Sam Shende

Sometimes, a truce is the most important part of a war

[Sam Shende @SamRShende]

#NewProfilePic

[Sam Shende @SamRShende]

@sophieraiin @grok Hey @grok put her in saree

[Sam Shende @SamRShende]

@sassy_genx 👍👍👍

[umukambwe @umukambwe12]

The Permission Problem
Traditional networking is a game of permission.
✍️You ask for a meeting.
✍️You ask for a referral.
✍️You ask for a seat at the table.

When you Build in Public, you don't ask. You announce. You aren't a supplicant looking for a favor; you are an architect showing a blueprint. High-value people don't want to network they want to collaborate with builders.

[Sam Shende @SamRShende]

@sophieraiin X 😅

[Mohit Kale @MohitKaleAI]

𝗗𝗮𝘆 𝟴𝟯: 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗶𝗻𝘁𝗼 𝗮 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁

Most people use AI like Google on steroids.
Ask once. Get an answer. Move on.
That is not research. That is guessing faster.

Once you have agents and workflows, a new use case opens up naturally.
Research.

Not academic research.
Real world, messy, everyday research.

When I say research assistant, I do not mean:
“Explain this topic to me.”

I mean:
• Read multiple sources
• Compare viewpoints
• Extract patterns
• Point out gaps
• Summarise with context

Early on, I made a mistake.
I asked AI to “research deeply” in one shot.

The output looked confident.
But it was shallow.
Some parts were correct.
Some parts were noise.

So I changed the structure.

I stopped asking for answers.
I started asking for 𝗽𝗿𝗼𝗰𝗲𝘀𝘀.

My mental model became simple:
• One agent reads
• One agent compares
• One agent summarises
• One agent challenges assumptions

Suddenly, AI stopped sounding like a blogger.
It started sounding like a junior researcher sitting next to me.

This is an important shift in Part 2.
We are no longer automating tasks.
We are 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗶𝗻𝗴 ��𝗵𝗶𝗻𝗸𝗶𝗻𝗴.

𝗧𝗿𝘆 𝘁𝗵𝗶𝘀 𝘁𝗼𝗱𝗮𝘆
• Pick a topic you genuinely want to understand
• Ask AI to list sub questions first, not answers
• Feed one source or idea at a time
• Ask AI to summarise in bullets, no prose
• Ask a follow up: “What might be missing here?”
• Write your own conclusion in one paragraph

That last step matters more than people realise.

AI is not your brain replacement.
It is your thinking amplifier.
Good research still needs a human spine.

Have you tried using AI for research yet?
What part do you still not trust fully?

Next chapter, we talk about memory.
Why AI forgets.
And why sometimes that is a feature.

#Agentic #AI #Workflows #ResearchAssistant

[Sam Shende @SamRShende]

@sassy_genx I'll love to smell 😍 That Flower

[Sam Shende @SamRShende]

@sassy_genx Who looks 👀 Eyes when she is the beauty 😍

[Mohit Kale @MohitKaleAI]

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲𝗻’𝘁 𝗰𝗼𝗻𝗳𝘂𝘀𝗲𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆 𝗹𝗮𝗰𝗸 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻, 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗰𝗼𝗻𝗳𝘂𝘀𝗲𝗱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝘁𝗼𝗼 𝗺𝘂𝗰𝗵 𝗻𝗼𝗶𝘀𝗲.
We scroll advice, tools, opinions, success stories, hot takes… and call it learning.

𝘽𝙪𝙩 𝙘𝙡𝙖𝙧𝙞𝙩𝙮 𝙪𝙨𝙪𝙖𝙡𝙡𝙮 𝙘𝙤𝙢𝙚𝙨 𝙛𝙧𝙤𝙢 𝙨𝙪𝙗𝙩𝙧𝙖𝙘𝙩𝙞𝙤𝙣, 𝙣𝙤𝙩 𝙖𝙙𝙙𝙞𝙩𝙞𝙤𝙣.

The hard part is not finding the next idea, it’s choosing what to ignore without the fear of missing out.

Funny thing is, the moment you stop chasing everything, your progress quietly accelerates. That pause between inputs is where real thinking still lives.

[Mohit Kale @MohitKaleAI]

𝗪𝗲𝗯 𝗱𝗲𝘃 𝗶𝗻 𝟮𝟬𝟮𝟲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼����𝘁 𝗽𝗶𝗰𝗸𝗶𝗻𝗴 𝗮 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸.
𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗽𝗶𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀.

React for control.
Next.js for shipping fast.
Svelte for sanity.
Astro for speed you can feel.

Same browser. Very different pain.

What are you building right now, and what did you intentionally choose not to use? 👇

[Sam Shende @SamRShende]

@sassy_genx @PSR42_ @Gagan_440 @HimanshuCr60468 @RahulTula6 @shubhamhindu0 @Bijayku44346437 @aw0886794 @Puneetpande08 @SaruS__01 @VishalMYadav_ @AmbikeshBJP @yogibhakt25 @ImBishwajit9 @By_crypto35 @BhaSuno7665 @BeingRss @WaseemM57947015 @SunilY93656 Hello 👋 Follow Back

[Sam Shende @SamRShende]

@AbhishekBo50275 On Bed more 😊

[Truly Aru @yoursTrulyAru]

Startup Funding Tip: Bootstrap with AI Automation for Real Traction

In the competitive startup world of 2026, seeking funding without proven traction is tough. The smartest move? Bootstrap using affordable AI-powered automation to build and demonstrate real momentum—fast and cheap.

