Techtter

🚨 Google just made it possible for anyone to build their own AI assistant without touching a single line of code.

It’s free to start, and it works right inside Gemini.

These assistants are called “Gems,” and you can create one in just a few minutes by giving it a name, telling it how it should behave, and adding files or notes for it to learn from.

Here’s how you can set one up:

1. Open Gemini
→ Go to [gemini dot google dot com]
→ Click on Gem Manager and hit "New Gem". 
→ You’ll also see some ready-made Gems like Brainstormer, Career Guide, and Coding Partner if you want to test those out.

2. Define your Gem
→ Give it a name, write down what you want it to do, and use the Magic Pencil to polish your instructions.

3. Feed it knowledge
→ Upload up to 10 PDFs or images that your Gem can use as a reference. 
→ You can swap them out whenever you want.

4. Test and adjust
→ Use the preview window to see how it responds. 
→ Tweak the instructions until it feels right.

5. Keep it flexible
→ You can always go back, edit your Gem, and update its instructions or knowledge.

Once ready, your Gem can live inside Gemini or show up in Google tools like Docs, Slides, Gmail, and Drive.

That means you can call on it to help with writing, planning, coding, or anything else you need while you work.

This is another step toward making AI feel personal, practical, and easy to use in everyday life. And now, anyone can create their own assistant in minutes.

--------

Credit: heygurisingh | X

#ai #geminiai #google #aiagents #agenticai #techtter

3 weeks ago | [YT] | 4

Techtter

🚨 NEW: Google offers completely free AI courses.

It is not necessary for you to have prior knowledge or pay any fee to learn AI.

Check out 10 must-see courses:

1 - Introduction to Generative AI

A simple and quick course on Generative AI.
Shows how to develop AI applications using Google tools.
It can be completed in just 45 minutes!

Link: lnkd.in/djD5Q32f

2 - Introduction to Large Language Models

Quick course for understanding large language models (LLMs).
It presents the applications of LLMs and methods to improve them.
Lasting 45 minutes, it teaches you how to create apps with AI!

Link: lnkd.in/dGvUvqn6

3 - Introduction to Responsible AI

Introductory course that teaches you how Google implements responsible AI in its products.
It presents the company's 7 AI principles.

Link: lnkd.in/dfHw4u4i

4 - Fundamentals of Generative AI

It combines the Introduction to Large Language Models and Introduction to Responsible AI courses.

Link: lnkd.in/dpakjh6C

5 - Introduction to Image Generation

Introduces diffusion models, a promising category of machine learning models in the field of imaging.

Link: lnkd.in/dvRMbqXh

6 - Encoder-Decoder Architecture

Explains the Encoder-Decoder Architecture used in tasks such as machine translation and summaries.

Link: lnkd.in/dhUGQMjr

7 - Attention Mechanism

Introduction to the attention mechanism, which allows neural networks to focus on specific parts of an input sequence.
The course takes approximately 45 minutes to complete.

Link: lnkd.in/dkNxSdqw

8 - Transformer and BERT models

By completing this course, it is possible to earn a special badge!

Link: lnkd.in/dQKKDrhh

9 - Creating Image Caption Templates

Teaches how to create models to caption images using deep learning.

Link: lnkd.in/dTEy-nGm

10 - Introduction to Generative AI Studio

It explains what Generative AI Studio is, its features, and how to use it through hands-on demonstrations.

Link: lnkd.in/dFtt-HYs
#ai #aiagents #googleai #careertips #careerguidance #techtter

3 weeks ago | [YT] | 6

Techtter

🚀 20 GenAI and AI Agents courses and curated 3 courses per Persona taught by Microsoft, NVIDIA, Google, and Hugging Face and ...👇

1. AI Agents for Beginners
lnkd.in/gSNnSK2k
Who: Devs and researchers starting from zero
11-lesson GitHub based course on agents, RAG, memory, and multi-agent systems
Why: Teaches real-world workflows with actual code

2. Hugging Face Agents Course
lnkd.in/gvkBA2vX
Who: Builders who want to learn by building
Interactive training with LangChain, LlamaIndex, memory, evals, and observability
Why: Feels like a playground. Open-source and community-driven

3. Generative AI Explained (NVIDIA)
lnkd.in/g4yNPvgK
Who: Curious minds, PMs, and analysts
Covers GenAI concepts, use cases, and applications without code
Why: Fast, clear, and digestible. Perfect before you start coding

4. Intro to Vibe coding🚀
bit.ly/46wv7Mq
Learn to Build Full-Stack Apps with AI
In this free 4-week course, master vibe coding—just describe your idea, and AI helps turn it into a working app.

