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

AI is making 10X engineers 1000X, and 1X engineers 0.1X 

stealing this quote from Shadi Copty 🙃

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

AI Evals Explainer

Generated with Nano Banana Pro

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'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)

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 ❤️

Building a Community of AI Agent Builders | Data & AI Tech Leader | Speaker | Insights via talks & podcasts 🎙️

Most AI demos impress. Few AI systems deliver.

After 18 years architecting data and AI systems - from 100TB to 500PB at scale - I've seen the same patterns: companies build AI that works in labs but fails in production.

The gap isn't technical capability. It's clarity about what success looks like.

I help organizations move from stalled AI pilots to systems that deliver measurable business value through the Production AI Program - an evaluation-first approach that defines success metrics before selecting tools.


At AWS and Databricks, I've driven end-to-end platform migrations, solved cost and performance bottlenecks (30-60% cost reductions), and built cultures of experimentation with governance at the core. I've architected serverless analytics for latency-sensitive SaaS, implemented AI guardrails and data quality frameworks, and mentored hundreds of engineers from junior to principal.

- Founder: AgentBuild - free 8-week AI agent syllabus with live study cohorts
- Speaker: 50+ data/AI events, published podcasts on pragmatic AI adoption
- Mentor: Global workshops for serious learners building production AI systems
- Podcasts to share pragmatic AI adoption lessons. DMs open for invites.

Stop guessing. Start measuring. Build AI that actually works in production.

I share field insights through my newsletter: https://newsletter.agentbuild.ai

Sandipan Bhaumik 🌱

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐯𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.

𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐢𝐧 𝐬𝐢𝐦𝐩𝐥𝐞 𝐭𝐞𝐫𝐦𝐬.

𝟏. 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬.
* An AI agent is a single autonomous system. 
* It thinks, decides, and acts on its own using internal reasoning loops. 
* This makes it powerful but also less predictable, since decision making happens inside the LLM and is harder to trace.

𝟐. 𝐇𝐨𝐰 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐞.
* They read memory, use an LLM to reason, pick tools, take action in the external environment, and update memory. 
* This loop keeps running, making real time decisions based on prompts and internal thinking.

𝟑. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.
* This is a structured, rule based system. 
* It uses multiple agents, tools, and APIs under orchestration. 
* Every step is transparent, logged, controlled, and follows workflow conditions instead of internal freeform reasoning.

𝟒. 𝐇𝐨𝐰 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐞.
* A decider chooses the right path, tasks run in sequence, and LLM evaluation happens only where needed. 
* The workflow follows branches like Path X, Path Y, or Path Z, producing consistent and predictable outcomes.

𝟓. 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞.
* AI agents offer autonomy but less control.
* Agentic workflows offer control, predictability, and visibility.
* Modern systems often combine both for balanced reasoning and reliability.

Which side do you think will dominate the next wave of enterprise AI systems: autonomous agents or structured workflows?

♻️ Repost this to help your network get started
➕ Follow Sandipan for more

#AIagents #AgenticAI #AIworkflows #LLMEngineering

Tech Leader - Data & AI | Community Founder | Speaker | Podcast Host

You're Losing Output Quality If You Don't Understand These 7 LLM Parameters
Most engineers use AI daily. Few understand what actually controls the output.

Here's the truth: 
LLM parameters are the hidden switches that determine whether your model gives you gold or garbage.
They control creativity, coherence, length, repetition everything that separates a useful response from a useless one.

If you want to engineer outputs instead of just hoping for good results, master these 7 Parameters:

1. max_tokens:
Sets the length limit. More tokens = detailed answers. Fewer = concise responses.

2. temperature:
Controls randomness. High = creative and diverse. Low = predictable and focused.

3. top_p:
Filters token choices by probability. Narrows the pool of possible words the model considers.

4. top_k:
Limits how many likely tokens the model can pick from. Higher = more freedom. Lower = tighter control.

