TechWorld with Nana | DevOps engagement report
@Njuchi_ - 54K followers on X
Measured over 2 original posts from a 30-day window, last computed on October 12, 2026.
Engagement
Early reading. We have captured 2 original posts for this account, below the 8 we require before treating a median as settled. The numbers above describe what we have seen so far, not a finished profile of the account.
A typical post picks up 72 interactions against 54K followers, an engagement rate of 0.133%. Posts are seen about 3.2K times each, and 2.22% of those impressions turn into an interaction. That is about 5.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 13:00 UTC, and Sunday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video, 100% are part of a thread and 100% link out. The account's strongest tracked post pulled 451 interactions, about 6.3x its own typical post. Only 2 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
Measured over 2 original posts from a 30-day window, last computed on October 12, 2026.
Where this sits in the catalog
At 0.133%, TechWorld with Nana | DevOps sits above the 25th percentile of the 199,658 accounts in this comparison. That places it in the below the median band, which runs 0.024% to 0.141%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.024% |
| 50th percentile | 0.141% |
| 75th percentile | 0.669% |
| 90th percentile | 2.53% |
| 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 Sunday 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 1442 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
- Mar 22, 20266.3x their median
Entry-level hiring at big tech is down roughly 50% compared to pre-pandemic levels. Junior roles are getting absorbed into smaller, more senior teams. Here's my take 👇 The job market is really harder right now. If you've been applying and hearing nothing back — you're not imagining it. But it doesn't mean that the opportunity has disappeared. It has simply shifted. The reason companies are hiring fewer engineers, is NOT because they need less done. They're hiring fewer engineers because they need more from each one. The times of getting paid to tick off a task list are ending. What they actually need — and can't find enough of — are engineers who understand systems deeply, take ownership, and make real decisions. A quick note on DevOps specifically: DevOps is not an entry-level path. Most job postings implicitly target mid-level or senior people — because a strong DevOps skillset means you already understand software development, operations, cloud, K8s, CI/CD, and observability. In many companies, DevOps isn't even a separate role. It's just what a senior engineer looks like. Now about AI. Because I know everyone's thinking it. Nobody actually knows what it means for jobs in 5 years. Not me. Not the LinkedIn thought leaders. Not the tech CEOs doing podcast rounds about it. Both extremes are wrong: "AI will replace all engineers" — wrong. "AI won't affect engineering at all" — also wrong. The roles that are under real pressure the most are ones doing narrow, siloed work — low-context, repetitive. AI is very good at those tasks. Including some of the specific DevOps automation stuff actually. The engineers I believe aren't going anywhere? The ones who can connect code, infrastructure, security, and business outcomes. And who can look at what AI produces and say "that's wrong — here's why" or "that's correct - and here's why" So what do you actually do right now? Not this → wait and see how it all turns out. Not this → skip fundamentals and just learn AI tools, hoping that's enough. Both lead nowhere good. My honest take: Broaden your skillset. Understand how the full system works — not just your piece of it. Cloud, CI/CD, K8s, observability, security — these keep showing up in job postings for a reason. And here's something people keep forgetting: every AI model you use runs on infrastructure that needs to be deployed, scaled, and maintained — that's Cloud and DevOps. Don't learn these things just enough to copy-paste from a tutorial. Learn them properly, to the point where you can design, debug, and explain what's going on. And yes — learn AI tools too. 🟢 Powerful when you know what you're doing. 🔴 Dangerous shortcuts when you don't. The best thing you can do in an uncertain market is become more skilled than the people around you. But skill alone isn't enough anymore. You could be the best engineer in the room. If nobody knows you exist, it doesn't matter. Visibility is part of the job now. That's what LinkedIn is actually for — and it matters more than ever right now. Bottom line is: the door hasn't closed. But it became higher and more competitive. What's your take?
