Morgan Linton's audit covers 7 public posts, including 7 with both reaction and comment counts. The measured median is 7 interactions. AI & building products is the most represented detected theme (4/7; themes can overlap). Start with a concrete follow-up to an existing subject, then test the opening and reply invitation across a consistent four-week sample.
7 of 7 posts have both public counts. Their median is 7 interactions. Compare future experiments with your own sample rather than an unrelated peer score.
“Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's a quick TL;DR” has 389 visible interactions. That is 55.6× the sample median. Its topic and opening are candidates for a follow-up experiment; this does not identify why it performed.
2 of 7 posts contain a question. Measured posts generated 16 comments per 100 reactions. Counts can include the author's replies and do not measure lead quality or sentiment.
The median gap between sampled dates is 0.9 days; the longest is 1.1 days (6 intervals). Missing posts can exaggerate gaps, so this is not a complete publishing calendar.
Profile & visual identityImage preview, section grades, missing data and specific fixesExplore
SOURCE PROFILE PREVIEW
Morgan Linton
Headline not supplied by the source
These are source images, which may be resized. An unavailable image does not mean it is missing on LinkedIn.
PROFILE CHECKLIST
Your first impression, section by section.
Open any section to see the evidence and a practical fix. Unknown data is not a failed check.
Profile picture
A+15/15 observed points
15 / 15 points assessable
PassProfile image supplied5/5
What we observed
The source supplied an image URL.
Keep it strong
Choose a current, recognizable photo. Preview the circular crop at comment size and leave space around your head.
PassImage decodes and fits its display shape10/10
What we observed
Returned image: 200 × 200px. Dimensions and decoding were measured on the returned image, which may be a resized LinkedIn thumbnail. Original resolution, face framing, lighting and professional suitability were not assessed automatically.
Keep it strong
Use a square original, check sharpness at full size, and preview the circular crop. A small provider thumbnail does not prove your original upload is low resolution.
Cover & visual identity
A+10/10 observed points
10 / 10 points assessable
PassCover image supplied5/5
What we observed
The source supplied an image URL.
Keep it strong
Use a simple cover that says who you help and what you do. Keep important text clear of the profile-photo overlay.
PassImage decodes and fits its display shape5/5
What we observed
Returned image: 798 × 200px. Dimensions and decoding were measured on the returned image, which may be a resized LinkedIn thumbnail. Original resolution, face framing, lighting and professional suitability were not assessed automatically.
Keep it strong
Preview your cover on desktop and mobile. Start with a 1584 × 396 canvas and keep the message readable without tiny text.
Name & identity
A+10/10 observed points
10 / 10 points assessable
PassReadable display name5/5
What we observed
Source text: “Morgan Linton”
Keep it strong
Use the name people know you by, consistently across your profile and other professional channels. Single names and all writing systems are supported.
PassName field stays focused on identity5/5
What we observed
Source text: “Morgan Linton”
Keep it strong
Move URLs, contact details and sales calls to action into About or Contact info. Keep your actual name unchanged.
Headline & positioning
UnknownNeeds source data
0 / 15 points assessable · partial coverage
UnknownHeadline supplied—
What we observed
Not returned in readable form by the source.
What to do
State your role or specialty in the headline, using terms your intended reader understands.
UnknownEnough context to explain your work—
What we observed
Not returned in readable form by the source.
What to do
Use this structure: [role or specialty] · Helping [audience] achieve [outcome]. Replace every bracket with a truthful detail.
UnknownHeadline is concise enough to scan—
What we observed
Not returned in readable form by the source.
What to do
Keep the headline focused on one main promise. Move your longer story into About.
About & descriptions
Partial5/5 observed points
5 / 20 points assessable · partial coverage
PassReadable introduction5/5
What we observed
Truncated source excerpt: “a little about me, in Gonzo journalistic style...because that's just more fun…”
Keep it strong
Lead with who you help, the problem you solve and the work you do today.
UnknownAn introduction with supporting detail—
What we observed
Truncated source excerpt: “a little about me, in Gonzo journalistic style...because that's just more fun…”
What to do
Add a specific example of your work, your approach, and the kinds of opportunities you welcome. Length is a structure check, not a quality guarantee.
UnknownEasy-to-scan paragraph structure—
What we observed
Truncated source excerpt: “a little about me, in Gonzo journalistic style...because that's just more fun…”
What to do
Break About into short paragraphs: current focus, relevant proof, then how to connect.
