Armand Ruiz

Director Data Science & AI Elite

Leading the Product Management org for the AI Portfolio of products focused on Generative AI and Traditional ML. Products include: watsonx.ai and watsonx.gov

Vice President of AI Platform

I am building the world's largest team of 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 by

- Recruiting hundreds of new engineers around the globe
- Reskilling our existing talent
- Offering them top-quality training in new-generation AI

Responsible for Pre-Sales Generative AI Pilots with the new IBM watsonx.ai Platform.

Director watsonx team - Client Engineering

Director of the group IBM Data Science Elite team. We prove the value of IBM Watson solutions by co-creating pilots with our customers. I managed a team of 90+ data scientists that worked on 100s of complex AI implementations.

Director Data Science

I manage a rockstar team of Product Managers leading the product portfolio of IBM Watson AI products.

Product Director - Product Management, Watson Studio and Machine Learning

Leading new Data Science products for IBM

Lead Product Manager - IBM Data Science and Watson Machine Learning

Bring Open Source data science tech to IBM stack

Product Manager for Data Science Open Source Extensibility 

In charge of Data Science projects in EMEA

Data Science Advocate - IBM Data Science & Machine Learning

Support sales in Data Science opportunities

Technical Seller Data Science & Smarter Cities

Internship to bring new innovations to IBM

Intern - Technical Software Engineer, Extreme Blue

Vodafone

Optimize 3G network coverage using Data Science

Data scientist - Radio Optimization Engineering

Federació Catalana de Tennis

Tennis Coach

VP of Product - AI Platform @IBM

As the Director of AI Engineering at IBM, I lead the watsonx team, a global group of over 1,000 AI Engineers who work on cutting-edge Generative AI solutions for various industries. With more than 12 years of experience in AI, I have a passion for democratizing practical AI skills and empowering professionals to leverage AI to transform their business.

Previously, I managed IBM's Data Science and AI Elite, delivering complex AI implementations for hundreds of clients. 

I also led the product management of IBM Watson Studio and Watson Machine Learning, an AI platform used by hundreds of customers and SaaS apps powered by AI. I hold a Master of Science in Telecommunications from Universitat Politècnica de Catalunya and speak three languages: Spanish, Catalan, French, and English.

Connect with me!

✉️ JOIN MY NEWSLETTER: Every Saturday morning, you'll get 1 actionable tip to launch and grow your AI strategies in business in less than 4 minutes. → GO HERE: https://www.nocode.ai/

📧 Email: armand@nocode.ai
X: @armand_ruiz


"It is kind of fun to do the impossible"
All opinions are my own and don't represent my employer.

Called it. 🎯 The Model Context Protocol (MCP) is officially moving to the Linux Foundation under the new Agentic AI Foundation.

I’ve been saying for a while that open governance is the only viable path forward for AI infrastructure. Today’s news proves it.

This brings Anthropic, OpenAI, Microsoft, Google, and others under one neutral banner. 

10,000+ active servers. 
97M+ SDK downloads. 
Zero vendor lock-in.

If you want an ecosystem to flourish, you have to open it up. The era of Agentic AI just got a serious upgrade.

Read the news here: https://lnkd.in/g7vy5-x2

At NeurIPS2025, Google presented Titans, a new architecture that combines the speed of RNNs with the performance of Transformers. 

The trade-off between infinite context and computational cost is the biggest hurdle in AI right now.

While Transformers are accurate but expensive to scale, and linear models are fast but lose detail, Titans delivers the best of both worlds by introducing Test-Time Memorization.

The Core Innovation: Instead of a static state, Titans uses a deep neural memory module that actively updates while running, utilizing a "Surprise Metric":
- Low Surprise: Routine data is processed efficiently without clogging memory.
- High Surprise: Anomalies and key details are instantly integrated into long-term memory.

The Results:
- Scales effectively to 2M+ token context windows.
- Outperforms larger models (including GPT-4) on extreme long-context reasoning benchmarks.
- Generalizes beyond text to genomics and time-series.

This is a move from passive processing to active, efficient learning.

Jensen just dropped a masterclass on the future of AI. 

It was great to hear from him in a long-format podcast conversation. 

