩 Context Engineering
📅 November 8
🕐 1.5 hours
🎙️ with Arvind Narayanamurthy & me

🎟️ Save your spot: https://lnkd.in/dvFmMiRr

If you’re an AI engineer today, you should absolutely get familiar with Model Context Protocol (MCP) and Responses API. These two are becoming the backbone of modern agentic systems.

Let’s unpack why 👇

1️⃣ MCP: The Connector Layer

Model Context Protocol (MCP) standardizes how models connect to external tools, APIs, and live data.

Think of MCP as the wiring system that allows your LLM to talk to the outside world, like tools, databases, vector stores, or your company APIs.

When you build or host your own MCP server, you’re giving your model new abilities to reason, act, and fetch context beyond what’s in its prompt.
This is what turns a plain LLM into an agent.

If you’re working on production-grade AI systems, you’ll often need to:
→ Integrate custom tools or data sources
→ Maintain context across multiple steps
→ Ensure reliable tool invocation and streaming

MCP makes all that possible.

2️⃣ Fireworks AI Responses API

Once your MCP setup is ready, the Responses API is how you connect it to the model.

It’s a next-generation SDK that powers conversational AI. It supports:
→ Stateful conversations (previous_response_id)
→ Real-time streaming for interactive apps
→ Tool calling and safe execution

The Fireworks Responses API follows the same schema and format as OpenAI’s Responses endpoint.

So if you’ve been using OpenAI models, you can switch to open-source models on Fireworks AI with almost zero code change.

You’ll unlock faster inference, lower latency, and broader model choice, including open models like Qwen, Mistral, Llama, and Gemma hosted natively on Fireworks AI.

You can even mix and match:
✅ Use MCP to extend model capabilities
✅ Use Responses API to orchestrate reasoning, tool use, and streaming

That’s the magic combo powering many of today’s most advanced agentic systems.

3️⃣ Why This Matters

Agentic systems aren’t built with just a model call.
They’re built with context, memory, and actions. MCP and the Responses API make that orchestration reliable, stateful, and production-ready.

If you’re serious about building enterprise-grade AI apps, start experimenting with:
→ MCP Examples (connecting to MCP servers and invoking tools)
→ Conversation Chaining (using previous_response_id to continue dialogue)
→ Streaming Output (for responsive real-time interfaces)
→ Ephemeral Mode (for privacy-friendly, non-persistent interactions)

Happy Building 🚀
〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re an AI engineer or an AI leader looking to level up your skills in LangChain and LangGraph, this one is for you.

I’m teaming up with Arvind Narayanamurthy  for a beginner–intermediate, 1.5 hour hands-on workshop where you’ll learn how to move from simple prompts to real AI applications and agentic workflows.

Why this matters
- Most engineers know how to prompt. Few know how to orchestrate.
- LangChain and LangGraph are becoming the backbone of production AI systems.
- If you want to build or lead AI engineering teams, you need these mental models.

What you’ll learn
- Foundations of LLM orchestration and where LangChain fits
- Essential building blocks: chat models, prompting patterns, structured outputs, LCEL
- Tool calling with MCP adapters
- RAG basics end to end
- LangGraph workflows and agentic architectures
- Two live labs: a micro-app and a simple agent
- Quick dive into LangSmith for debugging

The workshop is recorded, and everyone gets the replay.

If you want practical, real-world skills to build modern AI systems, this is the one to sign up for.

Grab your seats: https://lnkd.in/eW2WZdcf

Are you wondering where the AI industry is actually headed? 
Every week brings a new framework, a new toolkit, a new acronym. But beneath all the noise, one truth is becoming clearer: the real moat isn’t the model, it’s the combination of data and AI. 
 
AI is the toolkit. 
Data is the substance. 
AI gives us reasoning power. 
Data gives us understanding. 

That connection came to life at Snowflake BUILD 2025, where the  focus wasn’t on hype, it was on grounding AI in reality. 
 
We saw how Snowflake Intelligence lets teams ask complex questions in natural language and get trusted answers from governed data. How the Cortex Agents API enables developers to build domain-specific agents that reason and collaborate. And how the Managed MCP Server securely bridges external AI systems to enterprise data, turning “agentic AI” from an idea into something production-ready. 
 
Even unstructured data, the 80 % of information hidden in PDFs, chats, and documents, is now first-class and accessible through simple AISQL queries. 
 
This feels like a turning point. 
The next era of AI won’t be defined by bigger models, but by deeper integration between data and reasoning. 
 
AI without data is just potential. 
Data without AI is just storage. 
Together, they become intelligence. 
 
#SnowflakePartner #SnowflakeBUILD

If you’re an AI engineer trying to understand agentic systems, keep reading 👇

A single-agent system relies on one model to do everything: reasoning, planning, execution, and validation. It’s efficient for simple, sequential tasks but quickly breaks down as workflows grow more complex or interdependent.

