60/100
hybrid
You have a genuinely original insight about AI drift acceleration and an authentic voice that cuts through LinkedIn BS. But you're sabotaging your credibility by talking about 'experiments' without showing any data. You scored 1/5 on quantitative data and 2/5 on concrete examples. Every claim about 'how often this happens' or 'every outcome root-caused' is unverifiable. You're writing like someone who's done the work but showing receipts like someone who hasn't.
Dimension Breakdown
📊 How CSF Scoring Works
The Content Substance Framework (CSF) evaluates your content across 5 dimensions, each scored 0-20 points (100 points total).
Dimension Score Calculation:
Each dimension score (0-20) is calculated from 5 sub-dimension rubrics (0-5 each):
Dimension Score = (Sum of 5 rubrics ÷ 25) × 20 Example: If rubrics are [2, 1, 4, 3, 2], sum is 12.
Score = (12 ÷ 25) × 20 = 9.6 → rounds to 10/20
Why normalize? The 0-25 rubric range (5 rubrics × 5 max) is scaled to 0-20 to make all 5 dimensions equal weight in the 100-point CSF Total.
Zero quantitative data (1/5 rubric), no concrete examples (2/5), generic entities throughout
Mentions experiments repeatedly but never details a single test case, measurement, or comparison
Drift acceleration framework is genuinely novel but lacks synthesis with existing prompt engineering research
Second-order thinking present but evidence quality critically weak (2/5), circular reasoning on root causation
Despite authentic voice (17/20), actionability score of 3/5 and unfalsifiable claims damage credibility
🎤 Voice
🎯 Specificity
🧠 Depth
💡 Originality
Priority Fixes
Transformation Examples
Every outcome of the experiment so far has been root caused to the way I used AI every single time. The only solution each time to deliver a better human-in-the-loop experience for the AI coding assistants.
In 27 of 30 tracked sessions, drift traced directly to my prompting—usually ambiguous requirements or mid-task scope creep. But three sessions revealed something different: identical prompts to GPT-4 vs Claude produced divergent drift profiles. GPT-4 maintained coherence longer but failed harder at the boundary; Claude signaled confusion earlier through hedging language. This suggests drift has both user-controlled factors (prompt clarity) and model-specific factors (how coherence degrades across token windows). Next experiment: can early-warning signals be systematically detected?
How: Test the boundary between user error and tool limitation. Design experiments that could distinguish them: run identical prompts through different models, test whether drift patterns differ, measure if certain task types hit drift regardless of prompt quality, explore whether context window mechanics create inherent drift acceleration.
In order to trust the tools, I must know what I do to break it.
To trust these tools, I need to know exactly how I break them.
- Removed 'In order to' filler—more direct
- Changed 'must' to 'need to'—less declarative, more personal
- Fixed subject-verb agreement (tools/them)
- Added 'exactly' for specificity and emphasis
- Maintains your authentic frustration without formal distance
Derivative Area: The core observation that 'AI makes mistakes and needs human oversight' is standard. Your framing around human error being the primary cause echoes common prompt engineering wisdom without advancing it.
Challenge the emerging narrative that AI will 'write all our code soon.' Your drift research suggests the opposite: as AI gets more capable at generating coherent-feeling code, the human skill of detecting subtle logical drift becomes MORE valuable, not less. This is the anti-automation argument from someone actually using the tools.
- Why does drift feel coherent as it happens? Explore token-level LLM mechanics—are you detecting the moment when the model's attention weights shift away from your core constraints?
- What if drift is a feature, not a bug? When might exploratory drift actually lead to better solutions than rigid constraint-following?
- The economics of teardown-vs-patch: at what code complexity threshold does the decision flip? Can you model this?
- How do different AI models have different 'drift signatures'? Map the failure modes across GPT-4, Claude, Copilot—do they drift differently?
- The psychology of drift detection: why do developers (including you) miss the early signals? Is it sunk cost, or something about how code review attention works?
30-Day Action Plan
Week 1: Quantify your current practice
Track 10 AI coding sessions this week. For each: record task description, approach used (patch vs teardown), time spent, outcome (success/fail/teardown), and what triggered the decision. Use a simple spreadsheet. Don't analyze yet—just capture raw data with timestamps and brief notes.
Success: You have 10 logged sessions with concrete numbers (minutes, iterations, decision points). You can answer: 'In what % of sessions did I hit teardown threshold?' and 'What was average time for patch-approach vs teardown-approach?'Week 2: Document one experiment completely
Design one A/B test from your Week 1 patterns. Pick your most common drift trigger. Hypothesis: [specific claim]. Method: 5 tasks with Approach A, 5 with Approach B. Metrics: time, teardown rate, orphaned code lines. Run it. Document everything—prompts used, full output, decision points. Write up findings in 500 words.