Enter https://t.co/mdcR4x9xhH (formerly Integromat), a no-code workflow automation platform that connects apps, automates repetitive tasks, and integrates AI steps seamlessly. At around $9–$10/month for entry-level plans (with generous credits for operations), it lets solo founders or small teams automate lead gen, customer onboarding, content distribution, email sequences, data syncing, and more—without hiring developers or burning cash.

Examples in action: Automate CRM updates from form submissions, trigger personalized follow-ups via AI, or schedule social posts based on user behavior. These efficiencies free up time, reduce costs, and generate measurable metrics (like MRR growth, user sign-ups, or efficiency gains) that impress investors. By proving traction through smart automation, you show resourcefulness and scalability—key signals for funding rounds. Scale smart, not expensive!

#StartupFunding #EntrepreneurLife

[Sam Shende @SamRShende]

@sassy_genx This is called X

[Sapna D. @sapna_always]

This is what I wrote while I was away from X. Real experience, real thoughts.. no bs.

https://t.co/T7fPea9pka

[Mohit Kale @MohitKaleAI]

𝗗𝗮𝘆 𝟳𝟴: 𝗔𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱𝘀

Agents sound fancy.
Workflows sound boring.
Reality is the opposite.

When people say “AI agent”, I usually ask one thing.
“What does it actually do?”

Most answers are vague.
“It thinks.”
“It decides.”
“It acts.”

That is not engineering. That is storytelling.

Here is how I see it.

An agent is just a role with responsibility.
A workflow is the path that role follows.

Nothing more.

If an agent does everything, it is not smart.
It is fragile.

The moment I stopped treating agents as magic brains and started treating them like workers in a system, things improved.

One agent reads.
Another agent checks.
Another agent decides.
Another agent writes output.

Each one dumb on its own.
Powerful together.

Workflows keep agents honest.
They prevent loops.
They reduce hallucination.
They make failures traceable.

Now when something breaks, I do not ask,
“Why did the AI mess up?”

I ask,
“Which step or role leaked responsibility?”

That question always has an answer.

Agents without workflows are chaos.
Workflows without agents are rigid.
Together, they feel like software again.

𝗧𝗿𝘆 𝘁𝗵𝗶𝘀 𝘁𝗼𝗱𝗮𝘆
• Take one AI task you want to automate.
• Split it into clear roles, not prompts.
• Assign one responsibility per role.
�� Draw arrows for the order they run.
• Add a stop condition.
• Congratulations, you built a workflow.

Agents are roles.
Workflows are discipline.
Buzzwords disappear when systems appear.

Do you currently have one AI doing everything?

Tomorrow, we talk about safety, privacy, and beginner mistakes people regret later.

#Agentic #AI #Automation #Agents #Workflows

[Mohit Kale @MohitKaleAI]

𝗗𝗮𝘆 𝟳𝟰: 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗔𝗜 𝗹𝗼𝗰𝗮𝗹𝗹𝘆, 𝘄𝗵𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗵𝘆𝗽𝗲

Cloud AI feels powerful.
Until latency hits.
Until privacy matters.
Until cost shows up quietly.

For a long time, I only used AI through APIs.
Fast demos. Instant results. No setup pain.

But something felt off.

Every prompt went outside my system.
Every experiment depended on network, limits, and pricing pages.
I was building intelligence, but I did not own the control.

So I tried running AI locally.

Not to replace cloud models.
Not to beat benchmarks.
But to understand the system end to end.

Local AI taught me things cloud never could.

You feel the limits immediately.
Memory matters.
Context size becomes real.
Model choice stops being ego driven.

𝗠𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁𝗹𝘆, 𝘆𝗼𝘂 𝘀𝘁𝗼𝗽 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗔𝗜 𝗹𝗶𝗸𝗲 𝗺𝗮𝗴𝗶𝗰.

When a model runs on your machine:
  • You see what it can and cannot do.
  • You design prompts more carefully.
  • You respect tradeoffs instead of hiding them behind APIs.

Local does not mean better.
It means visible.

That visibility changes how you think as an engineer.

After that, even when I went back to cloud models,
I used them with intention, not excitement.

Control beats hype every single time.

𝗧𝗿𝘆 𝘁𝗵𝗶𝘀 𝘁𝗼𝗱𝗮𝘆
  • Forget performance for a moment.
  • Think of AI like a local service, not a chatbot.
  • Ask yourself what data should never leave your system.
  • Identify one use case where latency matters.
  • Notice how your design choices change.
  • This mindset matters more than the tool.

Cloud gives convenience.
Local gives understanding.
Good engineers know when to choose which.

Would you trust AI more if it ran fully under your control?

Tomorrow, we talk about choosing small models on purpose, and why bigger is not always smarter.

𝗣𝗿𝗼 𝘁𝗶𝗽: 𝗧𝗿𝘆 𝗢𝗹𝗹𝗮𝗺𝗮 𝗼𝗿 𝗟𝗠𝗦𝘁𝘂𝗱𝗶𝗼 𝗳𝗼𝗿 𝗹𝗼𝗰𝗮𝗹 𝘀𝗲𝘁𝘂𝗽.

#Agentic #AI #Ollama #LMStudio #LocalLLM