A multi-agent system, on the other hand, distributes intelligence across specialized agents that collaborate. Each has its own role, memory, & context - and together, they solve problems that a single model can’t handle alone.

But here’s what most people miss: there’s no standard architecture.

A multi-agent system is like Lego blocks, you design it for your use case.

You can build:
→ a supervisor agent managing sub-agents
→ a hierarchical setup with layered control
→ or a hybrid model combining centralized planning and peer coordination
The challenge isn’t in building agents, it's in managing how they talk to each other.

Three reasons multi-agent systems often fail:

❌Memory fragments across agents.
Each agent keeps its own context. When one agent hands work to another, subtle details get lost or duplicated. Without a shared memory layer or context synchronization, agents start “forgetting” what the others already know.

❌Operational costs multiply.
What costs $0.10 for a single agent might cost $1 when ten agents coordinate. Every message, validation step, or context handoff adds latency, token cost, and complexity. At scale, coordination overhead often outweighs the benefit of specialization.

❌ Write conflicts cascade.
If multiple agents write to the same source of truth: a shared database, knowledge base, or document, their changes can collide. One agent updates a schema, another updates logic on top of the old version, and suddenly the system’s state fragments.

TL;DR: Not every workflow needs a multi-agent setup.

When tasks are independent, read-heavy, and parallelizable, multi-agent systems shine. But for sequential logic and tight dependencies, a single-agent pipeline is still faster, cheaper, and easier to maintain.

As always, Galileo has published a new phenomenal free handbook- Mastering Multi-Agent Systems.
 
If you want to go deeper into how these systems work, why they fail, how to evaluate them, explore different architectures, and even build LLMOps around them, this is one of the best reads out there: https://lnkd.in/diptdEC2

#GalileoPartner

Multi-Agent AI System Cheatsheet 

I learned Design Thinking on my first day as a data scientist at IBM, and it quietly became one of the most useful mindsets in my AI career. It taught me to slow down, understand the real problem, and build solutions that actually land with users.

Here’s how I still use it today:

→ Solve the right problem
Most AI teams jump into modeling too early. Design Thinking forces you to reframe, question assumptions, and check if the problem is even defined correctly.  

→ Diverge before you converge
Explore many possibilities, then narrow down. This prevents tunnel vision and leads to better architectures, better experiments, and better product decisions

→ Use the 2x2 grid
Am I exploring or deciding?
Am I thinking about the problem or the solution?
This simple check saves me from going down the wrong path.  

→ Observe behavior, not opinions
The real insights come from watching users, not asking them. The same applies when evaluating agents, prototypes, or internal tools.  

→ Prototype fast, assign ownership
Ideas die when nobody owns them. Execution is where Design Thinking turns into impact.  

This mindset shaped how I build AI systems then, and it still shapes how I build them now.

♻️Share this with your network

Design Thinking Framework

Last week I came home to a little surprise from OpenAI filled with their new books, 100 Chats for Studying, Career, and College Life. 

I flipped through them right away and loved how visual and genuinely interesting they’ve made each section. It feels approachable, practical, and fun in a way most tech books don’t even try for.

If you want to explore the prompts yourself, you can check them out at this link: https://lnkd.in/ddycaDAg

It’s a pretty cool resource to keep on your desk as you build, study, or just experiment with new ideas.

Here is my absolute fav one which I wish I could have used as a student 👇  
I want to understand [concept]. Explain it with an analogy to real-world items.

Let me know in the comments below which ones is your favorite?

If you’re getting started as an AI engineer, this one’s for you 👇

Here are 12 things you need to learn if you want to become a real AI engineer (or level up as one):

1️⃣ LoRA & qLoRA fine-tuning
Learn low-rank adaptation to fine-tune massive models while keeping GPU and memory usage in check.
https://lnkd.in/dxAfNJjP
https://lnkd.in/dcfhk4YB

2️⃣ Reinforcement Learning from Human Feedback (RLHF)
Understand how to align model behavior with human expectations using curated reward signals.
https://lnkd.in/da4qf5JV
https://lnkd.in/drEbQ4x2

3️⃣ Reinforcement Learning from Verifiable Rewards (RLVR)
Use simulated or programmatic reward models when human feedback is too expensive.
https://lnkd.in/dFBgDAyd

4️⃣ RAG Fundamentals
Learn how to build Retrieval-Augmented Generation systems- ingest documents, create embeddings, stitch context, and prompt LLMs.
https://lnkd.in/dFkgD6RQ
https://lnkd.in/d7v6DzEG

5️⃣ Advanced RAG Architectures
Master multi-hop retrieval, hybrid sparse-dense search, and complex reranking for multi-document use cases.
https://lnkd.in/dUrAz2D3

6️⃣ Embedding Models & Vector Databases
Benchmark sentence encoders, tune indexes (FAISS/Pinecone), and understand latency vs recall trade-offs.
https://lnkd.in/d8zeWbmn
https://learn.pinecone.io/