Three key insights he shared:

1/ Human language is the new syntax: The "tech divide" is closing. You won't need C++ to code the future; you just need to speak.

2/ The "30-Day" Mindset: Nvidia operates like they are always 30 days from bankruptcy. This paranoia fuels their innovation and resilience.

3/ Safety is an engineering problem: Forget the sci-fi movie plots. AI safety is just like cybersecurity—as capabilities rise, so do the guardrails.

Pic taken during my epic commute

No alternative text description for this image

Current mood: "Joining a new company is akin to an organ transplant - and you're the new organ. If you're not thoughtful in adapting to the new situation, you could end up being attacked by the organizational immune system and rejected."

Learning fast, exploring the culture, and pushing myself to bring out the best work I’ve ever done. 

Loving the challenge. 🤩

As a leader, your job is to run a tight ship.

That doesn’t mean control. It means precision.

Clear goals. 
Clear roles. 
Clear standards.

In fast-moving teams, especially in AI, ambiguity is expensive and confusion kills momentum.  Alignment compounds it.

Gemini 3 is here and it isn’t just another model release. It’s Google firing on every AI cylinder: infra, research, product, and distribution.

A few things stand out:

1.	Full-stack advantage, finally realized.
Gemini 3 is the first model shipped into Google Search on day one. That’s not just marketing ; it’s how you put frontier AI in front of billions.

2.	The agent era is no longer theoretical.
With Deep Think mode, long context, multimodal understanding, and real-time planning, Gemini 3 moves beyond chat. It’s not a chatbot. It’s a problem-solver.

3.	AI is getting better at “reading the room.”
It reflects a core shift: models are starting to grasp intent and nuance and not just data.

Benchmarks back it up: topping LMArena, solving “Humanity’s Last Exam”, setting a new bar in math and science tasks. But that’s the sideshow.

More info here: https://lnkd.in/gWeXGwhC

I cannot wait to try it out

JSESSIONID

li_at

Talks about #ai, #nocode, #deeplearning, #machinelearning, and #artificialintelligence

building AI systems

𝗔𝗜 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 𝟭𝟬𝟭

Current AI is often described as a "statistical correlation machine." It predicts the next word or pixel based on patterns it has seen before. World Models represent a shift toward causal understanding—giving machines a way to simulate the future.

What is a World Model? Think of it as an internal flight simulator. Instead of just recognizing an environment, the AI builds a mental model of how that environment works. It uses this model to test hypotheses ("If I drop this glass, it will break") without needing to actually perform the action.

The "𝗟𝗮𝘂𝗻𝗱𝗿𝘆" Example Imagine training a robot to fold clothes.

- 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot must physically interact with the clothes millions of times, learning through endless trial and error.

- 𝗪𝗶𝘁𝗵 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot simulates the physics of the fabric in its head. It plans the fold internally, predicts the outcome, and only moves its arm when it is confident. This results in safer, faster, and more generalizable learning.

𝗛𝗼𝘄 𝘁𝗵𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗟𝗠𝘀: While Large Language Models (LLMs) are masters of syntax and semantics, they often struggle with physical causality. World Models differ because:

𝟭. 𝗧𝗵𝗲𝘆 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗰𝗮𝗹𝗹: They don't just retrieve information; they run mental experiments.

𝟮. 𝗧𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻 𝗹𝗮𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗹𝗲𝘀: They infer the physics of their environment from data rather than being hand-coded.

𝟯. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲: They predict "what happens next" in a state-space, not just a token-space.

𝗧𝗵𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: The next frontier is the convergence of these technologies. By combining the reasoning of LLMs with the grounding of World Models, we move closer to Embodied Intelligence—software that can read, watch, plan, and act in the physical world.

Scaling laws in AI are nowhere near finished. The latest Gemini 3 results show it clearly.

Pre-training: Many believed progress had plateaued. Yet the jump from 2.5 to 3.0 is one of the biggest leaps we have seen. When you improve data, architecture, and compute together, you keep breaking ceilings. No walls in sight.

Post-training: This might be the real frontier. We are still early in how we align, steer, and specialize models. The innovation curve here is just beginning, and the gains on 3.0 prove it.

