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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning LifecycleIntroduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data InsightsDescriptive StatisticsInferential StatisticsHow to Understand Population Distributions?
Reading Data Files into PythonDifferent Variable Datatypes
Probability for Data ScienceBasic Concepts of ProbabilityAxioms of ProbabilityConditional Probability
Central Tendencies for Continuous VariablesSpread of DataKDE plots for Continuous VariableOverview of Distribution for Continuous variablesNormal DistributionSkewed DistributionSkeweness and KurtosisDistribution for Continuous Variable
Central Tendencies for Categorical VariablesUnderstanding Discrete DistributionsPerforming EDA on Categorical Variables
Dealing with Missing ValuesUnderstanding OutliersIdentifying Outliers in DataOutlier Detection in PythonOutliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and PopulationCentral Limit TheoremConfidence Interval and Margin of Error
Bivariate Analysis Introduction
CovariancePearson CorrelationSpearman's Correlation & Kendall's TauCorrelation versus CausationTabular and Graphical methods for Bivariate AnalysisPerforming Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical VariablesIntroduction to Hypothesis TestingP-valueTwo sample Z-testT-testT-test vs Z-testPerforming Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares TestBivariate Analysis on Categorical Categorical Variables
Multivariate AnalysisA Comprehensive Guide to Data ExplorationThe Data Science behind IPL
Supervised Learning vs Unsupervised LearningReinforcement LearningGenerative and Descriminative ModelsParametric and Non Parametric model
Machine Learning PipelinePreparing DatasetBuild a Benchmark Model: RegressionBuild a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should knowConfusion MatrixAccuracyPrecision and RecallAUC-ROCLog LossR2 and Adjusted R2
Dealing with Missing ValuesReplacing Missing ValuesImputing Missing Values in DataWorking with Categorical VariablesWorking with OutliersPreprocessing Data for Model Building
Understanding Cost FunctionUnderstanding Gradient DescentMath Behind Gradient DescentAssumptions of Linear RegressionImplement Linear Regression from ScratchTrain Linear Regression in PythonImplementing Linear Regression in RDiagnosing Residual Plots in Linear Regression ModelsGeneralized Linear ModelsIntroduction to Logistic RegressionOdds RatioImplementing Logistic Regression from ScratchIntroduction to Scikit-learn in PythonTrain Logistic Regression in pythonMulticlass using Logistic RegressionHow to use Multinomial and Ordinal Logistic Regression in R ?Challenges with Linear RegressionIntroduction to RegularisationImplementing RegularisationRidge RegressionLasso Regression
Introduction to K Nearest NeighboursDetermining the Right Value of K in KNNImplement KNN from ScratchImplement KNN in Python
Bias Variance TradeoffIntroduction to Overfitting and UnderfittingVisualizing Overfitting and UnderfittingSelecting the Right ModelWhat is Validation?Hold-Out ValidationUnderstanding K Fold Cross Validation
Introduction to Feature SelectionFeature Selection AlgorithmsMissing Value RatioLow Variance FilterHigh Correlation FilterBackward Feature EliminationForward Feature SelectionImplement Feature Selection in PythonImplement Feature Selection in R
Introduction to Decision TreePurity in Decision TreeTerminologies Related to Decision TreeHow to Select Best Split Point in Decision Tree?Chi-SquaresInformation GainReduction in VarianceOptimizing Performance of Decision TreeTrain Decision Tree using Scikit LearnPruning of Decision Trees
Introduction to Feature EngineeringFeature TransformationFeature ScalingFeature EngineeringFrequency EncodingAutomated Feature Engineering: Feature Tools
Introduction to Naive BayesConditional Probability and Bayes TheoremIntroduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings!Working of Naive BayesMath behind Naive BayesTypes of Naive BayesImplementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification ProblemEvaluation Metrics: Multi Class Classification
Introduction to Ensemble TechniquesBasic Ensemble TechniquesImplementing Basic Ensemble TechniquesFinding Optimal Weights of Ensemble Learner using Neural NetworkWhy Ensemble Models Work well?
Introduction to StackingImplementing StackingVariants of StackingImplementing Variants of StackingIntroduction to BlendingBootstrap SamplingIntroduction to Random SamplingHyper-parameters of Random ForestImplementing Random ForestOut-of-Bag (OOB) Score in the Random ForestIPL Team Win Prediction Project Using Machine LearningIntroduction to BoostingGradient Boosting AlgorithmMath behind GBMImplementing GBM in pythonRegularized Greedy ForestsExtreme Gradient BoostingImplementing XGBM in pythonTuning Hyperparameters of XGBoost in PythonImplement XGBM in R/H2OAdaptive BoostingImplementing Adaptive BoosingLightGBMImplementing LightGBM in PythonCatboostImplementing Catboost in Python
Different Hyperparameter Tuning methodsImplementing Different Hyperparameter Tuning methodsGridsearchCVRandomizedsearchCVBayesian Optimization for Hyperparameter TuningHyperopt
Understanding SVM AlgorithmSVM Kernels In-depth Intuition and Practical ImplementationSVM Kernel TricksKernels and Hyperparameters in SVMImplementing SVM from Scratch in Python and R
Introduction to Principal Component AnalysisSteps to Perform Principal Compound AnalysisComputation of Covariance MatrixFinding Eigenvectors and EigenvaluesImplementing PCA in pythonVisualizing PCAA Brief Introduction to Linear Discriminant AnalysisIntroduction to Factor Analysis
Introduction to ClusteringApplications of ClusteringEvaluation Metrics for ClusteringUnderstanding K-MeansImplementation of K-Means in PythonImplementation of K-Means in RChoosing Right Value for KProfiling Market Segments using K-Means ClusteringHierarchical ClusteringImplementation of Hierarchial ClusteringDBSCANDefining Similarity between clustersBuild Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to DaskWorking with CuML
Introduction to Machine Learning InterpretabilityFramework and Interpretable Modelsmodel Agnostic Methods for InterpretabilityImplementing Interpretable ModelUnderstanding SHAPOut-of-Core MLIntroduction to Interpretable Machine Learning ModelsModel Agnostic Methods for InterpretabilityGame Theory & Shapley Values
Introduction to AutoMLImplementation of MLBoxIntroduction to PyCaretTPOTAuto-SklearnEvalML
Pickle and JoblibIntroduction to Model Deployment
Deploying Machine Learning Model using StreamlitDeploying ML Models in DockerDeploy Using StreamlitDeploy on HerokuDeploy Using NetlifyIntroduction to Amazon SagemakerSetting up Amazon SageMakerUsing SageMaker Endpoint to Generate InferenceDeploy on Microsoft Azure CloudIntroduction to Flask for ModelDeploying ML model using Flask
Model Deployment in AndroidModel Deployment in Iphone