7️⃣ Prompt Engineering & Chain-of-Thought
Design prompts that guide reasoning step-by-step, manage templates, and dynamically inject context.
https://lnkd.in/djbJD578
https://lnkd.in/d9M75H_i

8️⃣ MCP & Agent-to-Agent Integration
Use Model Context Protocol (MCP) to coordinate LLMs with APIs and other agents for seamless A2A handoffs.
https://lnkd.in/dfjvxhvP
https://lnkd.in/dJ5NWuaK

9️⃣ Long-Context Window Strategies
Handle 100k+ token sequences using smart chunking, hierarchical retrieval, and memory-augmented architectures.
https://lnkd.in/djgkXGaZ

🔟 Model Efficiency: Quantization & Distillation
Shrink models with post-training quantization (4/8-bit), pruning, and knowledge distillation for faster, cheaper inference.
https://lnkd.in/d2iurEyv

1️⃣1️⃣ LLMOps & Model Monitoring
Build CI/CD pipelines for LLM deployment, track drift, monitor hallucinations, and set up alerting for cost or performance issues.
https://lnkd.in/dxik45e6

1️⃣2️⃣ Evaluation & Benchmarking
Measure with perplexity, ROUGE, F1, and human evaluations. Automate benchmarks to compare models at scale.
https://lnkd.in/dP_NJXg9

〰️〰️〰️〰️
♻️ Share this with your network 
🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

If you’re getting started with building AI agents, this Agentic AI Toolkit Cheatsheet will come in handy 👇

You’ll quickly realize it’s not just about choosing a model. You need orchestration, memory, retrieval, evaluation, and a solid deployment stack.

And if you’re not careful, your architecture turns into a mess of disconnected tools.

That’s why I put together this Agentic AI Toolkit Cheatsheet.

It organizes the full stack of what goes into building production-grade AI agents- from model hosting to feedback loops, so you can stop guessing what’s missing in your pipeline and start shipping actual systems.

Here’s what it covers:

🔧 Foundation Models & Hosting
🎛️ Model Customization (SFT, RFT, RLHF)
🤖 Agent Frameworks
🧩 Orchestration & Workflow
📥 Data Ingestion & Preprocessing
🧠 Embedding & Retrieval
🧷 Memory & Knowledge Storage
🔌 Tool Integration
🧪 Evaluation & Monitoring
🖥️ User Interfaces
🚀 Deployment & Hosting


💡 A few tips if you’re working on your first agentic stack:

→ Choose one tool per layer to start, depth over breadth.
→ Build iteratively and introduce evaluation as early as possible.
→ Use event-driven workflows to manage complexity.
→ Think about memory and feedback as first-class components, not afterthoughts.


📌 Bookmark this.
📚 Use it to audit your stack.
🛠️ Build with it.
📤 Share it with a teammate who’s lost in the sea of GenAI tooling.

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

This Week of AI is here⚡️
Another packed week in the world of AI- here’s your quick TL;DR of everything that happened across the AI ecosystem this week 👇

- Apple Forges Billion-Dollar AI Alliance with Google for Siri Overhaul
- Anthropic Foils First Documented Large-Scale Autonomous AI Cyberattack
- World Labs Unveils Marble, A Multimodal World Model for Spatial Intelligence
- OpenAI Rolls Out GPT-5.1 with Adaptive Reasoning and Dual Personalities
- Microsoft Creates MAI Superintelligence Team Led by Mustafa Suleyman
- Google Launches ‘Agentic Checkout’ and AI Shopping Tools for Holiday Season
- IBM Joins OpenSearch Software Foundation as Premier Member

♻️ Share this with your network
🔔 Follow me (Aishwarya Srinivasan) for no BS data and AI updates and insights

This week, the AI landscape was defined by blockbuster partnerships, a major infrastructure upgrade, and a chilling preview of autonomous cyber warfar

Open article: This Week in AI: Week of 16th November 2025 by Aishwarya Srinivasan • 3 min read

This Week in AI: Week of 16th November 2025

Thanksgiving week, yet a lot happening in this week in AI!
Catch everything you need to know here 👇

Vision-Language Models connect what AI sees with what it reads and reasons.

They’re the foundation of AI systems that can interpret charts, medical images, retail shelves, or product catalogs.

But a generic VLM doesn’t understand your domain’s visual language.
That’s where fine-tuning becomes essential.

𝐖𝐡𝐲 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐚 𝐕𝐋𝐌 𝐦𝐚𝐭𝐭𝐞𝐫𝐬

A pretrained VLM already knows the basics of visual-text reasoning.
Fine-tuning helps it specialize for your domain.

→ In healthcare, it learns to detect anomalies in MRIs and X-rays.
→ In retail, it interprets shelf images and product layouts.
→ In enterprise, it extracts structured data from invoices and reports.

