No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

Paolo Perrone

LangChain just open-sourced their answer to Claude Code.

9.9K GitHub stars. 5 hours old.

Deep Agents: the batteries-included agent harness. MIT licensed. FREE.

Here's why this is a big deal:

Claude Code is amazing but closed-source and locked to Anthropic.

Deep Agents gives you the same architecture. Any model. Any provider.

What's included out of the box:

1️⃣ Planning
write_todos for task breakdown and progress tracking. The agent plans before it codes.

2️⃣ Filesystem access
read_file, write_file, edit_file, ls, glob, grep. Full codebase navigation.

3️⃣ Shell execution
Run commands with sandboxing. Build, test, deploy from the agent.

4️⃣ Sub-agents
Spawn isolated workers with their own context windows. Parallel work without context pollution.

5️⃣ Context management
Auto-summarization when conversations get long. Large outputs saved to files. The problem Claude Code solved, now OPEN SOURCE.

Two lines to start:

pip install deepagents

agent = create_deep_agent()

Works with Claude, OpenAI, Google, or any LangChain-compatible model. Swap providers without rebuilding.

Built on LangGraph. Streaming, persistence, checkpointing: all production-ready.

The CLI adds web search, remote sandboxes, persistent memory, custom skills, and human-in-the-loop approval.

From their README: "This project was primarily inspired by Claude Code."

They're not hiding it. They're DEMOCRATIZING it.

Are you still locked into a single AI coding provider? 👇

♻️ Repost for someone who wants Claude Code capabilities without the lock-in

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝

My Meta Staff Engineer friends shared their Claude Code setup with me.

Changed how I build software FOREVER.

10 tips. Here's why they matter:

1️⃣ Always start in Plan Mode
Never execute first. Argue with Claude. Challenge its assumptions. Spend 80% of your time here. The code generation is the easy part.

2️⃣ Run /init on every project
Claude scans your codebase and creates a CLAUDE.md rules file. This controls everything. Skip this and Claude is guessing blind.

3️⃣ CLAUDE.md under 300 lines
Every line bloats your context window. More bloat = worse output. Only add what's unique to your project. Think lint rules, not docs.

4️⃣ Build a validation loop
Build, test, compile commands inside CLAUDE.md. Claude self-corrects when it can validate its own output. Without this, it hallucinates fixes.

5️⃣ Interrupt early, interrupt often
Claude going off track? Escape. Don't wait. Don't hope. Fresh context beats bloated context every single time.

6️⃣ /clear between features
Old context contaminates new tasks. One feature per session. Start clean or pay the token tax.

7️⃣ Multiple instances simultaneously
This is the REAL unlock. Multiple terminals. Multiple Claude sessions. One builds, one plans, one debugs. Jump between them like Starcraft.

8️⃣ Never manually edit rules
"Update the rules so we never do this again." One sentence. Claude maintains its own CLAUDE.md. Stop hand-editing config files.

9️⃣ Audit with /context regularly
MCPs destroy your token budget. /context shows the biggest offenders. Disable what you don't need. Most engineers never check this.

🔟 Sub-agents for atomic tasks ONLY
Don't create CEO agents and product agents. Keep shared context in ONE session. Sub-agents lose the reasoning chain. Bring work to the context, not context to the work.

The meta-lesson:

AI coding is 80% context engineering, 20% code generation.

Most engineers optimize the 20%.

These 10 tips optimize the 80%.

That's why they ship faster than entire teams.

Are you still treating Claude Code like a chatbot? 👇

♻️ Repost for someone using Claude Code who hasn't optimized their workflow

2 founders built Claude Code but for marketing, and it's already compressing 10-month execution cycles into 10 days

After multi-billion dollar exits to Salesforce and Oracle, Tom Chavez and Vivek Vaidya noticed something:

AI eliminated manual work for engineers, but marketers were still dealing with analysis and dashboards.

I was watching their conversation with Navin Chaddha from Mayfield and it struck me what has driven their success as serial entrepreneurs:

•⁠ ⁠“A bad decision made quickly is better than a good decision made slowly.”
•⁠ ⁠“Everything we used to do in 10 months, we now try to do in 10 days.”

Hear them break it all down:

You don't need 14 courses to build AI agents.