I Built a Working App in 4 Minutes, Thanks to Manus 1.5

S
Sarthak Dogra Last Updated : 30 Oct, 2025
5 min read

When it comes to AI agents and workflow automation, Manus AI stands tall among the host of such solutions. The platform is quite renowned for its capabilities, so much so that us folks here at AnalyticsVidhya use it extensively for a range of tasks. And so, it shouldn’t be a surprise that we have covered it extensively over the months and years. With that continuum comes a big update in this story. Manus 1.5 is here!

I get it. Let’s back up a little. What even is Manus AI? How does it help you? Is it worth exploring (or even reading through this article)

All in time, starting now.

Table of contents

What is Manus AI?

For those unaware, Manus AI is a powerful general-purpose AI agent. Note, it is an AI agent, not your regular AI chatbot like ChatGPT. Which means it is designed to turn natural language into real-world outcomes. Unlike conventional AI chatbots that stop at explanations, Manus AI executes complex, multi-step tasks entirely in the cloud. Simply describe what you wish to achieve, and it gets to work step-by-step.

With Manus, you can perform tasks across research and data analysis to website development and automation. You can delegate a complete workflow like “analyze resumes, rank candidates, and generate a shortlist,” close your browser, and return to see it finished. It is that simple!

You can read about Manus AI in more detail here.

Enter Manus 1.5

Manus 1.5 is the latest model by Manus, with extended capabilities and faster performance. All in all, the new model promises improvements in “speed, reliability, and quality across all task types,” as the Manus team mentions in its release. In numbers, it is able to deliver 4X faster results, 15% better quality, and 6% higher user satisfaction. In addition, it can now handle larger and more complex problems.

However, the Manus team isn’t just promising faster or smarter results with this update. In fact, the biggest capability that the new model brings to the table is full-stack web application development. This means you can now describe what you want, and Manus 1.5 builds it, deploys it, and even maintains it, all in one seamless workflow.