5. Generative AI for Beginners (Microsoft)
lnkd.in/gKBNqFDQ
Who: Full-stack devs and curious tinkerers
21 lessons from prompt design to deployment
Why: Structured, example-rich, and project-based

6. Learn generative AI by Amazon Web Services (AWS)
lnkd.in/gG8Gvqnq
Who: Devs who want a structured on ramp to AWS AI services
Learning modules covering prompt engineering, AWS Bedrock agents, guardrails with hands-on labs.
Why: Learn how to build GenAI products using Amazon AI stack bridge theory and cloud implementation

7. Google Cloud Skills Boost: GenAI Track
lnkd.in/gYFcZmqb
Who: Professionals upskilling with certifications
Labs and badges covering LLMs, prompting, and safety
Why: Makes your resume GenAI-ready with cloud context


👇 Which path fits you best?

✔️The Student → Future AI Engineer
Goal: Learn GenAI fundamentals, hands-on coding, and build a portfolio
1-Generative AI for Beginners (Microsoft)
2-AI Agents for Beginners (Microsoft)
3-Into to Vibe coding

✔️The Entrepreneur → AI Product Builder
Goal: Build MVPs fast and evaluate GenAI platforms
1-Hugging Face Agents Course
2-Building RAG Agents (NVIDIA)
3-Into to Vibe coding

✔️The Educator or Researcher
Goal: Gain conceptual depth and teach others
1-AI for All (NVIDIA)
2-Generative AI Explained (NVIDIA)
3-AI Agents for Beginners (Microsoft)

✔️The Product Manager or Strategist
Goal: Evaluate tools and shape AI strategy
1-Google Cloud Skills Boost
2-Hugging Face Agents
3-Generative AI for Beginners (Microsoft)

Learn by doing, don’t wait for the perfect moment
Pick one small task, apply what you learn

1 month ago | [YT] | 10

Techtter

25 blogs, 25 data engineering concepts 👇


You want to learn Data Engineering from the ground up? Here’s a list that covers all the key concepts you will use in real projects.

Here are 25 blogs to guide you through every important concept 👇

1. Data Lake vs Data Warehouse
→ lnkd.in/gEpmTyMS
2. Delta Lake Architecture
→ lnkd.in/gk5x5uqR
3. Medallion Architecture
→ lnkd.in/gmyMpVpT
4. ETL vs ELT
→ lnkd.in/gvg3hgqe
5. Apache Airflow Basics
→ lnkd.in/gGwkvCXd
6. DAG Design Patterns
→ lnkd.in/gHTKQWyR
7. dbt Core Explained
→ lnkd.in/g5mQi8-y
8. Incremental Models in dbt
→ lnkd.in/gS25HCez
9. Spark Transformations vs Actions
→ lnkd.in/g2RRCGMW
10. Partitioning in Spark
→ lnkd.in/g5fXjSJD
11. Window Functions in SQL
→ lnkd.in/gupxmxvu
12. Slowly Changing Dimensions (SCD)
→ lnkd.in/gVFQmnuf
13. Data Modeling (Star vs Snowflake)
→ lnkd.in/gEP6Dacb
14. Data Quality with Great Expectations
→ lnkd.in/g84tGjBA
15. Data Lineage & Cataloging
→ lnkd.in/gT-GcF3a
16. Apache Kafka 101
→ lnkd.in/gHfDGa2d
17. Batch vs Stream Processing
→ lnkd.in/gPZt-pwd
18. PySpark Optimization Tips
→ lnkd.in/gQ6DXgDU
19. Auto Loader in Databricks
→ lnkd.in/gJiuYCQU
20. Delta Live Tables
→ lnkd.in/gn3AuZep