Good insights from Deepmind engineers this week.

Better models are not coming slowly. They are coming faster.

Plan for continuous capability jumps, not incremental upgrades. The disruption curve is steepening.

it is very cool to work everyday with people in person. i think  product innovation and magic really happens with real human interaction

It’s insane how many hours I’ve seen senior technical leaders burn on architecture charts. 

All that time spent polishing pixels... now you just describe it in plain English and boom and nano banana AI handles the rest.

By the way, this is a solid, modern and agile AI stack. To build at x10 speed you need to have the right building blocks.

Fortusight, Founder | AI advisor | Ex-Dir of Llama @Meta & Ex-VP of Watson @IBM

Fortusight's AI stack

A lot of people ask me this question, I'll start with the absolute core:
1. I use Agno for multi-agent framework, they're fast, elegant and robust.
2. Exa for web grounding, I consider Exa a bigger booster than LLM choice for my use case.
3. FMP for premium, clean, reliable financial data turned into tailored tools.

For LLMs, I use O3 for multi-agent orchestration and planning, GPT 4.1 for digesting web content and supporting my A1Facts flow; Flash2.5 for structured extraction and Gemini 3 pro for synthesizing investment thesis.

I constantly progress the choices of LLMs prioritizing quality over price.

For tools, I use Cursor with Opus 4.5 (recently replacing Gemini 2.5 pro); and Figma make - which I absolutely love.

For internal ops agents, (1) managing coverage requests (2) digesting user feedback (3) analyzing product analytics (4) writing my weekly newsletter -- all built with Agno and Gemini 3 pro.

Less exciting but just as critical :-) Streamlit for front end [being replaced with react], and Firebase for database/auth/analytics -- both choices have been really critical for prototyping rapidly with my users.

The "secret sauce" is secret (lol), but I will say solving factuality in evolving market dynamics is a significant effort, and using expert-system techniques and simulations augments LLMs where they are critically weak.

Happy to share more if you have any questions -- and obviously here's the link if you want to try it out (https://lnkd.in/g7eR86dr)

AI Evals Explainer

Generated with Nano Banana Pro

Introducing SAM 3D: Powerful 3D Reconstruction for Physical World Images

and it’s not your typical 3D reconstruction tool.

It does what previous models couldn’t:
- Reconstruct real-world objects and scenes from a single image.
- Handle occlusion, indirect views, and cluttered backgrounds.
- Estimate human pose and shape with surprising accuracy.

Why does this matter?
Because for the first time, we’re seeing 3D perception at the scale, quality, and accessibility of today’s 2D models.

The architecture behind SAM 3D borrows from LLMs: pre-training on synthetic data, followed by post-training on real-world images using a human-in-the-loop ranking engine. The result is a feedback loop that continuously improves both the data and the model.

The implications stretch far beyond creative media. Robotics. E-commerce. Sports medicine. Interactive avatars. You name it.

And the best part? 

It's fast. 
Real-time fast.

Meta’s already using SAM 3D to power a “View in Room” feature in Facebook Marketplace; turning static listings into immersive experiences.

The gap between virtual and physical is shrinking. SAM 3D is a serious leap forward.

Learn more:
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗯𝗹𝗼𝗴: https://lnkd.in/g8w6dvAB
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gd_HQE9c
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gp3p9jaf
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀: https://lnkd.in/gfwmcKGK
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆: https://lnkd.in/g9CPHEXJ
- 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: https://lnkd.in/guSDTnU3

A sharp, modern AI tech stack for building a serious startup.

Fortusight from Shadi Copty lays out a blueprint that actually reflects where the industry is going:

multi-agent architectures, real-time grounding, premium data, and a diversified LLM strategy tuned to specific jobs rather than brand loyalty.

1/ Agno for agent frameworks.
2/ Exa for grounding.
3/  FMP for clean financial data.
4/ A portfolio of models (O3, GPT-4.1, Flash2.5, Gemini 3) each picked for its superpower.

Cursor + Opus 4.5, Figma Make, Firebase, and Streamlit/React for fast product loops.

And a layer of expert systems + simulation to fix factuality where LLMs keep failing.

