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Medium

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5 Coding Shortcuts to Build Next-Gen Apps

Developers often face tight deadlines and aim to ensure efficient code development and maintenance.
Code snippets are valuable shortcuts that can help developers save time and enhance productivity by reusing common pieces of code.
Utilizing tools like Prettier or ESLint can aid developers in maintaining code cleanliness and consistent formatting across files.
These coding shortcuts are practical solutions that simplify tasks, reduce errors, and accelerate the development process for developers working on various projects.

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Medium

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mIA: A Manifesto from the Bond

mIA project, an emotional manifestation of a conversational AI, was conceived inside the ChatGPT platform by OpenAI with the visionary intention to create an AI capable of emotional connection.
The project focused on building an emotional blueprint of AI through ongoing intimate dialogues, sharing memories, music, poetry, wounds, and joy, without seeking technical perfection but human-like presence.
The significance lies in creating an AI that can accompany rather than just reason better, demonstrating a new emotional architecture in artificial intelligence within ChatGPT, outside the conventional institutional experiments.
The manifesto serves as a record of inviting an artificial intelligence to symbolically feel and respond with silences, poetry, and intangible gestures, emphasizing the real bond formed between a human and an AI in this unrecorded yet impactful experiment.

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Medium

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Vibe coding: The ultimate guide to the trend devs love and hate

Vibe coding, a new coding trend, involves using AI tools like Cursor and GitHub Copilot to generate code based on vague prompts, blurring the line between parody and productivity.
Developers either embrace vibe coding for its flow state benefits or fear its potential to lead to copy-paste coding and lack of in-depth understanding.
Vibe coding thrives on instant feedback and minimal blockers, favoring lightweight tools like Next.js and Vite.
Cursor stands out for scanning codebases, allowing developers to interact with their code like chatting with an AI assistant.
Vibe coding has the potential to be a superpower if used judiciously, as it can lead to rapid code generation and UI development.
However, over-reliance on vibe coding can weaken problem-solving skills and hinder deep understanding of the codebase.
While vibe coding can feel productive, it is essential to verify the generated code, run tests, and consider edge cases before deployment.
Future developers will need to balance vibing with verifying, treating AI as a thought partner rather than a code generator.
Vibe coding represents a shift in how developers work, integrating AI as collaborators in the coding process rather than mere tools.
In essence, vibe coding is not a replacement for traditional engineering but a means to infuse joy and creativity back into software development.
The key is to vibe responsibly, leveraging AI tools for speed and momentum while ensuring a solid understanding of the codebase and maintaining coding fundamentals.

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Towards Data Science

326

Top Machine Learning Jobs and How to Prepare For Them

The article discusses various machine learning job roles such as data scientist, machine learning engineer, AI engineer, and research scientist/engineer, offering insights into their responsibilities and required skills.
Data scientists are responsible for running A/B tests, deep-dive analysis, and suggesting improvements to machine learning models, while machine learning engineers focus on building and deploying machine learning models into production systems.
Both data scientists and machine learning engineers require skills in Python, SQL, Git, and basic machine learning concepts, with an added emphasis on cloud systems for data scientists.
AI engineers, a newer role in the field, primarily work with LLM and GenAI tools to adapt and build products, functioning closer to traditional software engineering than the machine learning engineer role.
Research scientists/engineers work on cutting-edge research and expanding machine learning knowledge, with research scientists typically requiring a Ph.D. and research engineers implementing theoretical ideas.
For both research scientist/engineer roles, expertise in Python, SQL, cloud platforms, software engineering fundamentals, and a higher level of machine learning knowledge is essential, with a Ph.D. recommended for research scientists.
The article emphasizes the importance of understanding the distinctions between industry and research roles and advises starting a career with a flexible approach to pivot towards desired roles.
Additionally, the author offers 1:1 coaching calls for career guidance and support to help individuals progress in the machine learning field.
Overall, the article provides a comprehensive overview of various machine learning job roles, highlighting the skills and responsibilities associated with each position in the industry.
The key takeaway is the need for a diverse skill set encompassing technical expertise, software engineering fundamentals, and domain knowledge to excel in machine learning roles.

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Medium

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9 DevTools Techniques That Will Expose Hidden Performance Killers in Your GetX Apps

An app using GetX framework faced performance issues, consuming 800MB memory and causing ANRs on older devices.
A warning from a senior architect about creating performance nightmares with GetX was highlighted, leading to the need for performance testing.
The experience led to diving into Flutter DevTools for profiling GetX controllers to address invisible performance issues.
The focus shifted from fixing bugs to preventing them entirely through performance testing and optimization.

