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softwaredevelopmentindia-one · 1 year

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India is a hub for technology and innovation and the field of machine learning Solution Development in India — Microlent Systems

India is a hub for technology and innovation, and the field of machine learning is no exception. With a growing number of companies specializing in this field, it can be challenging to know which one to choose. In this article, we will discuss the best machine learning companies in India, with a special focus on Microlent Systems.

Microlent Systems: Microlent Systems is a software development company located in Jodhpur, Rajasthan, that specializes in machine learning. They offer a range of services, including data analysis, predictive modeling, and natural language processing. They have worked with clients across various industries, from healthcare to finance.

Fractal Analytics: Fractal Analytics is a data analytics company that specializes in machine learning. They offer a range of services, including data engineering, data visualization, and artificial intelligence. They have worked with clients such as Microsoft, Coca-Cola, and PepsiCo.

Wipro: Wipro is a global technology company that offers machine learning solutions, including predictive maintenance, fraud detection, and chatbot development. They have worked with clients across various industries, including banking, healthcare, and retail.

Analytics India Magazine: Analytics India Magazine is a media company that covers the latest trends and developments in the field of machine learning. They offer training programs, research reports, and industry events to help businesses stay up-to-date with the latest developments in machine learning.

Tiger Analytics: Tiger Analytics is a consulting firm that offers machine learning solutions, including predictive modeling, optimization, and data visualization. They have worked with clients such as Adidas, Samsung, and Amazon.

LatentView Analytics: LatentView Analytics is a data analytics company that offers machine learning solutions, including customer segmentation, price optimization, and demand forecasting. They have worked with clients such as Microsoft, Coca-Cola, and Johnson & Johnson.

BRIDGEi2i: BRIDGEi2i is a consulting firm that offers machine learning solutions, including sales forecasting, customer segmentation, and supply chain optimization. They have worked with clients across various industries, including banking, retail, and healthcare.

Amazon Web Services: Amazon Web Services (AWS) offers a range of machine learning solutions, including image and speech recognition, chatbot development, and predictive analytics. They have worked with clients such as Netflix, Airbnb, and Samsung.

In conclusion, there are several excellent machine learning companies in India, each offering unique solutions and services. Microlent Systems stands out among them, with its expertise in data analysis, predictive modeling, and natural language processing. Located in Jodhpur, Rajasthan, they are well-positioned to provide high-quality machine learning solutions to clients across various industries.

https://microlent.com/blog/why-you-should-focus-on-improving-best-machine-learning-companies-in-india.html

#softwaredevelopment #machinelearning #india #dubai #uae #usa #web development #mobile app development #software #startups in india #innovation #technology #ai #ml solution #machine learning #machine learning course

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dugongglobalservices · 6 months

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https://dugong.one/ai-and-ml-services/

Intelligent Solutions, Real-World Impact: Dugong Global Services's AI and ML Services

Dugong Global Services pioneers the future with cutting-edge machine learning services, offering advanced solutions for businesses seeking to harness the power of AI. As a leading machine learning development company, we specialize in crafting tailored ML solutions that drive innovation. Our expert team excels in the development and deployment of machine learning models, ensuring optimal performance and efficiency. Whether you're exploring ML services for the first time or seeking advanced solutions, Dugong Global Services is your trusted partner. Visit https://dugong.one/ai-and-ml-services/ to delve into the world of artificial intelligence and machine learning, and transform your business with intelligent solutions.

#machine learning services #ml solution #ml services #machine learning development #machine learning development company

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botreetech · 11 months

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BoTree Technologies provides cutting-edge Machine Learning Solutions to businesses seeking to harness the power of data-driven intelligence. As a trusted provider of ML Solutions, our team of experienced data scientists and machine learning engineers designs and implements scalable and customized systems. With expertise in machine learning algorithms, predictive modeling, and pattern recognition, we help businesses unlock valuable insights, automate processes, and drive decision-making efficiency. Partner with BoTree Technologies to leverage our ML Solution expertise and stay ahead in the era of data-driven innovation. Visit our website to explore our Machine Learning Solutions in detail!

#Machine Learning Solutions #Machine Learning Solution #ML Solutions #ML Solution

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miraculousdeservesbetter2024 · 1 year

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I can’t believe Kagami confessed her love to both Adrien and Marinette at the same time.