This is the kind of stack that lets a small team punch far above its weight.

🚀

Open article: Financial Analysis App by fortusight.streamlit.app

Exciting project for you all to check! I tried an earlier prototype and it was very impressive

Startup life, here I come :-)

Thrilled to share with you my new adventure, Fortusight - a project I've labored on nights and weekends for the last 2 years.

It's a tool for the serious investor that wants to deeply understand a company before buying and holding for 5-10 years.

It uses AI Agents to read through filings, earning calls, analyst reports, news, press-releases - forms an opinion on a company's competitive position, management's friendliness to shareholders, and creates bull/base/bear scenarios for valuation.

To quote a friendly hedge fund manager "I used to have 5 people doing all of this" - each company is analyzed for 192 different perspectives, with A1Facts to ensure factuality for high stakes decisions.

Here's how you try it: https://lnkd.in/g7eR86dr

Let's go 🚀 🚀 🚀

🚨 Stop scrolling. The math on this is barely real.

Get a $799/mo institutional AI research platform for just $50/mo.

The catch: Only 10 spots open. 

RIAs & Fund Managers—this is a complete no-brainer. 👇

https://fortusight.ai/

I need your help 🙏

I'm looking for design partners for my baby startup that just got pre-seed funded.

If you know a wealth fund manager, a small RIA, a family office that wants to reduce their equity research costs from ~$75K to less than what they pay for starbucks coffee - share this link with them: https://fortusight.ai  

For 10 design partners, I'm giving away Fortusight for $50/month instead of $800/month for a year in exchange for feedback to help guide the product's development.

The beta with our users has been incredibly successful, with recurring usage of our equity deep dives.

This will propel the company forward, and one day I will find ways to repay your kindness - many thanks ❤️

Helping Techies Build Personal Brands for Career & Business Growth | AI-Assisted LinkedIn Strategist to CTOs, Directors, and Tech Founders & Coaches | 100K+ Insta | Mentored 1K+ Techies | Engineering Manager @ Jio

With over 9 years of industry experience, I help tech professionals achieve professional growth by crafting influential personal brands and leveraging platforms like LinkedIn.

As an Engineering Manager and Career Coach, I’ve mentored over 1000 professionals, helping them enhance their profiles, expand their networks, and implement proven growth strategies. My clients have successfully transitioned to better roles, expanded their influence, and built standout online presences.

What I Offer:
👉5-Step Career Branding Strategy: Tailored to job seekers, network builders, and those aiming to strengthen their LinkedIn presence.
👉Insights from 8+ Years in Tech: Experience across startups and giants like Byju’s and Jio, decoding how organizations operate in the digital era.

Recognized by: Byju’s, The Voice of Women, and Indian Express for my impact in career branding and coaching.

My Approach:
Speak like a leader. Work as an entrepreneur. Sell like Apple.

If you’re navigating a job transition, looking to boost your career, or want to become a recognized brand in tech, I’m here to help. Let’s connect and collaborate.

💡 Reach Me:
👉DM me on LinkedIn or Topmate.
👉Find me on other social media with @careerwithhina
👉For collaborations, email me at careerwithhina@gmail.com.

Let’s build a thriving community together. I’m just a message away!

Hina Arora

AI vs ML vs DL vs GenAI vs LLM vs AI Agent - What’s the Difference?

Credit: Prem N.

In the evolving AI landscape, it’s essential to distinguish between foundational technologies and their specialised branches. This guide provides a clear breakdown:

𝟏. 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐀𝐈):
A broad field focused on designing systems that replicate human intelligence — including reasoning, learning, and decision-making.

𝟐. 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐌𝐋):
A subset of AI where systems learn from data patterns and improve over time, enabling predictions and automation without explicit programming.

𝟑. 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐃𝐋):
A specialized area within ML that leverages layered neural networks to process complex data, such as images or natural language.

𝟒. 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 (𝐆𝐞𝐧𝐀𝐈):
An AI approach that creates original outputs - text, code, visuals - by learning from existing data structures and patterns.

𝟓. 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝐋𝐋𝐌𝐬):
Deep learning models trained on large text corporate, enabling machines to understand and generate human-like language at scale.