UnknownA clear next step—
What we observed
Truncated source excerpt: “a little about me, in Gonzo journalistic style...because that's just more fun…”
What to do
End with one relevant next step, such as who should message you and what to include. This automated check recognizes English phrases and links; review other languages manually.
Experience & proof
UnknownNeeds source data
0 / 15 points assessable · partial coverage
UnknownProfessional experience identified—
What we observed
No readable entries were returned. We cannot conclude this section is empty.
What to do
List relevant roles with an accurate title and organization. Connect them to the work you want to do next.
UnknownExperience includes useful descriptions—
What we observed
Not returned in readable form by the source.
What to do
For each relevant role, explain your responsibility, a concrete contribution, and an outcome you can substantiate. Do not invent numbers. Masked source descriptions need manual review.
Education & learning
UnknownNeeds source data
0 / 10 points assessable · partial coverage
UnknownEducation or learning entry identified—
What we observed
No readable entries were returned. We cannot conclude this section is empty.
What to do
Add relevant education or structured learning you want to share. Name the institution or course accurately; formal degrees are not required for a strong professional story.
UnknownLearning context is described—
What we observed
Not returned in readable form by the source.
What to do
Describe your subject, course or qualification and one relevant project or skill. Use the full qualification name where helpful. We assess clarity, never institution prestige or degree level.
Location & context
A+5/5 observed points
5 / 5 points assessable
PassReadable location5/5
What we observed
Source text: “Incline Village”
Keep it strong
Use an accurate city or region if you want it visible. Do not add a home address. Omitted provider data is not penalized.
Read all available profile text
Headline
Not returned in readable form by the source.
About
a little about me, in Gonzo journalistic style...because that's just more fun…
This is a truncated source excerpt.
Experience
No readable entries returned.
Education
Name unavailable
Carnegie Mellon University
Hidden entries are omitted from this readable preview. Source text is not independently verified.
VISUAL REVIEW
Check how your profile looks.
The automatic image grade checks file readability and shape. Use this checklist to review the visual choices it cannot judge.
Is your face recognizable in the small circular crop?
Is the image sharp, evenly lit, and free of distracting clutter?
Does the banner communicate your work without overlapping your photo?
Are colors and typography consistent with your professional identity?
A transparent presentation checklist, not a LinkedIn ranking, talent assessment or prediction of reach. Grade thresholds: A+ 90, A 80, B 65, C 50, D 35, F below 35.
Score = earned points ÷ assessed points × 100. Coverage is assessed weight out of 100. Unavailable and masked data are excluded. Scores with different coverage are not directly comparable.
Truncated text earns only checks already demonstrated in the visible excerpt; the unseen remainder is not marked wrong. Text rules measure structure, not factual accuracy, writing quality or every language equally.
Photo and banner grades cover image availability, decoding and shape only. Face framing, lighting, expression, visual style and authenticity require a human review. No appearance, identity or personality judgments are made.
An education entry is evidence of profile context, not verified credentials. School prestige, age, degree level and background do not affect the score.
Section weights: photo 15, cover 10, name 10, headline 15, About 20, experience 15, education 10, location 5. Passed checks earn their full weight; improvements earn zero; unknown checks are excluded.
PerformanceYour own baseline, strongest posts and publishing rhythmExplore
YOUR OWN BASELINE
Performance, with perspective.
Interactions across the sample
Publication date · UTC
389
09-09
3
09-10
45
09-11
7
09-12
7
09-13
3
09-14
3
09-14
View chart data
Measured interactions by post
Date UTC
Reactions
Comments
Total
2026-09-09 15:15
365
24
389
2026-09-10 13:58
2
1
3
2026-09-11 16:59
13
32
45
2026-09-12 13:59
3
4
7
2026-09-13 15:13
6
1
7
2026-09-14 04:20
2
1
3
2026-09-14 15:06
3
0
3
Post totals at collection time. Older posts have had more time to collect reactions and comments.
EARLY VS RECENT SAMPLE
45→3
Median visible interactions: 45 for the earliest 3 measured posts and 3 for the latest 3. The middle post is excluded to keep group sizes equal. This compares selected posts of different ages, not account growth.