You’re not rebuilding intelligence, you’re refining perception to fit your use case.

𝐇𝐨𝐰 𝐋𝐨𝐑𝐀 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭

Full model fine-tuning is expensive and compute-heavy.
Low-Rank Adaptation (LoRA) keeps the base model frozen and trains only small adapter layers.

That means:
→ Faster training cycles
→ Smaller memory footprint
→ Lower compute costs
→ Domain adapters that are easy to swap in and out

You can maintain one base model and multiple lightweight adapters for each use case such as invoices, medical forms, or retail analytics.

𝐈𝐧𝐬𝐢𝐝𝐞 𝐚 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥

A VLM has three main components:
→ 𝐕𝐢𝐬𝐢𝐨𝐧 𝐄𝐧𝐜𝐨𝐝𝐞𝐫 converts pixels into visual tokens.
→ 𝐅𝐮𝐬𝐢𝐨𝐧 𝐋𝐚𝐲𝐞𝐫 combines visual and text context for reasoning.
→ 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐃𝐞𝐜𝐨𝐝𝐞𝐫 generates captions, summaries, or structured responses.

Each layer introduces potential failure modes like poor resolution, misaligned regions, or verbose hallucinations. Fine-tuning improves alignment and reliability across these components.

𝐓𝐡𝐞 𝐟𝐢𝐧𝐞-𝐭𝐮𝐧𝐢𝐧𝐠 𝐥𝐢𝐟𝐞𝐜𝐲𝐜𝐥𝐞

→ 𝐃𝐚𝐭𝐚 𝐝𝐞𝐬𝐢𝐠𝐧: Collect diverse, high-quality, clearly labeled visuals.
→ 𝐓𝐚𝐬𝐤 𝐝𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 : Choose the right setup: captioning, VQA, extraction, or localization.
→ 𝐋𝐨𝐑𝐀 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠: Train adapters for each domain efficiently.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Use both quantitative metrics and human review for grounding and accuracy.

Always evaluate across different slices such as document type, lighting, and template to surface hidden biases.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Each domain adapter should have its own dataset lineage, version, and evaluation score.
Reliability requires attention to fairness, privacy, consistency, and uncertainty handling.
Fine-tuning doesn’t just improve accuracy, it strengthens governance and ethical alignment.

LoRA fine-tuning makes VLMs faster to adapt, cheaper to deploy, and more aligned with your real-world data. 

〰️〰️〰️
Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Aaron Lax

Singularity Systems, LLC

Due to my networking with the world's leaders and some of the best engineers, we have decided to take it upon ourselves to do what no large company has and #ChangeTheWorld, becoming an incubator for ideas rather than merely reporting them!

Chief Executive Officer

BioTone ZKI

Senior Business Advisor

Cybersecurity Insiders

This is a group of chats and rooms that I have had the fortunate opportunity to include the TOP Minds of the world of Cybersecurity and we are able to collaborate and work together to change the world by changing Tech and Cybersecurity and those that are employed in these positions now….

My official title is IT Administrator, which is a Full Stack Developer with knowledge in All Fields. I am a system administrator with 8 years of professional experience building everything from .NET to Python to Javascript applications, I have professionally administered Linux servers for 8 years, Windows Servers 10 years, and in a non-professional capacity I have been a Linux Administrator and Security Specialist for 26 years now. Technology is my passion and bringing that passion to the world is the goal of TheLaxerz.

Entrepreneur/Cybersecurity Specialist/Developer/Network Engineer

Dark Cryptonite

Advisory Board Member

The Intelligence Community Inc.

Administration and coordination with other admins for the most popular Defense Group on LinkedIn

Administrator

infosec.live

My own groups are important, I assumed the same with my friend I supported for all shows and members…

Co Host

Dragonchain

Advisor to CEO

Arkansas Public Service Commission

Maintain Network including Security.
Firewall
Servers
User machines
Maintain Sites (public and intranet) / Web Servers
Manage VEEAM
Setup/Manage VMWare ESXi, vSphere
Setup/Administration of Windows Servers

Additional Duties due to Programming Background:
Develops Future Application Architectures and Plans
Mentor Programmers
Help in Languages newer programmers do not know
Develop Applications
Ensure security of applications 
Deploy applications and configure deployment

Completed Project:
Move 2 development ESXi hosts into 1 new ESXi host capable of running our development and internal File Server. 

Move 9 physical and 2 hosts to new hosts. 

Migrate data from 35 year old database to a new CRM software. 

Multiple Integrations into CRM tools and other tools for operations. 

Fix 2 Internal Important Applications and 1 development application. 
Replacement of 50+ user workstations and monitors. 

Reconfiguration of Firewall Policies for new hosts, and integration applications. 

VEEAM reconfiguration for new environments and old systems to prepare for upcoming task 1. 

Upcoming Task:

Move 8 Physical Servers into a Virtual Environment with Failover.