𝟔. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬:
Autonomous systems powered by LLMs or other models, capable of perceiving, planning, and executing tasks based on defined goals.

Follow Hina for more such content

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping Tech Leaders & Coaches Build Personal Brand for Professional Growth | Partnered with 20+ CTOs, Directors & Founders | Done-for-You Service

𝐓𝐨𝐨 𝐦𝐚𝐧𝐲 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬. 𝐂𝐚𝐧'𝐭 𝐝𝐞𝐜𝐢𝐝𝐞 𝐰𝐡𝐢𝐜𝐡 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧? 
Here are the 6 categories, understand these and tool selection becomes obvious:

𝟏. 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐩𝐮𝐫𝐩𝐨𝐬𝐞 𝐋𝐋𝐌 𝐩𝐫𝐨𝐯𝐢𝐝𝐞𝐫𝐬
Models like Claude, OpenAI, Gemini, Llama, Mistral, Grok, DeepSeek, Cohere, and AI21Labs power everything from training to fine tuning to deployment.

𝟐. 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭 𝐚𝐧𝐝 𝐦𝐮𝐥𝐭𝐢 𝐚𝐠𝐞𝐧𝐭 𝐬𝐲𝐬𝐭𝐞𝐦𝐬
Tools like AutoGen, CrewAI, Superagent, CamelAI, MGX, Manus, AgentVerse, LangGraph, and AutoGPT help orchestrate agents, memory, planning, and complex workflows.

𝟑. 𝐋𝐋𝐌 𝐚𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬
Platforms like LangChain, LlamaIndex, Haystack, OpenLLM, Kernel, DSPy, and Guardrails AI let you build end to end LLM applications with chains, memory, and tools.

𝟒. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬
PromptLayer, Promptable, Langfuse, ChainForge, PromptPerfect, and PromptTools help you design, test, monitor, and optimize prompts at scale.

𝟓. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Systems like FAISS, Chroma, Pinecone, Weaviate, Redis, Qdrant, Milvus, and Relevance AI store embeddings for RAG pipelines and semantic search.

𝟔. 𝐍𝐨 𝐜𝐨𝐝𝐞 𝐚𝐧𝐝 𝐥𝐨𝐰 𝐜𝐨𝐝𝐞 𝐀𝐈 𝐛𝐮𝐢𝐥𝐝𝐞𝐫𝐬
FlowiseAI, Langflow, n8n, Replit, Cursor, Lovable, and Chatbase make it possible to build full LLM workflows without heavy coding.

Learning these six categories is the fastest way to understand how modern AI products are actually built.

♻️ Repost this to help your network get started
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Which category should I break down into a deep dive next?

#LLM #AItools #GenAI #AIAgents

Picture a digital world where software works together like a team of tiny superheroes, each with its own superpower, to create amazing applications that run smoothly and efficiently. 

Welcome to the world of microservices, where small is mighty!

Microservices are small, independent pieces of software that work together to build an application. They communicate using clear rules, and each piece can be developed, deployed, and scaled on its own.

Some commonly used design patterns that can be beneficial when designing and implementing microservices:

✅ Gateway Pattern
✅ Service Registry Pattern
✅ Circuit Breaker Pattern
✅ SAGA Pattern
✅ CQRS (Command Query Responsibility Segregation) Pattern
✅ Bulkhead Pattern
✅ Sidecar Pattern
✅ API Composition Pattern
✅ Event-Driven Architecture Pattern
✅ Database per Service Pattern
✅ Retry Pattern
✅ Configuration Externalization Pattern
✅ Strangler Fig Pattern
✅ Leader Election Pattern

--------------------------------------------------------------
I help technical individuals create their career brands on LinkedIn.
👉https://lnkd.in/gAK7PqUd

Follow Hina Arora to learn more

Microservice Design Patterns

❌ Don't only apply for the job but also ensure to establish a network with the recruiter for the future.

Interview Mail Tips-

You can achieve this by sending thoughtful emails.

✅ 𝗔𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝗳𝗼𝗿 𝗷𝗼𝗯 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Application for [Job Title] - [Your Name]

Dear [Hiring Manager's Name],

I hope this message finds you well. I am writing to express my interest in the [Job Title] position at [Company Name] that I recently came across. I believe my skills and experience align well with the requirements of the role.

