DeepLearning.AI engagement report
@DeepLearningAI - 355K followers on X
Measured over 28 original posts from a 30-day window, last computed on October 7, 2026.
Engagement
A typical post picks up 128 interactions against 355K followers, an engagement rate of 0.036%. Measured over 28 original posts, its engagement rate beats 54% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 1.03% of those impressions turn into an interaction. That is about 3.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.93 post a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 28 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 664 interactions, about 5.2x its own typical post. Recurring topics include #ai, #aiengineering, #vectorsearch.
Measured over 28 original posts from a 30-day window, last computed on October 7, 2026. Recurring tags: #ai, #aiengineering, #vectorsearch.
Compared with accounts its own size
DeepLearning.AI's engagement rate beats 54% of the tracked X accounts closest to it in follower count (20,444 accounts, accounts of similar size (decile 10 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 48% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.036%, DeepLearning.AI sits above the 25th percentile of the 205,374 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.143%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.143% |
| 75th percentile | 0.678% |
| 90th percentile | 2.55% |
| 99th percentile | 82.4% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 13:00 UTC, and Wednesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 100% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 100% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 100% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 100% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 567 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Jul 29, 20265.2x their median
AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with @QodoAI and taught by @nnennahacks, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free: https://t.co/ouU2lu3WDR
- Sep 6, 20263.8x their median
🛑 Stop letting your coding agents run autonomously for hours. It is costly and often ineffective. Andrew Ng just broke down the real workflows top AI practitioners use. The secret is skilled human judgment, highly iterative planning, and frequent output verification. 🧵👇 Most of your time can now be spent on architecture and writing specs, not writing individual lines of code. You must learn to direct agents’ workflow, calibrate their autonomy, and build robust testing regimes. Read Andrew's full letter to study the five foundational skills of using coding agents: https://t.co/kXbmj47XZd #DeepLearningAI #AIEngineering #CodingAgents #DeveloperTools
- Jul 15, 20263.8x their median
Fast inference makes a new class of real-time LLM applications possible. In our new short course, Fast LLM Inference with Cerebras, built in partnership with @Cerebras and taught by @zhennydez, @duerr_seb, and @MilksandMatcha, you'll build them on the Wafer-Scale Engine, where a model's weights sit on-chip and tokens come out several times faster than a typical GPU setup. You'll build a webpage that personalizes itself as users interact with it, assemble a multi-tool workflow that analyzes market signals in one response, and adopt habits for cleaner agentic coding with Codex. Enroll for free: https://t.co/n5qcWhR4Yo
- May 6, 20263.1x their median
Most AI agents still respond in plain text. In our latest course, Build Interactive Agents with Generative UI, you’ll create agents that generate UIs like charts, forms, and interactive components on demand. Work across the Generative UI spectrum and connect a LangChain agent to a React frontend using CopilotKit and the AG-UI protocol. Made in collaboration with @CopilotKit, and taught by its CEO, @ataiiam. Enroll for free! ➡️ https://t.co/KW2pzTZwH3
- Aug 26, 20263.6x their median
Build coding agents that learn from experience. In Building Adaptive AI Agents, you’ll turn an agent’s own traces into reusable skills and build a code knowledge graph that finds the right context where keyword search misses. Built in partnership with @Oracle and taught by Nacho Martínez (@jupiterwanderer) and Casius Lee. Enroll for free: https://t.co/xOsWcg4wGd
- Sep 1, 20263.3x their median
Coding agents can write functional code, but relying on "vibe coding" without knowing core software engineering fundamentals can compromise long-term system reliability, security, and extensibility. We’ve mapped out Pillar 2 of the AI Engineering Skills Map: Software Engineering Fundamentals to help you bridge the gap. To build production-ready full-stack applications, focus on building these foundational skills: 🌐 Building Full-Stack Applications 🗄️ Managing Data 📐 Designing System Architectures 🛡️ Making Systems Secure and Reliable 🚀 Scaling and Operating in Production Master these to steer your agents effectively. Read the breakdown from Andrew Ng here: https://t.co/h1wii8W4jS #AIEngineering #SoftwareArchitecture #BuildWithAI