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tempests-bards-and-birds · 2 years

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Adrien Agreste from Season 1 to Season 5

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erisluna35ocblog · 1 month

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This week on my Reverse AU OC portrait series, I'm using Blake to study how to do leather. Leather is such a complicated material to work with, its supposed to be so reflective hence the more intense highlights in his jacket compared to how I did Shizuke's black shirt and Blair's romper. There's also the contrast in rendering black fur compared to Blair's white fur coat. Rendering black in general is a whole different thought excersise compared to the whites on Shizuke and Blair.

Introducing the black cat for my OC's reverse AU, Blake Crawford! Compared to Blair's ring, his silver ring has an orangey tint, a referrence to how he's the fox in the original timeline. The inspiration to tie his hair back is from Attack on Titan's Eren, for the depressing edgyness. Unlike Fiona who's design took some cues from Ladybugs (Marinette and Bridgette), Blake's inner clothes is based on Nathanael's akuma form, Evillustrator. It's cause his original design takes cues from Nathanael. The reason for the aversion to referrencing Adrien and/or Felix is, other than their outfits just aren't very edgy emo, it's to signal he isn't supposed to be seen as a love interest for his Ladybug partner. I wanted there to be something of a hint with his role despite being the black cat. Instead of paralleling Fiona, his outfit parallel's Blair's more. The black fur lining and an oversized black jacket, the white (light gray) shirt underneath, the way his hair is parted, the stripes, the round details... I picked him to be the black cat so he can be more active with his arc where he's looking for his sister.

Why are the twins separated?

Much like in the original, he and Blair lost their parents and got separated because of the foster system. Blair ended up with their abusive relatives while Blake ended up with the Liungs... except they never got Blair back. It's too late by the time they found out those relatives sold Blair off to the Supreme.

Blake tried everything he could to reach his sister but the Supreme blocked him every time - she's their newest little child soldier, they still had use for her. Eventually, there was an attempt on his and the Liungs' lives. The only reason they survived was because the Resistance saved them.

There, Blake learned everything. His parents and the Liungs were members of the Resistance. Mrs. Liung, the only one left alive, stayed in contact with the Resistance despite semi retiring to focus on helping him get Blair back - it's the least she could've done for her fallen friends and husband. Yet they can't reach her no matter how many strings they pulled, which is really saying something as Mrs. Liung was a former celebrity and her strongest string is the Resistance Leader, Lorelei, who is the mayor's sister-in-law. They suspect the Supreme had something to do with it.

So to tally, the Supreme has: killed his parents, killed Mr. Liung, abducted his sister, tried to get him and the Liungs killed... Blake has plenty to be mad about. This is why Blake joined the Resistance.

At first, Blake was obsessed with taking down the Supreme for taking everything from him. He fought them from the shadows. He takes out his anger on their goons for getting in his way. It doesn't matter what they throw at him, he will destroy them all... This went on for years.

But alas, Blake is still young. After years of fighting the Supreme, things are looking more and more futile. He grew tired, both emotionally and physically. He was always fighting, always hiding, making sure the Supreme doesn't catch wind of both Blake surviving or the new black cat on the prowl... the stalemate was taking its toll on him. Nothing seems to be changing.

That is until Keagan brought a girl to their base.

It was a mistake, the fallen prince said. But Lorelei saw something in the girl and declared her the new Ladybug, his partner. The time to fight in the light has come.

Not long after this, the Supreme called in reinforcements to crush the Resistance. First was the infamous Ox-wielding Miraculous hunter they've imported from Japan. Second was their shiny new-bee who's brimming with potential... Blake's sister.

#ml au #ml oc #mlb oc #reverse au #blake crawford #somehow the real challange was making him even more emo than normal hahaha #even funnier was the solution #give him some eyeliner #and eyebags #blake is tired a lot in this au

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miraculouslycool · 1 year

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Ok this is totally not like, a dig at the show or anything but I wish there was a filler between Elation and Transmission that solely featured Adrien finding out that being moony eyed over your crush instead of listening to class is a struggle when said crush sits behind you

#marinette got the longer end of the stick there #alya: there is a simple solution to this plan *bodily throws Adrien into her seat*#ml spoilers #transmission #elation #adrienette

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iwasbored777 · 1 year

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Elation really confirmed where Marinette got all that "planning the future" stuff from cuz she and her parents won't give up until they give Adrien a perfect future and it shows

