Mayank A.'s LinkedIn Strategy — Follow for…

For 38 years, computer scientists believed Dijkstra's algorithm was optimal for sparse graphs. The logic seemed airtight: Dijkstra sorts vertices by distance. Sorting has a lower bound of O(n log n)…

LinkedIn post image: For 38 years, computer scientists believed Dijkstra's algorithm was optimal for sparse graphs.

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Algorithms3 months ago

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$10 hardware. 10MB RAM. 1 second boot. That's a full AI assistant. Just found PicoClaw. While everyone's running AI on $599 Mac Minis, these folks are running it on a $9.90 LicheeRV Nano. The comp…

LinkedIn post image: $10 hardware. 10MB RAM. 1 second boot.

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Edge AI3 months ago

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If you are preparing for your System Design Interview, these resources will be very helpful for you. 👇 Read short summaries here - Two Phase Commit (https://lnkd.in/g3Btwu3Z) Clock-Bound Wait (ht…

LinkedIn post image: If you are preparing for your System Design Interview, these resources will be very helpful for you. 👇

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System Design Interview Prep3 months ago

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Just because you understand something doesn’t mean you’re good at explaining it. I learned that the hard way. I used to think smart people were the ones who used big words and complicated explanatio…

LinkedIn post image: Just because you understand something

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Communication Skills3 months ago

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This guy built GPT from scratch in pure C. No PyTorch. No TensorFlow. No libraries. Just raw C code. What he implemented: → Custom random number generator (xorshift) → Character-level tokenizer → M…

LinkedIn post image: This guy built GPT from scratch in pure C.

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AI Engineering3 months ago

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Are you even a good SQL developer if you haven't tried such queries in production? 1. DROP DATABASE production; 2. WHILE 1=1 BEGIN SELECT * FROM VeryLargeTable CROSS JOIN AnotherVeryLargeTable; EN…

LinkedIn post image: Are you even a good SQL developer if you haven't tried such queries in production?

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Databases3 months ago

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Topics & Content Focus

Primary Topics

Frugal/efficient AI engineering (tiny hardware, fast boot, low-RAM assistants)From-scratch ML systems understanding (implementing transformer internals without frameworks)Developer-experience minimalism (zero-dependency UI, semantic HTML, no-build tooling)

Secondary Themes

AI-assisted software development (agents bootstrapping migrations, code-generation as leverage)Engineering culture: clarity in explanation, teaching mindset, curiosity preservationOperational caution via satire (SQL anti-patterns as community bait)

Industry Focus

Software engineering for AI infrastructure and edge/embedded deploymentsOpen-source developer tools and performance-oriented systems programmingPragmatic engineering culture in modern product teams (incl. India tech references)

Content Categories

Tool/Project discovery + teardownComparative benchmarks and constraints-based storytellingEducational mindset micro-essaysHumor/relatable dev ops posts

Performance Insights

450.8571428571428%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Frugal/efficient AI engineering with strong numeric contrastsAI building software (agents writing most of the code) + migration narrativeRelatable learning/communication lessons with an audience question

Virality Signals

Quantified novelty (dramatic cost/RAM/boot deltas) that’s easy to repeat/shareOpen-source adjacency (promised GitHub link) increases shares/saves behaviorConstraint/efficiency positioning (anti-bloat narrative) taps builder prideListicles/specs create “screenshot-friendly” structureLight controversy/edginess (SQL satire, “this is how you actually understand X”)

Structure & Quality

900

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.82/10

Uniqueness Score

Common Hooks

Constraint-first shock hook (price/RAM/boot time) to trigger disbeliefExtremes/absolutes framing (no libraries, pure C, zero dependencies)Curiosity hook via specific named artifact (project/library/person) + immediate payoffProvocative rhetorical question to invite replies (often playful/ironic)Self-reflective lesson opener (short personal reversal: “I used to think… now I realize…”)

Common Endings

Resource drop/redirect (e.g., “GitHub in comments”)Community prompt (“Can you suggest more…?”, “Have you ever…?”)Identity-based follow CTA (positioning following as a statement)

Value Delivery Methods

Curated frontier examples (small, concrete projects with big implications)Deconstruction into components/specs to make complexity legibleActionable takeaways via tools to try and repositories to checkTaste-making: strong opinions about simplicity, constraints, and craft

Formatting Style

Short, punchy line breaksArrow-bullets and tight feature listsMini-spec sheets (dimensions/heads/layers/context) to signal rigorComparison blocks (A vs B vs C) with quantified deltasOccasional hashtags and signature-style separators

Audience & Tone

YES

Question Usage

Response Rate

Detected Tone

Smart-casual builder voiceEnthusiastic curator of engineering odditiesOpinionated minimalism advocate (anti-bloat, pro-constraints)Playfully provocative (uses irony to spark replies)Teacher-mode clarity (simple explanations and meta-learning)informalmixed

Interaction Style

Community co-creation prompts (asking readers to add examples)Debate-bait framed as humor/irony (especially in database/ops posts)Builder-to-builder signaling (inviting technically literate agreement and sharing)

Community Building Signals

Uses questions to convert readers into contributorsUses follow CTA tied to identity/emotion (belonging/values)Uses comments as a delivery channel for resources (drives return visits)

Writing Style Patterns

Content Strategy

Hook: Constraint-first shock hook (price/RAM/boot time) to triggerTone: informalCTA: Soft community question CTA

Writing style breakdown

This developer built a full Postgres-compatible database in 1,000 lines of Zig.

No heavy engines. No legacy bloat.

Just a single binary.

What's inside

→ LSM-tree storage engine

→ Write-ahead logging (WAL)

→ Basic SQL parser

→ Network protocol compatibility

→ Zero-copy serialization

The performance

→ 50k writes/sec on a laptop

→ <2MB executable size

→ Starts in 5ms

We spend so much time learning how to 'use' databases.

We forget to learn how they actually work.

The best way to master a system is to build a 'toy' version of it from scratch.

It strips away the magic.

GitHub link in the first comment.

Mayank A.

Warm Analysis

Performance Overview

Top Posts by Engagement

Posting Patterns & Frequency

Best Performing Days

Best Performing Times To Post

Topics & Content Focus

Primary Topics

Secondary Themes

Industry Focus

Content Categories

Performance Insights

Best Performing Topics

Virality Signals

Structure & Quality

Common Hooks

Common Endings

Value Delivery Methods

Formatting Style

Audience & Tone

Detected Tone

Interaction Style

Community Building Signals

Writing Style Patterns

Content Strategy

What's inside

The performance