With a background in [Relevant Skills/Experience], I am excited about the opportunity to contribute to [Company Name]'s [specific project/department/goal], and I am confident in my ability to make a positive impact. I have attached my resume for your consideration.

I would appreciate the chance to discuss how my background and expertise could benefit your team. Please let me know if there is a convenient time for a call or a meeting.

Thank you for considering my application. I look forward to the opportunity to speak with you.

Best regards,
[Your Name]

✅ 𝗙𝗼𝗹𝗹𝗼𝘄-𝗨𝗽 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Follow-Up on My Interview

Hi [Hiring Manager's Name],

I hope you're doing well. I wanted to follow up on the interview we had for the [Job Title] position at [Company Name]. I'm really excited about the opportunity and would love to hear about the next steps in the process.

Looking forward to your response.

Best regards,
[Your Name]

✅ 𝗥𝗲𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Appreciation and Future Consideration

Hi [Hiring Manager's Name],

I hope this message finds you well. I wanted to express my gratitude for considering me for the [Job Title] position. Although I didn't make it to the next round, I'm thankful for the chance to learn about [Company Name]. I look forward to potentially crossing paths again in the future.

Thank you once again.

Best regards,
[Your Name]

✅ 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Accepting the [Job Title] Position

Hello [Hiring Manager's Name],

I hope you're doing well. I wanted to formally accept the offer for the [Job Title] position at [Company Name]. I'm really excited about joining the team and contributing to [Company Name]'s success. Please let me know the next steps and any additional information you need from my end.

Thank you and looking forward to starting on [Start Date].

Best regards,
[Your Name]

✅ 𝗦𝗮𝗹𝗮𝗿𝘆 𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗘𝗺𝗮𝗶𝗹:

𝗦𝘂𝗯𝗷𝗲𝗰𝘁: Salary Discussion for [Job Title] Position

Hello [Hiring Manager's Name],

I hope this message finds you well. I'm excited about the offer for the [Job Title] role at [Company Name]. I would like to discuss the compensation package to ensure that it aligns with my skills and experience. Could we set up a time to talk about this further?

Thank you and looking forward to your response.

Best regards,
[Your Name]




#emailtemplates

Everyone wants to work in AI, but 80% have no idea which role to choose.
And that’s where their journey breaks before it even starts.

In the last few months, I’ve spoken to engineers, analysts, PMs, and even designers who want to move into AI but feel completely lost.
Not because of lack of interest — but because the AI ecosystem is huge and evolving fast.

To make it easier, I created this detailed map of the Top Careers in AI.

It covers roles across engineering, product, research, strategy, and even education — because AI today needs more than just coding. It needs problem-solvers, system thinkers, researchers, communicators, and leaders.

Some interesting patterns I’ve noticed while helping tech professionals pick the right path:

• Many engineers naturally fit into ML Engineer, Data Engineer, or Algorithm roles — but hesitate because they think they need a PhD. Most don’t.

• Product and strategy-oriented folks shine in AI Product Strategist and AI Consultant roles, because they understand impact and business context.

• People who enjoy teaching, mentoring, or explaining complex topics often find a great fit as AI Educators or AI Trainers.

• Designers with curiosity about AI can transition into Voice Interaction roles, especially with strong UX skills.

• Research-heavy roles like AI Researcher or NLP Specialist need deeper math/ML foundations — but are incredible for those who love experimentation and problem-solving.

Every role has its own skill stack, learning path, and growth curve.
But once you understand the landscape, choosing becomes much easier.

If you’re planning a transition into AI in 2025, start with clarity.
Know your strengths.
Match them with the right direction.
Then build skills step by step.

Which AI role are you exploring for your career? Let’s discuss.

AI-Assisted Personal Branding for Tech Leaders | 9yrs+ in Tech | Helping 20+ CTO / Founders / Directors / Coaches / Consultant Build Personal Brand for Professional Growth | Keynote Speaker | Done-for-You Service

“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐲𝐨𝐮 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐛𝐞 𝐥𝐚𝐢𝐝 𝐨𝐟𝐟.”