#They really have the best solution how to help abused kids and it shows #They will give him a wife and kids and a job and a whole future #They are also really aggressive about it like you can't escape boy once you tie the knot you're one of us no looking back #Sjsksks #miraculous ladybug #marinette dupain cheng #tom dupain #sabine cheng #ml elation

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critical-thinking-is-mandatory · 2 years

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🎇💞Marinette as an engineer 💞🎇

could she be a mechanical engineer? yes <3

could she be a materials engineer? also yes <3

mechatronic? yes <3

electrical? yes <3

civil, agricultural, environmental, chemical? yes yes yes <3

Marinette is a creative, solution-oriented person, with no end of ideas.

if she can’t make it in fashion, she sure as hell can in engineering.

#ml #marinette #giving characters my interests #problem solving is a way of life <3 #you see that shit where marinette became class president by offering compromises and solutions??#that's engineer behaviour right there #anyone who says she should be an architect will be shot in the town square <3 #architects are fundamentally problem *causers*

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aibyrdidini · 21 days

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SEMANTIC TREE AND AI TECHNOLOGIES

Semantic Tree learning and AI technologies can be combined to solve problems by leveraging the power of natural language processing and machine learning.

Semantic trees are a knowledge representation technique that organizes information in a hierarchical, tree-like structure.

Each node in the tree represents a concept or entity, and the connections between nodes represent the relationships between those concepts.

This structure allows for the representation of complex, interconnected knowledge in a way that can be easily navigated and reasoned about.

CONCEPTS

Semantic Tree: A structured representation where nodes correspond to concepts and edges denote relationships (e.g., hyponyms, hyponyms, synonyms).

Meaning: Understanding the context, nuances, and associations related to words or concepts.

Natural Language Understanding (NLU): AI techniques for comprehending and interpreting human language.

First Principles: Fundamental building blocks or core concepts in a domain.

AI (Artificial Intelligence): AI refers to the development of computer systems that can perform tasks that typically require human intelligence. AI technologies include machine learning, natural language processing, computer vision, and more. These technologies enable computers to understand reason, learn, and make decisions.

Natural Language Processing (NLP): NLP is a branch of AI that focuses on the interaction between computers and human language. It involves the analysis and understanding of natural language text or speech by computers. NLP techniques are used to process, interpret, and generate human languages.

Machine Learning (ML): Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. ML algorithms can analyze data, identify patterns, and make predictions or decisions based on the learned patterns.

Deep Learning: A subset of machine learning that uses neural networks with multiple layers to learn complex patterns.

EXAMPLES OF APPLYING SEMANTIC TREE LEARNING WITH AI.

1. Text Classification: Semantic Tree learning can be combined with AI to solve text classification problems. By training a machine learning model on labeled data, the model can learn to classify text into different categories or labels. For example, a customer support system can use semantic tree learning to automatically categorize customer queries into different topics, such as billing, technical issues, or product inquiries.

2. Sentiment Analysis: Semantic Tree learning can be used with AI to perform sentiment analysis on text data. Sentiment analysis aims to determine the sentiment or emotion expressed in a piece of text, such as positive, negative, or neutral. By analyzing the semantic structure of the text using Semantic Tree learning techniques, machine learning models can classify the sentiment of customer reviews, social media posts, or feedback.

3. Question Answering: Semantic Tree learning combined with AI can be used for question answering systems. By understanding the semantic structure of questions and the context of the information being asked, machine learning models can provide accurate and relevant answers. For example, a Chabot can use Semantic Tree learning to understand user queries and provide appropriate responses based on the analyzed semantic structure.

4. Information Extraction: Semantic Tree learning can be applied with AI to extract structured information from unstructured text data. By analyzing the semantic relationships between entities and concepts in the text, machine learning models can identify and extract specific information. For example, an AI system can extract key information like names, dates, locations, or events from news articles or research papers.