- Sep 9, 20265.4x their median
Three things have changed in the engineering job market and most people have only noticed the first one. The first is the layoffs, and the story that got told about them — which is not the story the data tells. The second is that getting in got harder. The deal where you're hired junior and your employer pays for your depth over two years? That's basically gone. And the third is where AI actually sits in real engineering work. I've spoken to over fifty senior engineers about this. What's really running in their pipelines, not what their company posts on LinkedIn. Each of these on its own is bad news. Put together, they point somewhere completely different, and I didn't expect that when I started. I'm walking through all three live on September 13th. Three hours, free, and I'll answer your questions at the end. Register here, if you'd like to join: https://t.co/ikjCyoOs5Y
- Apr 29, 20264.2x their median
Most DevOps engineers can deploy to Kubernetes. Very few can explain what happens on the network when they do. That gap is why debugging networking issues feels like guesswork — and why so many engineers avoid infrastructure problems they don't fully understand. But the good thing is: you don't need to know everything about networking. You need to know the 20% that covers 80% of what you'll actually use on the job. In my latest video, I break down exactly that — from physical servers, VMs, cloud, Docker, all the way to Kubernetes networking — using one real-world example the whole way through. No dry theory. Just the concepts that actually come up when things break in production. If you work with containers, cloud, or Kubernetes — this one is worth 40 minutes of your time. 🎥 Watch the complete guide here: https://t.co/3u41DJhOgC What was the networking concept that confused you the most? Drop it in the comments 👇
- Jun 12, 20263.7x their median
Stop letting a non-tech background hold you back. 🛑 Ana completely shifted her life from Swiss medical device manufacturing to becoming an AI DevOps Engineer using nothing but structured self-study. Her exact career shift framework: → Python self-study fundamentals → Data Engineering specialization → Cloud DevOps training (Kubernetes and Docker) → Enterprise AI speech analytics optimization No tech degree. No prior code experience. Just an optimized strategy to break into tech. 👇 Watch her breakdown how she landed her first Cloud DevOps role: https://t.co/ohtG7DE6nx #CloudDevOps #DataEngineering #Kubernetes #WomenInTech #SoftwareEngineering #TechJobs
- Mar 30, 20262.9x their median
The first time I heard "multi-cloud", it sounded simple. Use AWS for this. Google Cloud for that. Azure for something else. Best tool for each job. Easy. Then I actually tried to make it work. Suddenly I was dealing with: ‼️ Three different credential systems ‼️ Cross-cloud networking that nobody talks about ‼️ Infrastructure I had to maintain in two or three places at once And a cloud bill that made no sense. Here's what you should know: Netflix, Spotify, and Uber use multi-cloud. But not because it's simple. Because they figured out the right architecture to make it manageable. So I made a full video breaking down exactly how multi-cloud works in practice: https://t.co/s5fBo7h00P Not the theory. The real problems. The real solutions. And a live demo deploying across AWS, Azure and GCP. If you're working with cloud infrastructure — or want to — this one is worth your time. 💬 Are you already working with multi-cloud at your company? Curious to hear where most teams actually are with this.
- Aug 12, 20262.8x their median
A junior developer writes 100 lines of code. A senior developer writes 20. Both solve the exact same problem. What's the real difference? It isn't about coding skills or memorizing syntax. It’s about understanding system architecture. When given a task like adding authentication: Juniors search for a tutorial, copy code, and stop once it works. Seniors step back to evaluate data storage, token vs. session auth, security protocols, and backend integration. When an application runs slow: Juniors try to optimize line-by-line code execution. Seniors trace the entire request across the frontend, backend, database queries, and network payloads to spot the true bottleneck. You don't become senior by writing more code. You become senior by grasping the full software development life cycle—how components communicate, where bottlenecks happen, and why architectural decisions matter. This system-level perspective is exactly what interviewers test in a system design interview. Ready to master IT fundamentals and start thinking like a senior engineer? Book a call with us here to take the next step: https://t.co/NID5sFgOQp #TechWorldWithNana #ITFundamentals #SoftwareEngineering #WebDevelopment #TechCareers
- Aug 15, 20262.6x their median
Developers keep getting rejected in tech interviews despite having 5 frameworks on their resume. The reason? They know how to use tools, but not what the tools actually do. When an app returns stale data from an API call 🔄: • A framework developer checks if they used the right React hook ⚛️. • A foundational developer checks HTTP request headers 🌐, browser caching ⚡, and API response payload integrity ⚙️. Frameworks change every 2 years. Syntaxes evolve. AI handles boilerplate. What doesn't change is fundamental architecture—HTTP protocols, client-server communication, caching strategies, and data flow. If you want to future-proof your dev career, master the underlying mechanics before chasing the next trending library. Knowing the fundamentals is the fastest shortcut to senior-level engineering. If you're ready to bridge the gap between syntax and true system architecture, check out our IT Fundamentals Course: https://t.co/drNCm8Od8C #SoftwareEngineering #WebDevelopment #TechCareers #TechWorldWithNana