You need 6 weeks and this path:

Week 1️⃣: Understand the pattern
→ Anthropic's Building Effective Agents: https://lnkd.in/ew-rnBgD
→ Chip Huyen's agent explainer: https://lnkd.in/gKNqjeGZ
→ 3 hours total. No courses yet.

Week 2️⃣: Build something ugly
→ Langflow (no-code, 1 hour to ship): langflow.org
→ A research agent. A Slack bot. Anything.
→ Now you have context for everything else.

Week 3️⃣–4️⃣: Understand what you built
→ AI Agents in LangGraph: https://lnkd.in/g7raxjpC
→ Agent Memory: https://lnkd.in/ehtwbCVz
→ Rebuild your ugly thing. Properly.

Week 5️⃣–6️⃣: Deploy it
→ LangSmith for tracing: smith.langchain.com
→ Railway or Render for hosting
→ Now it's a portfolio piece.

The trap:
"I need to understand transformers first."
"I should take the Andrew Ng course first."
"I'll start when I have more time."

Build ugly. Learn why it's ugly. Fix it.

💾 Save for your next "where do I even start" spiral
♻️ Repost if you've bookmarked 47 courses and finished zero

Everyone's building AI agent demos.

Palantir shipped an end-to-end agentic architecture.

12 layers. The ENTIRE production stack. Here's what's inside:

1️⃣ Secure LLM integration
GPT, Gemini, Claude, Llama, Meta: commercial and open-source. Zero data retention by providers. Swap models without rebuilding.

2️⃣ Context Engineering
Real-time, pro-code and no-code. Contextual data, contextual logic, and systems of action feeding the Ontology.

3️⃣ The Ontology
Not a data lake. A digital twin with semantic, kinetic, and dynamic layers. Human+AI decision model built in. This is their moat.

4️⃣ Agent Lifecycle
Agent building, orchestration, and evaluation suites. All integrated. No stitching 5 tools together.

5️⃣ Security & Governance
Role-based, marking-based, purpose-based controls. System-wide branching. Approvals. Checkpoints. What regulated industries ACTUALLY need.

6️⃣ End-to-end Observability
Every tool call. Every data access. Every decision traced. Full model catalog.

Plus 6 more layers: tool services, multimodal compute, development environments, human+AI applications, package/release/deploy, and enterprise automation.

12 layers. One architecture. Production-ready.

The framework wars were the warmup.

The real game is agentic architecture.

Are you still debating agent frameworks? 👇

♻️ Repost for someone building agents who hasn't seen this architecture

Google just gave every AI coding assistant NATIVE knowledge of how to build agents.

15,600 GitHub stars. One command. FREE.

Your coding assistant doesn't know ADK. It hallucinates APIs and invents methods that don't exist.

Here's why this changes everything:

npx skills add google/adk-docs/skills -y

One command. Your coding agent now knows:

1️⃣ ADK Cheatsheet
Full Python API reference. Every method, every parameter. No more hallucinated imports.

2️⃣ ADK Dev Guide
Development lifecycle and coding guidelines. The patterns Google's own team uses.

3️⃣ ADK Deploy Guide
Agent Engine and Cloud Run deployment. Prototype to production in the same workflow.

4️⃣ ADK Eval Guide
Evaluation methodology and scoring. Test your agents before they hit production.

5️⃣ ADK Observability Guide
Tracing, logging, and integrations. See what your agents are ACTUALLY doing.

6️⃣ ADK Scaffold
New agent project in seconds. Right structure from the start.

Works with everything:

⚡ Claude Code: one command install
⚡ Cursor: MCP server config
⚡ Gemini CLI: extension install
⚡ Any MCP-compatible tool

Plus a docs MCP server that connects your editor directly to ADK documentation. Always up to date. Machine-readable.

What this replaces:

Hours of Googling API docs → $0
Debugging hallucinated methods → $0
Manually reading changelogs → $0

ADK has 15,600 stars, 2,800+ dependent projects, and now ships in Python, TypeScript, Go, and Java.

Google isn't just building an agent framework.

They're teaching YOUR coding assistant how to use it.

