Amaka

Learn profitable crypto strategies for beginners in a bull run. Maximize gains with expert tips and tactics.

3 Best Crypto Trading Strategy for Beginners

amana is redefining trading in MENA with a cloud-native, crypto-inclusive platform built for local users—simple, fast, and designed for real regional needs.

The Platform Built for MENA: Why amana Isn’t Just Another Trading and Investing App

I have very recently started making some progress with my <a href="https://hackernoon.com/launching-my-company-a-scholarship-and-a-webinar-277e3b66e351" target="_blank">Self-Taught Machine Learning Journey</a>. But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. In this Series of Blog Posts, I talk with People that have really inspired me.

Interview with Kaggle Kernels Expert: Aakash Nain

Tesla has the data ocean. Waymo has the ground truth. Nvidia has the compute. Who wins the Gran Truismo of autonomous driving?

Gran Turismo 2026: The Great AI Showdown for Autonomous Driving

With a plug-and-play value model, Q★ improves LLM reasoning and achieves cutting-edge coding and math correctness without the need for fine-tuning.

Researchers Just Built a Plug-and-Play Brain for LLMs

Kateryna Korotieieva explores how product managers can build sustainable, AI-powered products by focusing on resilience, ethics, and long-term value.

Is AI the End of Sustainability?

Python tutorial describing process to design a custom bird vocalization classifier using audio-to-mel-spectrogram conversion and Google's EfficientNet B0 model.

Machine Learning for the Birds: Building Your Own Bird Vocalization Classifier

An in-depth analysis of 1,355 GitHub Copilot issues reveals key problems, causes, and solutions—and what Copilot’s team should improve next.

Common Problems with GitHub Copilot (And How to Solve Them)

What do developers and studies say about GitHub Copilot? We break down research on code quality, productivity, and security in real-world use.

How Effective Is GitHub Copilot?

We address threats to construct, external validity, and reliability in our Copilot study using multiple data sources and consensus-based data labeling.

⚡ TLDR 

How GitHub and Stack Overflow Data Were Verified for Research Accuracy

Research shows Copilot alters coding workflows, increasing code verification time and emphasizing the need for clear code explanations and flexible AI features.

Exploring User Needs and Satisfaction with GitHub Copilot

Users want more Copilot customization, better code quality, simpler setup, broader IDE support, and attention to IP and privacy concerns.

How Copilot Can Better Serve Developers

Learn how to use GitHub Copilot effectively—maintain control, improve code quality, and avoid platform issues with these research-backed insights.

How Developers Stay in Control While Using Copilot

Discover 11 solution types used to fix GitHub Copilot problems—based on 497 real cases across bugs, settings, versions, features, and more.

11 Proven Solutions to Common GitHub Copilot Problems

Explore 391 GitHub Copilot issues categorized into 16 root causes, from internal errors to IDE incompatibility and user misconfigurations.


The 16 Main Reasons Why GitHub Copilot Breaks

Explore 1,355 GitHub Copilot issues categorized by type, cause, and fix—plus insight into feature requests and what’s been resolved by GitHub.

How Developers Struggle with Copilot (And What GitHub Has Fixed)

A qualitative study of GitHub Copilot issues using data extraction and open coding to reveal root causes, real solutions, and recurring developer problems.

How Researchers Used Grounded Theory to Decode Copilot Issues

A large-scale study uncovers key problems developers face using GitHub Copilot, along with their causes and potential solutions.

What Developers Wish GitHub Copilot Did Better

An empirical study of GitHub Copilot reveals common problems, their causes, and solutions—based on 1,300+ developer discussions and issues.

What Developers Really Think About GitHub Copilot

While they may seem similar at first glance, there’s a big difference between spam and phishing, and understanding that difference is crucial for protecting you

Spam vs Phishing: Know the Difference

Exploring the impact of model size on COCOGEN's performance, CODEX-002 outperforms CODEX-001. Prompt sensitivity decreases as model size increases.

How CODEX Model Size Influences COCOGEN's Output Quality

Human evaluation shows COCOGEN outperforms DAVINCI in generating more relevant and correct commonsense graphs for tasks like EXPLAGRAPHS and PROSCRIPT.

COCOGEN vs DAVINCI: A Human Evaluation of Structured Commonsense Graph Generation

Coding remains only one part of the overall software engineering process.

Coding Might Not Be the Best Use of Your Time Any More

COCOGEN pioneers the use of Code-LLMs for structured commonsense generation, opening new avenues for NLP tasks that require structured prediction and reasoning.

Unlocking Structured Commonsense Reasoning with Code-LLMs

Integrate the systems you use every day in a matter of minutes and without a single line of code to start synchronising your data into Xero, QuickBooks and MYOB.

AMAKA

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