- Aug 25, 20262.2x their median
ChatGPT can write a Python script in 10 seconds that takes a developer an hour. ⏱️ But it still won't fix your latency issues. 🐢 Why? AI solves the code problem, not the system problem. 🤖 If a checkout process is slow, asking AI to optimize the code only speeds up execution by 1.2x. A developer who understands system architecture will trace the entire flow: 🔍 - Frontend payload design 💻 - Backend validation checks ⚙️ - 5 redundant database queries per transaction 🗄️ Caching the inventory data and batching updates fixes the real bottleneck and delivers a 3x speedup. ⚡ AI generates syntax—it doesn't understand your entire stack, database context, or system interconnectivity unless you instruct it. 🧠 The value in software engineering is no longer writing syntax. It’s system thinking: knowing where the code fits, why a feature is complex, and how components interact. 🏗️ Where have you seen AI code generation hit a hard wall when dealing with complex, multi-service architectures in production? 💬👇 Master the full software development lifecycle, architectural context, and systems-level thinking required to lead in the AI era. 🚀 Explore the IT Fundamentals Course here: https://t.co/5ukENduGSq 🎓 #TechWorldWithNana #SoftwareEngineering #SystemArchitecture #DevLife
- Jul 14, 20262.0x their median
🚀 The Ultimate DevOps Roadmap: From Fundamentals to Production 1️⃣ The Foundation (Weeks 1–2)Linux & Git: You cannot skip this. Master Bash scripting, Vim, standard I/O, and Git workflows. This is the absolute bedrock of everything you will build later. 2️⃣ The Language (1 Month)Go or Python: Pick one and stick to it. Focus heavily on strings, variables, maps, functions, interacting with APIs, and utilizing SDKs. 3️⃣ Cloud & Infrastructure as Code (1–2 Months)AWS + Terraform: Focus on AWS core services like EC2, VPC, and IAM. Pair this immediately with Terraform so you learn how to build scalable, repeatable infrastructure from day one. 4️⃣ Containers & Kubernetes (1 Month)Docker & K8s: Learn how to containerize applications with Docker, and then move on to managing and orchestrating those containers at scale using Kubernetes. 5️⃣ Master CI/CD (1–2 Months)The Core: This is the absolute backbone of DevOps. Dedicate the majority of your focus here to build end-to-end pipelines that take committed code and deploy it directly to a containerized environment (like K8s or AWS). 6️⃣ Observability (1 Month)The Stack: Dive into Prometheus and Grafana. You don't need to be an absolute guru right away—just master the core fundamentals, basic concepts, and how to implement observability in your systems. Looking for direct guidance? Secure a free orientation call with our team and map out a personalized study plan before the calendar fills up! https://t.co/4ryO9Q1hQZ
- Sep 10, 20262.0x their median
In 2018 Joseph bought a new house and a new car. Two weeks later he was laid off. He was on an H1B visa. He had 60 days to find a job or leave the country. He didn't find one in time. He packed up and flew back to India. Most people would have taken the first offer they got when they returned to the US. Joseph didn't. He used the time in India to figure out what he actually wanted to build his career around — and he chose DevOps. A few years later he's a Staff Cybersecurity Engineer at PayPal, a Docker Captain, and an AWS Community Builder. I sat down with Joseph to hear the full story — the setback, the pivot, the rebuild, and what he'd tell engineers going through the same thing right now. Full interview now on Youtube 👇 https://t.co/emV13ayqxt
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.
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Reading these numbers
A typical post picks up 72 interactions against 54K followers, an engagement rate of 0.133%. Posts are seen about 3.2K times each, and 2.22% of those impressions turn into an interaction. That is about 5.98% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 0.13 posts a day over the last 30 days, though only 7% of days saw any activity at all. Most posts go out around 13:00 UTC, and Sunday is the busiest day of the week. Of the 2 posts sampled, 100% carry an image or video, 100% are part of a thread and 100% link out. The account's strongest tracked post pulled 451 interactions, about 6.3x its own typical post. Only 2 original posts have been captured so far, fewer than the 8 posts we want behind a median before treating it as settled. Read the figures above as an early measurement of this account, not as a finished profile of it.
- What is TechWorld with Nana | DevOps's engagement rate on X?
- TechWorld with Nana | DevOps (@Njuchi_) has an engagement rate of 0.133%, based on the median interactions across 2 original posts from the last 30 days against 54,286 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.133%, TechWorld with Nana | DevOps sits above the 25th percentile of the 199,658 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 @Njuchi_ have real engagement?
- There is not yet enough sample to rank this account against others of its size.
- When does @Njuchi_ post?
- Most posts go out around 13:00 UTC, and Sunday is its busiest day, at roughly 0.13 posts per day across the measured window.