🔗 Install skills: npx skills add google/adk-docs/skills -y
🔗 Full docs: google.github.io/adk-docs

Are you still letting your coding assistant hallucinate ADK APIs? 👇

♻️ Repost for someone building with Google ADK who doesn't know about this

Google just dropped a FREE agent starter pack.

One command. 60 seconds. Full production stack.

3,100+ GitHub stars. Here's why:

5 agent templates, ready to deploy:

1️⃣ ReAct agent (ADK)
2️⃣ RAG agent: document retrieval + Q&A
3️⃣ Multi-agent with A2A Protocol
4️⃣ Live API: real-time multimodal
5️⃣ LangGraph base

That's the starting point. The infrastructure is what makes it DIFFERENT:

⚡ Cloud Run + Agent Engine deployment
⚡ Terraform configs included
⚡ CI/CD with Cloud Build + GitHub Actions
⚡ Vertex AI evaluation built in
⚡ Interactive playground for testing

One command:

uvx agent-starter-pack create my-awesome-agent

No boilerplate. No config hell. No "works on my machine."

Google gave away what most teams spend weeks building.

For $0.

Which template are you starting with? 👇

♻️ Repost for someone building agents from scratch

The complete AI Agents learning stack for 2026.

31 resources. Every single one free.

Most engineers bookmark 50 resources and finish ZERO.

This isn't a bookmark dump. It's a 5-phase path:

📹 UNDERSTAND

1️⃣ Intro to Large Language Models (Karpathy) → https://lnkd.in/egCPxzEx
2️⃣ Deep Dive into LLMs (Karpathy) → https://lnkd.in/euMCHnyv
3️⃣ Agentic AI Overview (Stanford) → https://lnkd.in/etjh6sYZ
4️⃣ Building and Evaluating Agents → https://lnkd.in/e45S6ZcZ
5️⃣ Building Effective Agents → https://lnkd.in/eKEaupVF
6️⃣ Building Agents with MCP → https://lnkd.in/eVhWN9kK
7️⃣ Building an Agent from Scratch → https://lnkd.in/eY3c5npC

📜 READ THE SCIENCE

1️⃣ ReAct → arxiv.org/abs/2210.03629
2️⃣ Chain-of-Thought Prompting → arxiv.org/abs/2201.11903
3️⃣ Tree of Thoughts → arxiv.org/abs/2305.10601
4️⃣ Reflexion → arxiv.org/abs/2303.11366
5️⃣ Toolformer → arxiv.org/abs/2302.04761
6️⃣ Generative Agents → arxiv.org/abs/2304.03442
7️⃣ RAG Survey → arxiv.org/abs/2312.10997

🗺️ STUDY THE ARCHITECTURES

1️⃣ Google's Agent Whitepaper → https://lnkd.in/ejiEnCdK
2️⃣ Building Effective Agents (Anthropic) → https://lnkd.in/ew-rnBgD
3️⃣ OpenAI's Practical Guide → https://lnkd.in/ef2vzH2C
4️⃣ Claude Code Best Practices → https://lnkd.in/eRBfw9k5
5️⃣ Google's Agent Companion → https://lnkd.in/eezmZrVs

🧑‍🏫 BUILD

1️⃣ HuggingFace Agent Course → https://lnkd.in/gxSFVqhY
2️⃣ MCP with Anthropic → https://lnkd.in/efqmGWaR
3️⃣ Agent Design Patterns → https://lnkd.in/gHhvHAKq
4️⃣ Multi-Agent Systems → https://lnkd.in/gkg3qzMG
5️⃣ Building Browser Agents → https://lnkd.in/etMGU_bi
6️⃣ Computer Use with Anthropic → https://lnkd.in/edR3Qg94
7️⃣ Evaluating AI Agents → https://lnkd.in/eFFiUue5
8️⃣ Building & Evaluating RAG Apps → https://lnkd.in/gWAbB363
9️⃣ Agent Memory → https://lnkd.in/eCf57tqh

🗂️ SHIP

1️⃣ GenAI Agents → https://lnkd.in/gykAzWMt
2️⃣ Microsoft AI Agents for Beginners → https://lnkd.in/efzMR5aU

5 phases. 31 resources. $0.

Bootcamps charge $5,000+ for LESS.

Which phase are you starting with? 👇

♻️ Repost for someone who wants to build agents but doesn't know where to start.

