Forem: Alex

What's Actually Happening When AI "Enhances" a Video

Alex — Wed, 13 May 2026 02:13:54 +0000

AI video enhancement is one of those features that gets marketed with dramatic before/after clips and very little explanation of what's actually going on under the hood.

This post covers the technical foundations — what models are doing, where they work well, and where they hit real limits.

The Problem with Traditional Approaches

Classical video processing tools (think FFmpeg filters, VirtualDub, early sharpening algorithms) work in pixel space with hand-crafted rules:

Sharpen: increase contrast at detected edges using kernels like Laplacian or Unsharp Mask
Denoise: average surrounding pixel values (Gaussian blur, median filter)
Upscale: bicubic or bilinear interpolation — essentially weighted averaging of neighboring pixels

These methods are fast and deterministic, but they have a fundamental ceiling: they have no model of what the content should look like. A denoising filter doesn't know whether a patch of variation is film grain or fabric texture. A sharpening kernel doesn't know it's looking at a face versus a background. They apply the same operation everywhere.

The results are predictable: over-sharpened halos, blurry textures, and that distinctive "processed" look.

How Deep Learning Changes the Approach

Modern AI enhancement uses convolutional neural networks (CNNs) and, more recently, transformer-based architectures trained on large datasets of paired low-quality/high-quality video frames.

The model learns a mapping: given a degraded input patch, predict what the clean output should look like. During training, the network sees millions of examples of noise patterns, compression artifacts, blur, and color degradation — paired with their ground-truth clean versions.

At inference time, the model isn't applying a rule. It's making a learned prediction based on context. That's why it can:

Preserve texture while removing noise (it has learned what noise looks like versus texture)
Reconstruct edges without halos (it knows edge context, not just gradient magnitude)
Restore color without over-saturation (it has learned natural color distributions)

The practical upside: the output looks like better source footage, not like filtered footage.

Frame Interpolation: A Separate Problem

Upscaling and denoising work on individual frames. Frame interpolation is a different challenge: synthesizing new frames between existing ones to increase frame rate.

The naive approach (blending adjacent frames) produces ghosting artifacts wherever there's motion. The AI approach uses optical flow estimation — computing the motion vectors between frames, then warping and blending source frames along the predicted motion path to generate the intermediate frame.

More recent approaches use neural networks that jointly estimate motion and synthesize the intermediate frame end-to-end, producing cleaner results on complex motion like fast camera pans or object occlusion.

The output: 24fps footage that plays at 60fps with synthesized intermediate frames, substantially reducing perceived choppiness.

Optical flow-based interpolation works well on predictable motion but can produce artifacts on very fast motion, motion blur, or scene cuts. Most tools detect cuts and skip interpolation at transitions.

Upscaling: What "4x" Actually Means

Traditional 4x upscaling takes a 480p frame and produces a 1920×1080 frame by interpolating between known pixels. The output has the right dimensions but no additional real detail.

AI super-resolution (SR) models — architectures like ESRGAN, Real-ESRGAN, or more recent diffusion-based SR models — don't just interpolate. They generate plausible high-frequency detail based on learned priors about natural images.

A 4x AI upscale of a face doesn't just make the face bigger. It generates plausible detail for skin texture, eye detail, and hair that wasn't present in the source. Whether this is "real" detail is philosophically debatable — it's the model's best prediction, not a recovery of original information. In practice, for most viewers, the result looks substantially more natural than bicubic upscaling.

The quality ceiling is largely determined by the source. AI SR works best when there's a coherent low-resolution signal to start from. Heavily over-compressed video with significant blocking artifacts gives the model a corrupted starting point — artifact removal typically runs as a pre-processing step before SR in production pipelines.

Where It Breaks Down

Knowing the limits is as important as knowing the capabilities:

Condition	Why It's Hard
Severe motion blur	Blur destroys spatial frequency information; deconvolution is ill-posed
Heavy MPEG blocking	Large blocks give the model very little signal to work from
Scene cuts during interpolation	Optical flow between unrelated frames produces obvious artifacts
Extreme color degradation	Color priors can hallucinate incorrect hues in ambiguous regions
Very low source resolution (< 240p)	Too little information for SR to produce plausible high-res output

The honest summary: AI enhancement is a powerful tool for footage that has some recoverable signal. It is not a reconstruction tool for footage that is fundamentally missing data.

Applied: What This Looks Like in Practice

Tools like TotalMedia VideoEnhance package these pipelines into a browser-based interface — upload a video, configure the AI model, frame interpolation target, and output resolution (up to 4K, 8K on Pro), then process.

The interesting engineering constraint in browser-based video AI is balancing model size against inference time. Running a full Real-ESRGAN pipeline on a 10-minute video client-side is not feasible. Server-side inference with progress streaming is the practical architecture — which is what most web-based tools use.

The before/after split preview is generated from a short sample clip, not the full video, which is how per-frame inference becomes interactive.

Launching TotalMedia: A Simpler Way to Fix and Convert Video Files

Alex — Thu, 30 Apr 2026 02:41:38 +0000

Most video tools are built around a single function.

Record. Edit. Stream.

But if you actually work with video files, the real friction shows up after that.

Wrong format for your editor
Files too large to share
Low-quality recordings that need cleanup
Repeating the same conversion steps across multiple files

We built TotalMedia to handle that layer.

What we launched

Two focused tools:

1. VideoEnhance
Link: https://www.totalmedia.ai/video/enhance-online/editor

Improves video quality using AI-based processing.

Upscaling (up to 4K/8K depending on mode)
Frame interpolation
Noise and artifact reduction
Multi-file queue support

2. VideoConverter

Handles conversion and compression at scale.
Link: https://www.totalmedia.ai/video/converter-online/convert

Multi-format support
Batch processing
Device and web presets
Custom output settings
Built-in downloader module

Why not just use existing tools?

Most existing tools fall into two categories:

Too simple → limited formats, no batch workflow
Too complex → heavy UI, slow for repetitive tasks

We optimized for:

Speed
Consistency
Minimal repeated work

Typical workflow we’re targeting

Instead of:

record → edit → export → convert → compress → retry

We want:

record → enhance (if needed) → convert → done

What we’re focusing on next

Faster batch processing
Better handling of mixed input formats
Cleaner output consistency across devices

If you work with screen recordings, downloaded videos, or large batches of files, this might save you some time.

