Product Insights for Emerging Technology

20 years in emerging tech — ex-Head of Product at Palantir, now founder of two ventures.

At Common Sense, we’re building modern agentic AI systems for local government — 100x increases in productivity across critical workflows that simplify civil service and delight citizens.

At First Principles, we help emerging tech companies generate massive value, ship faster, align teams, and unlock product-market fit or hypergrowth — through fractional product leadership and expert product coaching.

Whether it serves 10,000 users or 10 million residents, the system has to work — and keep working.

Sound like I can help you? Get in touch:

adam@firstprinciples.la or adam@commonsensetech.ai

Adam Judelson

A city once invited us in for an in-person working session.

We aligned the agenda, booked the flights, and landed.

Minutes before the meeting while we prepared in a nearby coffee shop, they cancelled due to an emergency.

Most teams would have stepped back and waited for a new date.

We didn’t.

We went to the building anyway, stood in the lobby, and said, “We’re here. We know the timing shifted. We’ll stay as long as you need.” Turns out, the opportunity to get ahead outweighed the crisis.

This should be the bare minimum when working with public institutions.

Show up. Make it easy. Remove friction. Respect their time.

The difference between us and the incumbents is simple. 

We want to have a conversation. 

We want to understand the work. 

We want to sit with the people responsible for keeping the city running.

And we believe that if the government is going to get faster and more effective, it starts with partners who are willing to be in the room, see the real constraints, and prove what they can do.

Building Common Sense (govtech) | Founder, First Principles | Product Insights for Emerging Tech | Ex-Palantir | 4x Venture-Backed Exec

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I just finished one of the most talked-about books in government right now, “Breakneck: China’s Quest to Engineer the Future,” and it made me rethink something we see every day inside cities.

Wang’s core argument is that China became an engineering state, one that builds at speed and scale, while America became a lawyerly society that regulates, litigates, and creates veto points that block building.

The American “lawyerly” model has crucial advantages in protecting individual rights, constraining state overreach, and fostering a flexible, innovative society, even though it often slows physical building. He sees these legal and institutional strengths as exactly what China most needs to import, just as the U.S. should import some of China’s engineering spirit.

Local government sits right at the collision point of these two mindsets – cities need to build (housing, infrastructure, services) but operate inside a thicket of legal constraints and procedural requirements.

Most of the people I meet in government, across agencies, roles, and floors, genuinely care. They want things to work. They want residents to feel the difference.

Both types of thinking and action are crucial to progress, and the lack of understanding from these opposite optics causes the work to drag on, because every decision becomes defensive or about optics instead of outcomes.

Individuals start protecting themselves instead of the mission.

Wang’s point isn’t just about internal dysfunction – it’s about what happens when a society only optimizes for preventing bad outcomes rather than producing good ones. Local government has become very good at avoiding mistakes and needs work building things and taking bold, calculated risks. 

The question is whether we can recover some of that engineering ethos without losing the legal protections that matter to the world’s most important democratic republic.

Every city leader I meet wants faster service delivery with AI. And increasingly, the teams running those services want to deliver it.

But there’s a gap between enthusiasm and implementation.

The same AI doomerism questions come up in almost every conversation: “What if the AI makes a mistake?” “What if the AI misses something?” “What if the work or answer is incomplete?”

These are the right questions to ask. Public sector teams are accountable to their communities in ways that private companies aren’t, with a litigious public ready to pounce on mistakes. Every strong AI company must be able to explain how accuracy and quality are measured, for sure.

But it’s also worth asking: what’s the error rate in your current workflow? How much work is left entirely undone right now? How long are people waiting for answers or approvals under the status quo?

Because in most cases, the baseline isn’t perfection. It’s human staff working through hundreds of repetitive tasks under time pressure, applying convoluted legal frameworks, with inconsistent results.

The challenge isn’t that teams are being too careful. It’s that we’re measuring AI against an impossible standard while accepting a much higher error rate and set of delays from manual processes.

Government operations are excellent at managing long-tail risk.

But managing opportunity requires a different muscle to be flexed, one that involves controlled testing rather than comprehensive policy first.