Python Snippet Codes for Semantic Tree Learning with AI

Here are four small Python code snippets that demonstrate how to apply Semantic Tree learning with AI using popular libraries:

1. Text Classification with scikit-learn:

```python

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.linear_model import LogisticRegression

# Training data

texts = ['This is a positive review', 'This is a negative review', 'This is a neutral review']

labels = ['positive', 'negative', 'neutral']

# Vectorize the text data

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(texts)

# Train a logistic regression classifier

classifier = LogisticRegression()

classifier.fit(X, labels)

# Predict the label for a new text

new_text = 'This is a positive sentiment'

new_text_vectorized = vectorizer.transform([new_text])

predicted_label = classifier.predict(new_text_vectorized)

print(predicted_label)

```

2. Sentiment Analysis with TextBlob:

```python

from textblob import TextBlob

# Analyze sentiment of a text

text = 'This is a positive sentence'

blob = TextBlob(text)

sentiment = blob.sentiment.polarity

# Classify sentiment based on polarity

if sentiment > 0:

sentiment_label = 'positive'

elif sentiment < 0:

sentiment_label = 'negative'

else:

sentiment_label = 'neutral'

print(sentiment_label)

```

3. Question Answering with Transformers:

```python

from transformers import pipeline

# Load the question answering model

qa_model = pipeline('question-answering')

# Provide context and ask a question

context = 'The Semantic Web is an extension of the World Wide Web.'

question = 'What is the Semantic Web?'

# Get the answer

answer = qa_model(question=question, context=context)

print(answer['answer'])

```

4. Information Extraction with spaCy:

```python

import spacy

# Load the English language model

nlp = spacy.load('en_core_web_sm')

# Process text and extract named entities

text = 'Apple Inc. is planning to open a new store in New York City.'

doc = nlp(text)

# Extract named entities

entities = [(ent.text, ent.label_) for ent in doc.ents]

print(entities)

```

APPLICATIONS OF SEMANTIC TREE LEARNING WITH AI

Semantic Tree learning combined with AI can be used in various domains and industries to solve problems. Here are some examples of where it can be applied:

1. Customer Support: Semantic Tree learning can be used to automatically categorize and route customer queries to the appropriate support teams, improving response times and customer satisfaction.

2. Social Media Analysis: Semantic Tree learning with AI can be applied to analyze social media posts, comments, and reviews to understand public sentiment, identify trends, and monitor brand reputation.

3. Information Retrieval: Semantic Tree learning can enhance search engines by understanding the meaning and context of user queries, providing more accurate and relevant search results.

4. Content Recommendation: By analyzing the semantic structure of user preferences and content metadata, Semantic Tree learning with AI can be used to personalize content recommendations in platforms like streaming services, news aggregators, or e-commerce websites.

Semantic Tree learning combined with AI technologies enables the understanding and analysis of text data, leading to improved problem-solving capabilities in various domains.

COMBINING SEMANTIC TREE AND AI FOR PROBLEM SOLVING

1. Semantic Reasoning: By integrating semantic trees with AI, systems can engage in more sophisticated reasoning and decision-making. The semantic tree provides a structured representation of knowledge, while AI techniques like natural language processing and knowledge representation can be used to navigate and reason about the information in the tree.

2. Explainable AI: Semantic trees can make AI systems more interpretable and explainable. The hierarchical structure of the tree can be used to trace the reasoning process and understand how the system arrived at a particular conclusion, which is important for building trust in AI-powered applications.

3. Knowledge Extraction and Representation: AI techniques like machine learning can be used to automatically construct semantic trees from unstructured data, such as text or images. This allows for the efficient extraction and representation of knowledge, which can then be used to power various problem-solving applications.

4. Hybrid Approaches: Combining semantic trees and AI can lead to hybrid approaches that leverage the strengths of both. For example, a system could use a semantic tree to represent domain knowledge and then apply AI techniques like reinforcement learning to optimize decision-making within that knowledge structure.

EXAMPLES OF APPLYING SEMANTIC TREE AND AI FOR PROBLEM SOLVING

1. Medical Diagnosis: A semantic tree could represent the relationships between symptoms, diseases, and treatments. AI techniques like natural language processing and machine learning could be used to analyze patient data, navigate the semantic tree, and provide personalized diagnosis and treatment recommendations.

2. Robotics and Autonomous Systems: Semantic trees could be used to represent the knowledge and decision-making processes of autonomous systems, such as self-driving cars or drones. AI techniques like computer vision and reinforcement learning could be used to navigate the semantic tree and make real-time decisions in dynamic environments.

3. Financial Analysis: Semantic trees could be used to model complex financial relationships and market dynamics. AI techniques like predictive analytics and natural language processing could be applied to the semantic tree to identify patterns, make forecasts, and support investment decisions.

