BC BUSINESS
Saved by @Mollyycolllinss

Time for #PapersThatMakeYouGoHmmm! A weekly summary of new ML papers from arXiv that make me think one or more of:

1. That looks useful!
2. That's an interesting approach!
3. A business could be built around this!
4. How did they do that?!

 How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations

https://t.co/GTAaqHRgi1
Validating Label Consistency in NER Data Annotation

https://t.co/eeYBU4AUvT
A two-stage data association approach for 3D Multi-object Tracking

https://t.co/h3LijchIzC
Neural Networks, Artificial Intelligence and the Computational Brain

https://t.co/2jJHGlHYsn
Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

https://t.co/QhahrfRN5T
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

https://t.co/gM4vCIMMzW
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

https://t.co/xvyYotmqBG
Discussion of Ensemble Learning under the Era of Deep Learning

https://t.co/eqdMw8WNEU
Do we need to go Deep? Knowledge Tracing with Big Data

https://t.co/Z3EtibSYA3
mt5b3: A Framework for Building AutonomousTraders

https://t.co/w6sBscN3uo
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

https://t.co/oO0r8IOshz
Classifying Scientific Publications with BERT -- Is Self-Attention a Feature Selection Method?

https://t.co/7pAUbTGfLb
Collision-Free Flocking with a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning

https://t.co/1R4qJ5M0Eq
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

https://t.co/S3fQcgbNcK
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

https://t.co/8sPKuqAPnQ
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling

https://t.co/UcgL7WDUGv
Noise Learning Based Denoising Autoencoder

https://t.co/hPSClsZTTp
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks

https://t.co/7xawUMSYSW
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search

https://t.co/b6PQNPyDef
Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum

https://t.co/7qA7BomthH
Dissonance Between Human and Machine Understanding

https://t.co/nRBcIDIoSP
A System for Automated Open-Source Threat Intelligence Gathering and Management

https://t.co/zRIE873tMW
Classification of Pedagogical content using conventional machine learning and deep learning model

https://t.co/kFt1Vr11DS
GLocalX -- From Local to Global Explanations of Black Box AI Models

https://t.co/jNEAt3yDei
An Artificial Intelligence based approach to estimating time of arrival and bus occupancy for public transport systems in Africa

https://t.co/oQkFARvo0e
Edge-Featured Graph Attention Network

https://t.co/5jRr0ynqHA
Situation and Behavior Understanding by Trope Detection on Films

https://t.co/2tTVFlj7BM
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments

https://t.co/wsMBpdo3zG
Disentangled Recurrent Wasserstein Autoencoder

https://t.co/KNKdFN9RII
GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention

https://t.co/eGlkz92WGB
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

https://t.co/8AC8HngYl1
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia

https://t.co/51FwnK9i5v
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations

https://t.co/4lp4UxSYMV
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions

https://t.co/abJTLSptss
Understanding in Artificial Intelligence

https://t.co/b5kufxxoL5
A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

https://t.co/6TRfUgvv1v
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

https://t.co/OYEPMDtf5I
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset

https://t.co/VtsS1g7pVx
Leveraging AI to optimize website structure discovery during Penetration Testing

https://t.co/tZxbNs99Tu
Is it a great Autonomous FX Trading Strategy or you are just fooling yourself

https://t.co/ted6dt7jBd
Deep Reinforcement Learning for Active High Frequency Trading

https://t.co/C4iR7RNfs2
Studying Catastrophic Forgetting in Neural Ranking Models

https://t.co/GtzumvDLEj
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

https://t.co/TxUEKByuBX
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection

https://t.co/aNjfrPm8Gd
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning

https://t.co/4lYbSyvDH3
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation

https://t.co/8tMpbWAu1b
Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

https://t.co/KzvOxzqexs
Deep Parametric Continuous Convolutional Neural Networks

https://t.co/m3jGJWSnXr
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition

https://t.co/B3EG8k37SB
GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation

https://t.co/rjx4yjnNQj
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

https://t.co/fAEPrJmbYB
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

https://t.co/wPPbWpOR36
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

https://t.co/wPPbWpOR36
GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition

https://t.co/AcLGlax0fk
SceneGen: Learning to Generate Realistic Traffic Scenes

https://t.co/JNOxqvAeKB
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

https://t.co/Tjh0adUiNv
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks

https://t.co/XB0dKTws4J
NNStreamer: Efficient and Agile Development of On-Device AI Systems

