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

As an AI engineer, LLM Twin is an important architecture to understand.

An LLM Twin refers to a system where a language model is designed to represent the knowledge, writing patterns, and decision logic of a specific person, team, or organization. The goal is consistency and reliability, not novelty.

At its core, an LLM Twin is built around how information flows. Think of it as 4 steps:

1️⃣ First, content is collected from real sources: blogs, internal documents, posts, code repositories, or knowledge bases. This data reflects how the person or organization actually thinks and communicates.

2️⃣ Next, the data is structured for two purposes.
One path prepares instruction-style examples that help the model learn how to respond.
Another path prepares retrieval data so the system can pull relevant context at inference time. This ensures responses stay grounded in real material.

3️⃣ Then comes training and evaluation. Models are fine-tuned where appropriate, tested against clear criteria, and versioned before deployment. This step looks very similar to traditional ML workflows, just adapted for language models.

4️⃣ Finally, at inference time, user requests are combined with retrieved context and passed through the deployed model. Monitoring is critical here: quality, latency, and cost all need to be observed continuously.

Where is this used in practice?

✦ Personal or creator assistants that help scale writing and communication
✦ Company-wide assistants that maintain a consistent product or brand voice
✦ Internal tools that answer questions based on code, docs, or prior decisions
✦ Support systems that reflect how experienced engineers respond to issues

Why should AI Engineers understand this?
This architecture reflects how GenAI systems are actually built and maintained. It forces you to think beyond prompts and focus on data quality, retrieval design, evaluation, and long-term system behavior.

My 2 cents 🤌
This is a GREAT topic to build a portfolio project around!! 

Credit: LLM Engineer’s Handbook by Paul Iusztin  and Maxime Labonne. I really like how this book breaks down AI engineering clearly and goes deep into different GenAI system architectures without hand-waving.

Over the past few months, Arvind and I've delivered seven deep technical workshops across agentic AI, RAG, context engineering, AI evals, MCP, vibe coding, and more.

One theme kept coming up: you want something more structured. Not just standalone sessions, but a clear end-to-end roadmap from A to Z.

So that’s what we’re building.

A structured, comprehensive course for:
- Hands-on builders: software, AI, and ML engineers
- AI product managers
- Tech leaders and CXOs looking to upskill meaningfully

Before we finalize it, I’d love your input. I’ve created a short survey to understand where you are, what you want to learn, and your career goals.

It takes under 10 minutes.

As a thank you, you’ll receive an additional 10% discount when we launch the course soon 🫶

Survey: https://lnkd.in/gQYeBCRP

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Here’s my honest pattern with almost every AI tool I try.

I end up using a lot of tools, usually early access. After repeating this cycle enough times, I realized my reactions are almost predictable.

👉First hour
There’s real excitement.
I’m clicking around, testing odd prompts, trying things that used to fail. No strategy yet, just my curiosity and momentum.

👉Next few hours
I start comparing.
Where would this actually fit in my work?
What would I stop using if I kept this?
Is this clearly better, or just new?

👉Later that day
I experiment without guidance.
I skip the docs and examples on purpose. I try incomplete ideas, messy workflows, prompts I would never put in a demo. This tells me more than any tutorial ever could.

👉Day two
Then I look at the examples the team shared.
They usually work smoothly. They’re polished. They show the tool at its best, which is exactly what they’re meant to do.

👉After a few days
This is where the real understanding kicks in.
I start tweaking those examples. Changing assumptions. Using it in ways that feel realistic, not ideal. That’s when the boundaries show up. Not frustration. Just clarity.

Here’s the takeaway I’ve learned the hard way:
If you haven’t noticed limitations yet, you probably haven’t pushed the tool very far.

And honestly, if I use something consistently for three days and still can’t find a meaningful flaw, that’s rare. That earns real respect.
I haven’t experienced that yet.

Curious how others see this.
Are there tools you’ve used deeply where the limits still haven’t shown up?

Just a couple of weeks ago, Arvind and I hosted an in-person AI meetup with The Hub Bengaluru.

Over 300 people showed up in person.

Every single one of them was either already working in AI or actively trying to break into the space. The energy in the room was unreal. It genuinely felt like a room full of builders, practitioners, and people who are curious enough to go deep.

During the event, we rolled out a small challenge. We asked attendees to share a recap of what they learned, and we said we would pick one winner.

Really excited to share that Suraj V is that lucky winner 🎉
Suraj gets free access to all the The Gen Academy workshops we have hosted so far.