#dataengineering #snowflake #spark #dbt #techtter

1 month ago | [YT] | 12

Techtter

90% aren’t prepared for an AI Engineer interview.
It’s not just “prompting” or “use LangChain.”
Here's a list of questions + concepts you need to know👇

🔹 𝗟𝗟𝗠 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
→ What is tokenization, and how does it affect generation?
→ How do embeddings really work?
→ What’s the role of attention, positional encoding?
→ What changes during fine-tuning? (optimizers, schedulers, layer freezing)
→ LoRA vs QLoRA vs full fine-tune - tradeoffs?

🔹 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
→ Few-shot vs zero-shot - which works better where?
→ How do you design system prompts that are robust across users?
→ How do you make output deterministic?
→ How do you track, version, and backfill changing context?
→ How do you build/maintain the memory?

🔹 𝗥𝗔𝗚 𝗦𝘆𝘀𝘁𝗲𝗺𝘀
→ What’s your chunking strategy - by length, semantics, or structure?
→ How do you choose a vector DB (Chroma, Pinecone, OpenSearch…)?
→ Can you update or backfill embeddings with zero downtime?
→ How do you evaluate retrieval quality (precision@k, reranking, citation)?

🔹 𝗠𝗟𝗢𝗽𝘀 & 𝗟𝗟𝗠𝗢𝗽𝘀
→ Sketch a pipeline: from raw data → model → serving → feedback
→ How would you monitor performance drift or hallucinations?
→ How do you log prompts and outputs for debugging and auditing?
→ CI/CD for LLM workflows - what’s different from ML?

🔹 𝗖𝗼𝘀𝘁 & 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀
→ How do you reduce token usage?
→ When should you quantize a model?
→ What’s your batching + caching strategy to reduce latency?
→ When to use hosted APIs vs open-source models?

🔹 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴
→ How do you make an AI system more deterministic and less brittle?
→ What fallback do you use if the LLM fails mid-task?
→ Can you solve this without an LLM or vector DB?
→ What’s the right database for this task - SQL, NoSQL, or vector?

🔹 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀:
1️⃣ What happens if your embedding model changes - how do you migrate safely?
2️⃣ How would you fine-tune a model on user behavior and deploy it?
3️⃣ How would you make this system cheaper without killing quality?
4️⃣ Can you walk me through a debugging session for incorrect LLM outputs?

What’s the realest AI/ML interview question you’ve encountered?
Please do comment below and support others 🫶

1 month ago | [YT] | 3

Techtter

Learning AI Agents can uplift your entire career in 2025

If you don't know where to start, here are my Fav 15+ resources...

This is part 3 of my AI Agent explained series, where I bring you the best AI Agent resources to learn AI Agents.

Let's get into it, and you can out parts 1 and 2 in the comments:

📌 YouTube Videos:

1. AI Agent for Beginners by Microsoft
- lnkd.in/gWzCe8dY

2. Intro to AI Agents by Google Cloud
- lnkd.in/gP2MHDWk

3. What are AI Agents? by IBM
- lnkd.in/g2e3uZhM

📌 Free Courses:

- OpenAI Academy: Building AI Agents and Assistants
lnkd.in/g-eKfayR

- 9+ Free courses divided as per experience: lnkd.in/gE6aaKmQ

📌 Reports:

1. Thomson Reuters: Agentic AI 101
lnkd.in/gbcFwkHN

2. Google: Agents Whitepaper
lnkd.in/gAe3RdWB

3. OpenAI: Practical Guide on Building AI Agents
lnkd.in/gBp48Frb

4. Anthropic: Building Effective AI Agents
lnkd.in/gXCVR_bu

5. Galileo: Mastering AI Agents
lnkd.in/g669eHdn

📌 Blogs and CookBooks:

1. MongoDB - Demystifying AI Agents: A Guide for Beginners
lnkd.in/gkxX7ibZ

2. IBM - Introduction and in-depth explanation of AI Agents
lnkd.in/gxddjuuk

3. Hugging Face - AI Agents Course Full in-depth Cookbook
lnkd.in/gbSvEEvB

4. N8N - Basics of AI Agent guide with practical examples
lnkd.in/gF95y-mm

#ai #aiagents #llm #langchain #langgraph #aiengineering

1 month ago | [YT] | 4

Techtter

🚀 We’ve been busy upgrading Career Compass AI – and it’s now more powerful than ever.