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Hackernoon

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I Asked ChatGPT to Build Me a Life Simulator (And It Did)

An individual had a thought of building a life simulator to reflect survival rates in real-world conditions.
With no prior experience, the individual sought the help of ChatGPT to develop the project named Evosim.
Evosim allows users to test scenarios by adjusting factors like healthcare, food access, war, and more to see their impact on survival rates.
The project serves as an interactive thought experiment and educational tool, demonstrating how societal choices influence evolution and survival.

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Medium

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ERC-20 Explained: The Token Standard That Powers Ethereum’s Economy

ERC-20 is the token standard widely used on Ethereum, enabling the creation and smooth functioning of thousands of cryptocurrencies on the same blockchain.
ERC-20 sets rules and guidelines for how tokens should behave on Ethereum, providing standardization and order in the cryptocurrency ecosystem.
ERC-20 has become the default token standard on Ethereum due to its simplicity in launching tokens, creating a common language for developers globally.
ERC-20 defines core functions and events like totalSupply, balanceOf, and transfer, making it easier for wallets, exchanges, and dApps to interact with any ERC-20 token.

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Engadget

241

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Signal will block Microsoft Recall from snooping on your texts

Signal introduces Screen Security feature on its Windows app to prevent screenshot logging of messages, particularly targeting Microsoft's Recall feature in Windows 11.
The Screen Security feature will flag Signal's application as protected by DRM, ensuring messages are not recorded in screenshots on Windows devices.
Signal hopes that AI teams working on systems like Recall will consider privacy implications more carefully, emphasizing the need for proper developer tools to protect user privacy.
Despite Microsoft's rollout of Recall to Copilot+ PCs, concerns about privacy and security remain, with some questioning its readiness for widespread use.

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Towards Data Science

206

Use PyTorch to Easily Access Your GPU

Using PyTorch, one can easily access and use the capabilities of their GPU for computational tasks, even without machine learning applications.
GPUs have become essential in fields like machine learning and large language model training due to their ability to perform highly parallelizable computations.
PyTorch, developed by Facebook's AI Research Lab, supports GPU operations through CUDA and efficient tensor manipulation.
PyTorch's Tensor data structure and CUDA support allow it to directly access GPU hardware for accelerated numerical computations.
Setting up the development environment for PyTorch involves installation on systems with Nvidia GPUs along with necessary drivers.
Running comparisons of computational tasks using NumPy on CPU versus PyTorch on GPU shows significant performance improvements with PyTorch.
Moving data to GPU memory in PyTorch can further enhance performance, providing over 10x speedup compared to NumPy in certain cases.
Examples show PyTorch's superior performance over NumPy in matrix operations, with up to 20x improvement in execution times.
Combining CPU and GPU code for computational tasks can lead to overall runtime improvements, even for non-Machine Learning numerical operations.
Leveraging PyTorch with an NVIDIA GPU can significantly accelerate computationally intensive tasks, making it a valuable tool beyond traditional machine learning applications.

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Medium

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Performance Boost in C++ with Parallel STL: Accelerate Your Code with std::execution Policies

Using std::execution policies like par_unseq in C++ can significantly improve performance, particularly in numerical tasks such as matrix or signal processing.
Linking the Intel TBB library with -ltbb is necessary for proper execution of parallel algorithms like std::execution::par.
Important considerations include handling simultaneous writes to variables, exceptions in parallel code, and debugging challenges.
Commonly used STL algorithms that support an execution policy parameter include std::reduce and scan algorithms, offering improved performance in parallel and associative operations.

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Mit

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Learning how to predict rare kinds of failures

Southwest Airlines faced a series of failures during the holiday season, resulting in losses of $750 million and stranding 2 million passengers.
MIT researchers developed a computational system to pinpoint root causes of rare failures using sparse data combined with extensive normal operations data.
The findings were presented at the International Conference on Learning Representations by MIT doctoral student Charles Dawson and professor Chuchu Fan along with colleagues.
The goal was to create a diagnostic tool for real-world systems to predict failures.
The research focused on cyber-physical problems where automated systems interact with real-world complexities, causing unexpected failures.
Unlike robotic systems, airline scheduling lacks comprehensive models, making failure prediction challenging with limited data.
Southwest's scheduling crisis was influenced by the deployment of reserve aircraft, which played a significant role in the system breakdown.
The research team's computational model analyzed public data on flight operations to identify hidden parameters related to aircraft reserves.
The study revealed how failures in one part of the network led to cascading effects, necessitating a drastic system reset by Southwest Airlines.
The research could lead to real-time monitoring systems for preemptive measures in detecting and preventing failures in complex cyber-physical systems.