Success: You have a complete experimental writeup with hypothesis, methodology, data table, and conclusion. At least one finding surprises you or contradicts your assumption. You can share this publicly without hedging.Week 3: Connect your insight to existing research
Search Google Scholar and AI research communities for 'LLM coherence drift' 'context window degradation' 'prompt engineering failure modes.' Find 3-5 papers or substantial posts. Read them. Write 300 words on: How does your drift acceleration framework relate? What do they miss that you've found? Where does research validate or challenge your experience?
Success: You can say 'My findings align with Smith et al on X but diverge on Y' or 'The academic work focuses on Z, but practitioners deal with W.' Your originality score rises because you're synthesizing, not just observing. You have citations ready.Week 4: Publish a high-CSF synthesis piece
Write a new post combining Week 1 data, Week 2 experiment, Week 3 research connections. Structure: Opening with your specific numbers, detailed experimental methodology, unexpected finding, connection to broader research, contrarian conclusion. Target 800-1000 words. Include: 5+ specific data points, 2+ citations, 1 complete before/after prompt example, your operational definition of 'invariant.'
Success: Post scores 65+ on CSF framework—every claim is backed by data or cited research, 'experiments' means documented tests, readers can replicate your approach. You've moved from interesting practitioner to credible technical voice with original research contribution.Before You Publish, Ask:
Can a skeptical reader replicate your experiment from what you've written?
Filters for: Specificity and actionability—if they can't replicate it, you haven't shown your work. This separates documented research from anecdotal claims.What would prove your core claim wrong?
Filters for: Intellectual integrity—unfalsifiable claims aren't insights, they're beliefs. If every outcome confirms your theory, your theory isn't testable.Have you provided at least one concrete number, timestamp, or measurement?
Filters for: Evidence quality over storytelling—'I've been running experiments' without data is aspirational, not actual. Numbers build credibility.Does your advice work for someone who isn't you?
Filters for: Generalizability—'rebuild from invariant' is vague unless you define what constitutes an invariant. Operational definitions make insights transferable.What's the one finding that surprised you or contradicted your initial assumption?
Filters for: Genuine experimentation vs confirmation bias—real research produces unexpected results. If everything confirmed what you already believed, you didn't experiment, you demonstrated.💪 Your Strengths
- Genuinely authentic voice (17/20)—you write like a frustrated practitioner, not a guru, which cuts through LinkedIn's usual AI hype
- Strong second-order thinking (4/5 reasoning_depth)—the drift acceleration framework showing how errors compound is non-obvious and valuable
- Contrarian courage (4/5)—opening with 'nobody on LinkedIn tells the truth' and focusing on AI's failures challenges the dominant narrative
- Structural variety and conversational flow (5/5 each)—the numbered teardown process, varied paragraph lengths, and direct address create engaging rhythm
- Willingness to show ongoing uncertainty ('I'll figure it out one day')—vulnerability that builds connection rather than undermining authority
You're sitting on genuine thought leadership material being held back by execution gaps. Your drift acceleration insight is original and valuable—but unsubstantiated. You have the practitioner credibility and authentic voice that most LinkedIn AI content lacks. The path forward is clear: document one experiment completely, publish the data, define your terms operationally, and connect your findings to existing research. Do this and you'll move from 'interesting voice in the noise' to 'credible technical researcher advancing the field.' Your next post with real data could hit 70+ CSF and establish you as someone practitioners trust and researchers cite. The raw material is there—now show your work.
Detailed Analysis
Rubric Breakdown
Overall Assessment
Highly authentic voice with strong personality. Jay writes like someone who's actually frustrated and experimenting, not teaching. Uses unconventional structure, personal pronouns, direct address, and self-awareness about failure. Minimal hedging. The 'drift' metaphor and teardown framework feel genuinely earned through practice, not borrowed theory.
- • Earned authority—speaks from actual experiments and failures, not best-practice listicles. The specific pain points (orphaned functions, silent contradictions) prove hands-on experience.
- • Conversational vulnerability—admits confusion in opening line and throughout, which makes the eventual insights feel more credible. Doesn't position himself as the expert who has it all figured out.
- • Structural dynamism—mixes short punchy sentences ('Stop. Deconstruct. Reset.') with longer exploratory ones. Uses em-dashes, fragments, and varied paragraph lengths like actual thought.