5. frequency_penalty:
Reduces repetition. Increase it to force fresh phrasing and varied language.

6. presence_penalty:
Forces new concepts. Stops the model from rehashing what it already said.

7. stop:
Defines where generation ends. Custom boundaries for clean, controlled outputs.

---

Here's what changes when you dial these in:

Your model stops giving rambling, repetitive, or off-brand responses.
It starts behaving exactly how your application needs it to.

Most people never tune these. That's why most AI outputs feel generic.

♻️ Repost this to help your network get started
➕ Follow  Sandipan for more

Reference: https://lnkd.in/emRDxKFV


#AI #LLM #GenAI #AIEngineering #AgenticAI #AIAgents #AgentBuildAI

I have curated 24 ready-to-deploy n8n workflows that saves you 100+ hours of development.

These are not beginner exercises, they are real systems solving real problems, and each one teaches you a pattern you can apply to your own work.

𝐒𝐭𝐮𝐝𝐲 𝐭𝐡𝐞𝐬𝐞. 𝐃𝐞𝐩𝐥𝐨𝐲 𝐭𝐡𝐞𝐦. 𝐓𝐡𝐞𝐧 𝐛𝐮𝐢𝐥𝐝 𝐲𝐨𝐮𝐫 𝐨𝐰𝐧.

𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭𝐬

• Angie, Personal AI Assistant with Telegram Voice and Text
https://lnkd.in/gWKrSE9X

• Multi-Modal Personal AI Assistant with Telegram & Google Gemini
https://lnkd.in/gMuBeFSr

• Multi-Platform AI Sales Agent with RAG, CRM Logging & Appointment Booking
https://lnkd.in/gCjxR82V

• Build a Financial Documents Assistant using Qdrant and Mistral.ai
https://lnkd.in/g4hfXFKE

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

• Automated Stock Analysis Reports with Technical & News Sentiment using GPT-4o
https://lnkd.in/gaZyy_bg

• Smart RSS News Alert System with DeepSeek AI and Slack Notifications
https://lnkd.in/gA57ajE3

• Process Receipt Images with GPT-4o-mini, WhatsApp & Google Sheets Automation
https://lnkd.in/gCVsnuPK

• Modular & Customizable AI Powered Email Routing: Text Classifier for eCommerce
https://lnkd.in/g9taqAZD

𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 & 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧

• Automate Multi-Platform Social Media Content Creation with AI
https://lnkd.in/gyepPfg6

• Automated Social Media Content Publishing Factory + System Prompt Composition
https://lnkd.in/g3B9EYWY

• Generate SEO-Optimized WordPress Content with AI Powered Perplexity Research
https://lnkd.in/gVTkxH5v

• Publish WordPress Posts to Social Media X, Facebook, LinkedIn, Instagram with AI
https://lnkd.in/g2DBTKSi

• Generate Unlimited E-Commerce Ad Creative with Nano Banana Image Generator
https://lnkd.in/gfNb8xkx

• Automated Generation of AI Advertising Photos for Product Marketing
https://lnkd.in/guAgStdG

• Automatically create YouTube short videos using Elevenlabs, Hailuo AI
https://lnkd.in/gAEAg2TW

𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 & 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬

• AI Agent for project management and meetings with Airtable and Fireflies
https://lnkd.in/gqpdwzCe

• Automated LinkedIn Lead Generation, Scoring & Communication with AI-Agent
https://lnkd.in/gnB4Gj5X

• Smart Job Search: Resume Scoring & Tailoring with OpenAI, Apify, and Airtable
https://lnkd.in/gmb9AwFh

• Jira Project Management Automation with Google Gemini & MCP Server
https://lnkd.in/gKAXXzg8

• Personal Finance Assistant: Query Notion Data via Telegram & WhatsApp with Claude AI
https://lnkd.in/gyRsvfAW

𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞

• Building Your First WhatsApp Chatbot
https://lnkd.in/g5nipsz8

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24 Workflows

Most AI projects fail because teams build before they know what success looks like. I've spent 18+ years in this game, and I've seen this pattern repeat itself: 

Teams rush to implement the latest AI tools, only to realize months later they have no way to measure if it's actually working.