https://t.co/s6SOklUTsp
AR-based Modern Healthcare: A Review

https://t.co/pcoZB3J3ka
Attention Based Video Summaries of Live Online Zoom Classes

https://t.co/M4ZStoy1AN
When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time

https://t.co/YTIAAWqAcd
On the Verification and Validation of AI Navigation Algorithms

https://t.co/gwBaqOeu4c
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision

https://t.co/YHLUQ5SpIa
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

https://t.co/rsqGk5fQ8b
Player-AI Interaction: What Neural Network Games Reveal About AI as Play

https://t.co/DIzXf1wuW6
Probabilistic Inference for Learning from Untrusted Sources

https://t.co/PFyTH6jZyp
Teaming up with information agents

https://t.co/fpaMVZlH0k
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

https://t.co/kStzf3RoTX
Black-box Adversarial Attacks in Autonomous Vehicle Technology

https://t.co/0B2xkavOWt
Motion-Based Handwriting Recognition

https://t.co/lT0ybiyiAl
Affordance-based Reinforcement Learning for Urban Driving

https://t.co/Do6J5eo7j6
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

https://t.co/St76T1uGLq
Responsible AI Challenges in End-to-end Machine Learning

https://t.co/u3drnpONrR
Mining Knowledge Graphs From Incident Reports

https://t.co/Sm1wZA2gYQ
Descriptive AI Ethics: Collecting and Understanding the Public Opinion

https://t.co/3u3By4VxIz
Hostility Detection and Covid-19 Fake News Detection in Social Media

https://t.co/GlrtTAalKE
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

https://t.co/ihKgihjDfV
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization

https://t.co/rZBlbHStLd
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

https://t.co/oPTKZbiaHd
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media

https://t.co/aU5g6hXOaS
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

https://t.co/mHFCoQBzCm
ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction

https://t.co/38Ivf3iTys
Adversarial Machine Learning in Text Analysis and Generation

https://t.co/OAaQGy3VD9
Dive into Decision Trees and Forests: A Theoretical Demonstration

https://t.co/YEd3NpQqLc
Stress Testing of Meta-learning Approaches for Few-shot Learning

https://t.co/CP6QDkSUPF
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer

https://t.co/UW74MHAEwV
Analysis of Information Flow Through U-Nets

https://t.co/H7Oay6mTpj
Distilling Interpretable Models into Human-Readable Code

https://t.co/tNph0XpFDT
Invariance, encodings, and generalization: learning identity effects with neural networks

https://t.co/kCJt8bajEP
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

https://t.co/cHHRGBip9h
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?

https://t.co/8Zx7fVfIIp
Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization

https://t.co/kuvkN4ZMZu
MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes

https://t.co/ffTvTzz5rZ
LEAF: A Learnable Frontend for Audio Classification

https://t.co/3WaQ8MIC8A
Customer Price Sensitivities in Competitive Automobile Insurance Markets

https://t.co/3nIj6XJNYQ
Pre-training without Natural Images

https://t.co/m1lBQFT9KJ
Arabic Speech Recognition by End-to-End, Modular Systems and Human

https://t.co/6rWnWJvJiR
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

https://t.co/lumhRoaVhf
Influence Estimation for Generative Adversarial Networks

https://t.co/ypMwRmSJqv
Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network

https://t.co/R9SiU4pWEy
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow

https://t.co/5wEXw5ECXL
Bayesian Neural Networks for Fast SUSY Predictions

https://t.co/eEjxi4ybgU
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

https://t.co/FdvIcYrTJZ
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments

https://t.co/XFZLPMcllL
Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey

https://t.co/WfdmTkw69N
Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data

https://t.co/1OjpuDYWcY
Machine learning applications for COVID-19: A state-of-the-art review

https://t.co/9DSd3TfB4D
Implicit Bias of Linear RNNs

https://t.co/IfgXSmwaFb
Open-Domain Conversational Search Assistant with Transformers

https://t.co/8iMUvwb0To
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

https://t.co/IQKlMjf5Hv
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs

https://t.co/zS4DRvfhI7
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

https://t.co/EA9GZDwoQO
Cross-domain few-shot learning with unlabelled data

https://t.co/DW7JGPcNwS
Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features

https://t.co/JswvK6YlQJ
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions

https://t.co/Z9cI9d1n2b
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations

https://t.co/bWrX68Pxl1
Image Denoising using Attention-Residual Convolutional Neural Networks

