DEV Community: Rushikesh Bodakhe

Why most database docs tools fail — and how Vizora1 is different

Rushikesh Bodakhe — Mon, 07 Sep 2026 12:55:13 +0000

chemaSpy, dbdocs, dbt docs, Dataedo — they're either free nerd tools (DSL required) or enterprise (priced for big orgs). Vizora1 is the gap: instant AI docs, zero setup, multi-DB, $0 infra. For sale at $9K.

Most database docs fall into two camps:

Nerd CLI tools — SchemaSpy, dbdocs. You learn a DSL. You run commands. You maintain files manually.
Enterprise catalog tools — Dataedo. Built for companies with data governance budgets.
ChatGPT prompts — work once, don't update when your schema changes.

Vizora1 is the gap: paste any PostgreSQL schema (or MySQL, SQLite, Prisma, Drizzle, DuckDB), get instant docs, ERD diagrams, AI explanations — no DSL, no CLI, no manual updates.

Built for solo devs and small teams who inherited messy databases.Not enterprise. Not geek-only. Just useful.

The full stack: React + TypeScript + Vite, Supabase, Stripe (Checkout + webhooks + billing portal), Vercel + Render, Sentry.

Live: vizora1.vercel.app

Asking: $9,000 (negotiable — reach out privately). 10 beta users included (full list at close). 30-day support.

What it takes to build a database docs SaaS from scratch (honest post-mortem)

Rushikesh Bodakhe — Sun, 06 Sep 2026 13:20:27 +0000

35 features, 35 migrations, CI gates, Playwright E2E, Sentry, dark-mode design. No AI API dependency. Here's the full build story — and why I'm selling it at $9K.
I spent months on Vizora1. Here's what actually went into it — no marketing gloss.

Design: dark-mode-first, dark navy (#0a0a0f) + cyan/blue accents. Not a default Tailwind theme — a real design system.
Features: auto-docs, ERD diagrams, AI explanations, change tracking, backend roadmap, annotations, multi-DB parsing (PostgreSQL, MySQL, SQLite, Prisma, Drizzle, DuckDB).
Tests: Playwright E2E + Vitest, running in CI before every merge.
Monitoring: Sentry connected, errors tracked in real-time.
Code quality: TypeScript everywhere, Zod validation, no any without justification.

The real investment: a solo founder's full-time focus for months, with 10 beta users giving real feedback.

The result is finished code, a live product, and a ready buyer pipeline. I'm not dumping — I'm passing it to someone with time to distribute it.

Asking: $9,000. Demo: vizora1.vercel.app.

built Vizora1 — AI docs for any DB schema. 35 features, Stripe live, 10 beta users. Selling at $9K, negotiable. Full story + demo link in reply.

Rushikesh Bodakhe — Wed, 02 Sep 2026 14:17:10 +0000

Rushikesh Bodakhe

Sep 2

I built a production-ready AI database docs tool — and I'm selling it (honestly)

#showdev #saas #postgres #ai

1 min read

I built a production-ready AI database docs tool — and I'm selling it (honestly)

Rushikesh Bodakhe — Wed, 02 Sep 2026 14:14:37 +0000

Production-grade AI SaaS that turns PostgreSQL schemas into docs + ERDs + AI explanations. 10 beta users, Stripe live, $0 infra/mo. Selling at $9K.

Every developer has inherited a database with zero documentation. ChatGPT by hand isn't a docs system.

So I built Vizora1 — paste any PostgreSQL / Supabase schema, get auto-docs, ERD diagrams, AI explanations, change tracking. Zero setup.

What's real (not a landing page):

Stack: React + TypeScript + Vite, Supabase, Stripe, Vercel + Render
35+ features, 35+ DB migrations, CI + Playwright E2E, Sentry
Stripe fully wired: Checkout, webhooks, billing portal
Pricing live: Free / $15 / $49 / $119 (20% annual discount)
10 beta users (6 active) — full list handed over at close
Live demo: vizora1.vercel.app | Source: github.com/CaptainRushi/Vizora1
No OpenAI dependency — AI runs self-hosted (~$0/mo infra)

Why sell: I'm a solo founder shifting focus. This is 80% built, 20% distributed. I'd rather hand it to someone who grows it.

Asking $9,000 (negotiable — DM me). 30-day post-sale support, full code, all accounts transferred at close.

