Hassan W. Bhatti

Snowflake Data Marketplace| Co-founder@CN (acq Snowflake)| Investor@Communityfund| I share insights on building start-ups and personal growth

I love building companies. Over the last 10 years, I’ve started, scaled, and exited 4 ventures—some bootstrapped to multi-million ARR, others acquired by industry leaders. Additionally - I was also fortunate to be part of Snowflake Pre-IPO to post-IPO growth phase - seeing what scaling looks like in the product and sales orgs. 

Right now, I’m deep in AI, co-running the world’s largest AI newsletter and building/researching in the Memory and AI agent space. I also invest actively—both through a fund and my own syndicate—backing founders who are building category-defining companies.

Always open to great conversations, whether it’s about building, investing, or the future of AI.

Founder roles currently (2024-Present):
- Superhuman AI (Co-leading fast growing media company)
- Stealth Co (Something in AI)

Founder roles previously (2017-2024):
- CryptoNumerics (Acquired Snowflake)
- Semantic Health (Acquired AAPC)
- Software Services business (Acquired by a family business) 
- Law Firm (Rebuilt and grew family law firm)

Previously worked at (2015-2024):
- Snowflake (Product and GTM roles)
- Iris Automation (1st Product and Biz hire)

Education:
- 2010-2015: BSc in Physics and Economics at University of British Columbia on a full ride scholarship 
- 2020-2023: LLB with Upper Second Class Honours (Grade 2:1) from University of London
- Multiple certifications in AI, business, and software from Uoft, Harvard, and other programs

Product & Partnerships - Data Marketplace 

Key focus areas:
- Native Apps (2023-Present)
- Global Lead for HCLS Vertical (2021 - 2023)
- Data Monetization Product and 0-1 growth strategy  (2021 - 2022)
- Working across all industries (2020 - 2022)
- Launched EMEA and APAC (2020-2021)
- Cryptonumerics integration (2020)

Think it. Say it. Done.

The average person spends 3 hours typing + switches 1,000 tabs per day.

That ends today.

Meet Lemon: The first voice-to-action AI agent that turns your voice commands into finished tasks.

Comment "Lemon" to get free access for 30 days.

(must be following so I can DM you)

Building | 2x exited founder (acq Snowflake) | Start-up to Post-IPO | Investor@Nextwave

If you’ve been in AI long enough, you know when something shifts.

The last 12 months the slope steepened.

Models now:
• Write and ship production-level code.
• Handle multi-step reasoning reliably.
• Work across large codebases and documents without collapsing.
• Orchestrate tools and execute structured workflows.
• Cost materially less per experiment than a year ago.

And the pace between releases is compressing.

What matters isn’t just that models are better. It’s that iteration cycles are collapsing.

You can now:
- Ship MVPs in days.
- Run 10x more experiments per sprint.
- Test product hypotheses without adding headcount.
- Refactor entire systems without touching most of the code yourself.

The builders who understand this are reorganizing around it.

Smaller teams > higher leverage > more shots on goal > faster feedback loops.

This is the first time in my career where capability, cost, and velocity all improved at once.

If you’re building on the app layer right now, this is an unusually asymmetric moment.

Lean in.

We've been extremely lucky to have some of the sharpest minds in tech sit down with us on Superhuman.

We’ve talked about 
- Building in monopolized markets
- Competing with giants
- AI-native companies
- Capital allocation
- Leadership under pressure

...and a lot more

If you care about where AI, startups, and ambition collide then this is for you.

Youtube: https://lnkd.in/eSzrdVj4
Spotify: https://lnkd.in/gWgSbXwa
Apple Podcast: https://lnkd.in/gTB2ENcU

Why is building a great team critical for entrepreneurs?

Here's ashfaq munshi, CEO of Pepperdata, talking about how your team is your biggest asset as a builder

Watch the full episode: https://lnkd.in/eQiJTSh8

Who says you can't have a little fun at work?

Founder Lowerated | AI Engineer (heylemon.ai)

Call me the Godfather or Batman, either one works.

With Lemon and your voice you can:
    - set your writing style and tone.
    - setup workflows and trigger them with a a single word 🙌

No extra menus or screens. Personalize Lemon however you want, just gotta use your voice.

Use it here: https://heylemon.ai
 
What would you teach your AI to remember?

Follow me to learn how you can leverage AI to boost your productivity and accelerate your career. Scaled products to 8 Million+ users and built the world's biggest AI newsletter with 1 Million+ readers.

Zain Kahn

This guy from Cambridge plays with Claude Code like he owns it.

He talks about his workflow in 2026 and here's what's new: 

1. Video-Based Specification (Spec Phase)

> Screen Recording: 

Instead of writing a spec from scratch, find an existing product that is similar to your idea. Record your screen while using it and talking through your specific feature ideas and changes.

> Generate PRD: 

Upload this video to Gemini 1.5 Pro (referred to as Gemini 3 Pro/Free Pro in the video context) and ask it to generate a Product Requirement Document (PRD).

> Refine Spec: 

Use the "Ask User Question" tool in Claude Code. Prompt it to interview you about the generated spec to fill in missing details (e.g., "How should the emoji picker be positioned?").

> Package Discovery: 

Feed the refined spec into ChatGPT with "Heavy Thinking" (likely OpenAI's o1/o3 models) to search for and recommend specific, well-maintained GitHub packages (e.g., for a WYSIWYG editor) to avoid building complex components from scratch.

2. The Orchestrator Role

> Design Feedback Loops: 

Your primary job is not to write code but to design loops where the agent can build, fail, and learn.

> Monitor & Update: 

Watch the agent’s reasoning. If it makes a mistake, don't just fix the code—update the claude. md (project instructions file) to prevent that specific mistake from happening again.