Huge congratulations, Suraj V!! 

Building The Gen Academy has always come from a very real observation that Arvind and I see every single day in our work. The adoption of generative AI and LLMs is moving incredibly fast, but the depth of AI engineering skills needed to build reliable systems is not keeping up.

There is a very real skill gap in AI engineering right now! There are so many curious souls who want to become AI engineers- but they aren't able to get those opportunities; and there are so many companies out there who  want to hire, but aren't finding the right talent. 

Our mission is to bridge that skill-gap!

As people who are deeply involved in this space, we felt a responsibility to help bridge that gap. Not just for people who are already experienced, but also for entry-level folks who want to build strong foundations the right way.

That is why our workshops are intentionally designed to be both intuitive and practical. We focus on theory that actually helps you reason about systems, and we pair that with hands-on demos where you build real things using modern toolkits and frameworks.

So far, we have hosted 6 workshops, and the response has been incredibly encouraging. We have trained 1500+ people already ❤️ 

Our next workshop is coming up on February 15th, and it will be focused on AI evaluations (link in comments)

Do follow The Gen Academy to stay updated on all our upcoming workshops.

Six months ago, Arvind and I started The Gen Academy with one simple goal- to create workshops that are deeply-technical, practical, cuts through the ton of noise out there, and that provides people clarity on building productive-grade AI systems.

So far we have trained 2100+ people. We’ve hosted 7 workshops, and consistently we’ve had 300–400 builders join us live.

The audience has been incredible.
- Software engineers moving into AI.
- AI engineers building LLM systems in production.
- Product leads thinking about reliability and evaluation.
- Directors and VPs from top tech and Fortune 500 companies who want to truly understand what’s under the hood.
- Even people from non-tech background who wanted to strengthen their technical fundamentals 

Recently, we hosted a workshop on AI Evaluations.

And this one felt especially important.

Because evaluating non-deterministic generative systems is fundamentally different from traditional ML evaluation.

We covered:

✦ How to evaluate non-deterministic generative AI systems
✦ How to think about benchmarks and what they actually tell you
✦ How to design your own evaluation dataset
✦ How to choose the right metrics for qualitative and system-level evaluation
✦ How to combine human evals, code-based evals, and LLM-as-a-judge
✦ A hands-on walkthrough so this isn’t just theory

If you’re building LLM applications, RAG pipelines, or agentic systems, evaluation is not optional. It is the difference between a demo and a reliable product.

If you missed the live session, we’ve shared the recording here: https://lnkd.in/gzMdbfWH

We’ll continue building these workshops for engineers who want to go beyond experimentation and into production.

More coming soon, so do follow us & The Gen Academy to get updates on our next workshops.

5 days to go! 
Arvind and I are super excited to be hosting an AI evals hands-on workshop on February 15.

It’s a 90-minute session where we’ll cover the fundamentals of AI evaluations and then actually do it together. You’ll walk away having built and reasoned through real evals yourself. 

If you’re aspiring to be an AI engineer or planning to upskill for AI roles in 2026, understanding how to evaluate LLM applications is critical.

Building an LLM app is often the easy part. Knowing whether it truly works in production is where most teams struggle.

LLM evaluation is not just about correctness anymore. You are judging usefulness, safety, faithfulness to sources, consistency across runs, cost, latency, and real user outcomes across prompts, RAG pipelines, tools, and agentic workflows.

Because this is hard, many teams fall back to vibe-evals. If a few outputs look good, they ship. That approach breaks quickly when models change, prompts regress, retrieval fails, or user behavior shifts. Without evals, problems surface only after users complain.

Why learning evals matters:
✦ Evals turn LLM development from guesswork into engineering
✦ They help catch regressions as models, prompts, or tools evolve
✦ They are essential for scaling RAG systems and agentic workflows
✦ In 2026, strong teams use hybrid evals combining automated metrics, LLM-as-a-judge, and targeted human review

Evals are no longer optional. They are a core AI skill.

In this workshop, Arvind and I will walk through how AI evals are done in real systems, with hands-on exercises so you build intuition.

This is designed for AI engineers and tech leads building systems, and even business leaders who want intuition for what “good” really means in production. Even if you are a beginner, don't worry, we will give you the foundations for you to understand everything comprehensively, and enough resources that you can review at your own pace.

If you want to level up your AI skills for 2026, register here:
https://lnkd.in/dQSR7yhU

Evaluation Resources

Fine-tuning is going to be one of the biggest focus areas for AI engineering teams this year.

