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John Crickett

John Crickett

LinkedIn Content Strategy & Writing Style

Building AI and building with AI

United Kingdom
27 Viral ScoreView on LinkedIn

0 people tracking this creator on ViralBrain

Warm Analysis

John Crickett positions himself as a seasoned engineering veteran who bridges the gap between principled software craftsmanship and the frontier of AI automation. His content strategy centers on debunking the "hype cycle" by applying 30 years of architectural rigor to modern AI agents, focusing on themes like context engineering, modularity, and the human responsibility of code ownership. He is notable for his pragmatic skepticism, often challenging the industry's obsession with raw output by arguing that 1,800 AI commits are useless without human-led clarity and maintainable foundations. This creates a unique intersection where legacy engineering wisdom meets cutting-edge AI workflows, transforming the conversation from how to generate code to how to architect systems that remain robust in an AI-first world.

Performance Overview

Followers

205.1K

Connections

27.1K

Avg. Engagement

433

Engagement Rate

Posts/Week

14.0

Viral Score

27

Posts Analyzed

2

Top Posts by Engagement

Software engineers: Context switching kills productivity. Also software engineers: I'm now managing 19 AI agents and doing 1800 commits a day. We’ve spent years complaining that managers who expect…

4931132510 viral
8 months ago
View on LinkedIn

Did you know you can build a GPT in just 200 lines of pure Python? Yesterday Andrej Karpathy did just that, sharing microgpt. Training and inference, all in one file. Study the code and you'll see ho…

LinkedIn post image: Did you know you can build a GPT in just 200 lines of pure Python?
83271347 viral
8 months ago
View on LinkedIn

I've loved AI for a long time. Not the hype cycle version. The real thing. Which is exactly why I can see its flaws. Something strange is happening in tech discourse. If you're not a full believer,…

33689144 viral
8 months ago
View on LinkedIn

"AI can't handle a multi-million line codebase." I keep hearing this. But I want to challenge something more fundamental. Is there really such a thing as a multi-million line codebase? Every large…

25411864 viral
8 months ago
View on LinkedIn

"You didn't write the code, so you don't truly understand it." Is a common argument against AI coding tools. But it doesn't hold up. I just read another post making this case. It sounds reasonable o…

20310643 viral
AI-Assisted Software Development8 months ago
View on LinkedIn

I've been documenting my views on AI-assisted coding. I believe in strong opinions, weakly held, so I'd love your thoughts, feedback, and challenges on what follows. When we argue about whether LLMs…

894172 viral
AI-Assisted Software Engineering8 months ago
View on LinkedIn

Posting Patterns & Frequency

14.0 posts/week

Posts / Week

0.6 days

Days Between Posts

2

Total Posts Analyzed

HIGH

Posting Frequency

Timezone: Europe/London

Best Performing Days

ThursdayTuesdayWednesday

Best Performing Times To Post

13:3016:30

Topics & Content Focus

Primary Topics

Engineering rigor in AI-assisted software development (verification, trust, and failure modes)AI agent workflows and the operational reality of building with agents (spikes, experimentation loops, ADRs)Context engineering as the bottleneck for production AI (retrieval, latency, cost, relevance)

Secondary Themes

Software architecture as an enabler/constraint for AI tooling (modularity, boundaries, testability)Anti-hype tech discourse and epistemic humility (nuance vs tribalism)Productivity narratives in engineering culture (context switching, deep work, managerial vs maker dynamics)

Industry Focus

Software engineering leadership and developer productivityAI developer tools (agents, coding copilots) and AI application architectureDevOps/Platform engineering adjacent concerns (context layers, data access, performance/cost tradeoffs)

Content Categories

Contrarian take / myth-bustingTechnical playbooks and workflowsIndustry meta-analysis / discourse critiqueArchitecture and engineering fundamentalsSponsored industry perspective (platform/context layer)