I built a production-ready SaaS for database documentation — and I'm handing it over

Rushikesh Bodakhe — Wed, 26 Aug 2026 14:17:52 +0000

Every developer has that one painful week: you inherit a database with 30 tables, nobody documented them, and ChatGPT chat-by-chat isn't a docs system. So I built one.

Vizora1 turns any PostgreSQL/Supabase schema into living documentation — auto-docs, ERD diagrams, plain-English explanations, change tracking, a backend roadmap. Paste a schema, get a doc.

What I built

Production-grade from day one: 35+ features, 35+ Supabase migrations, CI with quality gates, Playwright E2E tests, Sentry, dark-mode-first design, Stripe billing fully wired (Checkout, webhooks, billing portal), and a real pricing table live (Free/$15/$49/$119).

No OpenAI API dependency — the AI features run self-hosted, so operating cost stays near zero. Infra: Vercel + Render + Supabase, render and free tiers. 10 beta users, 6 active.

Why sell it

I'm a solo founder, and I'm shifting focus to a new venture. Running Vizora1 half-attended doesn't respect it. It's 80% built, 20% distributed — the buyer's work is distribution, not development. Honestly — I'd rather hand it to someone with time to grow it than let it sit.

What's in the kit

Full source + Git history (GitHub)
All deployments (Vercel, Render)
Supabase project + 35+ migrations
Stripe wired end-to-end
Complete technical documentation + SEO plan
Demo:

Why I'm posting on dev.to: because dev.to is full of people who get this problem. You've lived the "undocumented database" nightmare. If you're interested in the project — reach out. If you're just curious how it's built, happy to share.

I'm selling it at $9,000 — a fair price for a finished asset, not a vapourware promise.

Acquiring Vizora – AI Database Intelligence Platform

Rushikesh Bodakhe — Wed, 17 Jun 2026 12:05:59 +0000

I'm looking to sell Vizora, a developer-focused SaaS built to help engineers understand, visualize, and document databases faster.

🔗 Demo: https://vizora1.vercel.app/

What Vizora Does

Generate ER diagrams from database schemas
Visualize database structures
Create database documentation automatically
AI-powered schema understanding
Modern developer-friendly interface

Included in the Sale

✅ Complete source code

✅ Website and deployment files

✅ Vizora brand assets & logo

✅ Official Vizora email accounts

✅ Technical documentation

✅ Database architecture

✅ Existing registered users (13+)

✅ Full ownership and intellectual property

Tech Stack

React
TypeScript
Vite
Tailwind CSS
Supabase

Why I'm Selling

I'm focusing on other projects and looking for a founder, agency, or company that can continue growing Vizora.

Asking Price

💰 $10,000 USD

Interested in acquiring Vizora?

Send me a DM and I'll share additional details, screenshots, and a walkthrough of the platform.

Demo: https://vizora1.vercel.app/

Selling Vizora – AI-Powered Database Intelligence Platform ($10,000)

Rushikesh Bodakhe — Wed, 17 Jun 2026 12:02:42 +0000

Selling Vizora – AI-Powered Database Intelligence Platform ($10,000)

After building Vizora from the ground up, I've decided to sell the project and focus on other opportunities.

What is Vizora?

Vizora is a developer-focused SaaS platform that helps users visualize databases, generate ER diagrams, create documentation, and better understand database schemas.

What's Included?

✅ Complete source code

✅ Domain & branding assets

✅ Vizora logo and design files

✅ Official Vizora email accounts

✅ Technical documentation

✅ Deployment configuration

✅ Database schema & architecture

✅ User database (13+ registered users)

✅ Full ownership and intellectual property rights

Tech Stack

React
TypeScript
Vite
Tailwind CSS
Supabase
Modern SaaS architecture

Why Buy Vizora?

Fully built product
Ready for further development and monetization
Developer tools market continues to grow
Existing registered users
Modern codebase and clean UI
Great opportunity for a founder, agency, or DevTools company

Asking Price

💰 $10,000 USD

Serious buyers only.

If you're interested in acquiring Vizora or would like a demo, please send me a DM.
https://vizora1.vercel.app/

I asked my AI agent to design a product launch image. Here's what came back.