Note that Manus 1.5 is not the only new model to debut. Alongside, the company has announced a ‘Lite’ version, named Manus-1.5-Lite. This one is meant to provide “a streamlined experience optimized for cost efficiency.” Basically, a lighter version with limited capabilities. While Manus 1.5 brings all the power of the new architecture, Manus 1.5 Lite is meant to give you a demo of that power. We shall use the Lite version for a hands-on experience with no-code app building shortly.

All Aboard the ‘No-Code App Train’

Of course, Manus 1.5 isn’t the first attempt at such a no/low code app production. Just last week, Google announced similar app-building capabilities in its AI Studio. I built an Itinerary Generator app for Indian cities using the platform within minutes. You can read all about it here. Recently, Microsoft came up with similar capabilities for its Microsoft 365 Copilot platform, letting you build apps using simple English language.

Even though it is not the first, Manus 1.5 is certainly among the frontrunners in this segment. The fact that the team behind it analysed the requirement at hand and quickly churned out a model specifically for app development is commendable. Manus AI’s user base, mostly those wanting agentic AI experiences for automated workflows, will certainly love this development.

How to Access Manus 1.5

You can visit the official Manus website (click here) to use the new Manus 1.5, or do the same on the Manus Android or iOS app. Once on the app, you can simply select Manus 1.5 or Manus 1.5 Lite from the dropdown menu on the top left of your chat window.

Note that while Manus 1.5 Lite is free to use for a limited credits, you will have to get a paid plan to access Manus 1.5, which brings us to the next question.

Manus 1.5 Pricing

Other than the free tier that gives you 300 credits for Manus 1.5 Lite every day, Manus has kept 3 pricing options for its platform.

  • The Basic plan starts from $19/ month, and gives you features like 1900 credits/ month (in addition to the 300 refresh credits every day), 2 concurrent tasks, 2 scheduled tasks, Slides, image and video generation, and Wide Research.
  • The Plus plan starts from $39/ month, and offers 3 concurrent and scheduled tasks each, as well as 3900 credits per month.
  • The Pro plan costs $199/month and comes with 19,900 extra credits per month, 10 concurrent and scheduled tasks each, and early access to beta features.

Manus is running a limited-time offer wherein it is offering double the extra credits on each plan upon the purchase of any of the paid subscription plans. So, if you buy the Basic plan right now, you will effectively get 1900 + 1900 credits/ month.

Note that these monthly prices are discounted by 17% if you opt for the annual billing option.

With that out of the way, let’s dive right in to test the new Manus 1.5 Lite for a real-world task.

Manus 1.5 Lite Hands-on Vibe Coding

I replicated my attempt at a no-code app with Google AI Studio for Manus 1.5 Lite. This seemed like a logical option as it would help me judge the results with another app that I already have made through a similar platform. I used the following prompt for the same –

Prompt: create an itinerary generator for all places in India that works on the input of a location, number of tourists, and travelling dates. The app should call on Gemini to generate each answer. All answers should be in the form of a time table built across the dates.

Output:

Hand on my heart, this was one of the best no-code app developments I have tried so far. Manus 1.5 Lite took around 4 minutes to produce the entire app, which worked fabulously well in my testing. I have shared the screenshots for the same below. Look for yourself and be the judge.

  • Manus 1.5 no-code App Development
  • Manus 1.5 no-code App Development
  • Manus 1.5 no-code App Development

I even followed up the app development with a prompt to change the theme to Orange and Grey. Have a look at the results.

Manus 1.5 no-code App Development

Conclusion

Manus 1.5 marks a turning point for the platform. With the new architecture and capabilities, it moves beyond automating workflows, towards creating entire digital products. From faster task execution to full-stack app generation, it’s clear that Manus is no longer just an assistant or an agent working at the backend. You can rely on it for technical creations as well.

The genius move here is Manus 1.5 Lite lowering the entry barrier, while the full version pushes professional boundaries. From what I feel, the platform now caters to a broader audience than ever before. From curious beginners to power users, Manus 1.5 is surely a must-try for everyone wanting to enhance their productivity.

For more such AI tools on productivity enhancements, you can read my list of 20 best AI tools for work here.

S
Sarthak Dogra

Technical content strategist and communicator with a decade of experience in content creation and distribution across national media, Government of India, and private platforms

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