Armand Ruiz

Director Data Science & AI Elite

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.

Director watsonx team - Client Engineering

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.

I’ve been practically living in Claude Code lately. If you build with AI daily, you know that your orchestration workflow is just as important as the model itself.

Recently, Boris Cherny (the creator of Claude Code at Anthropic) shared the internal best practices and workflows his team actually uses on a daily basis. 

Someone brilliantly distilled those threads into a structured CLAUDE.md file that you can drop straight into the root of any project.

It essentially acts as a system prompt that turns Claude into a much more autonomous, rigorous engineering partner.

Here is what the file enforces:
1/ Workflow Orchestration: Mandates a "Plan Node Default" for any task over 3 steps and uses subagents liberally to keep the main context window clean.

2/ The Self-Improvement Loop: This is the real magic. After ANY correction, it updates a tasks/lessons.md file. You are building a compounding system where the mistake rate drops over time because it actively learns from your feedback.

3/ Verification Before Done: It cannot mark a task complete without proving it works (diffing behavior, running tests, checking logs). "Would a staff engineer approve this?"

4/ Autonomous Bug Fixing: Zero hand-holding. You point it at failing CI tests or error logs, and it goes to work without requiring constant context switching from you.

5/ Strict Task Management: Forces a "Plan First" approach written to a todo.md with checkable items before implementation even starts.

It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of its changes.

If you are spending hours a day in the terminal with AI, setting up a strong .md instruction file like this will save you a massive amount of time and frustration.

It takes time and effort to set this up. If you do, you are ahead of everyone else.

If your employer isn’t providing unlimited Claude Code + secured MCPs to connect your enterprise repos, email, work-chat, meeting AI transcripts, calendar, and docs... they are likely moving at legacy speed. 

Without that context and tool access, you’re stuck in a cycle of manual copy-pasting and reinventing the wheel while the rest of the market moves in parallel workflows.

My advice? Don’t wait for the corporate roadmap to catch up. If you aren't getting these tools at work, force yourself to learn these agentic workflows on the side. 

When companies move this slowly, they eventually run into trouble and you don't want to find yourself at a disadvantage in today's market because your professional environment kept you in the past.

Stay curious and keep your skills ahead of your company's tech stack.

Everyone's talking about which LLM is best for agents. But after reading Lance Martin's deep dive into Manus's architecture, I'm convinced the real differentiator isn't the model. It's the context engineering around it.

The problem: A typical Manus task makes ~50 tool calls. Without careful management, results pile up in the context window and the LLM gets dumber the longer it runs. 

Anthropic calls it "attention budget depletion." 

Chroma calls it "context rot."

Manus solves this with three strategies:
𝟏. 𝐑𝐞𝐝𝐮𝐜𝐞: Older tool results get compacted to file path references. When that's not enough, the full trajectory gets summarized via structured schema.

𝟐. 𝐎𝐟𝐟𝐥𝐨𝐚𝐝: Fewer than 20 atomic tools bound to the LLM. Everything else runs in a sandbox via bash. The model stays lean.

𝟑. 𝐈𝐬𝐨𝐥𝐚𝐭𝐞: Sub-agents exist to give each task a clean context window, not to mimic human roles. No context bleed.

On top of that, Manus doesn't commit to one model. It routes tasks; Claude for code, Gemini for multimodal, OpenAI for math. 

Model-agnostic by design.

The takeaway: Manus has been refactored 5 times since launch. If a stronger model doesn't improve your agent, your engineering is the bottleneck.

Stop obsessing over which model to pick. Start obsessing over the implementation around it.

Full article: https://lnkd.in/gaY3rAV5

Prediction: Token budgets will soon surpass human salaries.

We are entering an era where the cost of compute for reasoning is becoming a primary line item, and the numbers are scaling fast.

I am already seeing developers consuming $100k+ a week in tokens.

It sounds astronomical, but when you look at the output, the ROI is there. They aren't just running a single chat thread; they are spinning up massive parallel processes. Swarms of agents coding, debugging, testing, and architecting simultaneously. The velocity is incredible.