Learn more at our official website: https://totalmedia.ai

Add Real AI to Your Lovable Project with One Prompt

Alex — Fri, 08 Aug 2025 09:19:13 +0000

TL;DR:

Use Momen's Lovable Prompt Generator to connect a fully-configured AI agent (with schema + project URL) into your Lovable app using a single generated prompt. No need to manually configure APIs or write backend code. Ideal for integrating real agentic AI into UI-first Lovable apps — reliably and visually.

Try it now: Lovable Prompt Generator

When Lovable Isn’t Enough

If you’ve spent time building in Lovable, you know how fast it is to get a good-looking UI up and running. But once you try to add real AI agents, or complex logic, things start to get tricky. The platform was built for speed — not depth.

This tutorial introduces a workaround: a prompt generator that links your Lovable frontend to a real AI agent you’ve built and tested visually in Momen. It doesn’t require writing code, and gives you more control.

What’s Momen?

Momen is the no‑code full‑stack web app builder that powers this tool. It handles the parts Lovable doesn’t focus on — things like AI agents, backend workflows, and databases. You can visually create and test your AI agent in Momen, confirm it works, and then use the prompt generator to connect it back to Lovable with a single prompt.

If you’ve hit the wall trying to add real agentic AI to your Lovable projects, this tool is a way forward.

What You'll Learn

How to visually build a real AI agent in Momen
How to generate a Lovable-compatible prompt using your agent config
How to plug that prompt into Lovable to create a fully working AI app

What You'll Need

A Lovable account ready
A Momen account (free plan works)
A clear idea of what you want your AI to do

4 Steps to Build an AI App in Lovable

Before we dive in, here's the basic idea:

Momen powers your backend AI agent, and this tool helps you generate a single prompt that tells Lovable how to connect to it.

Step 1: Create Your Momen Project

You can either start from scratch or choose a template with AI agents already configured.

Step 2: Build Your AI Agents in Momen

Click the "AI" button on the top bar to open the agent panel. Then click “Add agent” to start building.

You’ll see several sections in the agent builder:

General Configuration

Choose your preferred LLM (GPT-4o, Gemini 2.5, etc.), and optionally adjust settings like temperature (personality), output length, and conversation rounds.

Input

In the input section, Define what the user needs to provide — e.g., description, style， etc.. These are dynamic fields that user fills in.

Prompt

Set the agent’s Role (what it acts as) and Goal (what it needs to do). Example: “You are a helpful assistant that summarizes user input.” You can also add Constraints like “Use markdown only” or “Don’t include sources.”

Use + to reference inputs, user info, constants, or formulas in the prompt.

Context

Want your agent to access knowledge? Add data from database tables or connect APIs. APIs can even link your agent to external services.

Tools (Advanced)

Configure backend workflows (Actionflow), trigger APIs, or invoke other agents. This is how you give your agent real functionality beyond LLM text.

Output

In the Output section, you can configure how the AI response is delivered:

Default Output: The model will respond freely in plain text. You can also enable Streaming, which shows the response as it’s being generated — a smoother UX, especially for chat-based interactions.
Structured Output: If your app needs structured data (a JSON object), you can define the expected format here. This is helpful when your Lovable UI relies on specific fields.

Note: Gemini 2.0 Flash (used for image generation) does not support structured outputs.

Debug

On the right, test your agent and inspect each round: input, output, tool usage, and system messages.

Once done, click “Sync” to save your configuration.

Once you configured your agent, click the sync button to save your configurations.

💡 Pro Tip: Test your agent first in Momen’s debug panel — if it works here, it’ll work in Lovable.

Step 3: Generate Your Prompt

Copy your project URL from your browser.

In the agent panel, click the ⋯ menu and select "Copy". This includes your agent setup.

Open the Lovable prompt generator.
Paste in:
1. Project URL
2. Agent Schema
3. Describe your app
4. Choose whether you need authentication
1. Click Generate — and your Lovable-compatible prompt will appear.

Step 4: Paste the Prompt in Lovable

In your Lovable project, paste the generated prompt.

Wait 1–5 minutes — Lovable will start generating your AI app powered by the Momen agent.

That’s it. You’ve now got a frontend built with Lovable, and real AI logic powered by Momen — all with one prompt.

FAQs

What exactly is the “agent schema”? Where do I find it?

The agent schema is just a copy of your agent’s “setup” — everything you configured in Momen (the model, prompts, tools, etc.) in one package. How to find it: Go to your agent panel in Momen, click the three-dot menu (⋯) in the top right, and select “Copy”

What happens if I paste the wrong schema or URL?

Our tool will immediately show an error message if something’s off — for example, if the URL isn’t a Momen project link or the schema doesn’t match the format.

You won’t waste time generating a broken prompt. Just fix the input (copy the correct project URL or schema) and try again.

Can I add multiple agents to the same Lovable project using this tool?

Not directly — right now, the tool only links one agent to a Lovable project at a time.

However, inside Momen’s AI Agent Builder, you can chain multiple agents together. That means you can design several agents to work as a team (e.g., one for research, one for summarizing, one for writing) and then connect that combined agent workflow to Lovable with a single prompt.

Is Momen free to use? Are there any limits?

Yes — you can start for free. The free plan gives you 100k AI Points for building AI agents.

Do I need to know what an “Actionflow” is to build an agent?

No. Actionflows are optional. You can create a fully working agent without ever touching them. But if you want your AI to trigger multi-step workflows (like “send email → update database → call API”), that’s when Actionflows shine.

Can I test/debug my agent before connecting it to Lovable?

Yes — right inside Momen. There’s a built-in debug panel where you can test the agent, see every step (inputs, outputs, tool calls), and fix issues before touching Lovable.

What models does Momen support? Can I choose between GPT and Gemini?

Yes. You can pick from:

OpenAI models (GPT-4o mini, GOT-4o, GPT-4.1, GPT-4.1 mini, GPT-4.1 nano GPT-o3 mini, GPT-o4 mini)
Gemini models (2.5 Pro, 2.5 Flash, 2.0 Flash for images)

Does the AI automatically work in Lovable once I paste the prompt?