TYPICAL VS STRONGEST
7/389
Median / highest measured interaction total. A breakout can lift the average; the median provides another view of a typical sampled post.
Publishing rhythm
Median gap: 0.9 days · Longest gap: 1.1 days · 6 observed intervals.
Explore 6 groups and supporting posts
Publishing days in UTC — descriptive, not recommended times
Unknown means the source did not verify the format. Small or unequal groups do not establish a winning format.
Hooks and writing structure
Read writing structure for 7 posts
Text rules applied to every sampled post
Opening
Hook type
Words
Paragraphs
Final CTA
Tags / mentions
Play more video games.
Statement
4
1
Not detected
0 / 0
Very interesting week ahead. There will be two main topics discussed:
Statement
52
5
Not detected
0 / 0
We are at a time in history where AI safety could possibly be most important way to make an impact i
Personal opening
172
7
Detected
0 / 0
So I had a post go super viral on LinkedIn, but I’m not a marketer, I’m an engineer.
Statement
55
3
Not detected
0 / 0
Totally shocking news, all these AI labs like Kimi, Qwen, and DeepSeek, that we thought had found a
Statement
81
3
Not detected
0 / 0
Okay, it took a week to get this done the right way, but it's finally all complete, my comparison of
Number-led
277
7
Not detected
0 / 0
Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's
Statement
202
9
Not detected
0 / 0
Hook type uses the first nonempty line. CTA detection checks the final paragraph for a question or action phrase. Paragraphs use blank-line breaks. These rules can miss meaning and linked mentions.
Hashtags in the sample
No data available for this grouping.
Tags travel with the topic, author and format. These associations cannot isolate a hashtag's effect.
Recommended next stepsPrioritized actions, effort and what to measureExplore
PRIORITIZED RECOMMENDATIONS
Less guessing. Clear next steps.
Ordered from building a useful baseline to testing individual writing choices. Potential impact describes the purpose of an experiment, not a promised uplift.
01Develop a specific follow-up
Medium effort
OBSERVATION
The sample includes “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ”. Its visible interaction total is 389.
DO THIS
Build a follow-up to “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ”. Add a concrete example, a decision you made and one lesson. Keep the format similar so topic development is the main experiment.
Potential impact: Test repeatable interest in an existing subject
Metric to watch: Reactions and comments after the same seven-day window; compare with the baseline median
Median text length is 81 words with 5 paragraphs. 5 of 7 openings are classified as statements.
DO THIS
For the next post related to “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ”, open with a specific problem and a concrete outcome you can substantiate. Move context into the second paragraph. Avoid invented results or curiosity gaps that the post does not resolve.
Potential impact: Improve clarity; any engagement effect remains to be tested
Metric to watch: Median interactions across at least three comparable posts per opening approach
1 of 7 final content lines contain a detected question or action phrase; 2 posts contain a question anywhere. These are text rules, not semantic judgments.
DO THIS
End one upcoming post with a focused trade-off question connected to ai & building products. Ask readers to share a decision and why. Read replies before deciding whether the discussion was useful.
Potential impact: Test whether a clearer invitation supports useful discussion
Metric to watch: Public comments per post and manual review of reply relevance
7 dated posts span 5 days. Observed cadence is 8.4 posts per week.
DO THIS
Choose two slots per week for the four-week experiment below, or reduce the schedule if your capacity is lower. Keep the slots consistent and log every published post. Two slots are a planning choice, not a LinkedIn benchmark.
Potential impact: Create comparable records and a manageable learning loop
Metric to watch: Planned versus published posts, time spent, and median interactions at seven days
0 of 7 posts contain hashtags and 0 contain visible @mentions. LinkedIn may return linked mentions as plain text, so detection can undercount.
DO THIS
Use relevant topic labels only when they clarify the subject. Mention people only when they contributed or are directly relevant. Test a consistent tagging approach on comparable posts; do not attribute performance changes to tags alone.
Potential impact: Keep context and attribution useful
Metric to watch: Median interactions for tagged/untagged samples, with sample sizes and topic differences
Turn one idea related to “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ” into a concise checklist or document post. Keep a similar topic and ask the same type of question. Label this a new format experiment, not a proven winning format.
Potential impact: Learn whether a different presentation helps explain the same idea
Metric to watch: Public interactions at the same post age; at least three posts in each format before comparison
Content ideas & basic outlineThree starting ideas and a simple structure for your next postExplore
INCLUDED IN YOUR FREE REPORT
A starting point for your next post.