Network Coordinator

AQED

Advising as to Quantum, Blockchains, and the technologies and security of the future.

Senior Consultant

Red Sky Alliance (Reporting Portal)

Sr. Advisory Consultant

Network Coordinator, Technologist and Futurist, Thinkers360 Thought Leader and CSI Group Founder. Manage The Intelligence Community and The Dept of Homeland Security LinkedIn Groups.

My technical career is filled with a range of tech technologies, all the which have led me to artificial intelligence, I have gone from data analyst all the way to server administrator and network coordination.

Began programming at age 10 with BASIC, making Cars, Animals, and eventually Blackjack. This changed to VB, followed by C++, and then, when I started working, and prior to that, Python was my weapon of choice. 

In addition to these positions listed above I was on a Project that was funded for 3 years but never fully funded for the Supreme Court of AR which would have modernized their antiquated system for data entry and accounting, and make an Oracle Forms application turn into a Single-Page application developed with Javascript (Angular Front-End, Node Middleware, and Node-Backend), but the true issue was connecting the Oracle Database designed for the Oracle Forms application and password entry work properly thus we used Redis and JWTs to solve this allow for a seamless integration without redesigning the entire Database.  

While injured from a Car Wreck in 2020, I created a group called Cybersecurity Insiders Scoop, broke my back, both arms, and right legs, those groups became filled with the top minds in the world and cybersecurity and data science communities. Also, worked with InfoSec. Live did several shows with Roger Grimes from KnowBe4 and built a community that’s 300 strong in group messages and 300k strong in the groups DHS Group, The Intelligence Community, Emerging Technologies, and Cybersecurity Insiders Scoop LinkedIn Group, not Group Chat. 

I continue to work as a Network Administrator, have worked for the largest Distributor of Eggs in the entire world, several large engineering firms, but I’m advisor to 5 companies and created Cybersecurity Insiders, LLC and Board Member to Dark Cryptonite. And my passion is connecting and ensuring the safety and security of the world not just in cybersecurity but also with the world’s intellectual prowess. 

I continue to build communities and make friends, as #networking is the key to life and I hope to see the world learn this and operate in a fashion consistent with humanity not the world we are seeing divide itself every minute over nothing....

I have worked for large corporations that have hundreds of millions of dollars in revenue as Server / Security Administrator.

𝗧𝗵𝗲 𝗪𝗼𝗿𝗹𝗱’𝘀 𝗧𝗼𝗽 𝟭𝟬𝟬 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗠𝗲𝗻: 𝗛𝗼𝗻𝗼𝗿𝗲𝗱 𝗧𝗼 𝗕𝗲 𝗔𝗺𝗼𝗻𝗴 𝗧𝗵𝗲𝗺

Honored beyond words to be recognized in the Top 100 Most Influential Men in the World by Leadership Magazine. Thank you to Sibel Terhaar and the entire Leadership Magazine team for amplifying voices of innovators, builders, and those relentlessly shaping tomorrow.

Grateful for this acknowledgment not as a destination, but as a signal that the mission resonated: building intelligent systems, shaping responsible AI, advancing robotics, quantum, and creating a future defined by capability, humanity, and vision.

To those walking this road of change with me, this recognition belongs to you too. Our work is just beginning.

Founder of Singularity Systems and Cybersecurity Insiders. Strategist, DOW SME [CSIAC/DSIAC/HDIAC], Multiple Thinkers360 Thought Leader and CSI Group Founder. Manage The Intelligence Community and The DHS Threat

𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐒𝐄𝐂𝐔𝐑𝐄 𝐔𝐍𝐈𝐓𝐘 — 𝐓𝐡𝐞 𝐀𝐫𝐢𝐬𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤
Standing at the convergence of quantum physics, cryptographic science, autonomous systems, and secure communications, we are witnessing something extraordinary. Twin-Field Quantum Key Distribution (TF-QKD) is more than a protocol — it is a redefinition of secure communication. A channel where photons become truth carriers, where trust is validated by quantum interference, and where distance is no longer the enemy of confidentiality.
In traditional systems, security declines as distance increases. With TF-QKD, the relationship is reversed. Using single-photon interference and phase-matched coherent signals, it generates secure keys at rates that scale with the square root of transmission efficiency. This allows secure quantum communication to expand beyond the classical bounds — breaking the long-standing repeaterless limit without the complexity of quantum memories or repeaters. Today we are generating quantum-secure keys across hundreds of kilometers of optical fiber, proving that unbreakable channels can span national lines, strategic infrastructures, and future global networks.

This is not merely a cryptographic upgrade. It is the beginning of quantum-secure intelligence. TF-QKD enables authentication and control for autonomous agents, robotic systems, distributed AI models, and critical decision networks — all protected not by encryption strength, but by the laws of physics. Spoofing, interception, and man-in-the-middle attacks are eliminated not through defense but through impossibility.