Yes. The prompt tells Lovable exactly how to connect to your Momen agent.

What do I do if Lovable gives an error after pasting the prompt?

Check the "data" in your Momen AI agent panel, you should see the information of every round of the conversation.
If things are alright in Momen, you should try to debug in Lovable.

How do I know if the agent is actually responding inside Lovable?

If your Lovable app feels unresponsive or you’re not sure the AI is doing anything, you can confirm directly in Momen’s agent logs:

Open your Momen project's agent panel → Data tab.

Here you’ll see every request your agent received from Lovable, along with the response status and details.

Resources & Next Steps

🔗 Learn more about building AI agents in Momen → Momen AI Docs

🔗 See how to build workflows → Actionflow Guide

🔗 See how to connect with APIs → API Guide

🔗 Explore the Momen Community Forum → forum.momen.app

🎬 Subscribe to Momen's Youtube channel → https://www.youtube.com/@Momen_HQ

Add Real AI to Your Lovable Project with One Prompt

Alex — Fri, 08 Aug 2025 09:19:13 +0000

TL;DR:

Try it now: Lovable Prompt Generator

When Lovable Isn’t Enough

What’s Momen?

If you’ve hit the wall trying to add real agentic AI to your Lovable projects, this tool is a way forward.

What You'll Learn

How to visually build a real AI agent in Momen
How to generate a Lovable-compatible prompt using your agent config
How to plug that prompt into Lovable to create a fully working AI app

What You'll Need

A Lovable account ready
A Momen account (free plan works)
A clear idea of what you want your AI to do

4 Steps to Build an AI App in Lovable

Before we dive in, here's the basic idea:

Momen powers your backend AI agent, and this tool helps you generate a single prompt that tells Lovable how to connect to it.

Step 1: Create Your Momen Project

You can either start from scratch or choose a template with AI agents already configured.

Step 2: Build Your AI Agents in Momen

Click the "AI" button on the top bar to open the agent panel. Then click “Add agent” to start building.

You’ll see several sections in the agent builder:

General Configuration

Choose your preferred LLM (GPT-4o, Gemini 2.5, etc.), and optionally adjust settings like temperature (personality), output length, and conversation rounds.

Input

In the input section, Define what the user needs to provide — e.g., description, style， etc.. These are dynamic fields that user fills in.

Prompt

Use + to reference inputs, user info, constants, or formulas in the prompt.

Context

Want your agent to access knowledge? Add data from database tables or connect APIs. APIs can even link your agent to external services.

Tools (Advanced)

Configure backend workflows (Actionflow), trigger APIs, or invoke other agents. This is how you give your agent real functionality beyond LLM text.

Output

In the Output section, you can configure how the AI response is delivered:

Default Output: The model will respond freely in plain text. You can also enable Streaming, which shows the response as it’s being generated — a smoother UX, especially for chat-based interactions.
Structured Output: If your app needs structured data (a JSON object), you can define the expected format here. This is helpful when your Lovable UI relies on specific fields.

Note: Gemini 2.0 Flash (used for image generation) does not support structured outputs.

Debug

On the right, test your agent and inspect each round: input, output, tool usage, and system messages.

Once done, click “Sync” to save your configuration.

Once you configured your agent, click the sync button to save your configurations.

💡 Pro Tip: Test your agent first in Momen’s debug panel — if it works here, it’ll work in Lovable.

Step 3: Generate Your Prompt

Copy your project URL from your browser.

In the agent panel, click the ⋯ menu and select "Copy". This includes your agent setup.

Open the Lovable prompt generator.
Paste in:
1. Project URL
2. Agent Schema
3. Describe your app
4. Choose whether you need authentication
1. Click Generate — and your Lovable-compatible prompt will appear.

Step 4: Paste the Prompt in Lovable

In your Lovable project, paste the generated prompt.

Wait 1–5 minutes — Lovable will start generating your AI app powered by the Momen agent.

That’s it. You’ve now got a frontend built with Lovable, and real AI logic powered by Momen — all with one prompt.

FAQs

What exactly is the “agent schema”? Where do I find it?

What happens if I paste the wrong schema or URL?

Our tool will immediately show an error message if something’s off — for example, if the URL isn’t a Momen project link or the schema doesn’t match the format.

You won’t waste time generating a broken prompt. Just fix the input (copy the correct project URL or schema) and try again.

Can I add multiple agents to the same Lovable project using this tool?

Not directly — right now, the tool only links one agent to a Lovable project at a time.

Is Momen free to use? Are there any limits?

Yes — you can start for free. The free plan gives you 100k AI Points for building AI agents.

Do I need to know what an “Actionflow” is to build an agent?

Can I test/debug my agent before connecting it to Lovable?

Yes — right inside Momen. There’s a built-in debug panel where you can test the agent, see every step (inputs, outputs, tool calls), and fix issues before touching Lovable.

What models does Momen support? Can I choose between GPT and Gemini?

Yes. You can pick from:

OpenAI models (GPT-4o mini, GOT-4o, GPT-4.1, GPT-4.1 mini, GPT-4.1 nano GPT-o3 mini, GPT-o4 mini)
Gemini models (2.5 Pro, 2.5 Flash, 2.0 Flash for images)

Does the AI automatically work in Lovable once I paste the prompt?

Yes. The prompt tells Lovable exactly how to connect to your Momen agent.

What do I do if Lovable gives an error after pasting the prompt?

Check the "data" in your Momen AI agent panel, you should see the information of every round of the conversation.
If things are alright in Momen, you should try to debug in Lovable.

How do I know if the agent is actually responding inside Lovable?

If your Lovable app feels unresponsive or you’re not sure the AI is doing anything, you can confirm directly in Momen’s agent logs:

Open your Momen project's agent panel → Data tab.

Here you’ll see every request your agent received from Lovable, along with the response status and details.