4. Personalized Recommendation Systems: Semantic trees could be used to represent user preferences, interests, and behaviors. AI techniques like collaborative filtering and content-based recommendation could be used to navigate the semantic tree and provide personalized recommendations for products, content, or services.

PYTHON CODE SNIPPETS

1. Semantic Tree Construction using NetworkX:

```python

import networkx as nx

import matplotlib.pyplot as plt

# Create a semantic tree

G = nx.DiGraph()

G.add_node("root", label="Root")

G.add_node("concept1", label="Concept 1")

G.add_node("concept2", label="Concept 2")

G.add_node("concept3", label="Concept 3")

G.add_edge("root", "concept1")

G.add_edge("root", "concept2")

G.add_edge("concept2", "concept3")

# Visualize the semantic tree

pos = nx.spring_layout(G)

nx.draw(G, pos, with_labels=True)

plt.show()

```

2. Semantic Reasoning using PyKEEN:

```python

from pykeen.models import TransE

from pykeen.triples import TriplesFactory

# Load a knowledge graph dataset

tf = TriplesFactory.from_path("./dataset/")

# Train a TransE model on the knowledge graph

model = TransE(triples_factory=tf)

model.fit(num_epochs=100)

# Perform semantic reasoning

head = "concept1"

relation = "isRelatedTo"

tail = "concept3"

score = model.score_hrt(head, relation, tail)

print(f"The score for the triple ({head}, {relation}, {tail}) is: {score}")

```

3. Knowledge Extraction using spaCy:

```python

import spacy

# Load the spaCy model

nlp = spacy.load("en_core_web_sm")

# Extract entities and relations from text

text = "The quick brown fox jumps over the lazy dog."

doc = nlp(text)

# Visualize the extracted knowledge

from spacy import displacy

displacy.render(doc, style="ent")

```

4. Hybrid Approach using Ray:

```python

import ray

from ray.rllib.agents.ppo import PPOTrainer

from ray.rllib.env.multi_agent_env import MultiAgentEnv

from ray.rllib.models.tf.tf_modelv2 import TFModelV2

# Define a custom model that integrates a semantic tree

class SemanticTreeModel(TFModelV2):

def __init__(self, obs_space, action_space, num_outputs, model_config, name):

super().__init__(obs_space, action_space, num_outputs, model_config, name)

# Implement the integration of the semantic tree with the neural network

# Define a multi-agent environment that uses the semantic tree model

class SemanticTreeEnv(MultiAgentEnv):

def __init__(self):

self.semantic_tree = # Initialize the semantic tree

self.agents = # Define the agents

def step(self, actions):

# Implement the environment dynamics using the semantic tree

# Train the hybrid model using Ray

ray.init()

config = {

"env": SemanticTreeEnv,

"model": {

"custom_model": SemanticTreeModel,

}

trainer = PPOTrainer(config=config)

trainer.train()

```

APPLICATIONS

The combination of semantic trees and AI can be applied to a wide range of problem domains, including:

- Healthcare: Improving medical diagnosis, treatment planning, and drug discovery.

- Finance: Enhancing investment strategies, risk management, and fraud detection.

- Robotics and Autonomous Systems: Enabling more intelligent and adaptable decision-making in complex environments.

- Education: Personalizing learning experiences and providing intelligent tutoring systems.

- Smart Cities: Optimizing urban planning, transportation, and resource management.

- Environmental Conservation: Modeling and predicting environmental changes, and supporting sustainable decision-making.

- Chatbots and Virtual Assistants:

Use semantic trees to understand user queries and provide context-aware responses.

Apply NLU models to extract meaning from user input.

- Information Retrieval:

Build semantic search engines that understand user intent beyond keyword matching.

Combine semantic trees with vector embeddings (e.g., BERT) for better search results.

- Medical Diagnosis:

Create semantic trees for medical conditions, symptoms, and treatments.

Use AI to match patient symptoms to relevant diagnoses.

- Automated Content Generation:

Construct semantic trees for topics (e.g., climate change, finance).

Generate articles, summaries, or reports based on semantic understanding.

RDIDINI PROMPT ENGINEER

#semantic tree #ai solutions #ai-driven #ai trends #ai system #ai model #ai prompt #ml #ai predictions #llm #dl #nlp

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codetradeinsights · 22 days

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𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗘𝗥𝗣 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀!