I have actually heard this line.
And honestly, it is one of the most damaging misconceptions in the job market.

A friend of mine, a highly respected CTO, was laid off earlier this year.
Not because he lacked vision.
Not because he was not delivering.
But because his company restructured and shut down an entire business unit overnight.

𝐆𝐮𝐞𝐬𝐬 𝐰𝐡𝐚𝐭? 𝐖𝐢𝐭𝐡𝐢𝐧 𝐰𝐞𝐞𝐤𝐬 𝐨𝐟 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐡𝐢𝐬 𝐣𝐨𝐛 𝐬𝐞𝐚𝐫𝐜𝐡, 𝐡𝐞 𝐤𝐞𝐩𝐭 𝐡𝐞𝐚𝐫𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐬𝐚𝐦𝐞 𝐛𝐢𝐚𝐬, 
“𝐈𝐟 𝐲𝐨𝐮 𝐰𝐞𝐫𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐠𝐨𝐨𝐝, 𝐰𝐡𝐲 𝐰𝐨𝐮𝐥𝐝 𝐭𝐡𝐞𝐲 𝐥𝐞𝐭 𝐲𝐨𝐮 𝐠𝐨?”

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐭𝐫𝐮𝐭𝐡 𝐈 𝐡𝐚𝐯𝐞 𝐬𝐞𝐞𝐧 𝐨𝐯𝐞𝐫 𝐚𝐧𝐝 𝐨𝐯𝐞𝐫 𝐚𝐠𝐚𝐢𝐧:
✔️ Layoffs are about business decisions, not talent.
✔️ Leadership skills do not vanish because a company restructures.
✔️ Some of the most resilient, driven professionals are the ones who have bounced back after layoffs.

If you are a hiring manager holding this mindset, you are not filtering out “𝐰𝐞𝐚𝐤” candidates.
You are filtering out leaders who could transform your team.

And if you are a tech leader who is been laid off, 
It does not define you. It repositions you.

𝐔𝐬𝐞 𝐭𝐡𝐢𝐬 𝐩𝐡𝐚𝐬𝐞 𝐭𝐨:
📌 Show up online and share your expertise.
📌 Build visibility around your leadership track record.
📌 Reframe your story, so people see your potential, not just your past.

Because in today’s market, skills and leadership get you in the door.
But personal branding makes people trust your story.

DM me ‘Brand’ if you want to reframe your career story after a layoff.

I wrote my first production code 9 years ago.
And like many of you, it didn’t go well.

I still remember the feeling.
Pushing the changes.
Refreshing the screen.
And watching everything break in real time.

No one prepares you for that moment.
You learn fast that production is a different world
and confidence doesn’t come from writing code
it comes from fixing the mess you created.

Over the years, I realised something important.
Good engineers aren’t the ones who never make mistakes.
They’re the ones who stay calm when things go wrong
own their decisions
and get better with every failure.

If your first production push didn’t go well
welcome to the club.
If your last one didn’t go well
you’re still learning.

And that’s the whole point of this journey.

Everyone is talking about AI skills right now.
But no one tells you the uncomfortable truth.

The real “AI skill” of 2025/206 isn’t prompt writing.
It isn’t fine-tuning.
It isn’t building agents.

It’s learning how to think with AI.

Because the people winning today aren’t the ones who know the most tools.
They’re the ones who know how to turn AI into an extension of their mind.

Here’s what I’m seeing everywhere:

People who treat AI like Google → stuck.
People who treat AI like a partner → unstoppable.

AI isn’t here to replace your skill.
It’s here to amplify it.

If you know how to break problems down
ask better questions
structure context
and guide the model’s reasoning —
you’ll be ahead of 90% of the industry.

The tools will change every 3 months.
Your ability to think with AI will stay.

So if you’re wondering what to learn next:
Learn how to work with AI the way leaders work with people —
with clarity, direction, and intention.

That’s the real skill that will define the next decade.

taplio

The Battle

Armand Ruiz

Hina Arora

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The Battle

Armand Ruiz

Hina Arora

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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);}Armand Ruiz's LinkedIn Performance

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