Strategy report · Updated Jul 31, 2026
What's working for @michellescomputer on Instagram.
Michelle Lawson
185.8K followers · 345 posts
“✨ I make AI make sense 🩵 helping you learn & grow with AI 🧠 cs student + ai engineer”
Engagement rate
5.82%
Per post, last 90 days
Posts per week
1.5
Niche avg 4.0
Average likes
10.5K
282 avg comments
Post mix
Last 90 days · 345 posts indexed
01
Top-performing posts
The three posts pulling the most engagement in the last 90 days.
carousel 134.32% engagedthe plot thickens.
reel 14.06% engagedLocking in. …and we made it! Now your turn 👀 Comment “data” and I’ll send you the link to join the NFL’s Big Data Bowl 🏆 The deadline to join is November 26th so run run run 🏃♀️ @amazonwebservices @nfl This video was sponsored by AWS #computerscience #aiml #machinelearning #bigdata #datascience #coding #bigdatabowl
carousel 11.10% engagedThere’s still so much I’m learning. But as long as we don’t stop, we’ll get better and better everyday. 🩵 If you want to join me on this mission, follow along!! P.S. My ears are always open to ideas, suggestions and collaborations 🙂↕️🤝 “Apply your heart to instruction, And your ears to words of knowledge.” Proverbs 23:12 NKJV
02
Content pillars
AI-clustered themes the account posts about, with example posts pulled live from Instagram.
03
Posting rhythm
Cadence and timing patterns over the last 12 weeks.
1.5 posts/week
Niche benchmark · 4.0/week
Best days
Thursday · Sunday · Monday
Best hours
9-11am · 6-8pm
04
Signature hooks
Caption patterns the account reuses across high-performing posts.
Comment "[keyword]" and I'll send you the link to [resource]
Used 18×
comment "question" and I'll send it to you
use internlist.com for FRESHLY released internships... comment and i'll send you the link
Comment "channels" and I'll send you the links to these six channels
I [did hard thing] in [short time] to ship this
Used 3×
learned flutter in 12 hours to ship this
we 🫶 vibe coding... tools i used: @lovable_dev_official for frontend
did this whole photoshoot with just my phone, a tripod, and canva
You're not [negative label]. Here's what to do instead.
Used 3×
it's november and you still don't have a 2026 internship? breathe. you're not late.
i was 11 the first time someone told me i was "too young"
nobody talks about learning payroll, taxes, compliance... but you're expected to know all of it overnight
Here are [number] things you can actually use [tool/concept] for
Used 4×
here are 15 things you can actually use one for
Here's all the tools I used in this process
Once you have a computer with good enough specs, you can start running your own local AI models
Most [things] look [bad result]. The ones that work get [X] things right.
Used 2×
Most websites built with AI look bland and bring no returns. The ones that actually work get three things right
Pop quiz: What are the two key factors that make an app a GOOD app?
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Strategy summary
What's working, what to fix, and what to ship next.
TL;DR
The strongest lever in this account is the comment-for-link CTA mechanic, which appears in roughly 18 of 30 posts and is directly responsible for the account's highest comment counts. Post 15 drove 551 comments and post 24 drove 2,365 comments using this format alone, while posts without it average fewer than 20 comments. The account has a clear editorial identity across AI tools, coding, and career content, and the creator's personal voice keeps the feed from feeling generic.
The biggest leak is posting cadence. At 1.5 posts per week, the account is running well below the 4 posts per week benchmark for tech education creators, which means the algorithm has little to amplify between spikes. Engagement is heavily clustered in a handful of breakout posts, post 25 at 247,174 likes and post 17 at 25,317 likes, while the recent feed shows single-digit to low triple-digit likes, suggesting the account is coasting on residual authority rather than compounding it.
Sponsored posts like post 17 (AWS), post 26 (NVIDIA), and post 8 (Gusto) perform solidly but are not the top organic drivers. The cs.girlies community content in posts 9, 14, and 19 generates strong comment volume relative to likes, signaling a highly activated niche audience worth feeding more consistently.
Working
5- Comment-for-link CTAs reliably convert views into comment activity and DMs, as proven by posts 15 and 24.
- Personal origin story carousels like post 13 and post 23 earn outsized saves and follower trust.
- Timely, urgent framing around deadlines (internship cycles, hackathon dates) spikes comment volume fast.
- Sponsored posts tied to genuinely relevant brands (AWS, NVIDIA) land without breaking the feed's credibility.
- The cs.girlies community angle creates a second identity that attracts a loyal sub-audience beyond the main feed.
Risks
5- Posting cadence is too low at 1.5 per week. Increasing to 4 per week would give the algorithm more entry points.
- Recent reels (posts 0 to 7) show very low likes, suggesting hooks are not landing as well as older breakout posts.
- Too many posts rely on the same comment CTA structure back to back. Varying the CTA format would reduce fatigue.
- Captions rarely open with a bold statement. Most start with a soft setup line, which loses viewers in the first second.
- The photoshoot and lifestyle content in post 0 is off-brand and underperformed with only 9 likes.
Ship next
5- Raise posting frequency to at least 3 reels per week for the next 30 days and track whether average likes per post recover.
- Test opening captions with a sharp, specific claim in the first 5 words rather than a setup line or emoji.
- Batch-produce 4 to 6 career strategy reels targeting the fall recruiting season and schedule them 2 days apart.
- Retire lifestyle or photoshoot content that does not connect directly to coding, AI, or career topics.
- Create a dedicated series format (numbered episodes) around AI tools so individual posts build cumulative follower expectation.
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