Simple ideas from this profile’s available evidence. Choose one, add your own experience, and review the result.
IDEA 1
The next chapter
Follow up on “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ”: what changed since publishing, what stayed difficult, and the next step. Only include outcomes you can verify.
Break the work behind “Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's ” into three decisions. Add an example or screenshot you have permission to share.
Very cool waking up to see Shopify acquired Tailwind. If you don't know much about Tailwind, here's a quick TL;DR
Tailwind is one of the most polished and well-built CSS frameworks, i.e. makes sites look really nice.
They're also kinda famous for having an insanely brilliant team.
So why did Shopify buy them?
I think it's for a few reasons, and many of these are related to the fact that Tobias Lütke really understand technology, and where things are going with agentic commerce.
Shopify has been really jamming with headless commerce w/Hydrogen, and Tailwind is the main styling tool here.
Hydrogen is a React-based framework, and React is all about easy, plug-and-play reusable components. It's super easy for developers to pair Tailwind UI libraries w/Hydrogen so they can essentially copy and paste modular layouts.
Couple this with the fact that agentic coding workflows though Claude, OpenAI, Grok, etc. all bias towards Tailwind, and it's honestly one of the most well-timed/best-fit acquisitions I've seen.
Once again, Tobi is a genius, and yeah, he now has another team of geniuses joining Shopify. I would not like to be a Shopify competitor right now, they are getting really far ahead.
Totally shocking news, all these AI labs like Kimi, Qwen, and DeepSeek, that we thought had found a way to provide open weight models that rivaled OpenAI and Anthropic...
It looks like they were actually cheating, and just serving Claude models in the backend the whole time.
I still need to do a deep dive here but if this does turn out to be true, it kinda changes the entire narrative on local ai and the pace it was actually moving.
We are at a time in history where AI safety could possibly be most important way to make an impact in the world.
As one human, I'm trying to increase the impact I can make. And I think there's an opportunity here, that I just can't stop thinking about.
I am starting to put the pieces together on an eval suite for VulcanBench, focused on AI safety.
It's a different approach from what companies like METR are taking. And I'm not diminishing what they are doing in any way, but I am saying there is room for other approaches.
I want to look at AI safety, as companies use AI today, in the actual harnesses they use, with the actual things, their teams are using AI for every single day.
It is very meaningful for me to be able to make these kinds of free, open source, contributions. This is what open source is all about, doing things to make an impact above all else.
More details in the first comment below.
So I had a post go super viral on LinkedIn, but I’m not a marketer, I’m an engineer.
Anyone following me know about how to analyze this so I can know what I did right?
Right now I’m like, let’s get more out like this!! But then I’m also wondering, what does that even mean 😳
Very interesting week ahead. There will be two main topics discussed:
1. Pacing AI model releases
2. A bunch of new AI model releases
Welcome to September 2026.
Oh, and AI stocks drop like a rock first thing Monday morning. I don’t make the rules, that’s just going to happen.
Buckle up.
Okay, it took a week to get this done the right way, but it's finally all complete, my comparison of Astra and Fable 5.1 with VulcanBench 🖖
A few things took longer here, the primary one being some updates to my benchmarking score to layer in code quality. This added 3-4 days of time, but for the right reasons.
This new class of models requires a new class of benchmarks. I don't think we can just look at things like accuracy any more, we also have to look at code quality/maintainability factors.
Now with VulcanBench-SWE v4, 33% of the score is code quality/maintainability. For me, and many other engineering leaders, seeing a model get a 98% on a benchmark doesn't really give us much signal.
I created VulcanBench to help make decisions around model and effort level, and this means not just building evals that represent the kind of work teams give to these models, but the kind of output we expect from these models when building scalable system and working in large codebases.
While I would normally share more about my thoughts, I'll let you come to your own conclusions about Astra and Fable 5.1. Both are excellent models, OpenAI and Anthropic have really created a new class of models here, now it's for us to decide if we need this horsepower for daily tasks, or just the hard stuff, and to be realistic about the quality of the output.
Model card below, and if you want to do a deep dive, you can find more on the VulcanBench site and Github, both shared in the first comment below.
Live long and benchmark 🖖
2reactions
1comments
A RECORD YOU CAN RETURN TO
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