𝐓𝐡𝐞 𝐑𝐞𝐯𝐞𝐫𝐬𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 flips this on its head.

This isn't theoretical. 

I've used this approach with enterprise teams moving from AI experiments to production systems. 

The difference? They ship working AI instead of expensive science projects.

I have shared an example in this video.

If you're tired of AI initiatives that stall after the demo phase, this framework is for you.

Watch the full video: 
[YouTube link in comment]

What's the biggest challenge you're facing in moving AI from demo to production? 

Drop a comment, tell me what you think. 👇

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I stood there, paralysed by a familiar fear: 
𝐖𝐡𝐚𝐭 𝐢𝐟 𝐈'𝐦 𝐧𝐨𝐭 𝐦𝐨𝐯𝐢𝐧𝐠 𝐟𝐚𝐬𝐭 𝐞𝐧𝐨𝐮𝐠𝐡?
Are you feeling the same?
I can feel you.

I have been there, 
felt that stress, 
that anxiety, 
that mind running on a hamster wheel, 
that "𝑛𝑜𝑡 𝑏𝑒𝑖𝑛𝑔 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑚𝑒𝑛𝑡".

Everytime I fall in that trap, I remind myself of what one of my mentors said - "𝐓𝐡𝐞 𝐩𝐚𝐭𝐡 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐜𝐚𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐲𝐨𝐮𝐫 𝐩𝐚𝐜𝐞. 𝐈𝐭 𝐨𝐧𝐥𝐲 𝐜𝐚𝐫𝐞𝐬 𝐭𝐡𝐚𝐭 𝐲𝐨𝐮'𝐫𝐞 𝐨𝐧 𝐢𝐭."

Progress isn't always visible from where you're standing. 

That conversation you had last month? 
It's germinating. 

That framework you're refining? 
It's deepening. 

That client you're serving with full presence? 
They're noticing.

The oak tree doesn't apologize for taking decades to reach its height. It simply grows, season after season, ring after ring.

I used to think success meant sprinting. 
Now I understand it means 𝐧𝐨𝐭 𝐬𝐭𝐨𝐩𝐩𝐢𝐧𝐠. 
There's a profound difference.

Some days, my progress is a published article. 
Other days, it's finally understanding a concept I've been wrestling with for weeks. Some weeks, it's a new idea. Other weeks, it's the courage to say no to work that doesn't align.

All of it counts. All of it compounds.

The tracks in that photo wind through uncertain terrain. 
You can't see where they end. 
But they're going somewhere. 
And so are you.

If you're building something meaningful - 
whether it's a business, 
a skill, 
a body of work, 
or a new version of yourself

→ Remember this: 𝐅𝐨𝐫𝐰𝐚𝐫𝐝 𝐢𝐬 𝐟𝐨𝐫𝐰𝐚𝐫𝐝. 
Slow is still 𝑚𝑜𝑡𝑖𝑜𝑛. 
And persistence, not speed, is what transforms the landscape.

The journey from here to there isn't measured in how quickly you arrive. It's measured in whether you keep walking when the destination is out of sight.

Keep going. 
𝐘𝐨𝐮𝐫 𝐩𝐚𝐜𝐞 𝐢𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐢𝐭'𝐬 𝐲𝐨𝐮𝐫𝐬.