RAG, CoT, ReAct, RLHF, LoRA, MoE

What do these acronyms actually mean?

If you're breaking into AI engineering, these come up constantly. 

Here's the breakdown:

🔹 RAG (Retrieval Augmented Generation)
Instead of relying only on what the model "knows," you retrieve relevant documents and feed them into the prompt. Reduces hallucination. Powers most production AI apps.

🔹 CoT (Chain of Thought)
Prompting the model to "think step by step" before answering. Dramatically improves reasoning on complex tasks. Free performance boost.

🔹 ReAct (Reasoning + Acting)
Agent pattern where the model alternates between thinking and taking actions. Think → Act → Observe → Repeat. Foundation of most agent frameworks.

🔹 RLHF (Reinforcement Learning from Human Feedback)
How ChatGPT became ChatGPT. Train the model to prefer outputs humans rate highly. Why models are helpful instead of just predictive.

🔹 LoRA (Low-Rank Adaptation)
Fine-tune a model by training only a small number of new parameters. Makes fine-tuning accessible without massive GPU costs.

🔹 MoE (Mixture of Experts)
Model architecture where only a subset of parameters activate per token. How Mixtral and Nemotron run 30B+ params on consumer hardware.

Frameworks change every quarter.
These acronyms won't.

💾 Save for your next "wait, what does that stand for?" moment
♻️ Repost so they don't have to ask you.

81k | Director, Data & Analytics/ AI @ Gartner

Summary

Making state-of-the-art Artificial Intelligence concepts accessible and profitable to professionals from any background for over 35 years!

Highlights
-  Veteran innovator with 3 decades of experience leading state-of-the-art AI and Analytics ventures
-  98% of students surveyed would recommend me to peers
-  Generative AI/ Agentic AI expert-  ChatGPT, Claude, Copilot, Perplexity, Lovable, n8n, more
-  Experienced AI instructor for Cisco, LinkedIn Learning, University of Pennsylvania, & Simplilearn
-  Senior AI Curriculum Advisor for Graduate School of Education at Ivy League university UPenn
-  Winner, International Society of Data Scientists Outstanding Voice Award
-  Social media leader with over 107k LinkedIn followers


Selected Clients
 
-  3M
-  Audi
-  Biogen 
-  Booz Allen Hamilton
-  Bristol-Myers Squibb
-  Cisco
-  Citicorp
-  Franklin Templeton
-  Gartner/ Evanta
-  Harvard Innovation Labs’ Experfy
-  IBM
-  International Olympic Committee
-  Los Angeles Times
-  Mercedes
-  Morgan Stanley
-  Royal Bank of Canada
-  St. Jude’s Children’s Hospital
-  Wells Fargo
-  US Homeland Security
-  US Veterans Administration 
-  Yale University

Partial Skills Inventory
 
-  AIOps
-  Accountable AI/ Ethics
-  Anomaly Detection
-  Anthropic/ Claude
-  Artificial Intelligence
-  AWS
-  Brokerage Firms
-  Client Management
-  Consulting
-  Compelling Presentations
-  Compliance Regs and Frameworks
-  Curriculum Development
-  Dashboards
-  Data Storytelling
-  Data Visualization
-  Education
-  Explainable AI
-  Feature Engineering
-  Financial Services
-  Fraud Detection
-  Gap Analysis
-  Generative AI
-  GitHub Models
-  Google Cloud
-  Google Gemini
-  Insurance
-  KPI's and Metrics
-  Lovable AI
-  Machine Learning
-  Microsoft Azure
-  MLOps
-  Model Ensembles
-  Nano Banana
-  OpenAI/ ChatGPT
-  Orange Data Mining
-  Perplexity
-  Postgres SQL
-  Professional Development
-  Research
-  Retail
-  Retention and Attrition
-  SPSS
-  SQL Server
-  Underwriting

T. Scott Clendaniel

🔬 #ArtificialIntelligence 𝗛𝗶𝗿𝗶𝗻𝗴- 𝗝𝘂𝘀𝘁 𝗪𝗵𝗮𝘁 𝗔𝗥𝗘 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗿𝘀 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿?

The Terrible Facts:

- Artificial Intelligence needs a lot more experts.
- Tons of qualified professionals aren't getting hired.
- The expectations of hiring managers are just out of control.