Resources & Next Steps

🔗 Learn more about building AI agents in Momen → Momen AI Docs

🔗 See how to build workflows → Actionflow Guide

🔗 See how to connect with APIs → API Guide

🔗 Explore the Momen Community Forum → forum.momen.app

🎬 Subscribe to Momen's Youtube channel → https://www.youtube.com/@Momen_HQ

How a Legal Advocate Built an AI-Powered Case Intake & Memo Generator — Without Writing Code

Alex — Tue, 22 Jul 2025 09:05:24 +0000

⚖️ Built a Legal AI Assistant in a Week — No Code

Problem:

Client intake in law firms is messy.

Manual forms, unclear info, and memo drafts that start from scratch.

Thomas, a non-technical legal advocate, wanted to automate the first mile of casework:

Intake → Clarification → Drafting

So he built it—in 1 week—with Momen. No engineers.

🛠️ The Setup

Feature	Built With
Intake Form	Momen's drag and drop UI builder
AI Follow-Up	Momen's AI agent builder (with RAG)
Memo Generation	Momen's Actionflow + LLM
PDF Export	LegalMarkdown
Roles & Auth	Momen's Built-in permission

🔄 The Workflow

Client submits a case
AI follows up with smart questions
Once complete, Actionflow generates a memo
Lawyer dashboard shows full history + PDF export

No polling, no glue code. Just declarative logic and built-in AI agents.

✅ Outcome

MVP in under 7 days
AI-powered summaries + drafts
Saves 2–3 hours per case
Cost: <$100/month

🧠 Why It Works

Momen gave him:

Structured data + UI
Backend workflows (Actionflow)
RAG-enabled AI agents
Full access control

Built like a dev, without writing code.

Try it yourself → https://momen.app
Read more details: https://momen.app/blogs/build-legal-assistant-with-momen/

How We Built an AI-Powered Legal Timeline Generator

Alex — Fri, 27 Jun 2025 09:40:32 +0000

TL;DR: At Hack the Law Cambridge, we built an AI-powered Event Timeline Generator using Momen, ChatGPT-4o, and Gemini 2.5—no code required. It extracts events from legal text, detects conflicts between witness statements, and visualizes everything in a timeline. This post breaks down the full architecture so you can reverse engineer it yourself.

⚖️ The Legal Pain Point: Reconstructing Timelines from Testimonies

Litigation teams often spend hours parsing through witness statements, deposition transcripts, and contradictory accounts to rebuild a factual timeline. It's manual, high-stakes, and prone to human error.

So we asked: Can we automate this?

🛠️ The Tool: AI-Powered Timeline Generator (Demo App)

We built this app for:

🧑‍⚖️ Litigation associates
🧑‍💼 Paralegals
🧑‍💻 Legal tech teams

Core Features:

Upload or paste raw text (statements or transcripts)
AI extracts events and timestamps
AI detects and highlights contradictions
Interactive timeline view with source quotes
Structured database for case history

🚫 Why Not Just Use ChatGPT?

Sure, you can paste text into ChatGPT—but legal workflows need:

Structure
Repeatability
Traceability
Collaboration

That’s why we used Momen, a no-code platform where you can build full-stack apps with backend logic, UI, databases, and AI agents.

⚙️ How It Works (Full Stack Breakdown)

🧩 Database Schema

We created 6 interrelated tables:

Statement
Timeline_event
Conflict
Event_evidence
Event_in_conflict
Analysis (parent record for each case)

🧠 Two AI Agents:

timeline_extractor (ChatGPT-4o)

→ Parses each statement, extracts events, timestamps, witnesses
conflict_detector (Gemini 2.5)

→ Compares statements and flags conflicting events

🔁 Actionflows = No-Code Backend Logic

✅ `process_statements`

Triggered when user clicks “Generate Timeline”
Creates an analysis_id
Saves input statements
Calls insert_events for each statement

✅ `insert_events`

Calls timeline_extractor
Saves structured events to timeline_event table

✅ `insert_conflicts`

Triggered when user clicks “Detect Conflicts”
Calls conflict_detector
Stores results in Conflict and Event_in_conflict tables

🖼️ Frontend (Also No Code)

Built using Momen’s visual UI components:

Text inputs for witness statements
Button to trigger timeline generation
Interactive timeline list view (subscribed to timeline_event)
Sidebar to show conflict details

💡 Conflicting events show in red, with side-by-side quote comparison.

🚀 Timeline: Built in 2 Days

Even under hackathon time pressure, the app:

Parsed multi-page legal text
Built a timeline in under 30 seconds
Flagged conflicts with full traceability

📉 Total cost? ~$99 (includes both LLM agents + infra).

🔍 Want to Reverse Engineer It?

You can explore the full setup—database, agents, and Actionflows—here:

👉 Reverse engineer the full Momen build (view-only link)

Yes, you can view every detail of how it works.

For a more detailed breakdown, check it here

🧭 What’s Next?

Because it’s built with Gemini 2.5 (multi-modal), future versions could:

Analyze deposition videos or CCTV
Merge transcripts + video into one timeline
Integrate with document management systems

Drop questions or ideas in the comments!

How a Used-Car Dealer Built an AI SaaS in 5 Days — With No Code

Alex — Tue, 17 Jun 2025 06:56:12 +0000

🚗 Introduction: When Manual Tasks Stall Growth

Leo, a seasoned used-car entrepreneur, had a vision. After 14 years of sourcing, importing, and selling vehicles, he was buried under browser tabs, spreadsheets, and tedious admin work. Every car listing he reviewed had to be copied, cleaned, and emailed manually.

He didn’t know how to code — but he knew something had to change. So he explored no-code tools.

After hitting roadblocks with Adalo and Softr (slow performance, poor backend support, no paywall integration), Leo nearly gave up.

Then he found Momen.

🔧 From Hustle to Builder: What Was the Problem?

In the used & repairable car business, most vehicle info comes from sites like Copart, IAAI, or AutoTrader. But these sites are inconsistent — many don’t have APIs. Critical details are buried in cluttered layouts.

Leo’s vision: build a custom system to scrape vehicle data, clean it with AI, and manage it all in a dashboard.

No-code tools weren’t up to the task. Adalo lagged with real data. Softr required complex Airtable setups and had no scraping support.

He needed something better. A real system. A SaaS.