#growth #consistency #development #learning

𝐘𝐨𝐮𝐫 𝐋𝐋𝐌 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐢𝐬 𝐬𝐥𝐨𝐰. 𝐘𝐨𝐮𝐫 𝐭𝐨𝐤𝐞𝐧𝐬 𝐚𝐫𝐞 𝐞𝐱𝐩𝐥𝐨𝐝𝐢𝐧𝐠. 𝐘𝐨𝐮𝐫 𝐜𝐚𝐜𝐡𝐞 𝐢𝐬𝐧'𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠. 
𝐍𝐨𝐛𝐨𝐝𝐲 𝐭𝐞𝐚𝐜𝐡𝐞𝐬 𝐭𝐡𝐢𝐬. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝟓-𝐜𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐭𝐡𝐚𝐭 𝐟𝐢𝐱𝐞𝐬 𝐚𝐥𝐥 𝐨𝐟 𝐢𝐭:

𝟏. 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 𝐁𝐮𝐝𝐠𝐞𝐭𝐬.
Every stage adds delay. Input prep, retrieval, inference, and postprocessing must each stay within tight limits. Set a clear end to end target and split workloads to avoid overhead. Choose model size based on latency needs, not just accuracy.

𝟐. 𝐓𝐨𝐤𝐞𝐧 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠.
Token bloat slows everything down. Use summarization, truncation, or embedding filters before passing content to the model. Optimize prompts, use shorter formats, and enforce model specific token rules to keep prompts lean.

𝟑. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠 𝐋𝐚𝐲𝐞𝐫𝐬.
Caching cuts cost and prevents repeated work. Cache prompts, embeddings, top k retrievals, fixed messages, partial responses, and near duplicate queries. Effective caching makes your system faster and cheaper.

𝟒. 𝐏𝐚𝐫𝐚𝐥𝐥𝐞𝐥𝐢𝐬𝐦 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬.
Increase throughput using async and parallel calls. Break big tasks into subtasks, run retrievals in parallel, and use speculative decoding with smaller models. Fan out and aggregate to reduce waiting time.

𝟓. 𝐅𝐚𝐢𝐥𝐮𝐫𝐞 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠.
Production LLM systems need strong fault tolerance. Use retries, logging, tracing, shadow model testing, timeouts, graceful degradation, and fallbacks. Add circuit breakers for unstable services and validate every input and output.

Which of these five areas do you want me to create a deeper guide for next?

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Your customers are screaming for help. Can you hear them?

Here's the uncomfortable truth: 
While you're celebrating new sign-ups, some of your best customers are quietly walking toward the exit. And most teams don't see it coming until it's too late.

But what if AI could tap you on the shoulder before disaster strikes?

I just mapped out an Agentic AI workflow that's basically a customer success team on steroids and it never sleeps.
Check out this powerful Agentic AI workflow that ensures no customer is left behind:

𝟏. 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞𝐬:
You need real-time data to start the process. Product analytics, customer support systems, and email engagement tools are the foundational sources that collect risk signals.

𝟐. 𝐀𝐠𝐞𝐧𝐭 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧:
Raw signals are routed to specialized agents who are set up to take action based on risk severity.

𝟑. 𝐀𝐠𝐞𝐧𝐭 𝟏: 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐌𝐨𝐧𝐢𝐭𝐨𝐫:
This agent monitors customer health scores. If scores drop from 88 to 42, indicating usage decline, it triggers further action.

𝟒. 𝐀𝐠𝐞𝐧𝐭 𝟐: 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐒𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐳𝐞𝐫:
This agent pulls a 90-day timeline, analyzes tickets, and identifies root causes, giving a complete view of why the customer is at risk.

𝟓. 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐓𝐢𝐞𝐫𝐬:
Risk severity is categorized into high, medium, and low priorities, ensuring that each case is handled with the appropriate urgency.

𝟔. 𝐀𝐠𝐞𝐧𝐭 𝟑: 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐨𝐫:
Once the case is identified, this agent pulls predefined playbooks and generates an action plan tailored to address the customer’s unique situation.

𝟕. 𝐀𝐠𝐞𝐧𝐭 𝟒: 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫:
This agent drafts emails, schedules Slack reminders, and triggers CRM notifications to keep everyone aligned and acting promptly.