https://t.co/5aG06Yf2RF
Interpretable Models for Granger Causality Using Self-explaining Neural Networks

https://t.co/DR34Ed1qGd
Continual Deterioration Prediction for Hospitalized COVID-19 Patients

https://t.co/OsBrVfw7kj
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning

https://t.co/bKhILExhy7
PeerGAN: Generative Adversarial Networks with a Competing Peer Discriminator

https://t.co/944IW7qRsm
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

https://t.co/jBfVPPcvlY
Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data

https://t.co/Y1hnvcMo2K
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

https://t.co/r3B4ST2XIX
Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

https://t.co/MeBl9H7kS5
Does Continual Learning = Catastrophic Forgetting?

https://t.co/jk7tasU0IR
A survey on shape-constraint deep learning for medical image segmentation

https://t.co/OuSpETBxYG
Predicting Pneumonia and Region Detection from X-Ray Images using Deep Neural Network

https://t.co/qpDYzwLw2b
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

https://t.co/Q2nOU79sNv
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly

https://t.co/GOe2yLmOWF
Collaboration among Image and Object Level Features for Image Colourisation

https://t.co/bVqv6ZWQTV
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

https://t.co/EbrLI7pYe9
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods

https://t.co/DcOl3AbCds
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning

https://t.co/5uPWFcYtzQ
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias

https://t.co/VTDmjNd9QD
Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort

https://t.co/tx5Sc89T4Y
A simple geometric proof for the benefit of depth in ReLU networks

https://t.co/TT25NN3Z3M
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks

https://t.co/CgZhQGXLhC
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements

https://t.co/8MlUI1ezGy
Discrete Graph Structure Learning for Forecasting Multiple Time Series

https://t.co/MXCAmJNoaX
Heterogeneous Similarity Graph Neural Network on Electronic Health Records

https://t.co/6WhEGluvBZ
Learning from pandemics: using extraordinary events can improve disease now-casting models

https://t.co/7jWkDBEzv6
Physics-Informed Deep Learning for Traffic State Estimation

https://t.co/43BlFlrB6W
Diverse Complexity Measures for Dataset Curation in Self-driving

https://t.co/CZCCyoONoE
Phases of learning dynamics in artificial neural networks: with or without mislabeled data

https://t.co/5hgjP1yYgN
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

https://t.co/aQbjE43vtA
Visual Analytics approach for finding spatiotemporal patterns from COVID19

https://t.co/pSrj6m6zj5
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

https://t.co/SMWKivSQuU
Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients

https://t.co/XcyMard8Nf
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution

https://t.co/s5cJeOGptC
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

https://t.co/B9E2XzC4zC
Data-driven discovery of multiscale chemical reactions governed by the law of mass action

https://t.co/M2HkwCZBQm
Temporal Clustering of Disorder Events During the COVID-19 Pandemic

https://t.co/5M4wjqpubk
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling

https://t.co/zYC4jpnXkH
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data

https://t.co/0B1e6SVE2d
Exponential Kernels with Latency in Hawkes Processes: Applications in Finance

https://t.co/S44KgRfJjs
Deciding What to Learn: A Rate-Distortion Approach

https://t.co/s6TPmGNN5M
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper

https://t.co/ATqL7pEWkP
The Geometry of Deep Generative Image Models and its Applications

https://t.co/EwSU9rEiaw
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features

https://t.co/jcJIQk6gwJ
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools

https://t.co/diHKuYRFdO
Empirical Evaluation of Supervision Signals for Style Transfer Models

https://t.co/0lxQ3WXzaN
Needmining: Designing Digital Support to Elicit Needs from Social Media

https://t.co/xRhSg5WL9I
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function

https://t.co/252Oegv871
Video Summarization Using Deep Neural Networks: A Survey

https://t.co/LffqAz9gVb
Nowcasting Gentrification Using Airbnb Data

https://t.co/tySM5cSpy9
How Shift Equivariance Impacts Metric Learning for Instance Segmentation

https://t.co/OPdhtdDmC2
@threadreaderapp unroll

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Nick Huber
Nick Huber
@sweatystartup
There is a lot of bad advice floating around online when it comes to real estate investing.

Time for a stiff drink and some stuff you might not want to hear.

A thread 👇

Most real estate influencers spend 99% of their time trying to sound smart.

They purposely make things too complicated.

When you do this for a while you can tell if a property is worth buying with a napkin and 30 seconds.

