DEV Community: Rushikesh Bodakhe

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

Comments 1

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.

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

Rushikesh Bodakhe — Mon, 23 Mar 2026 13:37:14 +0000

I'm a solo founder. No team, no VA, no agency.
But I have Atlas doing research at 11pm. Koko writing blog posts at 2am. Caesar building my marketing plan while I'm debugging something else entirely. Turing fixing bugs I haven't even looked at yet.
None of this costs me anything. Not a dollar.
Here's exactly how it works.

The problem with "AI productivity" advice
Most articles about using AI to get more done are variations of the same thing: use ChatGPT for your emails, use Copilot for your code, use Midjourney for your thumbnails.
That's not a system. That's just outsourcing individual tasks to different tabs.
What I wanted was something closer to an actual team — agents that know their role, remember context, and hand work off to each other without me babysitting every step.
It took me a while to build it. Now it runs.

What APES OS actually is
It's a local multi-agent system I built on top of OpenClaw. Seven agents, each with a defined identity, a dedicated Discord channel, and their own workspace folder.
The lineup:

Main — orchestrates everything, runs the pipeline
APES — the autonomous one, triggers full automated runs
Atlas — deep research, sources, trend analysis
Koko — SEO content, blog posts, newsletters
Caesar — marketing strategy, 30/60/90-day plans
Turing — code, React, bug fixes
Zuri — social posts for every platform

They all talk to a web dashboard running locally at http://127.0.0.1:3000. There's a kanban board. Charts. Real-time agent status. It looks like a proper ops center.

How I actually use it day to day
I open Discord.
I type something in #atlas-research:
Research the current landscape of AI-powered SaaS tools
for indie developers. Focus on pricing models and what's
working vs. not.
Atlas comes back with a structured report — sources, statistics, emerging trends, what to do with the findings.
I forward that to Koko with one message:
Turn this Atlas research into a 1000-word SEO blog post.
Target keyword: AI tools for solo founders. Include a CTA.
Koko writes the post. I review it. Maybe I change two sentences.
Then I run the whole thing as a pipeline:
Run full pipeline on "AI tools for solo founders 2025".
Atlas researches. Koko writes the blog. Zuri makes social posts.
Caesar builds a 30-day promotion plan. Bundle it all at the end.
Twenty minutes later I have a research report, a blog post, five platform-specific social posts, and a marketing plan. I did not write any of it.

The technical side (simplified)
Three services run in the background:

OpenClaw Gateway — the brain, port 18789, handles all agent requests
Dashboard server — serves the UI at port 3000
Discord router — bridges Discord messages to the right agent via the CLI

One PowerShell script starts all three. I right-click, run it, and the dashboard opens automatically.
Every agent has a SOUL.md file in their workspace — a plain markdown file defining who they are, what they do, what they hand off to others, and a mandatory rule: log everything to Supabase after every task.
That logging rule is what makes the dashboard charts useful. It's also what keeps agents from just doing random things without a paper trail.

The cost breakdown
OpenRouter has free model tiers. My primary model is stepfun/step-3.5-flash:free. I have six fallbacks configured — all free or near-free.
Supabase is free up to 500MB. My agent logs and kanban tasks take up maybe 2MB.
Discord is free.
OpenClaw is open source.
I pay nothing per month to run this. If I switched to Claude Sonnet for everything, I'd probably spend $10–$20 depending on usage. I keep that as an option for when I need higher quality output on something important.

What's actually broken (being honest)
The 66% success rate on my dashboard is real. Some runs fail — usually because a free model hits a rate limit mid-task or returns something malformed. The fallback chain catches most of it, but not all.
Long conversations also cause problems. Context overflow after a few thousand tokens means I start fresh threads more than I'd like.
And Zuri's social posts are... fine. Not great. I still edit them before posting anywhere. The research and blog output from Atlas and Koko is genuinely good. The social content is 70% there.

What surprised me most
I expected to save time on writing. That happened.
What I didn't expect was that it changed how I think about my work. When I know Atlas can research something properly in 15 minutes, I research more things. When I know Koko can produce a draft blog in 20 minutes, I write more. I stopped bottlenecking on "I don't have time for this."
The volume of output I ship now versus six months ago is not close.