𝟖. 𝐇𝐮𝐦𝐚𝐧 𝐑𝐞𝐯𝐢𝐞𝐰:
Although automated actions are initiated, human oversight ensures that decisions are backed by the right context and insights.

𝟗. 𝐈𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 𝐄𝐱𝐞𝐜𝐮𝐭𝐞𝐝:
The playbook is executed, and the customer is guided back on track, improving their engagement with the product.

𝟏𝟎. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐎𝐮𝐭𝐜𝐨𝐦𝐞:
With the correct intervention, outcomes like increasing the health score from 42 to 78 and saving $50K ARR become a reality.

𝟏𝟏. 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐒𝐭𝐨𝐫𝐞𝐝:
The results are stored in the MCP server to further improve the inference model and action outcomes in the future.

𝟏𝟐. 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤𝐬 𝐔𝐩𝐝𝐚𝐭𝐞𝐝:
As interventions improve, so do the playbooks. The system learns and updates its strategies to optimize future outcomes.

If you’re not using a workflow like this, you could be letting valuable customers slip away.
This is the future of customer retention and success powered by AI.

Are you currently leveraging any AI-driven workflows to reduce churn in your business?

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Cursor hit $1 billion ARR in 24 months. Meanwhile, research shows AI made experienced developers 19% slower. This gap is where the real money is being made.

I've been talking to CTOs for months about their coding agent deployments.
Most spent $50K+ pcm on enterprise seats. 
Most can't prove ROI.

Here's what I learned from the ones actually making money with this stuff:

The winners aren't using AI to "make developers faster."
They're using it to make customization cheaper.

𝐂𝐨𝐧𝐬𝐮𝐥𝐭𝐢𝐧𝐠 𝐟𝐢𝐫𝐦𝐬: 
Same $150K project fee, but finishing in 200 hours instead of 300.

𝐃𝐞𝐯 𝐬𝐡𝐨𝐩𝐬: 
Taking open-source foundations and building customer-specific features in days, not weeks.

𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐞𝐚𝐦𝐬: 
Customizing tools for different departments without hiring more engineers.

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐩𝐚𝐭𝐭𝐞𝐫𝐧 𝐈 𝐬𝐞𝐞:
Start with something solid. 
Use AI for the boring customization layer - 
interfaces, integrations, tweaks. 
Keep humans on the unique business logic.

One agency I know built custom Slack integrations for three clients last quarter. 

Same base, different features. 
Used to take 3 weeks per client. 
Now takes 5 days.
That's real ROI.

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐝𝐨 𝐢𝐭:

→ 𝐃𝐨𝐧'𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞 "𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲." Measure cost per customization and project delivery time.

→ 𝐃𝐨𝐧'𝐭 𝐝𝐞𝐩𝐥𝐨𝐲 𝐭𝐨 𝐞𝐯𝐞𝐫𝐲𝐨𝐧𝐞. Target teams doing repetitive customization: internal tools, integrations, platform extensions.

→ 𝐓𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐡𝐢𝐝𝐝𝐞𝐧 𝐜𝐨𝐬𝐭𝐬: review time (up 91% in some studies), bug increases (up 9%), technical debt from inconsistent AI-generated patterns.

→ 𝐊𝐢𝐥𝐥 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐟𝐚𝐬𝐭. No measurable savings in 30 days? Stop paying for those seats.

If you're struggling to prove value from your coding agent investment, you're not alone. Most companies are.

But the ones who figured it out stopped trying to make everything faster.
They started making 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐭𝐡𝐢𝐧𝐠𝐬 cheaper.

The business of coding agents isn't about replacing developers or speeding up code generation. It's about making software customization so cheap that "one size fits all" stops making economic sense.

That's the shift.

I've written a longer article in my newsletter. Link is in comment.

Which companies are getting this right in your experience? 
Drop a comment - I'm genuinely curious what's working in the wild.