Performance Insights

432.6666666666667%

Avg Engagement Rate

STABLE

Performance Trend

Best Performing Topics

Skepticism about AI-agent productivity claims (trust, review, and accountability)Architecture-first arguments that reframe AI limitations as engineering debtAnti-tribalism commentary about AI discourse (nuance as credibility)

Virality Signals

Polarizing contrarian stance that challenges a popular identity narrative ("agent wrangler" productivity)High-quotability moral framing (engineering vs gambling; nuance vs tribalism)Use of well-known references to anchor credibility (Paul Graham) then subvert expectationsComment-bait via professional identity threats (implies negligence/blind trust) prompting rebuttalsDebate-friendly prompts at the end (invites disagreement and examples)

Structure & Quality

230

Avg Length (Words)

HIGH

Depth Level

ADVANCED

Expertise Level

0.86/10

Uniqueness Score

Common Hooks

Hypocrisy/irony opener that flips a familiar engineering belief against current AI behaviorQuoted claim hook (e.g., a common refrain) followed by a direct challengeValues-based framing (progress requires criticism; nuance is not skepticism)Authority-by-experience setup (referencing prior systems shipped or notable scale)

Common Endings

Direct question to invite debate (experience-sharing prompts)Binary-choice takeaway with a sharp moral/identity close (e.g., engineering vs gambling)Actionable closing artifact (e.g., document the decision; write the ADR)

Value Delivery Methods

Reframes hype claims into concrete engineering risks (verification, reviewability, accountability)Turns abstract AI debates into actionable engineering heuristics (modularity, boundaries, ADRs)Provides repeatable workflows (time-boxed spikes, parallel agent exploration, decision capture)Separates model capability from system design constraints (context as performance/cost bottleneck)

Formatting Style

Short paragraphs with deliberate line breaks for pacingRhetorical contrast blocks ("Either X... Or Y...")Occasional quoted excerpts to borrow credibility (e.g., Paul Graham)Process bullets/arrows for playbooksMinimal emoji usage; appears mainly in sponsored/announcement-style sections

Audience & Tone

YES

Question Usage

0.2%

Response Rate

Detected Tone

Principled skeptic (pro-AI, anti-hype)Relatable expert with sharp standardsContrarian but evidence-seekingSystems thinker (architecture and constraints first)Mentor-like pragmatist (turns beliefs into practices)semi-formalmixed

Interaction Style

Debate-driven engagement (audience argues edge cases and counters)Practitioner knowledge-sharing (workflows, architecture patterns, war stories)Identity/standards enforcement (what "real engineering" means in an AI era)

Community Building Signals

Invites peer benchmarking ("what's your workflow" / "biggest codebase")Uses shared engineering canon to create in-group context (Maker vs Manager, architecture basics)Positions the comments as a collaborative refinement loop ("I'm still refining this")

Writing Style Patterns

Content Strategy

Hook: Hypocrisy/irony opener that flips a familiar engineering belTone: semi-formalCTA: Open-ended practitioner question ("what's your wor

Writing style breakdown

<start of post>

AI is going to replace junior developers." This is the headline I see every single day.

But it misses the point of what a junior developer actually is.

A junior isn't just a "code producer." They are the future seniors of your organisation. If you stop hiring them because an LLM can write a boilerplate React component, you aren't being efficient. You're eating your seed corn.

Think about the long-term implications for a moment.

In five years, where will your senior engineers come from? You can't hire "seniors" if no one was ever a "junior."

The work changes, but the need for growth doesn't. We need to shift our focus from "how much code can they write" to "how quickly can they learn to navigate complex systems." AI is a tool that helps them get there faster, not a reason to close the door.

The bottom line: Don't use AI as an excuse to stop investing in people. Use it as a way to accelerate their journey from junior to expert.

To the hiring managers out there: Has AI changed your headcount plans for the year? Or are you doubling down on mentorship?

<end of post>

1. OVERALL WRITING STYLE & VOICE
1. The Hook: Usually a quote, a common industry 'myth,' or a direct question.
1. Starting sentences with conjunctions: 'But it doesn't hold up.' or 'And it works.'

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