Photonic security becomes the backbone for emerging machine cognition. AI-powered swarms, autonomous decision engines, and future intelligence architectures require secure neural pathways, not just encrypted channels. TF-QKD provides that pathway — a quantum-verified trust fabric that no adversary, algorithm, or future quantum machine can decode or manipulate.

This is no longer about cybersecurity. It is about securing cognition.

Not about protecting networks — but protecting intelligence itself.

As we build the future of AI, robotics, quantum systems, and secure infrastructure, we must also build the trust layer that unites them. TF-QKD is that layer.

The quantum bridge is open. What we choose to send across it will define the future.

#changetheworld

A Deep Dive into Quantum-Resistant Architecture for Secure Communications Why This Matters Right Now Imagine waking up tomorrow to find that every enc

Open article: The Quantum Security Revolution: How Next-Generation Blockchain is Protecting Our Digital Future by Aaron Lax • 9 min read

The Quantum Security Revolution: How Next-Generation Blockchain is Protecting Our Digital Future

𝗧𝗼𝗼𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝘀 𝗛𝗲𝗿𝗲. 𝗧𝗿𝘂𝗲 𝗦𝘂𝗽𝗲𝗿𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗛𝗮𝘀 𝗡𝗼𝘁 𝗬𝗲𝘁 𝗔𝗿𝗿𝗶𝘃𝗲𝗱.

Grok 3, Claude 4, Gemini 2, o3. These systems can solve graduate-level physics, write code, fix software, and reach ninety percent on advanced reasoning tests. They perform tasks in seconds that take humans hours. They impress us. They assist us. They help us build faster.

But let us be honest with the world. Being fast is not the same as being truly intelligent.

These AI systems do not invent new science. They do not design their own goals. They do not create theories humanity has never seen. They cannot think across months or years without us. They are powerful tools. They learn patterns, process information, and give answers. But they do not yet imagine.

Artificial General Intelligence (AGI) means matching human intelligence.
Artificial Superintelligence (ASI) means surpassing it.
We have not reached either.

To reach superintelligence, we will need more than bigger models. We will need AI that can rewrite itself. AI that generates its own goals. AI that discovers, not just predicts.

So where are we today? At the edge. Standing at the door. Watching glimpses of what might be possible.

The moment AI begins to truly imagine will be the moment everything changes.

#changetheworld

Let's #changetheworld Chuck Brooks, Alexey Navolokin, Robert Liscouski, and others including IBM, Titans Space Industries Inc., and those that are looking to push the boundaries of what has been.

The idea that human consciousness directly shapes quantum reality has persisted as one of the most seductive myths of modern science. It suggests that

Open article: Quantum Observer Illusion: Disentangling Conscious Waves from Code and Curcuits by Aaron Lax • 6 min read

Quantum Observer Illusion: Disentangling Conscious Waves from Code and Curcuits

𝙐𝙣𝙛𝙤𝙡𝙙𝙞𝙣𝙜 𝙏𝙝𝙚 𝙉𝙚𝙭𝙪𝙨 𝙊𝙛 𝙍𝙤𝙗𝙤𝙩𝙞𝙘𝙨, 𝙌𝙪𝙖𝙣𝙩𝙪𝙢, 𝙖𝙣𝙙 𝘼𝙞 — 𝘼 𝙒𝙤𝙧𝙡𝙙 𝘽𝙚𝙞𝙣𝙜 𝙍𝙚𝙒𝙧𝙞𝙩𝙩𝙚𝙣

Human history has pivoted on moments when a single convergence of technologies reshaped not just how we work, but how we think, how we build, and how we understand reality itself. Today that convergence is no longer speculative. It is here. 

Robotics is giving form and motion to machine intelligence, allowing systems to perceive, interact, and reshape our physical environments. Quantum computing is unlocking the hidden mathematics of the universe, solving problems that classical machines cannot even define. Artificial Intelligence is no longer a tool. It is becoming a collaborator, an architect of innovation, capable of synthesizing vast domains of knowledge and making sense of systems too large or too complex for any single human mind.

Singularity is no longer a metaphor. It is a reality. The merger of Robotics, Quantum, and AI is dissolving the boundaries between human ingenuity and machine capability. What once required decades of R&D now evolves in weeks, accelerated by self-learning systems, simulated digital twins, humanoid platforms, and quantum-informed optimization. Entire industries, from defense to healthcare, energy, infrastructure, space, and education, are not being updated. They are being reinvented. We are not just teaching machines to think. 
We are teaching machines to build new forms of thinking.

And so begins a new dialogue.

I am honored to launch a new podcast with a brilliant mind and global visionary, Fin Barker. Together, we will explore what happens when Robotics gains sentience of motion, when Quantum reveals hidden pathways of possibility, and when AI stops assisting and starts reasoning, creating, predicting, adapting, and engineering at human and superhuman scales. This podcast is not about trends or buzzwords. It is about transformation. It is about the world that is arriving faster than anyone expected. It is about the systems that will carry us into that world, and the minds who are building them right now.

