Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | a data science team in your pocket for B2B SaaS.

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

How we built Expandi's data system

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

Most dashboards & reports fail for one specific reason.

It rarely has to do with the tool or the data.

The problem is much simpler: metric definitions.

A dashboard full of metrics means nothing if no one agrees on what they mean.

I see this pattern constantly:

❌ sales trusts a spreadsheet over the dashboard.
❌ finance / Product report conflicting churn %.
❌ "active user" has 3 different definitions.

Before you build the dashboard, build the Metrics Dictionary.

It can be a simple Google Sheet that lists out your metrics + descriptions.

Here are the columns we include in it:

1. unit → is it €, $, %
2. filters → how to slice the data
3. calculation → how it's calculated
4. department → who cares / uses it
5. metric owner →who is accountable
6. refresh rate → how often it updates
7. description → what the metric means
8. data source → where the data comes from

I attached the template we use in the comments.

Copy it, tweak it, use it. ✌️

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

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.

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