We’re past the stage where calling an API is enough. The teams creating real leverage are the ones adapting models to their own data, workflows, and edge cases. Out-of-the-box models are powerful, but they are generic. Fine-tuning is where enterprises unlock meaningful efficiency gains and build real differentiation.

If you want to upskill in AI engineering, this is a topic you cannot skip.
Arvind and I are hosting a deep, hands-on workshop on Fine-Tuning LLMs.

In 90 minutes, you’ll learn:
✦ When fine-tuning makes sense and when it does not
✦ How SFT, RLHF, and DPO fit into real-world pipelines
✦ An end-to-end, code-based demo from raw data to a specialized model
✦ How to evaluate true performance improvements and avoid production pitfalls

This session is designed for software engineers, AI engineers, and data scientists, and it is equally valuable for product managers, solutions architects, tech leads, and executives working closely with model ecosystems.

If you want to move from using LLMs to actually optimizing them, this workshop will give you a practical framework to do it: https://lnkd.in/g-5gHrsY

In 2026, AI isn’t judged by a wow factor anymore.
It’s judged by whether it actually improves your workflows.

One of the most common questions I get from founders and teams I advise is how to automate go-to-market and sales properly. Real GTM automation needs deep ICP research, clear definitions, personalized messaging, and credible sources. This isn’t a one-prompt problem.

Normally, building a solid GTM or sales report would take me several hours, even with existing AI tools helping with research.

I recently got early access to Airtable’s new Superagent, and what impressed me was the approach. It didn’t treat my request as a single task. It broke it into ~16 steps, ran multiple agents in parallel, did deep research per subtask, cited sources, and stitched everything together into one structured output called a Super Report.

I used it to build a technical, informational report on AI for GTM/Sales in minutes. No fluff. No hallucinations. Just deeply researched content I could actually use with business leaders.

If your work involves building reports, decks, or MVP websites that need accuracy and depth, this feels less like “AI that makes things pretty” and more like AI that does real work.

Do check it out here: https://lnkd.in/gDkfSSG2

PS: You can get 2 months free for @Superagent, using code: AISH2FREE

#SuperagentPartner, #ad, #SuperagentbyAirtable

I teach AI Agents and Lead Gen | Lead Gen Man(than) | 100K+ students

For brand collaboration: manthan@leadgenman.com

Hi, I'm Manthan.

TL;DR:
I help entrepreneurs, business owners, and students build AI agents and automation systems that actually generate revenue. I post about AI workflows, agentic frameworks, LLMs, lead generation, cold emails and everything I learn along the way. No fluff, just practical stuff you can use.

The longer version:
I run an AI Automation Agency and create content about AI agents, n8n workflows, LangGraph, Lead Gen and go-to-market strategy. I share what works, what doesn't, and the lessons I pick up building systems for real businesses.

→ 200,000+ students across my courses
→ Worked with YC-backed companies like Retell AI, VectorShift AI, and Persana AI
→ Panel speaker at international events
→ Bachelor's in Computer Science and Engineering 

Links:
Courses: courses.leadgenman.com
AI Workflows for your business: leadgenman.com
Lead Gen Outbound Systems: leadgenman.com/leadgen
Learn from me; grow with me.

Manthan Patel

MCP is the hottest thing under the sun right now.

And you’re still confused about MCP?

Simply put, MCP (Model Context Protocol) is a 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝘁𝗼 𝘂𝘀𝗲 𝘁𝗼𝗼𝗹𝘀 via a 𝗰𝗹𝗶𝗲𝗻𝘁-𝘀𝗲𝗿𝘃𝗲𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲.

What this means for 𝘺𝘰𝘶:

You can now build a 𝗳𝘂𝗹𝗹𝘆 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝘀𝗲𝗰𝗼𝗻𝗱 𝗯𝗿𝗮𝗶𝗻 using Claude Desktop + a few local MCP servers.

Each server runs 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 𝘃𝗶𝗮 𝗗𝗼𝗰𝗸𝗲𝗿 𝗼𝗿 `𝗻𝗽𝘅`, and can expose tools, memory, search, or even apps, all with standardized interfaces Claude understands.

MCP acts as a universal connector, allowing Claude to securely access and work with your data where it already lives. No more copying and pasting between apps!