We will not discuss the future.

Singularity Systems will reveal it and partners like IBM, AMD, NVIDIA, Malika Capital and friends like Alexey Navolokin

https://lnkd.in/eCeNeW55

#changetheworld

Founder of Singularity Systems Defense and Cybersecurity Insiders. Strategist, DOW SME [CSIAC/DSIAC/HDIAC], Multiple Thinkers360 Thought Leader and CSI Group Founder. Manage The Intelligence Community and The DHS Threat

Open video: EP:03 - Singularity Systems - How AI Will Change Everything (and Faster Than You Think) on youtube.com

EP:03 - Singularity Systems - How AI Will Change Everything (and Faster Than You Think)

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐋𝐚𝐰 𝐅𝐫𝐚𝐦𝐞

When Legal Becomes Cognitive, Autonomic, and Strategic

While I have a Legal Representative, whom I will keep private, unlike most things about Singularity Systems. 

I am not building a chatbot.

I am constructing a cognitive legal infrastructure to assist me when legal advice is not readily available.

What you see here is the first visible layer of our emerging legal-intelligence architecture within Singularity Systems — designed to protect, orchestrate, and weaponize intellectual systems in the age of autonomous robotics, agentic AI, and quantum-accelerated cognition.

In the program, as autonomous systems evolve beyond tools and begin to generate their own decisions, design their own subsystems, and even author their own blueprints, the single greatest threat to innovation will not be mechanical failure — it will be the inability to defend, enforce, and securitize the IP generated by intelligent machines.

The law was built to protect human ideas.

We are entering an era where intelligence — and invention — is non-human.

So we designed something new.

A multi-agent, legally-aware cognition engine capable of:
• Encoding and securing invention at the moment of its generation
• Translating technical invention into intellectual property claims
• Mapping strategic litigation and pre-emptive defense pathways
• Reconciling cross-jurisdictional compliance in real time
• Transforming robotic designs, neural architectures, and generated code
 into defensible, traceable, and enforceable IP assets

Each legal specialist you see is not a UI element — it is a cognitive agent:
IP and Technology Specialist:
 
Trained to interpret code, algorithms, robotics schematics, neural weight structures, and generative model outputs — and classify them as patentable, copyrightable, or trade secret-routable intellectual matter.

Litigation Specialist:
Built on adversarial reasoning chains, discovery protocols, argument construction engines, claim vulnerability analysis, and pre-trial resolution models.

Business Law Intelligence:
Designs corporate armor around innovation — holding structures, IP assignment strategy, protective layering, royalty architecture, and global protection paths.

Legal Coordinator:
The orchestrator — the meta-agent — fusing technical cognition, robotic architectures, quantum output, and human-authored legal strategy into defensible invention frameworks.

We are not automating documents. We are architecting legal cognition.
We are not just protecting IP.
We are building systems that will know how to protect their own.
Welcome to Singularity Systems.
Where intelligence invents — and invention defends itself.

𝗧𝗵𝗲 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗪𝗲 𝗕𝘂𝗶𝗹𝗱 𝗧𝗼𝗱𝗮𝘆 𝗪𝗶𝗹𝗹 𝗕𝗲 𝗧𝗵𝗲 𝗪𝗼𝗿𝗹𝗱 𝗢𝘂𝗿 𝗖𝗵𝗶𝗹𝗱𝗿𝗲𝗻 𝗜𝗻𝗵𝗲𝗿𝗶𝘁 𝗧𝗼𝗺𝗼𝗿𝗿𝗼𝘄

We now live in a world where algorithms make decisions faster than human conscience can even process. Tools once designed to assist now possess the speed, autonomy, and reach to act without our supervision. Artificial intelligence, biological engineering, swarm robotics, autonomous targeting, hypersonic precision, and total digital surveillance have collectively created something humanity has never faced before. A technological power capable not just of transformation but of irreversible consequence.

Yet our ethical systems—our laws, treaties, and moral frameworks—were built for slower, visible, and controllable technologies. They assume there is always time to react, to deliberate, to choose. But machines work at a different speed. They do not pause for reflection. They do not weigh conscience. They only optimize.

History teaches that progress in technology has never guaranteed progress in wisdom. The crossbow, the chemical weapon, the atomic bomb. Every innovation arrived faster than the values needed to govern it. Each time, humanity learned after the fact.

Today that luxury is gone. We are crossing a threshold where learning after the fact may mean there is no after.

The breakthrough technologies being developed—right now, in real time—are extraordinary. They can heal, build, protect, and elevate humanity beyond anything seen before. But in the wrong context, without reflection, they can rewrite the story of our species in a single line.

This is not a call to stop progress. It is a call to match technical power with human responsibility. To build systems that reflect our humanity, not just our efficiency. To design intelligence that expands our capacity for wisdom, not just our capacity for speed.