If you're a hiring manager, please STOP looking for an entire department in just one employee.  It simply doesn't work that way.  You need a team.

Personally, I find the job search to be truly dreadful. It is exhausting, soul-crushing, depressing, dispiriting- actually, I will run out time before I run of negative emotions on this topic!  

But I hold out hope that one day everyone else will figure this out!

Please follow me for more analysis! 

- T. Scott Clendaniel

🥳 || #AI Training for 3.1 Million Worldwide || 111K Followers || Follow me for genuine ROI from AI!!

🔬 #AI for the Environment

The Ocean Cleanup provides remarkable technology in the form of the Interceptor 006, which I discovered courtesy of Pascal BORNET!

▶▶ Interceptor 006, the new Interceptor Barricade, shows early promise following deployment in Rio Las Vacas, Guatemala.

▶▶ Estimated 856,973 kg of trash removed and prevented from polluting the Caribbean Sea during installation and first trials; still awaiting anticipated ‘trash tsunami’ conditions to fully validate solution.

▶▶ Strong initial performance provides hope of ending annual trash floods into the Gulf of Honduras each year.

▶▶ Contributes to restoration and improvement of environment throughout Guatemala and to The Ocean Cleanup’s global mission of ridding the oceans of plastic.

Enjoy,

T. Scott Clendaniel

🥳 || #AI Training for 3.1 Million Worldwide || 109K Followers || Follow me for genuine ROI from Artificial Intelligence!

🔬 #Learning #AI:  𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲!

PLEASE 𝗦𝗔𝗩𝗘 𝗮𝗻𝗱 𝗦𝗛𝗔𝗥𝗘 this so your team can take advantage of it!

Claude Code has absolutely EXPLODED and taken the lead by a country mile.

So please don't let poor project structure defeat you!  Fortunately, we have the amazing Brij kishore Pandey, the king of project structures, to show a better approach.


🧭

🥳 || #AI Training for 3.1 Million Worldwide || 110K Followers || Follow me for genuine ROI from Artificial Intelligence and LinkedIn!

#AI #ArtificialIntelligece: Claude Code Project Structure!

Sharing some PURE #Wisdom!  🥳






(Go follow Carmen Morin- she is constantly sharing gems 💎 like these!)

#1 LinkedIn Education Creator 🇨🇦 | Performance-Based Learning Strategist & Keynote Speaker | Concert Pianist Turned 7-Figure Education Founder

It's not about the money you offer. 

It's about the environment you create.

Most "retention strategies" completely miss the point.

Leaders obsess over compensation packages.

While their people dread Monday mornings, toxic meetings, and feeling invisible. 

A raise loses its shine in weeks.

But culture impacts you from the moment you wake up.

❌ Toxic cultures drain talent regardless of salary because they:

↳ Create stress that money can't compensate for 
↳ Limit growth potential and career advancement 
↳ Foster competition over collaboration 
↳ Ignore individual strengths and development needs 
↳ Prioritize output over human well-being

✅ Healthy cultures outperform pay raises because they:

↳ Build psychological safety where people can be authentic 
↳ Foster continuous learning and growth opportunities 
↳ Create meaningful connections between team members 
↳ Recognize contributions beyond just compensation 
↳ Align individual purpose with organizational mission 
↳ Support work-life integration, not just balance 
↳ Encourage experimentation without fear of failure

You can't buy your way out of toxic culture. 

You have to build your way out.

The best talent chooses environments, 
not just paychecks.

♻ Repost to champion culture-first leadership. 
🔔 Follow Carmen Morin for more on human-centered systems.

🔬 #AI: The Problem With All Those Plans to Replace Senior Executives

𝗡𝗲𝘄 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝘃𝘀. 𝗮 𝗠𝗮𝘀𝘁𝗲𝗿 by Ravi Samrat Mishra 

So you have AI.  You're going to wipe out BOTH expensive existing talent AND new hires. You think you can just bring in agents to replace all that experience.

You might want to think again.  

Check out the video.  The younger employee is doing a heck of a lot of work.  In fact, he creates the crack that the "master" later exploits.

AI didn't swing a single hammer.  

YOU NEED to be onboarding the next generation!


