Mohan

[Rohit Kumar Tiwari @_rohit_tiwari_]

AI Engineering from Scratch.

503 lessons. 20 phases. 320 hours.

https://t.co/UuX9N62VCU

Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)

[Aman Kharwal @amankk_9]

AI/ML Certificates That Get You Hired.
https://t.co/6YMOFESwfR

[Gaurav Sen @gkcs_]

Valuable lessons by Karthik Ramgopal (Distinguished Engineer at LinkedIn).

1. AI should only be used for tasks that can be done manually.

If you cannot do the task yourself, you can't validate, guardrail, or eval the output.

To make a reliable system, we need AI output to mimic human output.

2. Developer productivity is up. Getting a job is difficult.

The engineers most at risk are those who treat AI as a black box.

They skip the fundamentals (context engineering, retrieval, evals) and lose the ability to judge whether the work is right or wrong.

Strong fundamentals and knowing how to harness AI are essential. I really feel like plugging in my cohort here, but I will resist the temptation :p

3. Both juniors and seniors are learning AI together. We are all on the same boat.

Juniors entering the workforce are AI-native by default. Seniors carry years of real-world judgment but are ossified into old habits.

Drop the ego. Let the learning flow.

1. Transformers
1.1 Tokens
1.2 Vectors
1.3 Attention

2. Retrieval Augmented Generation
2.1 Chunking
2.2 Vector Databases
2.3 Query Rewriting
2.4 Chunk reranking

3. Agents
3.1 Short-term and long-term memory
3.2 Tool use
3.3 MCP
3.4 Orchestration

4. Evals
4.1 Manual Evals
4.2 LLM as a Judge

-------------------------------

If you are looking to transition into an FDE or AI engineer role, join the AI Engineering cohort here:
https://t.co/lsqvTnoxvE

Cheers!

[krishna @OccupyingM]

Agentic Engineering has completely changed it's shape in the past few weeks if you want to get ahead here are some concepts you absolutely need to know for your own scaffolding

1. Continual Traces → Traditional logging has been rendered obsolete by these god tier models and harnesses. so instead you use continual traces logs that build upon themselves as the context grows

2. Agent Security Stack → Trust provision provenience and containment by design.

3. Stochastic Hierarchical Iterative Topology → Adaptive multi-level exploration of solution spaces. Using tree traces and the inherent laws of math to direct your agent harness for long running tasks

4. Relational Context Retrieval → Retrieve relationships, not documents, using relational dbs to constantly improve and connect your growing context

5. Filter Using Constraint Context: Current harnesses will have you believing that semantic search has been rendered obsolete but this technique allows you to improve your harness by leveraging the inherent semantic understanding of LLMs to efficently search relevant context.

DM me for the complete doc to 100x your agentic capabilities

[NIELIT Lucknow @LNW_NIELIT]

Build AI applications with NIELIT Lucknow

[prathosh ap @prathoshap]

I'm offering my Mathematics of GenAI course on NPTEL @nptel_official from July 26. We'll look at the mathematical underpinnings of algorithms powering modern AI. Interested folks can register (free of cost). Link in the first reply

[ᴀʀᴛ ᴏꜰ ᴘʜʏꜱɪQᴜᴇ @Artofphysique_]

Core strength doesn’t lie — this move tests your control, balance and endurance from the first rep.

[Vaishnavi @_vmlops]

Agentic AI
- A Complete Learning Guide
for High School Students

https://t.co/QRU2VqUNFG

[Tech with Mak @techNmak]

The right order for learning AI engineering in 2026.

What most people do:
→ Jump to agents
→ Skip foundations
→ Ignore MLOps
→ Wonder why nothing works

What this roadmap shows:

1. Foundation (Python, APIs, clean code)
2. Semantic intelligence (embeddings, vector DBs)
3. RAG (grounded outputs)
4. Agents (autonomous workflows)
5. MLOps (CI/CD, containers)
6. Evaluation (metrics, drift, bias)
7. Inference optimization (quantization, caching)
8. Portfolio
9. Specialization

The sequence builds on itself. Skip steps and you'll hit walls later.