I've been watching this shift. The lines between data, AI, and product are blurring. And it's not being discussed much.

Last month, the Head of Data at a Retail company shared: "My team used to get tickets for dashboards. Now they're getting asked to build agents that make decisions. I have no idea how to staff for that."

He's not alone.
Data teams are quietly becoming product teams.

Not because someone decreed it. 
But because the work itself demands it.

When your "data pipeline" starts making decisions, it needs an owner, SLAs, feedback loops, and versioning. 

Guess what? That's product management 101.

I'm watching job descriptions evolve in real time.

AI Product Managers are the hardest to find.
The FDE role is going viral with AI companies.

The companies that get this are restructuring now. 

The ones that don't are still running their AI efforts like IT projects - and wondering why nothing makes it to production.

New roles are emerging:
𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬 → 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐨𝐫𝐬
Less "pick the database," more "design the behavior of a system that runs 24/7 without supervision."

𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 → 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐎𝐰𝐧𝐞𝐫𝐬 
The bottleneck isn't writing code faster. It's building systems you can trust when they make desisions with LLMs connecting to APIs.

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 → 𝐎𝐮𝐭𝐜𝐨𝐦𝐞 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐫𝐬
Not just reporting what happened, but shaping how AI systems synthesize information and make recommendations.

𝐏𝐌𝐬 → 𝐑𝐢𝐬𝐤 & 𝐈𝐦𝐩𝐚𝐜𝐭 𝐒𝐭𝐞𝐰𝐚𝐫𝐝𝐬 
Every AI workflow creates measurable impact. Someone needs to monitor it, adjust it, and shut it down when it drifts.

𝐌𝐲 𝐭𝐚𝐤𝐞:
The teams shipping production AI are the ones who figured out that data + workflows + agents need to be treated as products - with ownership, accountability, and iteration baked in from day one.

Most companies are still structured for the old world. They're trying to build autonomous AI systems with org charts designed for BI dashboards.

It's like trying to run a software company with a manufacturing org chart.

𝐒𝐨 𝐡𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤:
Are you structuring for the org you have... or the org you're about to become?

Because six months from now, your CEO is going to ask you to deploy agents that handle customer support, process claims, or manage inventory.

And the question won't be "do we have GPT-5 access?"

It'll be: "Do we have people who know how to ship this safely, measure if it's working, and iterate when it's not?"

The companies restructuring now will have an answer.

👉 If you're a data, AI, or product leader navigating this shift, tell me in comments or DM me:

What roles are you creating? 
What's breaking in your current structure?

#futureofwork #aiagents #aiinwork #enterpriseai #aiteams

Still confused about RAG?
Here's a simple workflow for you.
Read the post to learn more.

When we ask an LLM a question, it often struggles if it has not seen the right data during training - like business specific data.

That’s where 𝐑𝐀𝐆 (𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧) comes in combining search(of the business doucments/data) with generation of accurate and relevant responses.

𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:

𝟏. 𝐔𝐩𝐥𝐨𝐚𝐝 𝐏𝐃𝐅
A user provides a knowledge source, like a PDF or document.

𝟐. 𝐂𝐡𝐮𝐧𝐤, 𝐄𝐦𝐛𝐞𝐝, 𝐒𝐭𝐨𝐫𝐞
The orchestrator breaks it into smaller pieces (chunks), converts them into embeddings (using an LLM), and saves them into a Vector Database.

𝟑. 𝐀𝐬𝐤 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧
The user sends a query to the system.

𝟒. 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 𝐓𝐨𝐩-𝐊
The Vector DB retrieves the most relevant pieces of information (chunks).

𝟓. 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐂𝐡𝐮𝐧𝐤𝐬
The orchestrator receives the matching chunks.

𝟔. 𝐏𝐫𝐨𝐦𝐩𝐭 = 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 + 𝐂𝐡𝐮𝐧𝐤𝐬
The orchestrator combines the user’s query with these relevant chunks and forwards it to the LLM.