But at that scale, cloud inference costs become the bottleneck.

The big unlock? Local Compute.

Putting a Mac Mini (or equivalent high-performance local hardware) on every developer's desk to run local models is the inevitable next step. We need to move the heavy lifting to the edge to make this sustainable. 

Invest $10,000 on a local computer and you have unlimited AI.

This creates a massive new need for the Enterprise: Fleet Management for Models.

Just as we manage laptops with MDM, companies will need a way to manage these distributed AI systems; securely pushing the latest Open Source models to local machines, updating weights, and orchestrating the workflow between local "thinking" and cloud "verification."

The future is hybrid, and the infrastructure to support it is being built right now.

I’m building my Personal OS to automate my mental overhead. For now, it’s simple, but the potential is already showing. I’ve moved away from "checking notifications" to a Pulse-based system.

How it works: 
1️⃣ Orchestration: Claude.md acts as the central hub, invoked by a launchd scheduler. 
2️⃣ Sequential Logic: Every "Pulse" (Morning/Midday/EOD) triggers a sequence of my agents: Scout → Horizon → Focus. 
3️⃣ Multi-Channel Delivery: I get a unified briefing via macOS notifications, an HTML digest, and my terminal.

The Agent Roster:
- Scout (/scout): Google Chat & Email monitor. It knows my VIP list and drafts replies before I even see the message. If something is important it will notify me, and even give me a call.
- Horizon (/horizon): The calendar expert. No more walking into meetings cold.
- Focus (/focus): The filter. It turns the noise into a top actionable list.

I feel spend less time "managing" my day and more time executing it. 

I also turn all the context for the day ahead into a fun podcast, and that gets me ready for the day and fully prepared.

I've been using computers in a completely different way lately. No much clicking. No browsing through tabs. No filling out forms. I describe what I want done, and agents go do it.

Many are still building software for humans. Replicating existing apps with AI. 

But that's not where this is going. The moment I stopped thinking of myself as a "user" and started thinking of myself as an orchestrator, everything changed. I set goals. I define constraints. I verify outcomes. That's it. The entire middle — the actual work — I don't touch it anymore.

And now I can't unsee it: most software we're building today is already obsolete. It's designed for humans to operate. But humans aren't going to be the operators anymore.

The new generation of software won't have dashboards. It won't have onboarding flows. It'll have APIs, state machines, and feedback loops built for AI agents, not for people.

The real question isn't "how do we develop this product/feature for humans using AI?" --> It's "if no human ever had to touch this, what would we build from scratch?"

if you are in a leadership position, you are punishing your team by keeping them busy.

If your team is too busy doing their "normal jobs" to spend hours a week experimenting with AI, you are failing them.

You are setting them up to be replaced by people who have the time to learn.

They will get disrupted, either by another department within the company or by a competitor, not because of AI, but because of your lack of urgency and understanding of the current landscape.

You have to give people the time, because integrating AI into daily workflows isn't something that happens overnight.

You have to actively remove work from their plates so they can learn the new way to work.

Because we are now all beginners again.

If you don't give them the time, they will never learn it, and your company will suffer a slow, painful decline into inefficiency.

No matter how big you are.

you need to clear your calendar and invest in AI implementation for your own productivity asap

The hardest part isn't the technology. It's making the time to actually sit down and try it.

Block 2 hours a day. Thank me later

Parallelism is the biggest unlock in AI. The real shift isn't speed; it's concurrency.

 I can now run 10 "hard things" simultaneously while I'm in meetings.

My workflow has flipped:
- Old way: Block time to do the work. Maybe use AI to do it faster.
- New way: Block time to review the results and give more context knowledge to the AIs.

We are moving from being "doers" to orchestrators. Your output is no longer limited by your hours, but by the number of parallel streams you can manage.

I get 100s of emails and chats daily. The only way to manage it is to stop everything, dive in, and manually filter the signal from the noise. It's inefficient and a total focus-killer.