Btw, DataCamp has opened 680+ courses for free from June 1-7. Worth exploring.
https://t.co/vi6gtDZS66

Image credits - @agenticgirl (Follow her)

[liminal @Liminal1988]

Sentences to finish before getting married

[Ben Lang @benln]

Pulled the fastest-growing startups by hiring velocity over the past 90 days:

[Mens Mastering Style @MasteringStylez]

Top 7 Stylish Haircuts Every Man Must Try in 2026 :

1.

[Priyansh Agarwal @Priyansh_31Dec]

A senior (L7) adds a comment on my doc, I think about it for 10 min and come up with a response. Then I take a step back and think for 1 more hour only to realise how deep and nuanced that comment was.

Please tell me how these people think what they think. It is a privilege to feel dumb honestly because it keeps you grounded and hungry to keep learning.

[Indian Tech & Infra @IndianTechGuide]

🚨 H1-B Visa workers to USA in the last 10 years.

[Santiago @svpino]

Context engineering is the single most important area you can focus on right now.

We already have amazing models.

Agents no longer fail because models are dumb. They fail because they don't have the right context.

Here are the 4 ingredients of good context:

[Neo Kim @systemdesignone]

If you want to become good at AI engineering (in 3 weeks), then learn these 15 concepts:

1 AI Agents: Memory, State & Consistency
→ https://t.co/v8H7O00jub

2 Machine Learning System Design 101
→ https://t.co/9MkHcLb5e0

3 Design Personal AI Chat Assistant
→ https://t.co/nNWq3onTnW

4 How RAG Works
→ https://t.co/cGmunPTUlb

5 LLM Concepts - A Deep Dive
→ https://t.co/5lCKxq2g4N

6 How to Design an AI Agent
→ https://t.co/JvnPd9773A

7 What is Reinforcement Learning
→ https://t.co/AVpl9j1oit

8 How Vector Databases Work
→ https://t.co/FVxan8xHH3

9 Context Engineering 101
→ https://t.co/OMkiZhkODL

10 AI Coding Workflow 101
→ https://t.co/paIf9ksIU9

11 LLM Evals Explained
→ https://t.co/nv3Ol8W53p

12 How AI Agents Work
→ https://t.co/tk3zkCjRvg

13 How MCP Works
→ https://t.co/wgf8gHnnkn

14 Agentic Patterns Explained
→ https://t.co/8YdBBWvTj1

15 Multi-Agent Architecture Explained
→ https://t.co/rS5QQS7Jln

What else should make this list?

===

👋 PS - Want my System Design Playbook for FREE?

Join my newsletter with 210K+ software engineers right now:
→ https://t.co/ByOFTtOihX

===

💾 Save & RT to help others ace AI engineering.
👤 Follow @systemdesignone + turn on notifications.

[Zeb Evans @DJ_CURFEW]

Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why.

First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.

Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands.

Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition.

I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively.

THE 100X ORGANIZATION
The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.

Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken.

The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.

These roles will evolve. But waiting for that to happen naturally means falling behind now.

The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.

THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS

— THE BUILDERS: 10X ENGINEERS
I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality.

Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment.

AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?

And how do you want your best engineers to spend their time?

If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code.

The new world is about enabling your 10x engineers to become 100x.

The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated.

I call this the great reckoning of AI coding, and every company will face this soon if not already.

More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well.

— THE BUILDERS: 10X PRODUCT MANAGERS
Product management and design roles are merging.

Designers that have customer focus, become more like product managers.

And product managers that have intuition for UX become more like designers.

The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results.

The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy.

Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on.

To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production.

Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck.
That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time.

— THE SYSTEM MANAGERS
Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp.

The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world.

You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is.

— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.

This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings.

One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers.

REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In our case, much of the savings in this new operating model will flow directly back to those that enabled it.

We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them.

You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace.

Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.

THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.

The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago.

ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.

[Aaron Levie @levie]

Great post on FDEs. Everyone should read it if you’re interested in this job category. This is a job that is going to be around as long as AI keeps changing rapidly, which it inevitably will.

People often wonder why isn’t this like just deploying other forms of technology in the past, like cloud.

Because something like cloud adoption affected a fairly concentrated set of users (developers and IT), and generally didn’t require a fundamental change to the workflows of employees to get the benefits of the new service being delivered on the cloud. At best you went to one training session and you were done.

With agents, the work to implement them is not only highly technical, but they directly impact the underlying workflows that people participate in. This means there’s a ton of technical work and change management that comes with it.

Further, the pace of change of cloud wasn’t nearly as quick, so there was a lot more time for best practices to propagate. Now, every model change means either something new can be done that wasn’t possible before, or some piece of scaffolding is now redundant or holding you back.

This is why it’s commonly easier for a vendor or partner that’s seen the implementation hundreds or thousands of times help do the work, even with internal support from the customer.

So, this job isn’t going away any time soon, and will be a great path for a lot of technical talent, especially early career.

[Neo Kim @systemdesignone]

If you want to become good at system design (in 30 days), learn these 30 case studies:

1 How Stock Exchange Works
→ https://t.co/ckLlZUh4UR

2 How YouTube Works
→ https://t.co/dTVLjI8EYh

3 How Google Docs Works
→ https://t.co/lXjTlb3Vm9

4 How Kafka Works
→ https://t.co/1D04tpNm2q

5 How URL Shorteners Work
→ https://t.co/SNxRzuzV6B

6 How WhatsApp Works
→ https://t.co/phAf30nR2M

7 How Airbnb Works
→ https://t.co/4NZMIlN70F

8 How Spotify Works
→ https://t.co/d1rGAvPIxA

9 How Slack Works
→ https://t.co/dpjG03ZvlL

10 How Reddit Works
→ https://t.co/J3ZrmwJ0q4

11 How Bluesky Works
→ https://t.co/wfo35CdFvm

12 How Tinder Works
→ https://t.co/uTLfmUajeG

13 How Twitter Timeline Works
→ https://t.co/T7xJTWL30C

14 How Uber Finds Nearby Drivers
→ https://t.co/UX8AA8yNmv

15 How Amazon S3 Works
→ https://t.co/fOchSbdw3C

16 How Apple AirTags Work
→ https://t.co/02ChJDY5Y5

17 How LLMs Actually Work
→ https://t.co/VW4fD9fH8P

18 How ChatGPT Apps Work
→ https://t.co/cK51NCi6OQ

19 How Uber Computes ETA
→ https://t.co/t5G2mhzahX

20 How Meta Serverless Works
→ https://t.co/jVCIuoN4wj

21 How Live Comments Work
→ https://t.co/UzdZPXinxX

22 How Real-Time Leaderboards Work
→ https://t.co/tPqC4Cear5

23 How Live Presence Works
→ https://t.co/u7LWmkQ9UB

24 How YouTube Scales MySQL
→ https://t.co/vm2enNgV16

25 How Vector Databases Work
→ https://t.co/UAtlY6Ntle

26 How Pastebin Works
→ https://t.co/nVS9TKAluk

27 How ChatGPT Works
→ https://t.co/wgE4cxO7i2

28 How Nginx Works
→ https://t.co/XX0ukXAQuG

29 How Lyft Works
→ https://t.co/T9fvldjqC4

30 How Google Search Works
→ https://t.co/DVlsy0vLPq

What else should make this list?

===

👋 PS - Want my System Design Playbook for FREE?

Try the link below to join my newsletter right now:

→ https://t.co/ByOFTtOihX

(200K+ software engineers are already inside.)

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💾 Save & RT to help others learn system design.
👤 Follow @systemdesignone + turn on notifications.

[Matt Harney @SaaSletter]

👀 New "AI Is Eating The World" 79-slide deck from @benedictevans

https://t.co/UD7gYPmEXT