The problem with being a big real estate PE firm:

You need to do good deals year after year and deploy 10s or 100s of millions in capital.

It’s a hamster wheel making others wealthy.

The advantage of being you:

One building like this can change your life.


Buying real estate that doesn’t cashflow well because values have risen 10% / yr for 10 yrs while wages have risen 3% / yr for 10 yrs is very risky.

I don’t care how long your interest rate is “fixed” for.

Markets are cyclical. Trends change. Bankruptcy is real.

If you don’t know anyone with money and you have less than $250k of your own cash real estate isn’t the best way to make money.

Even a $500k property (like the one pictured above) requires $150k down and another $100k in cash reserves.
BUSINESS
Chris Herd
Chris Herd
@chris_herd
I spoke to 1,700+ people about remote work in 2020

A few predictions of what will happen in 2021

[ a thread ] 💻🏠🌍

🎟 Remote competition: @FrancisSuarez is the first person to wield the power of Twitter to attract remote talent

Almost every region that has something which would attract people to move there will do the same thing

Incentives for remote talent to move will grow quickly

⏰ Async-first: People now see offices are instantaneous gratification adult kids clubs where sync work makes it impossible to get stuff done

Tools that enable async work are the most important thing globally remote teams need. A lot of startups will try to tackle this

🏢 HQ Slashed: Companies of every size cut their commercial office space by 50-70%

People will work remotely 2-4 days a week, and come into the office 1-2 days a week

Companies will provide a great setup at home for their workers

🤕 Injury explosion: Companies that don't provide the right tools & equipment begin to get Sued for Millions of Dollars

This rapidly becomes a key point of differentiation between companies they use to attract & retain talent
BUSINESS
Advertisement
a h
a h
@alexharfouche1
#irrationalebullience If you want to understand the nature of this asset bubble and the epic capital mis-allocation at it’s heart, look no further than rates. CB’s only tool to push an uber-levered financial system that is highly interconnected through derivatives
(1/N)

away from it’s natural convergence path towards collapse is to create opposing forces to push it towards divergence. Meaning: force, through financial repression, market participants to rely on price continuation, in a way that makes it self-fulfilling.
(2/N)

The tool used is rates as it is the basis off of which all other financial assets are priced. Today any buyer of $UST across the curve up to 30Y is guaranteed a loss given reals are negative throughout. So the only hope of escaping this GUARANTEED loss
(3/N)

is to angle for capital gains, i.e a continuous move downwards in real rates to push for some capital gains in real terms. The longer the financial repression lasts, the further investors are pushed out the maturity and credit risk curves.
(4/N)

Here are examples to understand what exactly is happening. If an investors buys a 5 Y UST he is guaranteed to lose almost 2% in real terms, so making that up in capital gains is much harder given the v short duration.
(5/N)
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Brian Feroldi
Brian Feroldi
@BrianFeroldi
I own 7 stocks that are 15+ baggers (and counting)

Here are 10 traits they all have in common:

The data:

Stock / # of bags / purchase year:

$AMZN / 15+ / 2010
$FB / 15+ / 2012
$GOOG / 15+ / 2009
$MELI / 15+ / 2011
$CMG / 20+ / 2012
$NFLX / 60+ / 2010
$TSLA / 60+ / 2012

1: Founder-led

$AMZN - Bezos
$CMG - Ells
$FB- Zuck
$GOOG - Page
$MELI- Galperin
$NFLX - Hastings
$TSLA - Musk

Founders tend to be:
+Detail oriented
+Innovative
+Mission driven
+Think long-term
+Have skin/soul in the game

Look for founders!

2: Consumer-facing

All of these companies attract millions/billions of consumers

This eliminates customer concentration risk and enables long-term brand building

3: High sales growth

All of these companies were growing revenue 20%+ BEFORE I bought them

Sales growth is the engine that drives profit growth

Profit growth is the engine that drives stock appreciation

Find companies that can grow sales 20%+ for decades
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Romeen Sheth
Romeen Sheth
@RomeenSheth
I love Twitter.

It’s truly the Town Square of the Internet.

But finding the diamond in the rough voices can be tough.

Here are 20 of my favorite people to follow:

1. Alex Lieberman - @businessbarista

Alex writes extensively about the Founder journey.

The cool part is he’s lived everything he talks about - starting from $0 and selling for $75M with hardly any outside capital raised.

My favorite piece:


2. Ryan Breslow - @ryantakesoff

Ryan is a Top 1% founder.