AI won’t move in the public sector through white papers or policies alone. It will move through small, measured pilots that prove value in real-world conditions.

Aspiring PM? Want To Crack Product Manager Role?

No Experience! No Tech Background! 

But Serious about Career Building

Welcome 👋

You're at right place.. 

Hello, I'm Sachin Sharma

IT Product Career Mentor. 

With a 99.1% satisfaction rating 🥇

Ex-Sr. Product Manager @BharatPe. 

Mentor IT Professionals to become Elite Product Managers in just 90 Days..

Some more about me:

𝐌𝐲 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:

➥ Entered Product role in 2013 after 20 rejections
➥ Overcame Rs 10,000 monthly salary
➥ Turn obstacles into growth opportunities

𝐌𝐲 𝐌𝐢𝐬𝐬𝐢𝐨𝐧:

➥ Empower 100,000 individuals to become Elite PMs

𝐌𝐲 𝐂𝐚𝐫𝐞𝐞𝐫 𝐆𝐫𝐨𝐰𝐭𝐡:

➥ Found a crucial mentor who propelled career
➥ Invested over 18 lakhs in real-world education and mentorship
➥ Got Salary Hike - Rs 10,000 monthly to Rs 1 Crore annual CTC in 9 years

𝐈𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐭𝐫𝐚𝐜𝐤 𝐫𝐞𝐜𝐨𝐫𝐝🥇

➥ Leading dynamic community assisting Aspiring Product Managers

➥ Mentored 1000+ Aspiring PMs with 99.1% satisfaction

𝐆𝐞𝐭 𝐎𝐧 𝟏:𝟏 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞 𝐂𝐚𝐥𝐥 &

𝐁𝐫𝐞𝐚𝐤 𝐈𝐧𝐭𝐨 𝐏𝐌 𝐈𝐧 𝐉𝐮𝐬𝐭 𝟗𝟎 𝐃𝐚𝐲𝐬

➥ Product Portfolio Building 
➥ Tips & Hacks For Top MNC's Q's
➥ Resume Building & Mock Interviews
➥ Product Management Free Resources & Templates 

Get 90 days roadmap to crack PM role!!

Shoot a DM For 1:1 Demo Call Link!

Sachin Sharma

If you want to win as a Product Manager in 2026, this is the real skill map complied by Sid Arora 😀

Not tools. Not buzzwords. But knowing what to use, when, and why in AI-first products.

Here’s a clean, practical AI skill stack for Product Managers, with the right tools to explore each layer:

Core skills + tools PMs should know:

1/ ML Models (basics, trade-offs)

TensorFlow: https://www.tensorflow.org
IBM Watson: https://www.ibm.com/watson

2/ GenAI, LLMs & SLMs

Claude: https://lnkd.in/g_JYCQBq
OpenAI: https://openai.com
Gemini: https://lnkd.in/giaTMfCF

3/ Fine-tuning & Instruction Tuning

Hugging Face: https://huggingface.co
Labellerr: https://www.labellerr.com
Kili: https://lnkd.in/g8BeuEmz

4/ Data Quality & Labeling

Labelbox: https://labelbox.com
SuperAnnotate: https://lnkd.in/g4kEwtXZ

5/ Embeddings & Vector Search
Pinecone: https://www.pinecone.io
Milvus: https://milvus.io

6/ RAG (Retrieval-Augmented Generation)
LangChain: https://www.langchain.com
LlamaIndex: https://www.llamaindex.ai

7/ Context Engineering
Contextual AI: https://contextual.ai
LangChain: https://www.langchain.com

8/ Prompt Engineering
PromptLayer: https://promptlayer.com
PromptPerfect: https://lnkd.in/gmZWCada

9/ AI Agents & Orchestration
AutoGen: https://lnkd.in/gaAfV2ww
MetaGPT: https://lnkd.in/ghYtstsE

10/ AI Workflows & Automation
Zapier: https://zapier.com
Make: https://www.make.com

11/ Composable AI Systems
Bubble: https://bubble.io
Supabase: https://supabase.com

12/ LLM Evals & Benchmarks
Helicone: https://www.helicone.ai
TruLens: https://www.trulens.org

13/ Observability & Feedback Loops
Helicone: https://www.helicone.ai
PromptLayer: https://promptlayer.com

14/ AI Governance & Ethics
Guardrails AI: https://lnkd.in/gwtF7_VJ
Rebuff AI: https://www.rebuff.ai

15/ Model Context Protocol (MCP)
Model Context Protocol: https://lnkd.in/g7AYdYYs

You don’t need to master everything. But you must know how the pieces fit together.