Because the real question is no longer Can we build it?
We already are.

The question is:
Will we remain the authors of our future—or become subjects to the systems we created?

Pause. Reflect. What we permit today becomes the world our children inherit tomorrow.

𝗦𝗼 𝗜’𝗺 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗱𝗼 𝘁𝗵𝗶𝘀.

I am going to shamelessly plug my X account.

This is where I am building in the open. Humanoid robotics. Artificial Superintelligence. Quantum architectures. Self healing systems that blur the line between biology and machine. The beginning of the next era.

If you want to see it unfolding in real time, raw, unfiltered, where the next breakthroughs will emerge, follow along:

https://x.com/mast3r0x1a4

The world is changing faster than anyone realizes. I plan to show you how it’s actually happening.

𝐒𝐨𝐫𝐚

https://lnkd.in/etAs4mNi

Open article: Aaron L. (@MAST3R0x1A4) on X by x.com

𝐓𝐡𝐞 𝐆𝐞𝐧𝐞𝐬𝐢𝐬 𝐎𝐟 𝐀 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐖𝐨𝐫𝐥𝐝

In ancient texts, creation was spoken into existence across days. Light separated from darkness, oceans from land, data encoded into biology long before we understood what "code" even was. In that story, the Earth took form through intention, order, and an unfolding logic of complexity.

Today, creation requires a different language.

Not spoken. Not written. Queried.

I sat with a blank digital canvas. No form, no shape. Then I opened Google's BigQuery Earth Engine. A planetary-scale geospatial system. A living database of our world. Not narrative, but granular truth. Petabytes of satellites, atmospheric chemistry, land cover, human footprints, vegetation health, waterways, thermal gradients… every living and shifting contour of Earth — indexed, time-stamped, queryable.

I didn’t wait 7 days.

I built an Earth in hours.

Not an illustration or simulation — but a computationally reconstructed, data-born Earth. With mountains whose elevations stream from global DEM matrices. Forests powered by NDVI signatures of living chlorophyll. Clouds calculated through temporal MODIS composites. Cities detected from spectral urban encroachment. Rivers traced by historical hydrological flows.

My “Let there be light” was a SQL statement.
And light appeared across continents — extracted from VIIRS night maps. You could watch human civilization glow.

My “Let the waters be gathered” was a temporal hydrological mask — live-updating the global water cycle.

My “Let the land bring forth life” was an ecosystem model — mapping biodiversity across Costa Rica, the Amazon Basin, Madagascar, and my hometown of Hot Springs.

In Genesis, creation ended with rest.

But in my genesis, creation begins with iteration.

Because now, Earth is not static. It evolves. It responds, learns, predicts, warns, rebuilds — and invites us to understand the complexity we once thought was divine mystery.

The next Genesis will not be written.

It will be computed.

And some of us will architect its language.

If you work with Earth systems, climate models, robotics, GIS, AI, or planetary-scale analytics — I invite you to join me.

The era of worldbuilding is no longer mythology.

It's executable. 

Singularity Systems with partners like AMD, IBM, Titans Space Industries Inc., and running on and looking to partner with Google and Google DeepMind. 

#changetheworld

𝐓𝐡𝐞 𝐀𝐫𝐭 𝐨𝐟 𝐋𝐞𝐠𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞

The legal profession has never lacked knowledge.

It has lacked reach, speed, and clarity.

Geopolitical negotiations, trade regulation, sanctions, cross-border licensing, sovereign contracts, and international compliance require a mind that can think across jurisdictions, infer intent, interpret structure, and advise with diplomatic precision.

I have built that mind.

An analytical legal intelligence engine capable of reading treaties like arguments, legislation like frameworks, and policy like architecture. It does not search. It interprets. It does not summarize. It advises.

It drafts with context, not keywords.
It supports attorneys, advisors, foreign ministries, and organizations that operate at the intersection of law, business strategy, governance, and commerce. It offers the sophistication of institutional counsel at a fraction of the cost and in a fraction of the time.

It places precision legal insight into the hands of those who could never afford it, expands the reach of law beyond borders, and beyond privilege. It allows intelligence to travel farther than influence.

This is not automation.
This is augmentation.

An instrument of clarity for those who negotiate, define, govern, or defend.

The first system was built not to practice law, but to elevate it.

Singularity Systems is moving to #changetheworld. We are in the pre-seed stage now. Please reach out if you are interested in helping bring the world tomorrow.

Check Out the Beta of My Already Completed but Not Yet Served Legal Advisor
https://lnkd.in/ejeksJcQ

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Aishwarya Srinivasan

Aaron Lax

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taplio

The Battle

Aishwarya Srinivasan

Aaron Lax

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Aishwarya Srinivasan's LinkedIn Performance

Performance ScoreAaron Lax's LinkedIn Performance

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Performance ScoreAishwarya Srinivasan's LinkedIn Performance