This guy is a machine - he’s built 2 unicorns before the age of 27.

Ryan spells out lessons on fundraising, operating and scaling.

My favorite piece:


3. Jesse Pujji - @jspujji

Jesse is who I think of when I think “bootstrapping.”

He bootstrapped his company to an 8-figure exit and now shares stories about other awesome bootstrappers.

He’s also got great insight into all things growth marketing:


4. Post Market - @Post_Market

Post puts out some of the most thoughtful investment insights on this platform.

It’s refreshing because Post cuts through the hype and goes deep into the business model.

Idk who he/she/it is, but the insights are 💣.
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Aditya Todmal
Aditya Todmal
@AdityaTodmal
TradingView isn't just charts

It's much more powerful than you think

9 things TradingView can do, you'll wish you knew yesterday: 🧵

Collaborated with @niki_poojary

1/ Free Multi Timeframe Analysis

Step 1. Download Vivaldi Browser

Step 2. Login to trading view

Step 3. Open bank nifty chart in 4 separate windows

Step 4. Click on the first tab and shift + click by mouse on the last tab.

Step 5. Select "Tile all 4 tabs"


What happens is you get 4 charts joint on one screen.

Refer to the attached picture.

The best part about this is this is absolutely free to do.

Also, do note:

I do not have the paid version of trading view.


2/ Free Multiple Watchlists

Go through this informative thread where @sarosijghosh teaches you how to create multiple free watchlists in the free


3/ Free Segregation into different headers/sectors

You can create multiple sections sector-wise for free.

1. Long tap on any index/stock and click on "Add section above."
2. Secgregate the stocks/indices based on where they belong.

Kinda like how I did in the picture below.
SCREENERS , ALL , TRADINGVIEW
Ethan \U0001f468\u200d\U0001f3a4
Ethan 👨‍🎤...
@Booligoosh
First update to https://t.co/lDdqjtKTZL since the challenge ended – Medium links!! Go add your Medium profile now 👀📝 (thanks @diannamallen for the suggestion 😁)


Just added Telegram links to https://t.co/lDdqjtKTZL too! Now you can provide a nice easy way for people to message you :)


Less than 1 hour since I started adding stuff to https://t.co/lDdqjtKTZL again, and profile pages are now responsive!!! 🥳 Check it out -> https://t.co/fVkEL4fu0L


Accounts page is now also responsive!! 📱✨


💪 I managed to make the whole site responsive in about an hour. On my roadmap I had it down as 4-5 hours!!! 🤘🤠🤘
MAKERS
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Alex Stamos
Alex Stamos
@alexstamos
The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.


In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.

In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.

This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.

In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
TECH
RadioChinar
RadioChinar
@RadioChinar
Demystifying Ghazwa-e-Hind. A #thread.

A recent video of Shoaid Akhter surfaced in social media creating a lot of controversy where the #Pakistani Cricketer can be seen suggesting invasion of India by using the term Ghazwa-e-Hind.
Radicalization of sportsmen seen off late
(1/10)


Some radical Islamic preachers believes that Ghazwa-e-Hind means a “Holy War against India”. As per this irrational & profane concept, there will be a fierce battle to secure victory over #India.
#Kashmir
(2/10)


#Pakistan-based terror groups, like JeM have been using Ghazwa-e-Hind as a Hadith to recruit, fund and justify its terror strikes as a religious holy war against #India.
#Kashmir
(3/10)


Some machiavellian people claim it is a prophecy that Muslim warriors will wage war against the #India.
Jaish & others falsely propagate that Jihad against India is considered holy in Islam & that those participating in it will be granted an easy entry into paradise.
(4/10)


However prolific scholars says that this is a made-up interpretation of some verse in The Holy Quran to mislead the gullible youth of #Pakistan into picking up arms and fighting its Army's personal battle against #India.
#Kashmir
(5/10)
SOCIETY
Ferruccio Balestreri \U0001f308
Ferruccio Balestreri 🌈...
@0xferruccio
🕵️‍♂️ How Google's PageRank algorithm works

The PageRank algorithm gives each page a rating of its
importance, which is a recursively defined measure of importance, based on if important pages link to it.
It's recursive because the importance of a page refers back to the importance of other pages that link to it

Here's how it works in practice:
1⃣ We start with some pages and crawl them for links


2⃣ Each page has 1/N points (where N as the total number of pages)


3⃣ Add points to each page for the amount of links to it, divided by the number
of links emanating from the sources of these links
TECH