𝟕. 𝐋𝐋𝐌 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐬 𝐀𝐧𝐬𝐰𝐞𝐫
The LLM uses both the query and the retrieved knowledge to produce a contextual answer.

𝟖. 𝐅𝐢𝐧𝐚𝐥 𝐀𝐧𝐬𝐰𝐞𝐫 + 𝐂𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬
The orchestrator delivers a refined answer along with source citations for transparency.

In simple terms, RAG makes AI grounded, accurate, and explainable by connecting responses with actual knowledge sources.

𝐃𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐑𝐀𝐆 𝐰𝐢𝐥𝐥 𝐛𝐞𝐜𝐨𝐦𝐞 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐟𝐞𝐰 𝐲𝐞𝐚𝐫𝐬?

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#RetrievalAugmentedGeneration #AI #VectorDatabases #LLM #RAG #AgentBuildAI

My gym sessions teach me about transformer architectures. 
My morning walks break down multi-agent failures. 
Cooking dinner? I'm learning why RAG systems collapse in production.
Commuting to office? I am leanring about the latest investments in AI, what the near future looks like across different industries.

The lessons nobody puts in their LinkedIn announcements.

I don't have extra hours in the day. But I've turned 10+ hours of "dead time" each week into my competitive advantage. 

These 20 podcasts bring war stories from the trenches straight into my headphones - while I'm living my life.

Use your "dead time" wisely, subscribe to these podcasts to learn about the real non-shiny AI stuff.


1. All-in-1
Tech investors debate startups, markets, and news.
Link: https://allin.com/

2. The TWIML AI Podcast
ML research and trends with Sam Charrington.
Link: https://lnkd.in/egy_WjAZ

3. Practical AI Podcast
Real-world AI workflows and tools.
Link: https://practicalai.fm/

4. 20VC
Quick founder and VC insights on building AI companies.
Link: https://lnkd.in/e_bH5FBr

5. Machine Learning Street Talk (MLST) 
Deep ML theory discussions.
Link: https://lnkd.in/ebimf96G

6. AI Podcast (by NVIDIA)
Industry applications in AI.
Link: https://lnkd.in/eHyEyqka

7. Eye on AI
AI trends and ethics.
Link: https://www.eye-on.ai/

8. The Artificial Intelligence Show
AI trends and use cases.
Link: https://www.theaishow.com/

9. Latent Space Podcast
AI and software development.
Link: https://lnkd.in/emWqCCYQ

10. High Agency Podcast
Building and deploying AI products.
Link: https://lnkd.in/eAn37-m4

11. AI Today Podcast
Enterprise AI applications.
Link: https://lnkd.in/em7qbFnN

12. AI in Business Podcast
AI and business strategy.
Link: https://lnkd.in/ezeAZpuv

13. Cognitive Revolution
AI research and its societal impacts.
Link: https://lnkd.in/e2WbnuNc

14. The AI X-risk Research
AI safety and risks.
Link: https://lnkd.in/eSy-2zn3

15. AI Breakdown
Quick AI news updates.
Link: https://lnkd.in/ehFP92zh

16. Talking Machines
ML theory and discussions.
Link: https://lnkd.in/e68sfR2x

17. Google DeepMind
Short talks on AI research and breakthroughs.
Link: https://lnkd.in/efd2HJWr

18. Me, Myself, and AI
Focuses on enterprise AI adoption and scaling.
Link: https://lnkd.in/euQamdRn

19. In Machines We Trust
Explores AI’s societal and ethical implications.
Link: https://lnkd.in/ehjAz37U

20. Hard Fork
Tracks tech and AI industry trends.
Link: https://lnkd.in/enJpMQSN

These podcasts are a goldmine of knowledge, whether you’re just starting out in AI or you’re a seasoned expert. Start listening today to level up your AI expertise!

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