> High-Level Decisions: 

Make the architectural decisions the agent can't, such as choosing the database or specific tools.

3. Model Choice & Tools

> Model Selection: 

Use Opus 4.5 for building large-scale features and GPT-5.2 for architecture and debugging (specific future models mentioned in his 2026 context).

> Voice Dictation: 

Use HyperWhisper to dictate prompts significantly faster than typing.

4. Execution & "Parallel Vibe Coding"

> Parallel Agents: 

For small, well-defined tasks (like extracting hard-coded strings for translations), spin up multiple sub-agents to work on different parts of the project simultaneously.

> Avoid Conflicts: 

Do not use parallel agents for large features within the same project to avoid complex merge conflicts ("meshing issues").

> Sub-agents for Multi-Project Fixes: 

If a bug exists in a template used by multiple projects, spin up one sub-agent per project to fix them all in parallel.

5. Review & Maintenance

> Planning Mode: 

Use Claude Code’s "Planning Mode" to prevent Architectural Drift, ensuring the agent sticks to the original design vision over time.

> Shape of Diffs: 

Inspect the "shape" of the code changes (diffs) to ensure they are manageable and align with expectations before accepting them.

> Forking for Learning: 

If the agent does something surprising or complex, fork the session. In the forked session, ask "Why did you do that?" or request diagrams to understand the code without polluting the context of the main working session.

Get more tutorials here: https://lnkd.in/e64Jvdrt

Follow me to learn how you can leverage AI to boost your productivity and accelerate your career. Scaled products to 10 Million+ users.

Traditional computer science degrees can't keep up with this pace.

Here are the 15 engineering blogs that actually teach real-world scale:

1 → THE CODE
Scale at global level.
Teaches you how to code with AI and AI Agents
https://lnkd.in/e64Jvdrt

2 → Airbnb Tech Blog
Product engineering done right.
User focused systems.
https://airbnb.tech/blog/

3 → Meta Engineering
Billions of users infrastructure.
Performance at extreme scale.
https://engineering.fb.com

4 → AWS Architecture
Cloud design patterns revealed.
Production ready blueprints.
https://lnkd.in/gqjh88PS

5 → Netflix TechBlog
Streaming at massive scale.
Chaos engineering explained.
https://lnkd.in/gNE8Cs6Z

6 → Discord Engineering
Real time systems mastery.
Handling millions of connections.
https://lnkd.in/gea6kSwt

7 → Google Research
Cutting edge tech breakthroughs.
Tomorrow's solutions today.
https://lnkd.in/gjzDu8wp

8 → NVIDIA Developer
GPU powered computing insights.
AI infrastructure secrets.
https://lnkd.in/dt_BW7qF

9 → Slack Engineering
Collaboration platform internals.
Distributed systems at scale.
https://slack.engineering

10 → Cloudflare Blog
Internet infrastructure deep dives.
Security and performance wins.
https://lnkd.in/gqa-HE6h

11 → Figma Tech Blog
Collaborative design systems.
Browser performance magic.
https://lnkd.in/g2KF_eX9

12 → Shopify Engineering
E-commerce at scale.
Black Friday traffic handled.
https://lnkd.in/g7DVzRAc

13 → Stripe Engineering
Payment systems that work.
Financial infrastructure explained.
https://lnkd.in/gfMr-jEr

14 → Microsoft Engineering
Enterprise scale solutions.
Developer tools insights.
https://lnkd.in/g3Mi8XES

15 → GitHub Engineering
Code collaboration at scale.
Developer platform secrets.
https://lnkd.in/gPHaD6AC

16 → Bonus
System design deep dives.
https://lnkd.in/g-tP7eEc

Save this for later.
Repost ♻️ for aspiring engineers.

Follow me for more AI playbooks and engineering resources.

Claude Code is only as good as your orchestration workflow.

I spent 6 months testing it so you don't have to. 

[ P.S. You can get my Ultimate Claude Code guide for engineers here at no cost: https://lnkd.in/e64Jvdrt ]

So Boris Cherny (the creator of Claude Code at Anthropic) recently shared the internal best practices his team actually uses daily. 

Someone brilliantly distilled those threads into a structured CLAUDE .md file you can drop straight into any project root.

It acts as a system prompt. Turns Claude into a far more autonomous, rigorous engineering partner.

Here's what it does:

→ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Mandates "Plan Node Default" for any task over 3 steps. Uses subagents liberally to keep the main context window clean.

→ 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗽: This is the real magic. After ANY correction, it updates a tasks/lessons .md file. You're building a compounding system where the mistake rate drops over time because it actively learns from your feedback.

→ 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗗𝗼𝗻𝗲: Can't mark a task complete without proving it works. Diffs behavior. Runs tests. Checks logs. The bar? "Would a staff engineer approve this?"

→ 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗕𝘂𝗴 𝗙𝗶𝘅𝗶𝗻𝗴: Zero hand-holding. Point it at failing CI tests or error logs... it just goes to work. No constant context switching from you.

→ 𝗦𝘁𝗿𝗶𝗰𝘁 𝗧𝗮𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Forces a "Plan First" approach written to a todo .md with checkable items before any implementation starts.

And perhaps the most important part?

It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of every change. Senior developer standards. Not shortcuts.

If you're spending hours a day in the terminal with AI, setting up a strong .md instruction file like this isn't optional anymore. It's the difference between AI that drifts and AI that compounds.

It takes time to set up. But if you do, you're ahead of almost everyone else.

Lemon is the most powerful tool I've used since ChatGPT.

You literally say things with your voice and it does it for you instantly.

Pure magic.

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The Battle

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Zain Kahn

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Posts

Engagements

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);}Hassan W. Bhatti's LinkedIn Performance

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