Save this. This is what modern Product Managers are actually learning now.

#tools #pms #apm #ai #productmanagement #resources #helpful #india #linkedin

Become Elite PM In 90 Days ~ AI Product Career Coach : Mentor IT Professionals to Break into AI & Product Management Role || Aspiring PMs Resources & 1:1 Call (Demo Call) ↓

21 Metrics That Show Tech Companies Are Crushing It 🤯

Every successful tech company has one thing in common: 

They know their North Star Metric

➥  It’s a clear, customer-focused metric
➥  It drives focus and aligns teams
➥  It measures progress towards the mission

These companies are laser-focused on what moves the needle for them

Here’s how top tech giants track their growth

Checkout below ⤵️

Hope this helps many  ♥️

Repost ♻ to help Aspiring PMs

They told me to “learn AI.” But no one told me what kind. So, I wasted 3 months playing with chatbots.


Then I found the real game-changer:
👉 AI Agents 

These aren't just fancy chatbots with better memory.

They plan, act, analyze, adapt all on their own.

They use tools, remember past steps, and work like autonomous interns who actually do things right.


As a PM? This blew my mind.

Because in 2026, building products won’t be enough. You’ll be orchestrating intelligent agents.

Here’s where to start - Best guide compiled by Paweł Huryn 👇 (No coding? No problem.)

1/ Understand the foundation

•  Building Effective Agents by Anthropic: https://lnkd.in/dtnwnuqb

•  Agents Companion whitepaper by Google : https://lnkd.in/d7T_mfCT

•  GPT-4.1 Prompting Guide: https://lnkd.in/dt8FxriE

•  Anthropic Prompt Engineering: https://lnkd.in/dc-kucif

•  Prompt Engineering by Google: https://lnkd.in/dEU2Y_9v

•  How to Build an AI Agent Step-by-Step: https://lnkd.in/d6Q6nduV


2/ Build step-by-step, even if you’re not technical

A free GitHub repo found by Rubén Domínguez Ibar.  

Well documented projects built with RAG, AI agents, multi-agent systems, MCP, etc.


3/ Explore 50+ AI Agent Projects

•  RAG Chatbot: https://lnkd.in/dew--RqD
•  AI Voice Agent: https://lnkd.in/dSW6_KiT
•  AI Agent Architectures: https://lnkd.in/dnmQ_kP8

Don’t wait for your company to “catch up.”
Start now. Play, break, build.

P.S. Bookmark this. In 6 months, this could be your unfair advantage.

Special Thanks To Paweł Huryn ł for this real goldmine :)

♻️ Repost to help PMs and engineers jump ahead of the curve.

#apm #career #careercoach #job #aiagents #ai #corporate #india #linkedin

Sachin Sharmma

The best resume template for Product Managers by Malay Krishna. Must save.

Built from 300+ PM interviews, this resume works because it focuses on impact, not tasks.

✅ Quantified outcomes over responsibilities
✅ Clean, recruiter-friendly formatting
✅ Easy to customize for each JD
✅ No fluff. No personal noise. Just results.

If you’re applying for PM roles in 2026,
this should be your benchmark.

👉 Apply for PM mentorship & prep: DM "Career" 

PMs: When did you last update your resume for impact?

P.s. Repost ♻️ to your network 

#pms #apm #resume #interview #corporate #india #companies #employees #linkedin

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The Battle

Adam Judelson

Sachin Sharma

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The Battle

Adam Judelson

Sachin Sharma

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Adam Judelson's LinkedIn Performance

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