- Aug 29, 20263.2x their median
Everyone has done it: You build a new application with coding agents and love the toy version so much that you wind up deploying it without first evaluating trade-offs like latency, uptime, and compute costs. And we all know vibe coders who do this every day and only discover the production consequences later. When a developer lacks the proper grounding in software engineering fundamentals, they can’t steer an agent to make the structural decisions needed for building great applications. In this week's letter, Andrew Ng outlines why understanding the full software development stack, from data management to user interfaces, remains essential for AI engineering. Here’s Andrew’s list of the key technical capabilities requiring a knowledgeable human’s guidance: 🏗️ Full-Stack Development: Planning out API design, session management, caching strategies, and asynchronous processing. 🗄️ Data Lifecycle Management: Picking data models and storage infrastructure to maintain consistency and clean feeds for downstream AI systems. 📐 System Architecture Design: Choosing everything from the monolith versus microservice dilemma to load balancing. 🔒 Security and Reliability: Building in smart failure-handling policies to ensure graceful degradation, unit and integration testing, and security and testing — early and often. 🚀 Production Operations: Configuring CI/CD pipelines, managing databases, and creating observability tools for understanding workloads. Read Andrew’s full letter to explore how software fundamentals shape effective AI engineering, and how those fundamentals fit into our overall map of AI Engineering Skills: https://t.co/QE4DsmAJzy
- Jun 3, 20263.1x their median
New short course: Fast & Efficient LLM Inference with vLLM, built in partnership with @RedHat and taught by @cedricclyburn. Learn to quantize an open-source LLM, serve it with vLLM, and benchmark your deployment across speed, cost, and accuracy. Free to enroll: https://t.co/czVwJBnLZ6
- Aug 21, 20262.9x their median
🧠 Traditional software is predictable. AI is not. What fundamental skills do developers need in order to build and deploy AI applications? Here's Andrew Ng's list: 🏗️ LLM foundations 📂 Grounding models with data 🤖 Building agentic systems 🧪 Evaluation-driven development ⚙️ Operating in production 📉 Machine learning foundations 🔗 This is the second installment of the AI Engineering Skills Map. Read more and subscribe: https://t.co/BbKURqJtoG #AI #MachineLearning #AIEngineering
- Apr 28, 20262.2x their median
We're opening the doors at AI Dev 26 today, and celebrating with a little something for everyone: AI Prompting for Everyone, a new course taught by @AndrewYNg, is now live! 🎉 Learn how to get accurate answers, better writing, and more useful outputs from the AI tools you already use — whether you're an engineer, a marketer, a salesperson, in finance, or anything in between. Enroll today: https://t.co/gfhK6E1Zf5
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 128 interactions against 355K followers, an engagement rate of 0.036%. Measured over 28 original posts, its engagement rate beats 54% of 20,444 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 1.03% of those impressions turn into an interaction. That is about 3.48% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.93 post a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 13:00 UTC, and Wednesday is the busiest day of the week. Of the 28 posts sampled, 100% carry an image or video and 100% link out. The account's strongest tracked post pulled 664 interactions, about 5.2x its own typical post. Recurring topics include #ai, #aiengineering, #vectorsearch.
- What is DeepLearning.AI's engagement rate on X?
- DeepLearning.AI (@DeepLearningAI) has an engagement rate of 0.036%, based on the median interactions across 28 original posts from the last 30 days against 355,117 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.036%, DeepLearning.AI sits above the 25th percentile of the 205,374 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @DeepLearningAI have real engagement?
- Its engagement rate beats 54% of the tracked X accounts closest to it in follower count (20,444 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @DeepLearningAI post?
- Most posts go out around 13:00 UTC, and Wednesday is its busiest day, at roughly 0.93 posts per day across the measured window.