So i built Mark, my Communications AI Agent. Its job is simple:
1/ Monitor Everything: It watches all my inboxes and chats 24/7.
2/ Surface What Matters: It identifies critical messages and ignores the rest.
3/ Notify Intelligently: I get a clean Terminal and Desktop notification with a summary file. No more context switching.

But that's just the start. The real magic is next → I'm connecting Mark to a shared knowledge base of all my documents, past conversations, and daily meeting notes.

Soon, it won't just notify me. It will draft context-aware replies for almost everything.

That's my way to solve this. I'm curious...have you developed something similar?

I moved from the EU to the US more than 10 years ago, and while I miss many things about Europe, I have to agree with the man of the hour 🦄 Peter Steinberger : the US is an incredible place for builders right now. 

There's an undeniable energy and enthusiasm here that's hard to find elsewhere. 

It feels like a special time to be building here.

Manus just shared why most people are still stuck in the "chat" era: the real value has shifted from conversational bots to custom superagents.

Chatbots answer questions; superagents execute workflows. 

But they aren't magic, you need to understand the underlying mechanics and put in the time to customize them for your specific needs. If you treat them like a standard search bar, you’re leaving 90% of the value on the table.

Here is how to actually get them to work for you:
1/ Move from Prompting to Engineering: Stop asking "Can you do X?" and start defining the architecture of the task.

2/ Invest in the Setup: Spend the time upfront to define personas, data sources, and specific tool-use constraints. The more "narrow" the agent's focus, the higher the quality.

3/ Iterative Refinement: A superagent is never "done." You have to monitor the execution and tune the instructions based on real-world failures.

Swipe through for the tips that actually matter for high-stakes execution.

Manus Intro

What’s the point of buying software when you can just create it on the fly?

It might take our generation a minute to adapt, but the next generation is growing up expecting to build exactly what they need. For them, the only thing that matters is the infrastructure to power it.

I’ve been building non-stop lately. I’ve never felt this empowered in my career. I can literally turn my ideas into reality in a matter of hours.

yesterday at the gym I heard someone pitching their new AI tool that helps you “do more, faster.” i feel like doing less and slower. am i the only one?

ok, so you moved from a chatbot to an AI agent. and you think you are ahead already.

but if you are still the one triggering all the actions...

if the agent isn't working autonomously for you all day long...

and if you don't have 10 of them running in parallel...

you are nowhere close to calling yourself AI-native. 

i recommend you spend the weekend learning. this is a good place to start: https://lnkd.in/eW4Sam_6

Every PM should understand evals. Not "be an expert." Not "build the eval framework yourself." Just understand them.

Here's why.

A/B testing changed product management forever. Before it, PMs guessed. After it, they measured. Evals are doing the same thing for AI products and systems but most PMs have heard the word but they don't know what they're talking about. 

An eval is how you answer one simple question: "Is my AI actually good?"

Not vibes. Not "it seems better." Not "the demo looked great." Actual, measurable, repeatable quality assessment.

Here's what you should tell tell your product leads:

1. Start with a golden set: a curated dataset where you know the right answers. This becomes your source of truth.

2. Define your graders: what does "good" look like? Accuracy? Tone? Factual correctness? Be specific.

3. Run your model against the golden set and score it. That's your baseline.

4. Every time you change something - new prompt, new model, new data — run evals again. Did it get better or worse?

5. Automate this. The speed of your eval loop determines how fast your AI improves.

That's it. That's the core.

The teams that ship reliable AI products aren't the ones with the best models. They're the ones with the best evals practice.

If you're a PM working on anything AI-related and you can't answer "how do we know it's good?"... start there.

Evals are the new A/B testing. And just like A/B testing, the PMs who get it early will have a massive advantage.

Manus Agents just launched on Telegram and it changes how you interact with AI agents entirely. I've been using this all weekend non-stop.