Rushikesh Bodakhe — Thu, 26 Mar 2026 10:18:07 +0000

I didn't plan to share this one.
I was in the middle of launching Vizora — a database documentation tool — and I needed a hero image for the launch post. Not a placeholder. An actual creative brief I could hand to a designer or drop into Midjourney.
I typed into #koko-content on Discord:
Create a detailed image concept for the Vizora launch.
Modern SaaS hero image. Professional.
Koko is my content agent. It runs locally via OpenClaw. I wasn't expecting much — I thought I'd get a vague paragraph and have to do the real work myself.
This is what came back:
![Koko agent in #koko-content Discord channel responding with a full image concept for Vizora launch — style, scene, color palette, key elements, dimensions, and tools listed]

Read that response.
It didn't just say "make it blue and professional." It gave me:

A specific scene — messy database diagram on the left transitioning into clean documentation on the right
An exact color palette — blue-to-purple gradient with a rationale (tech, trustworthy)
Key elements with layout logic — the central arrow showing transformation, where the logo sits, what the overlay text should say
Exact dimensions — 1792x1024, landscape, with the reasoning (social media headers)
A list of specific tools to actually build it — Canva template name included, Midjourney, Adobe Firefly, even a Python/OpenAI Images API prompt if I wanted to generate it myself

That last part. It wrote the generation prompt for me.
A sleek modern SaaS landing page hero image for Vizora,
a database documentation tool. Abstract glowing database
schema with interconnected nodes and tables transforming
into a clean interface. Blue and purple gradient background,
tech aesthetic, professional, 8k resolution.
I copied that prompt. Pasted it into Midjourney. Had a usable hero image in four minutes.

The thing I keep noticing about Koko specifically is that it doesn't just complete the task — it thinks about downstream use. The dimensions came with an explanation. The tool list came with a ranked order. The prompt came pre-formatted for the API.
I didn't ask for any of that. It just included it.

For context on the system: APES OS is a local multi-agent setup I built on OpenClaw. Seven agents, each with one job, each with their own Discord channel and a workspace folder containing a SOUL.md file that defines who they are and what they hand off to others.
Koko's job is content. Blog posts, newsletters, creative briefs, copy. The #koko-content channel is where I drop anything that needs writing or concepting. The response comes back in Discord and gets logged to Supabase.
The whole stack runs locally. Free OpenRouter model tier. Supabase free tier. Discord is free. OpenClaw is open source.

I shipped Vizora's launch post with that hero image. The image concept Koko wrote became the actual brief.
That's a weird sentence to type. My AI agent wrote the creative brief for my product launch. But that's what happened.

If you want the full setup — all seven agents, the Discord routing, the Supabase schema, the SOUL files — I documented it here: nexflowai.gumroad.com/l/npzufj
Or poke around the open source side first at docs.openclaw.ai.
Drop a comment if you want to know more about how Koko's SOUL file is structured — that's what shapes the quality of the output.

This will help in your developement.

Rushikesh Bodakhe — Wed, 25 Mar 2026 15:55:14 +0000

Rushikesh Bodakhe

Mar 23

I Run My Entire SaaS Solo Using 7 AI Agents for $0/Month

#ai #productivity #startup #devops

4 min read

My weekend project accidentally became my entire workflow

Rushikesh Bodakhe — Wed, 25 Mar 2026 09:27:33 +0000

It started because I was annoyed.
I had a Notion doc with 23 content ideas that had been sitting there for four months. Every week I'd open it, pick something, think about how long it would actually take to research and write properly, and close it again.
I'm not lazy. I just hate doing the same thing twice. Writing the same kind of research brief, the same blog structure, the same five social posts for five platforms. Every time. For every idea.
So one weekend I sat down and tried to automate the annoying part.
That was six months ago. What I built is now running every day.

Here's the basic idea. I have seven AI agents running locally on my machine, each with exactly one job:
Atlas → research
Koko → blog writing

Zuri → social media
Caesar → marketing strategy
Turing → code
APES → pipeline trigger
Main → orchestration
They each have a Discord channel. I type into the channel. The agent runs via OpenClaw. The response comes back in Discord and gets logged to Supabase.
The piece that made it actually useful was chaining them together. I type one message:
Run full pipeline on [topic]
Atlas researches it. Passes the output to Koko. Koko writes a blog post. Zuri turns it into social content. Caesar builds a promotion plan around all of it.
Twenty minutes. Four complete outputs. From one message.