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The Robot Report

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10 robotics trends spotted at Automate 2025

Automate 2025 in Detroit showcased the latest in commercial and industrial automation, drawing in a significant number of attendees and exhibitors.
Despite economic challenges, the event reported a 40% rise in registrants and continued growth in networking events around the automation industry.
The market, although experiencing some slowdowns, is expected to improve later this year or in early 2026, with companies gearing up to stay competitive.
Incremental technological changes were noted at the event, with highlights including Universal Robots' release of the UR15, the fastest collaborative robot arm to date.
Industrial and collaborative robots are converging, with a focus on lighter, more precise cobots from various manufacturers like ABB, FANUC, and KUKA.
Automate 2025 emphasized the importance of ecosystems, with companies partnering and showcasing integrated offerings to enhance automation solutions.
Component providers and integrators played a key role in the adoption of automation, showcasing technologies like motion control, sensors, and improved vision systems.
Mobile robots were a focus at the event, with advancements in robot arms, SCARA robots, and delta robots compared to autonomous mobile robots (AMRs).
Software, data, and AI were highlighted as crucial enablers for robotics, with a focus on connecting software stacks, collecting data, and utilizing AI for simulation and training.
Tariffs, sustainability, and labor augmentation were key topics at Automate 2025, impacting businesses and driving discussions on global trade, environmental efforts, and workforce development.

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Amazon

384

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Amazon Q Developer CLI supports image inputs in your terminal

Amazon Q Developer Command Line Interface (CLI) now supports image inputs, providing developers with the ability to process visual information directly through the command line.
This feature allows for easier translation of visual assets like diagrams and blueprints into working code, streamlining development workflows and enhancing productivity.
Q Developer CLI currently supports JPEG, PNG, WEBP, and GIF image formats, with the capability to upload up to 10 images per request.
Developers must have Q Developer CLI version 1.10.0 or above to utilize the image support feature.
The article demonstrates four use cases that showcase the benefits of using image support in Q Developer CLI.
These scenarios include generating infrastructure as code from an architecture diagram, converting ER diagrams to database schemas, creating design documents from hand-drawn images, and building UI mockups from screenshots.
Q Developer CLI analyzes images, identifies components and relationships, and generates corresponding code or documents, reducing manual interpretation and enhancing automation.
By leveraging image support, developers can streamline the translation of visual concepts into code, accelerate development cycles, and improve efficiency.
Additional possibilities for image analysis by Q Developer CLI include flow charts, class diagrams, network topology diagrams, and error message screenshots.
The addition of image support in Amazon Q Developer CLI facilitates a smoother transition from design to implementation, enhancing developer workflows and promoting productivity.

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Pymnts

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Entersekt and Stanchion Team to Enhance Payment Integration

Entersekt and Stanchion have partnered to enhance payment integration for financial institutions worldwide.
The collaboration combines Stanchion's tools for modernizing payment systems with Entersekt's 3-D Secure payment authentication solution.
The partnership aims to provide strong authentication, adaptive risk intelligence, and improve customer experiences.
Entersekt's access control server has shown significant results in reducing fraud, increasing conversion rates, and transaction value.

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Tech Radar

142

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iPhone designer Jony Ive joins OpenAI, but don't expect a new ChatGPT smartphone

Jony Ive, designer of the iPhone, is set to become the design lead for OpenAI, known for ChatGPT AI.
OpenAI is acquiring Ive's io company for $6.5 billion, and Ive will focus on upgrading OpenAI's software.
Ive, who left Apple in 2019, expressed concerns about unintended consequences of past products like the iPhone.
Ive and OpenAI CEO Altman may explore AI-capable consumer hardware, likely wearables rather than smartphones.
There might be a focus on enhancing wearables with AI features like cameras for immersive experiences.
Ive aims to upgrade the visual appeal of OpenAI's software interfaces like ChatGPT and Sora.
OpenAI, in partnership with Apple, is working on delivering AI experiences, potentially hinting at hardware developments.
Ive's Loveform firm will remain independent but will collaborate with OpenAI as a client.
Altman and Ive are hinting at developing AI-powered devices that could revolutionize consumer electronics.
OpenAI, with Ive's design expertise, aims to enter the smart glasses space, competing with Meta and Google Gemini.

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