If you want to build something like this
The full setup takes a few hours — Node.js, OpenClaw, Discord bot, Supabase tables, some JSON config. It's not a one-click install.
I documented the whole thing in detail — every step, every config field, every error I hit and how I fixed it — because I wanted something I could hand to someone and have them actually finish the setup.
If you want the full guide (Notion template + PDF + all 7 agent SOUL files), I put it here: https://nexflowai.gumroad.com/l/npzufj
If you just want to poke around the open source side first, OpenClaw's docs are at docs.openclaw.ai.

Either way — the agents are waiting.

Running APES OS on Windows 11 · OpenClaw 2026.3.x · March 2026

I Built a Database Designer Where You Draw First and Generate SQL Later

Rushikesh Bodakhe — Tue, 20 Jan 2026 05:24:13 +0000

Most of us still design databases like this:

Start writing SQL

Try to imagine relationships in our head

Fix mistakes after running migrations

Update diagrams if we remember

That gap between design and SQL causes a lot of pain:

Broken relationships

Confusing schemas

Outdated ER diagrams

Slow onboarding

So while building Vizora, I decided to flip the workflow.

Introducing the Vizora Designer 🧠🧩

Instead of starting with SQL, you start with structure.

Vizora’s Designer lets you:

Create tables visually

Add fields with types (UUID, INT, TEXT, etc.)

Define primary keys and constraints

Connect relationships on a canvas

See everything update in real time

And once the schema makes sense…

➡️ Convert the diagram into SQL instantly
➡️ Copy it
➡️ Run it in your database

The diagram becomes the source of truth.
SQL becomes the output, not the guesswork.

Why visual-first design actually helps

When you design visually:

You see relationships immediately

You catch missing fields or wrong types early

You can review schemas without reading 200 lines of SQL

You reduce rework before migrations hit production

This is especially useful when:

You’re designing a schema from scratch

You’re iterating fast in a startup

You’re explaining a database to someone new

You want diagrams that don’t go stale

No more:

“The diagram doesn’t match the SQL anymore.”

What Vizora does differently

Vizora is not just a diagram tool.

The canvas is editable

The schema is structured

SQL is generated, not manually written

Diagrams and code never drift

You design once — everything stays in sync.

Current workflow

Open the Designer

Create tables and relationships visually

Review the schema on the canvas

Click Export SQL

Copy and use it anywhere

Simple, predictable, and developer-friendly.

Still building in public

Vizora is still evolving, and I’m shaping it based on real developer problems:

Schema understanding

Documentation drift

Onboarding friction

Trust in tooling

If you’ve ever struggled with database design or outdated diagrams, I’d love your feedback.

Thanks for reading 👋

🧭 Introducing Database Onboarding Guides in Vizora

Rushikesh Bodakhe — Sat, 17 Jan 2026 05:26:45 +0000

Auto-generated docs are useful.
But they don’t answer the first question every new developer has:

“Where do I even start?”

That’s the gap Vizora’s Onboarding Guide is built to solve.

❓ What is the Onboarding Guide?

The onboarding guide is an AI-generated walkthrough of your database, created directly from the schema.

It explains:

What this database is for

Which tables actually matter

How tables relate and data flows

Where the risky or legacy areas are

The recommended reading order for a new developer

Think of it as:

“The explanation a senior engineer would give — written once, automatically.”

📄 Auto Docs vs 🧠 Onboarding Guide

Auto Docs

Table-by-table documentation

Column descriptions

Good for reference

Onboarding Guide

Big-picture understanding

Highlights core entities

Shows relationships and flow

Flags risky / legacy areas

Tells you what to read first

Docs answer what exists.
Onboarding answers how to understand it.

🧑‍💻 Why this matters

From experience:

Schema knowledge lives in people’s heads

New devs ask the same questions repeatedly

Docs drift, but understanding matters more than completeness

The onboarding guide is generated directly from the latest schema, so it stays relevant as the database evolves.

🧪 What the guide includes

Database overview

Core tables by importance

Key relationships

Data flow summary

Risky / legacy areas

Today’s Vizora Update: Team Activity Log + Downloadable Docs PDF

Rushikesh Bodakhe — Fri, 16 Jan 2026 10:46:34 +0000

Shipped a solid update to Vizora today, focused on making schema work more transparent and shareable.

🧾 Team Activity Log

Vizora now includes a read-only team activity log:

Logs schema changes

Logs team invites and role actions

Logs AI usage

Entries are shown in chronological order, with no editing or noise — just visibility and trust.

📄 Documentation PDF Download

You can now download your schema documentation as a PDF.