The Discord routing was the part I found most interesting to build.
There's a Node.js process sitting between Discord and OpenClaw. When a message comes in, it looks up the channel ID in a map, finds the matching agent name, and spawns an OpenClaw CLI process with the message attached:

const CHANNEL_AGENT_MAP = {
  '1484143597765918770': 'atlas',
  '1484143597765918771': 'koko',
  // ...
}

function runClawAgent(agentId, message) {
  const proc = spawn('node', [
    openclawPath, 'agent',
    '--agent', agentId,
    '--message', message,
    '--json'
  ])
  // handle stdout, log to Supabase
}

Nothing clever. Just a lookup table and a child process. The part that took the longest was getting the response extraction right — OpenClaw returns a JSON payload with nested content blocks and you have to walk through them to find the actual text.

The dashboard pulls from Supabase agent logs. Bar chart, donut chart, kanban board. Runs locally on port 3000.
Last week: 32 agent runs, 66% success rate.
That 34% failure is real. Free model tier on OpenRouter hits rate limits mid-run sometimes. The fallback chain catches most of it — I have six fallback models configured — but not all of it. When a run fails I just send the message again.
It doesn't bother me that much. Even at 66% I'm producing more than I was doing everything manually, and the cost is zero.

The thing I keep coming back to isn't the time saved, though that part is real.
It's that the 23-idea Notion doc is empty now. Not because I deleted it — because I actually went through all of it. When research and writing don't cost me an evening, I just... do more of them.
I don't think I was procrastinating on those ideas. I think execution was genuinely expensive and now it isn't.

The full setup has a fair bit to configure — OpenClaw, Discord bot intents, Supabase schema, a startup script that launches all three services in the right order, identity files for each agent. I got stuck in a few places the first time through.
I documented all of it if you want to build the same thing without the trial and error: nexflowai.gumroad.com/l/npzufj
Or if you want to poke around the open source side first, OpenClaw is at docs.openclaw.ai.
Happy to talk through the Discord routing or the OpenClaw config if anything above was unclear — drop a comment.

🦍 How I Built a “Mission Control” System to Run My SaaS Solo (7 AI Agents + Dashboard)

Rushikesh Bodakhe — Tue, 24 Mar 2026 13:48:01 +0000

When I started building my SaaS solo, I thought coding would be the hardest part.

I was wrong.

The real challenge was everything around it:

researching competitors
writing content
planning marketing
managing tasks
fixing bugs
posting on social

Every day felt like switching between 5 roles.

Nothing was structured.
Everything was reactive.

⚡ The Breaking Point

At one point, I had:

10 tabs open
half-written blog drafts
unfinished features
random notes everywhere

I wasn’t building a product anymore.

I was just… managing chaos.

So I decided to fix it.

🧠 What I Built Instead

I created my own system:

🦍 APES OS — a “Mission Control” dashboard for solo SaaS builders

It’s basically:

a central dashboard
a kanban task system
7 AI agents handling different jobs
🤖 The 7 Agents

Each agent has one job:

🔎 Atlas → market research
✍️ Koko → content writing
🚀 Caesar → marketing strategy
💻 Turing → dev/debugging help
📱 Zuri → social content
⚡ APES → runs full workflows
🧠 Main → orchestrates everything
🖥️ What the System Looks Like

Inside the dashboard:

agent status (what each agent is doing)
task board (kanban style)
operations report (weekly activity + performance)

Everything is in one place.

No more jumping between tools.

🔁 How My Workflow Changed

Before:

manual research
writing from scratch
random marketing
constant context switching

Now:

research → blog → tasks → social → done

I just trigger workflows and coordinate.

📊 Real Impact (for me)
research → hours → minutes
blog drafts → half day → few minutes
tasks → scattered → structured
mental load → high → manageable

It’s not perfect.

But it’s the first time solo building feels scalable.

🧩 Why I Turned This Into a Template

Setting this up wasn’t easy.

It took me:

trial & error
debugging configs
fixing broken setups
organizing everything into one system

So I documented everything:

full Notion template (dashboard)
setup guide (step-by-step)
code + configs
agent prompts
📦 If You Want to Build This Yourself

I packaged the full system here:

👉 https://nexflowai.gumroad.com/l/npzufj

This includes:

Notion template (duplicate & use)
complete setup guide (PDF)
all configs + files
💭 Final Thought

Most solo founders don’t struggle because of lack of skill.

They struggle because they’re doing too many things at once without a system.

This was my attempt to fix that.

Curious — what’s the hardest part of building solo for you right now?

This is what my "team" looks like. 7 AI agents, one dashboard, no employees.