Discover how integrating Artificial Intelligence with Enterprise Resource Planning can transform your manufacturing operations. Check out our latest blog post to explore the future of smart manufacturing:

Read more about the next wave of intelligent ERP systems and stay ahead in the digital transformation race!

https://www.codetrade.io/blog/ai-driven-erp-solutions-for-the-manufacturing-industry/

#artificial intelligence #machine learning #erp solution #manufacturing industry #ERP in AI #enterprise resource planning #intelligent ERP system #AI ML #AI-driven ERP solutions

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friendfromdsmp · 1 year

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All these ‘which of these Miraculous episodes was your favourite?’ polls is making me realise just how little I can enjoy the individual episodes outside of just mindless consumption because it’s a brilliant show for turning your brain off and having a laugh to relax after a hard day but the moment I put any thought into the show I’m like “Hold on a second that’s not good… wait a second why are they acting like it’s fine? No no no you can’t blame Marinette for that why would that be her fault? Woah easy Chat what are you- noooooo I wanna like you so bad please at very least take responsibility I want to like you!”

#half of the episodes constitute of Marinette being over on the bench with John Mulaney why does the world hate her #and also I don’t mind toxic characters or good characters doing bad things or whatever #but the show itself not only moves on from it but portrays it as a good thing?#even a brief nod that ‘oh we’re doing this? not the best solution but I’m down’ would do wonders #do all the bad shit you like but don’t pretend it’s good #especially when every episode has a rule that Mari needs to learn a lesson #meaning you WANT people to take a lesson from the show #miraculous ladybug #adrien agreste #chat noir #marinette dupain cheng #ladybug #ml ladybug #polls #mlb

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botreetech · 2 years

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Machine Learning company offering complete Machine Learning solutions for digital transformation. Get Machine Learning consulting for high-quality data-based applications.

#ml consulting #ml consulting services #ml solution #machine learning company #machine learning services #machine learning applications

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captain-harpo · 2 years

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The real takeaway from this ep of ml is that data collection technology will ruin us all

#that one ep with mylene was about being wary of corps pushing surface-level green solutions #this one is about big data #im here for it i think they should do more eps exactly like this #in the next one marinette will watch an episode of a tv show through a piracy website #and the villain will be an employee from the media company who thinks they'll be paid more if ppl don't pirate #ml jubilation #miraculous #miraculous ladybug #ml spoilers #miraculous s5 #captain's log

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casadefreewill · 2 years

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Daily Fics

These Are The Things, The Things We Lost by sariahsue ( @sariahsue )

"As Marinette reached to pull the blanket up, she looked down, surprised to see that her husband was already lying there, and her stomach curled with uneasiness. She shouldn't be climbing into bed with him. It felt so wrong.

He felt like a stranger."

After an akuma attack gone wrong, will Marinette and Adrien be able to find their ways back to each other?

Sitting here I’m just like, this will either be very sweet or incredibly angsty and I’m fully on board to find out which.

☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★

what excellent boiled potatoes by Boogum ( @botherkupo )

There's only one thing to do when stuck on a disastrous blind date: call your local cat boy to come to your rescue.

Ahhh! Oh this is adorable (and hilarious as always)

☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★

scales and steps by summerset ( @settledownsummer )

Adrien left something behind while running from an Akuma. Ladybug has a five-step plan to return it to him.

Ladrien bedroom hang out and Ladybug’s five step plan to get a date ❤️

☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★

red is his favourite colour by summerset ( @settledownsummer )

Adrien realized that the best way to get Marinette's attention was through arguments. He knew it was a desperate ploy for her attention; he might as well have been pulling her pigtails on the playground. But he just couldn’t help himself; she was intoxicating, her little chin tilted up in a challenge, her eyes lit with a fire and determination as they gazed right back into his, the bright blue closer than ever before. It was like a game, and he was always winning.

This would have to be set season 1/season 2, Adrien and Marinette not really being friends yet. Really leans into the ‘as soon as Adrien sees Marinette be Marinette he falls head over heels’. Basically as soon as she’s able to talk to him she’s golden!

☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★

An Unconventional Solution by Nomolosk ( @nomolosk )

Chat Blanc needed saving again. Who better to send than his favorite little mouse? Bonus: she might just learn the truth this time around.

The one thing I always want from a chat blanc story is for her to actually learn why it all happened, and this one finally delivered 🤍🤍

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