This isn't a chatbot bolted onto a messaging app. It's the full Manus — reasoning, multi-step task execution, tool use — running inside a conversation you already have open.

Here's what makes it different:
1/ Zero setup friction. Scan a QR code. You're connected in under a minute. No API tokens. No config files. No terminal.

2/ Full task execution from chat. Research, data processing, structured reports, PDFs — initiated and delivered without ever leaving the conversation.

3/ Multimodal by default. Send a voice message, a photo, a document. The agent transcribes, interprets, and acts. No manual file handling.

4/ It remembers how you work. Set your communication style — concise, structured, conversational — and it adapts across every interaction.

5/ You pick the model. Manus 1.6 Max for deep reasoning and creative work. Manus 1.6 Lite for fast, everyday tasks. Both go end-to-end.

But here's what got my attention even more — the cofounder just shared what's coming next:

1/ Create your own specialized agents and plug them into any group chat.

2/ WhatsApp, LINE, Slack, Discord support landing soon.

3/ Native Windows and Mac apps that let Manus operate your computer — like Browser Operator, but way more powerful.

And a lot more already in the shipping pipeline. Big updates dropping over the next 30 days.

Your AI agent shouldn't live behind a login screen on a separate platform. It should be wherever you already are — one message away, ready the moment you need it.

Learn more here: https://lnkd.in/gSuAx5UX

trust your AI overlord and... just hit accept 🤣

Recently, I stumbled across a YouTube video that completely stopped me in my tracks.

An urban explorer infiltrates the abandoned IBM center on the Côte d'Azur in La Gaude in South of France. Hearing the narrator describe it as a "vestige of Silicon Valley" and seeing that massive concrete colossus sitting empty in the middle of the forest is absolutely heartbreaking 💔 

For me, that place wasn't just a building. It was pure magic. It was where my professional life truly began and where my life completely transformed.

Early in my career, I had the chance to visit this unbelievable, high-energy campus while I was interning for IBM in Belgium. I knew immediately I belonged there, and I did absolutely everything in my power to get hired and eventually, I earned my spot.

When I look at the footage of those abandoned offices and empty reception halls, I don't see decay. I see endless, vibrant memories:

- Going from a nervous intern to earning my first full-time role.
- Working late into the night and battling those early-career nerves before major presentations.
- Learning a completely new language (French!).
- The incredible lunches, coffee breaks and walks around the campus with colleagues who became amazing, lifelong friends.
- Spending hours on the campus soccer & tennis courts, both competing in tournaments and playing with colleagues. I also learned to play la pétanque!
- And most importantly, it’s where I met my incredible wife.

That campus was the launchpad that gave me the opportunity to eventually relocate to the US. 

Looking back, I can draw a direct line from walking through those doors in France to my time as VP of AI Platform at IBM, and to do now the most exciting work in my career in Silicon Valley.

The doors might be closed, but the energy of what we experienced there is still very much alive. 

I will forever be grateful for that chapter, those people, and those memories.

Link to the video: https://lnkd.in/gA-Wja2s

Open video: INFILTRATION d’IBM, le plus gros site abandonné de la Côte D’Azur mais aussi le plus Surveillé !!!  on youtu.be

INFILTRATION d’IBM, le plus gros site abandonné de la Côte D’Azur mais aussi le plus Surveillé !!! 

I thought I was a heavy AI user. Then my usage 3x'd in the last 30 days. The pace of change is accelerating and what was cutting-edge a month ago is now the baseline. The new default is managing AI agents, not just using them.

This chart below is my AI usage (tokens)

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

I got promoted to Engineering Manager in 6 months of joining.

Not because I was the best engineer. But because I understood something most people miss:
Performance gets you in the room. Positioning gets you promoted.

After 9+ years in tech at companies like Jio and Byju's, I saw brilliant Directors getting passed over for VP roles, CTOs delivering results but invisible to boards, and Founders with great products struggling to raise capital.

