The AI impact
Will AI take all the jobs in the coming future? Is AI already helping founders do everything on their own? A single-person startup founder with AI agents working for them isn’t far away? Let’s find out.
Every day - literally every day there’s a new model, a new LLM, a new breakthrough popping up in the field of AI. Everything today is built on the research done over past decades and stacked on top of that. Now, every other company is jumping into AI, either to grab the first-mover advantage or just to slap 'AI' into their company portfolio. Whatever the reason, there are legit apps out there that are super cool and deliver massive productivity gains. You can create websites, the initial ones simplified using tools like Rollout. Video streaming and editing? Combine StreamYard, DaVinci Resolve, and Canva. General tooling like ChatGPT, Claude, and Grok handles your day-to-day needs for search, explanations, and help. Then there’s tooling AI like Docker Gordon or full-stack app-building platforms like Lovable to ship something faster. There’s so much out there that, obviously, I can’t list them all - the tools mentioned here are just what popped into my head while writing. As a founder, when you’re building a product and you’ve got an idea you want to execute, I think these helper tools and AI agents focused on specific tasks can actually help a ton. Let’s take content, for example, since I’ve done a lot in that space. As a content creator, there are tools, but there’s still so much innovation yet to happen in this area.
How a content creator can be happy and do it as a single person?
Let’s say I created a video or wrote a Blog post. Now, here’s how I want the flow to go:
A tool that can read my GitHub repo with code, and I prompt it about the video and blog I need to write. The AI agent/tool gives me a solid skeleton for my video and blog, and then the other agents kick in, working in parallel.
An AI agent to create shorts with SEO-optimized titles, descriptions, and tags.
An AI agent with social account integration that can curate high-ranking posts based on the blog and video I created.
An AI agent tracking the content timeline, making sure posts are timed right based on analytics.
AI agents handling posting across a day, week, or months, then improvising based on keyword searches to repost if something similar becomes a popular buzz later.
All in all, an SEO agent, a Creator Agent, a Social Media Agent, and an Editor Agent.
This keeps a balance and becomes a huge help for a creator who can now focus on building content and demos while leaving the rest to AI. There are tons of tools doing these things in bits and pieces, but the reality is none work the way I described here - with all the integrations, doing it right and smooth.
This leads to a question: will AI take all the jobs in the future based on the scenario above?
Short answer: In the short term, no. In the midterm, people using AI will replace people not using AI. Long term? Still uncertain. Like, if we’re talking 10 years down the line, things are progressing so fast we don’t know how impactful they’ll become. There will be some extent of jobs taken over by AI, for sure. That being said, a ton of new opportunities will pop up too, so it won’t be a 'no more jobs' kind of scenario.
Yes, AI is powerful, and it’s heading toward a path where everyone can be solo founders or creators with helping AI agents.
What are your thoughts on this and which is your favourite AI tool do let me know in the comments.
Coming to what I have been working on - I have been trying to create a new minimalistic project and I am happy to soft announce this first in this edition of my newsletter:
Kubehatch - Hatching vClusters on demand.
This is an open-source, minimalist Kubernetes platform designed for creating virtual clusters through a simple UI. I built this platform entirely myself → from the code and documentation to the logo design - leveraging both my skills + AI tooling.
KubeHatch is a cli that lets you create virtual Kubernetes cluster, it comes with a simple front-end where you can provide a KubeConfig file and select some options, based on that a virtual cluster will be created and the kubeconfig file for the virtual cluster will be displayed back in the UI, there is an ingress too with basic auth behind which frontend is running. The repo has a goreleaser to release the project and the complete quick start guide that you can try on your own.
It was so fun building it as I learned a lot of concepts, putting pieces together and developing something end to end. If you like do star this GitHub Repo and keep supporting.
Repo - https://github.com/LoftLabs-Experiments/kubehatch
CNCF Chandigarh meetup
I along with CNCF Chandigarh meetup organizers are organising “Cloud native an AI“ meetup on 22nd March. If you are around, then do register and also submit the CFP.
Kubesimplify
We are trying to create more content and some cool ones in preparation isn
Kubernetes crash course 2025 edition
Kubernetes CKA - on Hindi channel already going and one shot will be done post that for English
Kubernetes Secrets course
Kubernetes GitOps course
Kubernetes CKS series
More blogs on our website too so make sure to check our our awesome blogs. Keep supporting and subscribe to our YouTube channel, we are trying really hard to push more content.
WasmI/O barcelona and KubeCon London
I will be at WasmI/O in Barcelona where I have a session with Shivay and a Panel too.
then I will pair up with Paco on KubeCon talk in London. Make sure to add them to your schedule.
Awesome Reads
Operationalizing Machine Learning: An Interview Study - Operationalizing ML (MLOps) involves continuously collecting and labeling data, experimenting to enhance model performance, evaluating models across deployment stages, and monitoring production performance. Through interviews with practitioners responsible for deploying ML pipelines, we identify three key factors for successful ML deployments—Velocity, Validation, and Versioning—and summarize best practices, pain points, and anti-patterns relevant to tool design.
Looking Ahead to WASIp3 - it summarises the new features coming with WASIp3, gives a deeper look at the status of the implementation efforts, and ends with a real-world example which puts those new features to use.
Announcing Dapr AI Agents - Dapr Agents, a framework built on top of Dapr that combines stateful workflow coordination with advanced Agentic AI features.
Goroutines in Go: A Practical Guide to Concurrency - This guide explains why sequential task execution in programs can cause bottlenecks and introduces concurrency as a solution. It explores concurrency using Go, focusing on goroutines and channels to enhance performance.
Templating Alertmanager Config in kube-prometheus-stack - this article explains Alertmanager within Kubernetes (GKE) using the kube-prometheus-stack Helm Chart to route Prometheus and Loki alerts to Slack and PagerDuty, highlighting the challenges of a growing 1700-line YAML configuration across 15+ engineering teams. It introduces a templating solution that reduces complexity for dev teams by shifting the burden to the infrastructure team, using Helm templates to generate a 6500-line configuration from a simple team interface, cutting team-specific config by up to 94.59% and adding quality-of-life features like team aliases and custom routing.
Automating Kro Deployments Across Kubernetes Fleets - This tutorial explains how to streamline the deployment and management of complex applications across multiple Kubernetes clusters using Kro, the Kube Resource Orchestrator, and Sveltos, a multi-cluster management tool. It provides a step-by-step guide to automate Kro deployments with Sveltos, leveraging ClusterProfiles to configure and deploy resources efficiently to targeted staging and production clusters while handling dependencies and simplifying Kubernetes operations for developers.
Awesome Repo’s/Learning Resources
AI Researcher - AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents" & "Open-Sourced Alternative to Google AI Co-Scientist.
Awesome LLMOps - An awesome & curated list of best LLMOps tools.
llmaz - Easy, advanced inference platform for large language models on Kubernetes.
Do let me know your thoughts on the AI agents, how its impacting your life?
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