DEV Community: senthil kumar

Building Marlo: An Open-Source AI CMO for Founders and Startups

senthil kumar — Fri, 06 Nov 2026 07:30:33 +0000

As founders, we spend countless hours building products, fixing bugs, and talking to customers. But when it comes to marketing, things get complicated quickly.

You need to research competitors, find keywords, write blog posts, create content for LinkedIn and X, engage on Reddit, optimize SEO, generate leads, and keep everything consistent. Most founders either spend too much time on marketing or can't afford a dedicated team.

That's why I built Marlo.

What is Marlo?

Marlo is an open-source AI CMO that helps founders and startups automate marketing research, strategy, and execution.

Instead of using multiple tools, Marlo combines everything into one platform powered by specialized AI agents.

What Marlo Can Do
🔍 Research & Strategy
Product and market research
Competitor analysis
Marketing strategy generation
Brand voice creation
Content strategy planning
📈 SEO & Content
Technical SEO audits
AI search visibility monitoring
SEO content generation
Website crawling and analysis
Content optimization recommendations
📣 Social Media
X (Twitter) posts and threads
LinkedIn content creation
Reddit engagement ideas
Hacker News launch content
🎯 Lead Generation
Prospect discovery
Outreach preparation
Growth opportunity identification
Gmail-powered outreach workflows
Why Open Source?

Most AI marketing tools are black boxes.

I wanted Marlo to be:

Open and transparent
Self-hostable
Customizable
Developer-friendly
Affordable for startups and indie hackers

You own your data, choose your models, and extend the platform based on your needs.

Who Is Marlo For?
Indie Hackers
Startup Founders
SaaS Teams
Solopreneurs
Growth Marketers
Agencies
Open-Source Builders
The Vision

My vision is simple:

Every founder should have access to an AI-powered marketing team without paying agency prices or juggling dozens of disconnected tools.

Marlo is still evolving, and I'm actively looking for feedback from builders, marketers, and early adopters.

If you're interested in AI-powered growth, open-source software, or marketing automation, I'd love to hear your thoughts.

🚀 Try Marlo: https://marlo.mlforge.in

⭐ GitHub: https://github.com/Kumar2421/marlo-self-hosted-ai-cmo

I Got Tired of Using 6 AI Tools, So I Built One Instead

senthil kumar — Tue, 28 Jul 2026 05:10:30 +0000

I Got Tired of Switching Between AI Tools, So I Built One Platform

For every computer vision project, my workflow looked the same:

Search for a model.
Search for a dataset.
Annotate data.
Benchmark different models.
Train.
Deploy.

Every step required a different tool, different UI, and different workflow.

After doing this repeatedly, I started building MLForge to see if I could bring everything into one place.

Today it includes:

20,000+ AI models and datasets from major providers
Built-in annotation studio
Benchmark dashboard
Inference workspace
Computer vision training pipeline
Deployment interface

The project is completely free and designed to run locally on Windows, Linux, and macOS.

This is still an active project, and I'm continuously improving it based on feedback from other developers.

I'd really appreciate your thoughts:

What features would you expect from a platform like this?
What would stop you from using it?
What would make it part of your daily workflow?

Website:(https://mlforge.in)

Demo: [https://youtu.be/V0YBemyTkG8?si=CkOniaPa_Mwp7_B9]

Building a desktop-first Vision AI IDE — early thoughts & feedback welcome

senthil kumar — Thu, 01 Jan 2026 07:12:56 +0000

Post:

Hi DEV friends 👋,

Over the past month I’ve been tinkering with an idea: Ml Forge , a desktop-first IDE for Vision AI.

The motivation is simple: most Vision AI workflows feel fragmented. You jump between scripts, configs, dataset tools, training dashboards, and export utilities. My goal is to unify that into one place — with a no-code but still controllable approach.

Here’s what I’ve got so far:

Dataset manager (versioning, splits, validation)

Annotation studio

Training engine with live GPU metrics

Reproducible runs (dataset → config → model)

Logs & metrics

Inference & benchmarking

Export to ONNX, TensorRT, CoreML, TFLite, OpenVINO

Everything is UI-driven — no training scripts or deployment code.

I’d love to hear your perspective:

Does “no-code but controllable” resonate for Vision AI workflows?

What would make a tool like this useful in your day-to-day?

What blockers or concerns do you see?

I’m still early in development, so feedback from this community would be super valuable. Happy to dive into technical details or design trade-offs if anyone’s curious.

This tone works well on DEV because it feels like you’re sharing a journey and inviting collaboration, rather than promoting a product.

Would you like me to also add a short “storytelling hook” at the start (like how you struggled with fragmented workflows yourself) to make it more engaging for DEV readers?
refer the website to get info