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Vivekjyoti Bhowmik
LinkedIn Content Strategy & Writing Style
Astrophysicist turned AI founder | $15 Upwork gig → $1M raised → Building again | Road to $1M ARR in public 📍
India
35 Viral ScoreView on LinkedIn
LinkedIn Content Strategy & Writing Style
Astrophysicist turned AI founder | $15 Upwork gig → $1M raised → Building again | Road to $1M ARR in public 📍
1 person tracking this creator on Viral Brain
Vivekjyoti Bhowmik positions himself as a high-stakes builder-practitioner, leveraging his background as an astrophysicist to bring scientific rigor to the operational chaos of AI implementation. His content strategy centers on "Raw AI Thoughts," a series that moves past surface-level hype to expose the technical vulnerabilities and structural shifts within the AI stack, from voice concurrency issues to agentic code reviews. He is notable for his unfiltered technical skepticism, often questioning the stability of popular tools while simultaneously providing the "checklists" and "workflow maps" necessary to survive them. This creates a compelling intersection of deep-tech research and aggressive transparency, where he successfully bridges the gap between high-level market narratives (like NVIDIA’s GPU monopoly) and the gritty, line-by-line reality of building a $1M ARR company in public.
3.3K
3.0K
10
—
3.5
35
1
3.5 posts/week
Posts / Week
2.2 days
Days Between Posts
1
Total Posts Analyzed
HIGH
Posting Frequency
10.1%
Avg Engagement Rate
STABLE
Performance Trend
170
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.78/10
Uniqueness Score
YES
Question Usage
0.7%
Response Rate
Writing style breakdown
<start of post>
🚨The "AI Agent" hype is hitting a wall...
And it’s not because the models aren't smart enough...
It’s because your data is a mess.
Everyone is focused on the "brain"...
but nobody is talking about the "nervous system"...
An agent is only as good as the context it can pull in 100ms.
If your vector DB is slow...
or your API documentation is outdated...
the agent doesn't just "fail"...
It hallucinates a solution and bills you for the compute.
That is where the "Agentic ROI" dies.
The Prototype: "It works on my machine" with a clean CSV.
The Pilot: It hits real-world data and starts looping indefinitely.
The Pivot: Realizing you need a data cleaning layer before the agent layer.
The shift isn't about "better prompts."
It's about "better infrastructure."
If agents are going to run your ops...
who is running the agents?
Comment DATA and I’ll DM the "Agent-Ready Infrastructure" audit we use to prep legacy stacks for LLM automation.
——————————————————————
Day 12 of Raw AI Thoughts.
I’ve spent the last 3 years deep in AI from research to building and I’m going to share the stuff most people won’t say out loud.
If this hit a nerve, repost ♻️ it so your network gets used to hearing it… and follow if you want the next drop.
<end of post>
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