𝙏𝙝𝙚 𝙜𝙖𝙥? 𝙎𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙘 𝙫𝙞𝙨𝙞𝙗𝙞𝙡𝙞𝙩𝙮.
So I applied positioning principles to build my LinkedIn presence to 300K+ followers. Then CTOs, VPs, Directors, and Founders started asking: "Can you help me do the same?"

That's when I quit my EM role and built a personal branding agency specifically for tech executives.

𝙒𝙝𝙖𝙩 𝙄 𝘿𝙤?
I work with tech leaders including CTOs, VPs, Directors, Founders, AI Strategists, and Senior Tech Consultants.

Past clients have landed:
→ VP and C-suite promotions
→ Board positions  
→ Advisory roles
→ Speaking opportunities at major conferences

I don't make you an influencer. I position you as THE authority in your domain.

𝙈𝙮 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝: Your expertise + Our AI-powered systems + Your voice
𝒀𝒐𝒖 𝒔𝒑𝒆𝒏𝒅: 30 minutes per week
𝙒𝙚 𝙝𝙖𝙣𝙙𝙡𝙚: Everything else (strategy, content, engagement)

Because at your level, your time is worth ₹30K-50K+ per day. Spending 10 hours per week on content = ₹3-5L+ monthly opportunity cost.

That's why we built a 𝙙𝙤𝙣𝙚-𝙛𝙤𝙧-𝙮𝙤𝙪 model for executives who value results over DIY learning.

𝙒𝙝𝙤 𝙄 𝙒𝙤𝙧𝙠 𝙒𝙞𝙩𝙝?

→ CTOs and VPs positioning for board opportunities
→ Directors building visibility for VP roles
→ Founders using personal brands
→ AI Strategists establishing thought leadership
→ Tech Consultants & Coaches scaling influence

If you're leading teams, making strategic decisions, let's talk.

𝙈𝙮 𝙋𝙝𝙞𝙡𝙤𝙨𝙤𝙥𝙝𝙮?
Speak like a leader. Work as an entrepreneur. Sell like Apple.

Most tech executives don't need more skills. They need better positioning.

You're already brilliant. The problem isn't competence, it's VISIBILITY.

And in 2025, visibility isn't optional. It's career infrastructure.

Let's Connect?

If you're a tech executive who has deep expertise but limited visibility, let's talk.

📩 Book Your Brand Strategy Call on topmate
💼 DM me "POSITION" for a 30-min positioning call
🌐 Follow Journey: @careerwithhina 
✉️ Business: careerwithhina@gmail.com

Recognition: LinkedIn Community Top Voice | Spoke at IIT Delhi | Featured by Indian Express, Byju's, The Voice of Women, Strong Women in IT 2025

Hina Arora

Everyone asks this question lately
Which AI model should I actually use in 2026?

Here’s a practical ranking based on real work, not hype.

Top AI models people are actually using and why

1) Claude by Anthropic 
Best when you deal with deep thinking, long documents, analysis, and serious professional writing.

Learn more: https://lnkd.in/gRwxGbz4

2) ChatGPT From OpenAI
Still the most versatile option. Content, workflows, brainstorming, automation, coding support.

Learn more: https://openai.com/chatgpt

3) Gemini By Google
Works really well inside Google Docs, Sheets, Gmail, and research-heavy tasks.

Learn more: https://lnkd.in/gnEymgGy

4) Perplexity
If you want fast, clean, cited answers without noise, this is hard to beat.

Learn more: https://www.perplexity.ai

5) Grok 4.1 Built by Twitter (X)
Strong for real-time trends, internet culture, and what’s happening right now.

Learn more: https://x.ai

6) DeepSeek AI
A favorite among engineers and technical teams who prefer open-source and control.

Learn more: https://www.deepseek.com

📌 Pro tip
Most power users don’t rely on one AI anymore.
They use 2 or 3 models together.

Which AI are you using the most right now and why
Curious to hear real experiences in the comments.

---
[If you're a tech executive looking for expert for your LinkedIn visbility & positioning, DM me "POSITION”]

Google just quietly launched something powerful and most people missed it.