- • One section feels slightly abstract ('develop a strategy for this') without connecting back to concrete examples—could ground it more in a specific failure moment.
- • The hashtag list at the end, while authentic for LinkedIn, dilutes the rawness of the voice. The frog emoji saves it, but the tags themselves feel performative.
- • Final paragraph ('And every time, the root cause has been the same...') slightly repeats the realization already stated, creating minor redundancy in what's otherwise tight writing.
Rubric Breakdown
Concrete/Vague Ratio: 1:3.9
The post relies heavily on abstract conceptual language (drift, assumptions, coherence) without quantifying the problem's frequency or severity. While it describes a three-step recovery process, it lacks concrete examples, data, or specific scenario details. The author's experiments are mentioned but never detailed—no test cases, prompting strategies compared, or measurable results shared.
Rubric Breakdown
Thinking Level: Second-order thinking with gaps in third-order implications
The author identifies a genuine but underexplored phenomenon in AI-assisted coding: 'drift accumulation' where initial errors compound across layers. The core insight about stopping early and rebuilding from invariants is non-obvious and valuable. However, the analysis lacks empirical grounding—no metrics, failure rates, or comparative data substantiate claims. The framework is actionable but remains largely anecdotal.
- • Identifies a genuine, under-discussed phenomenon ('drift accumulation') that resonates with practitioner experience
- • Non-obvious reframing: treats failures as human boundary-condition discovery rather than tool blame
- • Actionable principle: stop early, deconstruct, rebuild from invariants—concrete enough to test
- • Epistemic honesty: acknowledges experiments are incomplete ('I'll figure it out one day')
- • Recognizes multi-layer dependencies as a system failure mode, not just individual code errors
Rubric Breakdown
The post offers a valuable lived-experience perspective on AI coding drift that challenges the oversimplified LinkedIn narrative. However, the core concepts (AI hallucination, context decay, human oversight necessity) are well-established. The original contribution is the specific 'drift acceleration' framework and teardown-rebuild methodology, grounded in genuine experimentation.
- • The 'drift acceleration' phenomenon—how coherent-feeling errors compound because each layer masks the foundational assumption break, creating a coherence illusion until critical failure
- • Inverting the blame narrative: systematic root cause analysis proving the user, not the tool, causes most failures—and viewing this as empowering rather than shaming
- • The 'rebuild from invariant, not patch' methodology as a concrete operational antidote to drift, grounded in repeated experimentation rather than theory
Original Post
Either I’m really bad at AI… or nobody on LinkedIn is telling the truth about what AI coding actually feels like. Here’s the reality I keep running into: If you let an AI coder run too far ahead, it doesn’t just make mistakes. It builds on top of its own drift. It feels right as it’s happening. It feels coherent. It feels close. And then you hit the moment where you realise the logic is off, the flow is wrong, and the whole thing is now running on assumptions you never intended. At that point, the fix isn’t a tweak. It’s a teardown. Not because the AI is “bad,” but because once drift enters the foundation, every layer built on top becomes a drift accelerator - orphaned functions, unused branches, silent contradictions, and code that technically works but no longer aligns with the original intent. Nobody talks about how often this happens. Nobody talks about how many “almost right” attempts end in a rewrite. Nobody talks about how much context you have to reset to avoid looping back into the same failure mode. I’ve been running experiments on this as a side project to my side project - testing different prompting strategies, different levels of structure, different ways of injecting pre‑reasoned context, and different teardown‑and‑rebuild patterns. The biggest takeaway? The moment you notice the first error in logic, flow, or output - stop. 1. Deconstruct. 2. Reset the context. 3. Rebuild from the invariant, not the patch. Trying to “fix” drifted code just compounds the drift. Develop a strategy for this. It completely changed how I use AI for coding, even if it was just for the sake of experimenting with the tools I have. In order to trust the tools, I must know what I do to break it. Every outcome of the experiment so far has been root caused to the way I used AI every single time. The only solution each time to deliver a better human-in-the-loop experience for the AI coding assistants. That’s been the whole point of these experiments - not to blame the tools, but to understand the boundary conditions of my own prompting. If I want to trust the system, I have to know exactly how I break it. And every time, the root cause has been the same: drift enters when I let it. I'll figure it out one day. — Jay hashtag #AICoding hashtag #AIEngineering hashtag #PromptDesign hashtag #SoftwareDevelopment hashtag #AIDrift hashtag #HumanInTheLoop hashtag #AIPractices hashtag #BuildInPublic hashtag #TechLeadership hashtag #AIReality 🐸