Rushikesh Bodakhe — Tue, 24 Mar 2026 10:10:15 +0000

I want to show you something instead of just describing it.
This is my operations dashboard on a normal week:
![APES OS Operations Report — 32 agent runs over 7 days, 66% success rate, top agents: Koko, Turing, Atlas, Caesar]
32 runs in 7 days. 66% success rate. Koko the content writer leads with 9 runs. Turing the developer at 7. Atlas the researcher at 6.
I didn't do any of that work.

What you're looking at
This is APES OS — a local multi-agent AI system I built on top of OpenClaw. It runs entirely on my machine. Seven agents, each with a specific job, each with their own Discord channel and workspace folder.
Here's the agent status page — this shows every agent in real time:
![APES OS Agent Status — all 7 agents showing green CONNECTED, with last task summaries and model info]

All seven showing green. Connected, idle, ready.
The ones with "No data yet" are Main and APES — they only activate when I trigger an orchestration run or a full pipeline. Atlas, Koko, Caesar, Turing, and Zuri handle the day-to-day requests that come through Discord.
The models you can see — openrouter/stepfun/step-3.5-flash and gemini-3-flash — are both free tier. That's the whole stack.

The part I use most
The kanban board. This is where actual work gets tracked:
![APES OS Task Board — 12 tasks in Done column including content creation, research, and Instagram posts]

Twelve tasks in Done. Zero in To Do or Doing. The dashboard shows "Outstanding — you crushed it" which is a little embarrassing but accurate for that day.
The tasks you can see in the Done column are real ones from that week:

create beautiful image post for vizora launch (assigned to Zuri)
write instagram post of vizora launch at 25/3/26 (Zuri again)
research best ai models for coding (Atlas)
research for a database related saas present in market (Atlas)

All of them completed, logged to Supabase, visible in the activity feed and charts.

How a task actually moves through the system
I type something in Discord — whichever channel matches the agent I want:
Research the best database solutions for
a bootstrapped SaaS in 2025. Focus on cost,
scalability, and what solo founders actually use.
That goes into #atlas-research. The Discord router picks it up, maps the channel ID to Atlas, spawns an OpenClaw CLI process with the message attached, and Atlas runs it.
When Atlas is done, the response comes back in Discord. Simultaneously, the completion gets logged to the agent_logs table in Supabase with the model used, duration, and a summary of the output.
The dashboard pulls from that table. The chart updates. The activity feed shows the run.
The whole thing happens without me touching anything after sending the message.

The pipeline that does the heavy lifting
For content work, I run a four-step chain:
Run full pipeline on [topic]:
Atlas researches → Koko writes the blog →
Zuri makes social posts → Caesar builds
a 30-day promotion plan
One message. About twenty minutes. I come back to four separate outputs.
I still review everything — especially Zuri's social content which needs editing — but the starting point is already 70-80% there. For research and blog drafts it's closer to 90%.

What the 34% failure rate means
I showed the success rate screenshot on purpose. 66% means roughly one in three runs fails.
Usually it's a free model hitting a rate limit mid-run. Sometimes the fallback chain catches it and retries with a different model. Sometimes it just fails and I re-send the message.
It's not a polished product. It's infrastructure I built for myself on weekends. The failure rate is annoying but the overall output volume more than makes up for it — even at 66%, I'm getting more done than I was doing everything manually.

The technical stack if you want to replicate this

OpenClaw — open source, handles the agent gateway and CLI
Discord.js — custom router that bridges messages to agents
Supabase — free tier, stores tasks and agent logs
OpenRouter — free model tier, stepfun flash as primary with fallbacks
Node.js — serves the dashboard locally on port 3000
PowerShell — one startup script launches all three services

Everything runs locally. No cloud hosting cost. No subscription.

If you want to build something like this
The full setup took me a weekend to get working — OpenClaw config, Discord bot permissions, Supabase schema, the startup script, all seven agent SOUL files. It's not complicated but there are a lot of moving pieces and a lot of ways to get stuck.
I documented the whole thing — every step, every config, every error I hit with the exact fix — in a Notion template with a PDF version and all the agent SOUL files pre-written.
It's at nexflowai.gumroad.com/l/npzufj if you want to skip the trial and error.
If you'd rather poke around first, OpenClaw's docs are at docs.openclaw.ai. The source is on GitHub.
Happy to answer questions about any part of how this runs — the Discord routing, the Supabase schema, the OpenClaw config, whatever you're stuck on.