⚙️ The Build: An AI SaaS in Under a Week

Leo used Momen to build a full SaaS, in 3 parts:

1. Chrome Extension (Built in Cursor)

Handles user auth and permission checks via Momen APIs.
Scrapes vehicle info and location data from listing pages.
Sends raw text to Momen backend for processing.

2. Momen Backend

Actionflow kicks off when new data arrives.
AI Agent structures data (VIN, damage status, pricing).
Stores clean records in a structured, queryable database.

3. Momen Frontend (Management Dashboard)

Staff can view/edit vehicle listings.
Set prices and trigger automated emails.
Built-in Stripe paywall for subscription-based access.

All of this was built in less than 5 days for under $100.

💡 Why Momen Made the Difference

AI Where It Matters: Instead of spamming ChatGPT prompts, Leo’s agent cleaned the messiest data with structured outputs.
Bridge to the Web: No APIs? No problem. The extension + Momen turned any site into a structured data source.
No Glue Code: No Airtable juggling, no Zapier madness. One platform for backend, frontend, AI, and auth.

⏱️ Time + 💸 Money Saved

Traditional dev: ~$20K+ in engineers & infra.

With Momen: <$100 in tools + one week.

Leo saved 95% in startup cost and now processes car data in minutes, not hours.

✅ Final Thoughts

Momen didn’t just help Leo automate — it helped him build a sellable SaaS product, without writing a line of code.

This is what’s possible when non-technical founders combine domain expertise with the right no-code + AI platform.

See the detailed breakdown of his project here: https://momen.app/blogs/how-this-used-car-business-founder-built-an-ai-automation-saas/

From Energy Expertise to Working AI App — Without Writing a Line of Code

Alex — Wed, 04 Jun 2025 06:16:11 +0000

When Lucy, a renewable energy founder based in Singapore, set out to build a solar calculator, she wasn’t looking to code. She was looking to solve a real problem — homeowners were overwhelmed with energy choices, and suppliers were struggling to stand out.

She had the know-how, the formula, and the user pain point. What she didn’t have? A dev team.

So she turned to no-code — and more specifically, a full-stack builder called Momen — to bring her idea to life in less than a week.

The Problem: Too Much Tech, Not Enough Clarity

Anyone who’s tried to size a solar panel system knows it’s not simple. You need to:

Estimate your household power needs
Factor in daily solar irradiance at your location
Choose the right mix of batteries, inverters, and panels

Lucy wanted to make this approachable — ideally through an AI-guided assistant that could talk to users, gather relevant info, and run the calculations behind the scenes.

How She Built It: AI-Guided, No-Code, All-in-One

Here’s what her app does:

Conversational Input — Users interact with an AI assistant that dynamically asks questions based on their answers. Have a pool? It’ll ask about pumps. Live in a cloudy area? It adjusts expectations.
Real-Time Data Processing — The app uses Google Maps API to convert an address into coordinates, then runs a solar irradiance formula on the backend.
Energy Recommendation — Once the info is gathered, a second AI agent calculates the number of solar panels and batteries needed. All results are shown in a clean UI.

The best part? Lucy built all this using Momen, a visual development tool that lets you create full-stack apps with or without code. She used its agent builder for the conversation logic, conditional views for smart UI rendering, and a native database for appliance and solar data.

Why Not Just Use Bubble or ChatGPT?

Lucy had tried those paths before. Bubble gave her UI flexibility, but building logic-heavy flows became frustrating. AI tools like GPT were great at answering questions — not running full workflows.

With Momen, she could:

Write and tweak prompts for logic
Use code where needed, without losing the visual builder
Keep frontend, backend, and AI all in one tool

It wasn’t a hacky prototype. It was a real app — production-ready, and fast.

For Founders Without Code

If you’re a non-technical founder sitting on years of industry expertise, this is a reminder: you don’t need to be technical to build something valuable.

Lucy didn’t start with “what tech stack should I use.” She started with “what problem am I solving?” Then used the tool that let her move fast and stay in control.

👉 Want to see how it works?
Check out the full case study (with screenshots + app breakdown):
🔗 https://momen.app/blogs/ai-energy-app-built-with-momen/

Inside Our Stack: Lovable + Cursor + Momen = A Custom Analytics Tool

Alex — Tue, 20 May 2025 09:20:40 +0000

Problem

We needed a better way to track engagement on our content across platforms—our old RPA workflow was limited, messy, and unscalable.

So we built our own internal dashboard by combining:

Lovable → For generating a clean UI from our product brief
Cursor → To help write custom React chart components (with AI)
Momen → A full-stack no-code platform where we stitched everything together

Why Momen?

Unlike Lovable or Cursor alone, Momen gave us structure, control, and flexibility:

Fully configurable backend (data modeling, workflows, automation)
Code components for embedding real React
Visual editor for app logic, roles, and data flows

🛠️ Key features:

Add a content link → auto-fetch and store details
Visualize engagement over time
Automated updates via Actionflow
Fully interactive UI built with real code components

⚙️ Built 90% visually. Coded only where it mattered.

📽️ Want to See It in Action?

🎥 Watch the 10-min video walkthrough:
✍️ Read the full blog post:

How We Used Lovable + Cursor + Momen to Build Our Social Metrics Tool

See how to build a content dashboard with Momen, Lovable and Cursor. Explore how to increase the dev efficiency while gaining flexibility and control.

momen.app
🌐 Explore the live project: https://editor.momen.app/tool/L6ARjmem6RK/WEB?code=sgllui0YcYinL

We build an AI mental health assistant with 7 AI agents! (no-code needed)

Alex — Wed, 26 Mar 2025 06:51:39 +0000

TL;DR

AI is no longer just about chatbots—it can provide real, tangible support. This blog explores how an AI-powered mental health assistant was built using Momen, leveraging multi-agent collaboration, real-time data retrieval, and automated workflows.

A Momen user, Eric, built this assistant to offer personalized crisis intervention. The app retrieves user medical history, symptoms, and emotional state to generate real-time AI-driven support—all while running seamlessly in the background.

Watch the video breakdown:

🛠 How It Works

1️⃣ User Input & Data Retrieval
The user submits an inquiry about their health or emotional state.

The system retrieves relevant medical history, past diagnoses, and emotional feedback from the database.