Awesome MCP Integrations available:

1. Communication & Social
- WhatsApp: Message analysis and automation - https://lnkd.in/dkaEekCj
- Slack: Get Claude analyzing your conversations - https://lnkd.in/dXm5A-EP
- Discord: Community engagement and moderation - https://lnkd.in/dpiwxt8S

2. Search & Discovery
- Brave Search: Real-time web information - https://lnkd.in/dQq-hjsx
- Airbnb: Accommodation search - https://lnkd.in/dacWq5ww

3. Business & E-commerce
- Shopify: E-commerce platform management - https://lnkd.in/dH-4V768
- Asana: Project management - https://lnkd.in/dT2XQcjy
- HubSpot: Marketing, sales, and service - https://lnkd.in/dGKefb4e

4. Development & Creative Tools
- GitHub: Code analysis and repo management - https://lnkd.in/dbhpTkJd
- VS Code: Code editing and IDE integration - https://lnkd.in/dMVBPYih
- Blender: 3D modeling and animation - https://lnkd.in/dPjNDyVg
- Figma: Design collaboration and prototyping - https://lnkd.in/duB_J2iX

5. Knowledge & Workspace
- Notion: Workspaces management - https://lnkd.in/dkwWNCWp
- Google Drive: Document storage - https://lnkd.in/d9DAntRF
- Airtable: Structured database management - https://lnkd.in/dt9rJmEM

6. Data & Storage
- PostgreSQL: Enterprise-grade relational database - https://lnkd.in/dwHxuN5x
- MySQL: Popular open-source database - https://lnkd.in/ddma4iiA
- SQLite: Lightweight file-based database - https://lnkd.in/djnNDy5m

Just install Claude Desktop App, download the MCP server for your tool of choice, and configure the connection. Most take less than 5 minutes!

How to Plug These Into Claude Desktop:

1. Open 𝗦𝗲𝘁𝘁𝗶𝗻𝗴𝘀 → 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 → 𝗘𝗱𝗶𝘁 𝗖𝗼𝗻𝗳𝗶𝗴
2. Add each server in the JSON config file 
3. Restart Claude Desktop. 

You’re now MCP-boosted!

Over to you: Have you tried MCP yet?

Everyone's building AI agents, but few understand the Agentic frameworks that power them.

These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development:

𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻)
- Creates visual connections between AI agents and business tools
- Flow: Trigger → AI Agent → Tools/APIs → Action
- Solves integration complexity and enables rapid deployment
- Think of it as the visual orchestrator connecting AI to your entire tech stack

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain
- Enables stateful, cyclical agent workflows with precise control
- Flow: State → Agents → Conditional Logic → State (cycles)
- Solves complex reasoning and multi-step agent coordination
- Think of it as the brain that manages sophisticated agent decision-making

Beyond technicality, each framework has its core strengths.

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻:
- Integrating AI agents with existing business tools
- Building customer support automation
- Creating no-code AI workflows for teams
- Needing quick deployment with 700+ integrations

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵:
- Building complex multi-agent reasoning systems
- Creating enterprise-grade AI applications
- Developing agents with cyclical workflows
- Needing fine-grained state management

Both frameworks are gaining significant traction:

𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Visual workflow builder for non-developers
- Self-hostable open-source option
- Strong business automation community

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Full LangChain ecosystem integration
- LangSmith observability and debugging
- Advanced state persistence capabilities

Top AI solutions integrate both n8n and LangGraph to maximize their potential.
- Use n8n for visual orchestration and business tool integration
- Use LangGraph for complex agent logic and state management
- Think in layers: business automation AND sophisticated reasoning

Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

This guy is running AI street interviews as paid ads with 4.5x ROAS 🤯

No creator. No film crew. No budget. 

Just a prompt + Calico AI

Here are 11 AI video tactics brands are using to create viral ads right now:
→ Street interviews (indistinguishable from real ones)
→ Podcast-style product endorsements
→ Viral food videos
→ Try-on videos with zero models
→ Shock hooks (47% higher completion rate)
→ Instant localization into any language
→ Product commercials
→ POV adventure content
→ Real estate transformations
→ Anatomical animations
→ Unboxing videos

Most marketers have no idea these exist yet.

I put together a free PDF with the exact prompts for all 11 along with the tool I'm using.

You can get all the prompts for these videos for free: https://lnkd.in/eteM-anD

The tool I'm using to generate these videos is Calico AI:
https://heycalico.ai/

Your SEO is working. Your AEO is broken.
 
SEO gets you ranked on Google.
AEO gets you cited by ChatGPT, Perplexity, and Claude.
 
Two completely different systems. Most founders are only playing one game.
 