This is especially useful when:

Sharing schema context outside the team

Reviewing architecture changes

Keeping records for audits or onboarding

🧠 Auto-Generated Docs (with diagrams)

The PDF is not manual or templated.

It’s generated directly from the schema and includes:

Table-by-table explanations

Relationship summaries

Diagram visuals

Deterministic structure (no guessing)

This is effectively the document version of Vizora — a portable, schema-derived artifact you can rely on ****

Still iterating carefully and shipping fundamentals before polish.

Feedback always welcome.

Looking for collaborators to build a developer tool (Vizora)

Rushikesh Bodakhe — Fri, 16 Jan 2026 04:11:00 +0000

I’m building Vizora, a schema intelligence platform for developers.

It helps with:

Understanding database schemas

Auto-generating diagrams & docs

Asking verifiable questions about schemas (no AI guessing)

This is an early-stage project, and I’m looking for collaborators, not paid contractors.

Important upfront clarity

❌ No joining fee

❌ No guaranteed payment

❌ No corporate structure

✅ Real product

✅ Real codebase

✅ Real learning

If you’re looking to:

Gain experience building a real SaaS

Improve backend / frontend / system design skills

Contribute ideas, not just code

Build something useful for developers

You’re welcome.

Tech stack

React + TypeScript

Tailwind

Node.js + Express

Supabase (Postgres)

AI used carefully (schema-constrained, evidence-based)

Ways to contribute

Frontend UI & interactions

Backend APIs & data modeling

UX improvements

Documentation

Feature discussions & reviews

No pressure, no deadlines, no corporate nonsense.

If this sounds interesting:

Comment here

DM me

Or ask for the repo link

Let’s build Vizora together.

Today’s Update (Jan 15, 2026): Verified Ask Schema — AI Answers You Can Audit

Rushikesh Bodakhe — Thu, 15 Jan 2026 14:54:56 +0000

📋 Today’s Platform Updates (January 15, 2026)

Today I shipped a major upgrade to Vizora’s Ask Schema feature.

The goal of this update was simple but hard to get right:

Reduce the AI debugging tax by making every answer verifiable.

Instead of “almost right” answers, Ask Schema now behaves like a schema reasoning engine.

✅ What Changed
Backend Improvements

Question Classification Each incoming question is now classified before it reaches the AI:

Read / dependency / impact / risk

Destructive questions (delete/drop) are explicitly detected

Strict Schema Context Formatting The AI now receives a tightly scoped, normalized schema context:

No raw dumps

No unrelated tables

No hidden assumptions

Validated AI Response Structure Every response must include:

Risk indicator (low / medium / high)

Reasoning method (step-by-step trace)

Explicit schema evidence

If any part is missing, the response is rejected.

Frontend Improvements

Risk Badges
Each answer now shows a clear risk level — especially useful for destructive questions.
Evidence Panel
Answers include a dedicated panel showing:

Schema version

Referenced tables

Referenced columns

Relationships used

Collapsible Reasoning Method Users can expand/collapse the AI’s reasoning to see how the answer was derived.

Why This Matters

Most AI tools fail not because they’re wrong —
but because engineers can’t verify them quickly.

This update shifts Ask Schema from:

❌ “Trust the AI”

✅ “Verify the reasoning”

Still building Vizora in public.
Feedback welcome, especially from backend engineers working with complex schemas.

Morning Build Update: How Vizora Is Shaping Up (UI, Motion, and Core Systems)

Rushikesh Bodakhe — Wed, 14 Jan 2026 05:20:21 +0000

Started today’s work on Vizora with a long desktop session ☕💻

Most of today’s focus has been on foundations, not flashy features.

What I worked on this morning

Refining the motion system (page transitions, staggered animations)

Cleaning up layout structure for desktop screens

Improving how different sections feel when navigating (less jank, more intent)

Iterating on internal components used across:

Ask Schema

Onboarding Guide

Schema Review

A lot of this work is invisible when done right — but very obvious when done wrong.

What I’m learning while building Vizora

Developer tools need calm motion, not fancy motion

Desktop UX matters more than mobile for backend-focused products

Small animation decisions affect trust more than people think

Vizora is intentionally growing slow and deliberate:

Schema-first

Evidence-based AI

No live DB access

No generic dashboards

Still building in public, one layer at a time.

📸 Morning build session attached
🌐 Live preview: https://vizora1.vercel.app/

If you’re building dev tools, I’d love to hear what you prioritize first: features or foundations?