𝗧𝗼𝗽 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗡𝗲𝘄 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀
-------------------------------------------

𝗦𝘁𝗮𝗿𝘁 𝗲𝗮𝗿𝗹𝘆: Begin training new hires on their first day so they are eager to learn and don't get lost to another organization. 
 
𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗽𝗹𝗮𝗻: A plan with clear goals helps employees understand expectations and gives managers a consistent way to train new hires. 
 
𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Make training materials easily accessible and prepare onboarding resources. 
 
𝗨𝘀𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆: Create courses, test learning, and track progress using technology. 
 
𝗔𝘀𝘀𝗶𝗴𝗻 𝗮 𝗺𝗲𝗻𝘁𝗼𝗿 𝗼𝗿 𝗯𝘂𝗱𝗱𝘆: A mentor can help new employees feel valued and learn from someone already in the organization. 
 
𝗣𝗿𝗼𝘃𝗶𝗱𝗲 𝗿𝗲𝗴𝘂𝗹𝗮𝗿 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸: Feedback helps employees develop their skills and recognize their potential. 
 
𝗧𝗿𝗮𝗶𝗻 𝗼𝗻 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘃𝗮𝗹𝘂𝗲𝘀: Ensure new hires understand the company's vision, goals, and values. 
 
𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝘀𝗵𝗮𝗱𝗼𝘄𝗶𝗻𝗴: Allow new employees to shadow more experienced employees. 
 
𝗨𝘀𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Onboarding automation can streamline and modernize the onboarding process. 

Credit: https://bit.ly/3O3cWn6

- T. Scott Clendaniel, AI Educator

LinkedIn Top Voice | Personal Branding Strategist & Executive Mentor | 81M+ Views | Helping Leaders & Founders Build Authority that Converts.

Hardest Part of Parenting?

Raising a human is the hardest work we will ever do. It is much more than just keeping them safe or making sure they have what they need. Our children are like little mirrors that reflect every single thing we do back at us. They do not just listen to our words, but they watch how we move through the world, even when we think they are not looking.

They see how we treat a stranger on the street and how we react when we are tired or frustrated. Our children mimic our morals and our behaviours every single moment of the day. They are learning from us even when we are not trying to teach them anything at all. It is a heavy weight to realise we are their primary map for life.

Every choice we make today becomes the voice inside their heads tomorrow. It makes me stop and look at my own actions with a lot of honesty. Am I being the kind of person I want them to grow up to be? We are not just raising kids, but we are shaping the future of how people treat one another.

How would you like them to be when they grow up?

#Parenting #Character #Legacy #Mindfulness #Inspiration

Children & Teen Personal Development Coach | I help students build self-belief/confidence through my Lead With Love program, empowering parents & teachers with leadership & emotional intelligence, grounded in Taekwondo.

View Ligia Chacón Hernández’s profile

Don’t Wait Too Long to Love ❤️

A little girl ran through the animal shelter, excited to find a dog.🐶

Kennel after kennel… barking, jumping, tails wagging.

Until she stopped.

In the corner sat an old brown dog with gray around his eyes. Quiet. Still.

His tag read: Milo — 10 years old.🐶

While the others barked for attention, Milo simply walked over and rested his head against the bars when she approached.

“I want him,” she said.

Her parents gently explained that Milo was very sick.
He might not have much time left.

She didn’t understand time.

She only understood that he had been waiting.

They promised to think about it and come back.

But the next morning, when they called the shelter…

Milo had passed away during the night.

The volunteer said he was found lying by the kennel door.

As if he was still waiting.

Sometimes the ones who wait the longest
are the ones we overlook the fastest.

Adopt the older dog.
Choose the one no one else sees.

Because love doesn’t need years.

Sometimes it just needs a chance. ❤️


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𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀:

 ▶▶ Audi
 ▶▶ BMW
 ▶▶ Coca-Cola
 ▶▶ Google 
 ▶▶ Louis Vuitton 
 ▶▶ Mercedes
 ▶▶ Netflix
 ▶▶ Nike
 ▶▶ NINTENDO WORLD™
 ▶▶ Spider-Man
 ▶▶ Super Mario Bros 
 ▶▶ VERSACE
 ▶▶ WhatsAPP


T. Scott Clendaniel

🔬#Learning #AI: Ten Commandments of AI Agents

How I wish I thought of these myself!  But I'm happy to share them.