2️⃣ AI-Driven Analysis
Vector search is used to match symptoms with existing intervention strategies.

AI cross-references symptoms with predefined crisis plans to suggest the most relevant response.

3️⃣ Personalized Intervention
The system analyzes the user’s condition in real-time and formulates a tailored intervention strategy.

AI recommendations evolve based on user interactions, improving accuracy over time.

🔧 How It Was Built

🖌 Generating UI with Loveable

Eric used Loveable to generate a UI based on a Product Requirements Document (PRD). Loveable provided an auto-generated UI, which was then refined in Figma before being built in Momen.

🏗 Structuring the Database in Momen

A structured database was essential for AI-driven interactions. The app's database stores:

User medical history & medications
Journal entries & emotional feedback
Predefined crisis intervention plans

Momen's backend ensures fast retrieval and scalability, handling complex user data efficiently.

💻 Building the UI with Momen's Drag and Drop Editor

Eric quickly builds out the UI and binds the data in the database to make it interactive.

🤖 AI Agents & Actionflows

Unlike a basic chatbot, this app operates through 7 AI agents, each handling a specific function:

Classification Agent → Determines if input is a casual chat or a medical concern.
Symptom Agent → Matches symptoms with crisis intervention strategies.
Chat Agent → Engages users in casual conversations while maintaining context.
Summary Agents (x4) → Summarize user interactions to ensure context-aware responses.

Each AI agent runs in Momen’s Actionflows, ensuring seamless automation.

✨ Key Highlights

✔ Multi-Agent Collaboration → AI agents work together to retrieve and analyze user data.
✔ Real-Time Context Awareness → The assistant pulls the latest user data for informed responses.
✔ Seamless Backend Execution → Everything runs in the background, ensuring a smooth experience.
✔ Scalable & Cost-Effective → Built in under 10 hours using Momen’s no-code platform.

🚀 Try It Yourself!

Eric’s project will be soon launched as a Momen template! Clone it to explore the setup and customize it for your own AI-driven applications.

🔗 Check out the template in Momen → Momen editor
📺 Watch the full breakdown → How to build an AI mental health assistant
📝 Read more on the blog → A deep dive of the agentic workflow

DeepSeek + GPT-4o: We build an AI dietitian with no-code

Alex — Mon, 10 Mar 2025 07:07:53 +0000

TL;DR

We built an AI app with DeepSeek and GPT-4o. See how we did it:

Introduction

In early 2025, DeepSeek R1 gained widespread recognition for its cost-effectiveness, strong logical reasoning, and open-source nature. Its exceptional coding and language-processing capabilities made it popular among AI enthusiasts. However, its potential extends beyond simple chatbot applications—it can serve as the foundation for custom AI assistants and customer service bots. For startups seeking an affordable alternative to GPT-based APIs, DeepSeek presents a compelling choice.

Yet, DeepSeek has limitations—it lacks the essential features of an AI agent. Because of this, DeepSeek alone isn't enough to build a fully functional AI agent (as of March 2025). However, with Momen’s integrated AI agent builder and BYOM (Bring Your Own Model) support, we integrated DeepSeek into an agentic workflow without writing any code. In this guide, we’ll demonstrate how to build a diet assistant app called "Angry Dietitian", combining DeepSeek’s capabilities in reasoning and text generation, with GPT-4o for image processing.

Here's a preview of the DeepSeek app we build with Momen :

Why DeepSeek for AI App Development?

Cost effectiveness: DeepSeek R1 offers a significantly cheaper alternative to other AI models:

OpenAI GPT-4o: $5.50 per 1M tokens (weighted cost, assuming a 1.5 input/output ratio).
DeepSeek R1: $1.21 per 1M tokens—78% cheaper than GPT-4o.

Advanced reasoning capabilities: DeepSeek R1 is optimized for complex reasoning tasks, making it suitable for AI applications requiring logical processing and in-depth analysis.
Efficient Resource Utilization: Unlike some larger models, DeepSeek R1 can run efficiently on consumer-grade hardware, making it more accessible for AI developers and startups.

What DeepSeek Can’t Do (And How to Work Around It)

While DeepSeek excels in reasoning and cost efficiency, it lacks native support for (as of March 2025):

Multimodal inputs (e.g., image recognition).
Native JSON structured output.
Tool invocation for dynamic API calls.

To overcome these limitations, we use Momen’s AI agent builder to integrate multiple AI models into a single application.

Why Momen? The Power of BYOM (Bring Your Own Model)

Momen enables AI agent orchestration—allowing users to:

Integrate their preferred AI models (e.g., DeepSeek, GPT-4o, GPT-4o mini).

GPT-4o and GPT-4o mini are built-in in Momen, but you can also

AI | Momen Docs

Based on LLMs, leveraging RAG and tool invocation, you can create customized business AI apps and agents.

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Generate structured JSON outputs (ideal for frontend display and inter-agent collaboration).
Deploy your AI app with one click, no-code needed.

With Momen’s new BYOM feature, you can configure your AI agent using your own deployed DeepSeek model—ensuring greater control, reliability, and reduced downtime.

Core Functions

Main Features

AI capabilities:

DeepSeek-powered analysis: The app leverages DeepSeek’s text generation and reasoning abilities to provide personalized dietary suggestions.
Multi-agent collaboration: GPT-4o handles image recognition, while DeepSeek provides text-based insights.
AI message storage: Momen has default tables in the database that log each conversation, ensuring debugging and auditing.

Customizable UI: Build the UI tailored to your needs.

Step-by-Step: Build the AI diet app

Data

This app requires 5 tables, all of which are default tables that are automatically created when you set up a new project and configure the AI. This means no additional tables need to be manually created.

Account table: Stores user information. We need to add some fields to store their body data, like height, weight, date of birth, fitness goals etc.

The following four tables are automatically generated when configuring the AI:

Message Content: Stores the content of each message from the AI. In this template, it records the reasoning and output of DeepSeek.
Conversation: Tracks the status of each conversation, including the user, the AI model, input data, conversation status, and any error messages.
Tool Usage: Typically records which tools the AI has invoked, but for this case, this table is not used.
Message: Tracks the token usage for each message in a conversation. You can view how many tokens were used by the user, the AI, or the system.