The data is clear:
 
→ 44% of users now trust AI recommendations over traditional search
→ AI referral traffic converts 2x better in 1/3 the sessions
→ 80% of companies have zero visibility into whether AI even knows they exist
 
The top-cited sources on LLMs are Reddit, LinkedIn, and articles.
 
Not your homepage. Not your product pages. Content.
 
I've been using outwrite ai to improve my brand visibility in AI search.
 
How It Works:
 
1. Enter your business URL
2. It performs competitor research automatically
3. Shows you every topic your business can rank for in AI search
4. Generates citation-optimized blog articles and social posts
5. Tracks your mentions, sentiment, and industry ranking
 
And the content it creates follows Google's E-E-A-T guidelines while being optimized for AI citations. So you're not choosing between Google and AI. You get both.
 
Whether you're a solo founder or SMB, this is how you stop being invisible to AI.
 
I put together a complete breakdown on how AEO works and how to actually get cited on AI models.
 
Comment "AEO" and I'll send it your way.

There is no "right" or "wrong" way to grow on LinkedIn.
 
But there is a conventional way and a better way.
 
Here's what most people get wrong:
 
❌ Conventional way:
→ Post random selfies whenever you feel like it
→ Spam 30 hashtags and pray
→ Ignore comments like you're Beyoncé
→ Drop 12 random links in bio and confuse everyone
→ Expect 1 Zillion followers in 30 days
 
✅ Better way:
→ Post content that solves your audience's problems
→ Make shareable content and collab with others
→ Reply fast and make people feel seen
→ Have one clear link and one clear CTA
→ Focus on steady, engaged growth that lasts
 
Two things matter for LinkedIn growth:
 
1️⃣ Know your audience
2️⃣ Know "what you should NOT do" more than "what you should do"
 
Most people overcomplicate this.
 
They chase hacks. They copy viral formats. They wonder why nothing sticks.
 
The creators who win are the ones who post consistently, engage genuinely, and build trust over time.
 
But here's the hard part: creating quality content consistently takes hours.
 
That's why I use Stanley (Your Content Coach).
 
What Stanley does for me:
 
→ Learns my voice automatically by scanning my past posts (no manual training)
→ Brainstorms content ideas based on what's actually working
→ Sends weekly analytics so I know what resonates and why
→ Writes posts that sound like me - not generic AI slop
 
The results speak for themselves: 3-5x better engagement, hours saved every week, and content that actually sounds like me.
 
Stronger content leads to more reach, more credibility, and more doors opening.
 
Stop hesitating. Start posting.
 
With your very honest content coach: https://lnkd.in/djr5JRe5

Grow your LinkedIn Account!

I was spending 30 minutes every morning scrolling through tech news sites just to stay updated.
 
So I built an AI agent that does it for me. It pulls news from my favorite sources, reads through everything, and sends me a clean summary every single morning.
 
Now I consume all of that in a 5-minute email with my morning coffee.
 
And I built the entire thing just by talking to AI. No code. No workflows. No node editors.
 
I used Appy.AI, and here's what surprised me:
 
→ I typed one sentence: "Build me a news AI agent that monitors and summarizes daily tech news"
→ Their agent builder Violet started asking me clarifying questions
→ Within minutes, it built the full application with a landing page, settings, triggers, and database
→ It even generated a name, logo, and personalized the whole experience
 
But here's what really sets it apart from every other AI builder I've tried:
 
1️⃣ Full authentication system built in. Users can sign up, log in, and manage their accounts
2️⃣ Organization management with roles and team invites
3️⃣ Third-party integrations like Airtable, Calendly, and your existing tool stack
4️⃣ Scheduled triggers so my agent runs automatically every morning
5️⃣ Built-in Stripe infrastructure so you can start charging customers immediately
 
I connected my agent to Airtable, customized the theme to cyberpunk (because why not), and the whole thing was production-ready without touching a single line of code.
 
You can build agents for yourself, your team, or to sell and monetize. The entire business infrastructure is already there.
 
This is the first platform I've used where building and shipping the product wasn't the hard part at all.
 
Try it yourself: https://lnkd.in/ezrHd5v5
 
Over to you: If you could build any AI agent and start selling it tomorrow, what would it do?

taplio

The Battle

Aishwarya Srinivasan

Manthan Patel

Followers

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Performance ScoreAishwarya Srinivasan's LinkedIn Performance

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Ready to grow your LinkedIn presence?

taplio

The Battle

Aishwarya Srinivasan

Manthan Patel

Followers

Posts

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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

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