The Commandments

➡️  Don't use agents just to "show off"
➡️  Agents need 2 characteristics: small, specialized, and decoupled
➡️  Enforce structured output
➡️  Explain the "why"
➡️  Orchestration is more important than autonomy 

➡️  Prompt Engineering Beats Fine-Tuning 
➡️  Tool 🔧 descriptions are VITAL
➡️  Cache like you mean it
➡️  Leverage shared artifacts
➡️  Log everything (seriously!)


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-T. Scott Clendaniel

𝟏𝟎 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐄𝐯𝐞𝐫𝐲 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐅𝐨𝐥𝐥𝐨𝐰 𝐖𝐡𝐞𝐧 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
Many teams rush into using AI agents without a clear strategy, often ending up with bloated, unreliable systems. Building powerful, production-grade AI agents requires discipline, structure, and thoughtful design choices. Here are 10 essential principles that separate hobby projects from scalable solutions:

𝟏. 𝐃𝐨𝐧’𝐭 𝐔𝐬𝐞 𝐀𝐠𝐞𝐧𝐭𝐬 𝐉𝐮𝐬𝐭 𝐭𝐨 𝐒𝐚𝐲 𝐘𝐨𝐮 𝐃𝐢𝐝
Use agents only where they truly add value. If a simple tool integration works, don’t overcomplicate it with orchestration.

𝟐. 𝐒𝐦𝐚𝐥𝐥, 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝, 𝐚𝐧𝐝 𝐃𝐞𝐜𝐨𝐮𝐩𝐥𝐞𝐝
Break big workflows into smaller specialized agents: Support, Manager, Verifier. Each should have a clear role.

𝟑. 𝐄𝐧𝐟𝐨𝐫𝐜𝐞 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐎𝐮𝐭𝐩𝐮𝐭
Always define output formats. This reduces ambiguity, improves reliability, and makes downstream processing easier.

𝟒. 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐖𝐡𝐲, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐖𝐡𝐚𝐭
Agents should understand context. When they know the why, they make better decisions.

𝟓. 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐎𝐯𝐞𝐫 𝐁𝐥𝐢𝐧𝐝 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐲
Orchestrate multiple steps intelligently instead of relying on a single agent to do everything. This boosts flexibility and reduces failure.

𝟔. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 > 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠
Maximize what you can do with retrieval, RAG, prompt patterns, and few-shot techniques before reaching for expensive fine-tuning.

𝟕. 𝐃𝐨𝐮𝐛𝐥𝐞 𝐃𝐨𝐰𝐧 𝐨𝐧 𝐓𝐨𝐨𝐥 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧𝐬
Agents need crystal clear instructions on how and when to use tools, including what to avoid and constraints to follow.

𝟖. 𝐂𝐚𝐜𝐡𝐞 𝐋𝐢𝐤𝐞 𝐘𝐨𝐮 𝐌𝐞𝐚𝐧 𝐈𝐭
Avoid unnecessary calls. Cache results and orchestrate smartly to reduce costs and latency.

𝟗. 𝐔𝐬𝐞 𝐒𝐡𝐚𝐫𝐞𝐝 𝐀𝐫𝐭𝐢𝐟𝐚𝐜𝐭𝐬
Use shared document IDs or structured outputs to avoid data duplication and improve collaboration.

𝟏𝟎. 𝐋𝐨𝐠 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐒𝐞𝐫𝐢𝐨𝐮𝐬𝐥𝐲
Good logging makes debugging, optimization, and scaling infinitely easier. If it’s not logged, it did not happen.

𝐓𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚 𝐛𝐚𝐬𝐢𝐜 𝐚𝐠𝐞𝐧𝐭 𝐚𝐧𝐝 𝐚 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐥𝐢𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞𝐬𝐞 𝐝𝐞𝐬𝐢𝐠𝐧 𝐜𝐡𝐨𝐢𝐜𝐞𝐬. 𝐖𝐡𝐢𝐜𝐡 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐝𝐨 𝐲𝐨𝐮 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐟𝐨𝐥𝐥𝐨𝐰 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐛𝐮𝐢𝐥𝐝𝐬?

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