AI Configuration

Configuring GPT-4o (Image Processing)

Input: User-uploaded food images.
Prompt Setup: Configured as a "food analyzer" to extract food type and estimated weight.
Output Format: JSON structured output for better agent collaboration.

Once it's done. We can debug on the right side to see if it works.

Integrating DeepSeek (Diet Analysis)
Note: ⚠️ You must deploy your own DeepSeek model through a cloud provider (e.g., Azure, AWS) when before configuring DeepSeek.

Deploy your model: Head to the cloud providers, for example, Azure. Sign in, choose the AI foundry service. Search and deploy your own model. You'll find the endpoint and key after you deploy.
Set up in Momen

Navigate to "Add Model" in Momen.
Copy the API endpoint and key from the cloud provider.
Paste the credentials into Momen’s model integration page.
Select the model when configuring the agent.

Input: User body data (from the Account table) and food details (from GPT-4o).
Output: Personalized diet suggestions based on streaming output.

Here's an example of the prompt:

UI Design & Data Binding

Once the AI configuration is complete, we move on to UI building. The app consists of three main pages:

Sign Up/Sign In Page – User authentication
Form Page – Collects user body data
Roaster Page – The main interaction hub where AI agents analyze the user's food

Sign up/ Sign in

This page allows users to create an account or log in.

Components:

A title at the top

A tab view with two options: "Sign Up" and "Sign In"

Two labels and input boxes under each tab (for email and password)

A button to handle authentication

Functionality:

The button is linked to user sign-up and login actions

Once authenticated, the user is redirected to the Form Page

Form Page

This page collects key body metrics required for dietary recommendations.

Components:

Input boxes for height, weight, date of birth, and fitness goals

Labels corresponding to each field

A button to submit the data

Functionality:

The button updates the Account Table, storing the user’s body data

After submission, the user is taken to the Roaster Page

Roaster

This is the core interface where AI agents analyze food and provide feedback.

The Roaster Page has a central circular UI design, consisting of:

A large circle in the center – Displays AI results and changes based on interaction.
Multiple smaller circles surrounding it – Each displays a key user body metric.

Small circles - Displaying user body metric

Surrounding the main interaction area are small circles, each showing an essential body metric. These values are dynamically pulled from the Account Table and displayed in real-time. If the user updates their body data, the circles automatically refresh.

Large center circle - AI interaction area

This is the main interaction zone, where the AI agents analyze food images and provide suggestions. It's a conditional view with four cases: before upload, uploaded, roasting, roasted. The conditions will be the page state.

Before upload:

Components:

Image Picker: Allows users to upload a food image.

Prompt Text: “Show me your food!”

Action: Once an image is uploaded, the page state is updated (state = uploaded).

Uploaded:

Components:

Displays the selected image in the circle.

A "Roast" button appears, allowing users to initiate AI analysis.

Action: Clicking "Roast" triggers:

Page state updates to "roasting" (state = roasting).

"The Eye" (GPT-4o) is called, processing the image into structured text (JSON).

If the image is invalid (not food), an error message is displayed.

Roasting:

Components: An animated loading effect to indicate AI processing.
Action:

GPT-4o extracts structured data (food type & estimated weight).
If the image does not contain food, a toast notification appears:
If successful:

Page state updates to "roasted" (state = roasted).

The DeepSeek agent ("The Roaster") is triggered, processing the food data into dietary advice.

The reasoning process is stored in the reason variable.

The final AI suggestion is stored in the answer variable.

Roasted:

Components:

Dietary recommendations displayed inside the central circle.

A "Why?" button appears, revealing the AI reasoning process in a modal.

Actions:

The dietary advice is bound to the answer variable and displayed.

Clicking “Why?” opens a modal showing the reasoning stored in the reason variable.

Preview & Publish

Once everything is set up. Click "Mirror" to check UI and interactions. Finally, click "publish" to deploy your project.

Cost & Time Estimate

Cost

Since this project involves two AI agents working together, you'll need Momen’s Basic plan or higher, starting at $33 per month. The Bring Your Own Model (BYOM) feature is available for free until May 30th 2025, allowing you to integrate your own deployed DeepSeek model at no additional cost. If you're building an AI app exclusively with DeepSeek, you can use Momen for free—no subscription required.

However, keep in mind that deploying your own model requires a cloud provider, such as Azure, and any associated costs will be covered by you.

Time

The entire build process, from setup to deployment, takes approximately 1.5 hours.

Template

We've made this project into a template. You can explore and customize it here: https://o2w08d4qv40yk.villa.momen.app

The usage guide: https://docs.momen.app/template/angry-dietitian

Conclusion

This is just the starting point. You can expand your AI app by adding features like payment integration to monetize it. DeepSeek isn’t just for text generation—it can power AI apps tailored to your needs.

If you're ready to build, bring your model to Momen and start creating—no coding required!

How to build an online store with Momen: No transaction fees, full customization

Alex — Wed, 05 Mar 2025 02:32:44 +0000

Introduction

In 2025, the eCommerce is bouncing back even stronger. Customers now make decision based on the internet. Whether you are selling hadcraft goods, digital products or a new brand, having an online presence is crucial for the growth. In this blog, we’ll walk you through how to build an easy online store with Momen.

What Makes Momen a Better Choice

Popular platforms like Shopify, Wix, and WooCommerce make it easy to start but come with serious downsides:

High transaction fees: Shopify’s basic plan costs $29/month, but if you make $5,000 in sales from 100 customers, you’ll end up paying at least $204/month in platform fees and transaction charges.
Limited customization: Many e-commerce builders lock you into pre-set themes and functionalities. You’ll need plugins or developers for custom features.
Scaling Constraints: As your business grows, you may need more features — many of which come as expensive add-ons or higher-tier plans. If you build with Momen, you own your store, your data, and your business logic with no code — no unnecessary costs or platform restrictions. You can keep 100% of your revenue. Owning full control of your design and logic. And Momen’s built-in backend make sure it’s scalable from day 1.

Core Features of Your Online Store

Core features

In this online store we build, you can get the following core functions:

Product management: Add, edit, and organize products dynamically. Shopping cart & checkout: Let customers add/remove items and complete purchases with Stripe integration.
User account system: Allow users to sign up, log in, and save their order history.
Payment integration: Process payments directly via Stripe without needing extra coding.