A free AI learning platform by Google.

Link is here 👉 https://lnkd.in/gwS9gdEP

Not because it’s another certificate.
But because of how it’s built.

You get learning paths from beginner to advanced.
AI, data, cloud, productivity.
Hands-on learning, not surface level theory.
Career focused tracks, not random topics.

And the biggest part?
These courses are built by the same teams who work on Gemini and real production AI systems at Google.

This is not “learn AI in 2 hours” content.
This is structured, practical, and job relevant.

Also, it’s completely free.

If you are a student, a working professional, or someone trying to stay relevant in an AI-first world, this is worth your time.

Most people will ignore it.
The ones who don’t will quietly compound their skills.

LinkedIn has introduced a new AI ranking system called 𝟑𝟔𝟎𝐁𝐫𝐞𝐰.
That’s one of the biggest reasons your posts aren’t performing the way they used to.

This algorithm doesn’t reward activity.
It rewards alignment.

Here’s how content is being judged now.

𝟏. 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐦𝐚𝐭𝐜𝐡 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐟𝐢𝐥𝐭𝐞𝐫:
Before reach, the system checks credibility.
It compares your post with:
– your profile headline
– your past content
– your professional experience

If there’s a mismatch, distribution slows down early.

𝐀𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟:
“Would someone hire me based on this post alone?”

If the answer is no, the algorithm feels it too.

𝟐. 𝐘𝐨𝐮𝐫 𝐟𝐢𝐫𝐬𝐭 𝐭𝐰𝐨 𝐥𝐢𝐧𝐞𝐬 𝐝𝐞𝐜𝐢𝐝𝐞 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠:
360Brew gives disproportionate weight to your opening.
Not for creativity, but for direction.

High-performing hooks do one thing clearly:
They state 𝐰𝐡𝐨 𝐭𝐡𝐞 𝐩𝐨𝐬𝐭 𝐢𝐬 𝐟𝐨𝐫 and 𝐰𝐡𝐚𝐭 𝐞𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐬𝐡𝐚𝐫𝐞𝐝.

If your hook sounds generic, the system assumes the content is generic.

𝟑. 𝐋𝐢𝐤𝐞𝐬 𝐚𝐫𝐞 𝐰𝐞𝐚𝐤 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐧𝐨𝐰
What matters more:
– saves
– time spent reading
– scrolling back up

This is why longer posts with structure outperform short takes.

Write to be revisited, not just noticed.

𝟒. 𝐃𝐞𝐩𝐭𝐡 𝐛𝐞𝐚𝐭𝐬 𝐨𝐩𝐢𝐧𝐢𝐨𝐧
Surface-level hot takes get initial reactions, then stall.
Posts that explain 𝐖𝐇𝐘, 𝐇𝐎𝐖, 𝐨𝐫 𝐖𝐇𝐄𝐍 keep spreading.

Frameworks > motivation
Breakdowns > announcements

𝟓. 𝐅𝐨𝐫𝐦𝐚𝐭 𝐬𝐮𝐩𝐩𝐨𝐫𝐭𝐬:
𝐂𝐚𝐫𝐨𝐮𝐬𝐞𝐥𝐬 & 𝐢𝐧𝐟𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 are still on top because they slow the reader down.
They increase dwell time only when the content inside is worth it.

The shift is simple but uncomfortable.

LinkedIn is no longer rewarding people who post more.
It’s rewarding people who mean something consistently.

If reach is dropping, the fix isn’t frequency.
It’s sharper positioning and deeper thinking.

Follow Hina Arora for more such content

taplio

The Battle

Armand Ruiz

Hina Arora

Followers

Posts

Engagements

Likes

Performance ScoreArmand Ruiz's LinkedIn Performance

Performance ScoreHina Arora's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Armand Ruiz

Hina Arora

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);}Armand Ruiz's LinkedIn Performance

Performance ScoreHina Arora's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreArmand Ruiz's LinkedIn Performance