Whether you’re actively job hunting or just planning your next career move, this AI-powered tool is designed to give you a serious edge.

Here’s what’s new and improved:

✅ Smarter Job Matching – Personalized job listings tailored to your goals and skills
✅ Advanced Career Planner – Visualize your growth path with real AI insights
✅ Resume Optimizer 2.0 – Rewrites, rates, and customizes your resume for each job
✅ AI Interview Coach – Prepares you for tough questions based on real job descriptions
✅ Intuitive Dashboard & User Flow – Redesigned for a cleaner, faster, and smarter experience
📸 (Screenshots added below: homepage, dashboard, features in action)

We built this with feedback from early users — and we’d love to hear what you think!
🎯 Whether you're switching industries or climbing in your field, Career Compass AI is your personal co-pilot.
🧠 Try it out and share your feedback
👉 Link: CareerCompassAI.io/

Best viewed on desktop 💻

Let’s make career growth smarter — together.

#CareerDevelopment #JobSearch #AI #SaaS #CareerCompassAI #JobHunting #ResumeTips #InterviewPrep

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1 2 3 4

1 month ago | [YT] | 0

Techtter

LLMs don’t break from weak instructions.
They break from unclear thinking.

Most AI devs obsess over prompts.

Context is the system’s oxygen.
No context, no performance.

Use this cheat sheet and resource list to master the stack:

1️⃣ Context vs Prompt Engineering
By Lena Hall
lnkd.in/deZmsdzv
Breaks down where prompting fails
Covers tools, workflows, and core principles

2️⃣ 12-Factor Agents
By Dexter Horthy
lnkd.in/dSd7qydi
Forget tweaking temperature
Master flexible context delivery

3️⃣ Context Is the New Skill
By Phil Schmid
lnkd.in/dN6fu5Jb
Tobi Lutke said it best:
“Context makes the task solvable.”

4️⃣ Context for Agentic Systems
By Lance Martin
lnkd.in/dY2MMPjK
Maps context strategies to agent lifecycles
Make agents smarter at every step

5️⃣ Tools & Techniques That Work
By LlamaIndex Team
lnkd.in/durYZDUV
Covers real tools like LlamaCloud
Move beyond prompting into full systems

If you want scale, stop engineering prompts.
Start engineering what the AI actually sees.

Visual credits to Lena Hall

1 month ago | [YT] | 8

Techtter

🧭 Where do you see yourself in 5 years?

If that question makes you pause — we built something to help.

Career Compass AI uses AI to help you:
🔹 Plan your career path
🔹 Analyze your skill gaps
🔹 Get personalized job suggestions
🔹 Optimize & build your resume

We’re excited to hear what YOU think about it!

Try it out 👉 careercompassai.io

Your feedback will help us make it smarter and more impactful.

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1 2 3 4

1 month ago | [YT] | 0

Techtter

🔥 One of the most exciting conversations in AI right now is about “context engineering” — how we design and deliver the right data and information for AI to make better decisions.

Context engineering is emerging as a critical discipline: designing, assembling, and optimizing what you feed into an LLM. It’s the art and science behind how RAG, agents, copilots, and AI apps create real business value.

And it’s much more than just writing prompts (which is just one small part).

This excellent diagram from Philipp Schmid (Google DeepMind) illustrates the key components that shape a model’s behavior:
✅ Instructions & system prompts
✅ User intent
✅ State & short-term memory
✅ Long-term memory
✅ Retrieved context (RAG)
✅ Tools & structured outputs

The future of AI isn’t just about better prompts — it’s about context engineering at scale.
Think of it as the new software architecture — but for AI reasoning. Like any engineering discipline, it’s becoming repeatable, measurable, and mission-critical.
#ai #promptengineering #contextebgineering #aiagents

📌 Let me know your thoughts in the comments!

2 months ago | [YT] | 4