How We’ll Build It with Momen

The process of building this online store will be divided into data, UI and payment logic.

Setting Up Data Model

The foundation of any app is its data model. For our store, we need four key tables:

Account Table: Stores user information.
Product Table: Holds product details like name, price, description, and images.
Shopping Cart Table: Tracks selected items, quantities, and prices.
Order Table: Stores order details and payment status.

Setting Up Data Relationships

Products → Shopping Cart (1:N): A product can be in multiple carts.
Accounts → Orders (1:N): A user can place multiple orders.
Accounts → Shopping Cart (1:N): A user can have multiple cart records.
Orders → Shopping Cart (1:N): A single order can include multiple cart items. Once the data model is in place, we can add product data in the database. Then we can move to UI design.

UI & Data Binding

Navigation & Login Page

The login page consists of a navigation bar and a login/signup window.

Navigation Bar: A fixed bar containing the store name, cart icon, and login/logout buttons.
Login/Signup Window: Uses a conditional view to toggle between login and registration forms.
Data Binding: Inputs are bound to the Account Table to validate user credentials. Once logged in, users are redirected to the product listing page.

Product Listing Page

Displaying Products: Uses a dynamic list bound to the Product Table.
Product Components: Each item container includes an image, name, description, price, and an “Add to Cart” button.
Conditional Button Logic: If a product is already in the cart, the button changes to indicate it’s been added.
Action Handling: Clicking “Add to Cart” updates the Shopping Cart Table and redirects users to the cart page.

Shopping Cart Page

Cart Items: Displays products added to the cart with quantity controls.
Quantity Adjustments: Clicking “+” increases quantity and updates total price; clicking “-” removes items if quantity reaches zero.
Data Filtering: Ensures only the logged-in user’s cart data is displayed.

Payment Processing

Checkout Button & Stripe Integration

Activating Stripe: Set up API keys and connect payments to the Order Table. Payment Workflow:
User clicks Checkout.
If the cart is empty, a warning appears.
If items exist, an order is created and cart items are linked to it.
Stripe processes payment. On success, the order status updates, and the cart clears.

Final Preview & Testing

With everything set up, you can click “Mirror” to test if the UI looks nice. And you can click “preview” to mock the real web environment to test the flow:

Sign up/login.
Browse and add products to the cart.
Adjust quantities.
Proceed to checkout and complete payment.
Verify the database updates accordingly.

Cost & Time Estimate

Time

For a non-technical, building such an online store takes about 2–3 hours. This is a demo-level project which doesn’t contain inventory tracking or order management. You can expand features incrementally, like product detail pages, order tracking or even AI customer service as your business grows.

Cost

If you want to integrate with payment, you only need to pay for Momen’s Pro plan starting from $85 per month, no transaction fee needed. If you run out of your computing resources, pay as you needed.

Preview

If you want a more detailed building guide, we made a 40-minute step-by-step guide so you can build along with:

Conclusion

This demo store is just the beginning. With Momen, you can scale your store into a full-fledged eCommerce platform — complete with on-page SEO, AI-powered recommendations, inventory tracking, and community features.

🚀 Start building your online business today with Momen: https:/momen.app

Forem: Alex

What's Actually Happening When AI "Enhances" a Video

The Problem with Traditional Approaches

How Deep Learning Changes the Approach

Frame Interpolation: A Separate Problem

Upscaling: What "4x" Actually Means

Where It Breaks Down

Applied: What This Looks Like in Practice

Further Reading

Launching TotalMedia: A Simpler Way to Fix and Convert Video Files

What we launched

Why not just use existing tools?

Typical workflow we’re targeting

What we’re focusing on next

Add Real AI to Your Lovable Project with One Prompt

TL;DR:

When Lovable Isn’t Enough

What’s Momen?

What You'll Learn

What You'll Need

4 Steps to Build an AI App in Lovable

Step 1: Create Your Momen Project

Step 2: Build Your AI Agents in Momen

General Configuration

Input

Prompt

Context

Tools (Advanced)

Output

Debug

Step 3: Generate Your Prompt

Step 4: Paste the Prompt in Lovable

FAQs

What exactly is the “agent schema”? Where do I find it?

What happens if I paste the wrong schema or URL?

Can I add multiple agents to the same Lovable project using this tool?

Is Momen free to use? Are there any limits?

Do I need to know what an “Actionflow” is to build an agent?

Can I test/debug my agent before connecting it to Lovable?

What models does Momen support? Can I choose between GPT and Gemini?

Does the AI automatically work in Lovable once I paste the prompt?

What do I do if Lovable gives an error after pasting the prompt?

How do I know if the agent is actually responding inside Lovable?

Resources & Next Steps

Add Real AI to Your Lovable Project with One Prompt

TL;DR:

When Lovable Isn’t Enough

What’s Momen?

What You'll Learn

What You'll Need

4 Steps to Build an AI App in Lovable

Step 1: Create Your Momen Project

Step 2: Build Your AI Agents in Momen

General Configuration

Input

Prompt

Context

Tools (Advanced)

Output

Debug

Step 3: Generate Your Prompt

Step 4: Paste the Prompt in Lovable

FAQs

What exactly is the “agent schema”? Where do I find it?

What happens if I paste the wrong schema or URL?

Can I add multiple agents to the same Lovable project using this tool?

Is Momen free to use? Are there any limits?

Do I need to know what an “Actionflow” is to build an agent?

Can I test/debug my agent before connecting it to Lovable?

What models does Momen support? Can I choose between GPT and Gemini?

Does the AI automatically work in Lovable once I paste the prompt?

What do I do if Lovable gives an error after pasting the prompt?

How do I know if the agent is actually responding inside Lovable?

Resources & Next Steps

How a Legal Advocate Built an AI-Powered Case Intake & Memo Generator — Without Writing Code

⚖️ Built a Legal AI Assistant in a Week — No Code

🛠️ The Setup

🔄 The Workflow

✅ Outcome

🧠 Why It Works

✅ `process_statements`

✅ `insert_events`

✅ `insert_conflicts`