State of AI-assisted software development

Google Cloud's 2025 DORA report reveals that AI adoption among software development professionals has surged to 90%, with developers dedicating a median of two hours daily to working with AI, and 65% heavily relying on AI for software development. The report, which surveyed nearly 5,000 technology professionals globally, shows that AI is no longer a novelty, but a near-universal part of a developer's toolkit, leading to significant productivity gains.

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America's top companies keep talking about AI – but can't explain the upsides

The provided text appears to be a webpage from the Financial Times, with various links and sections related to news, markets, and opinion pieces. There is no specific article or text to summarize, but rather a collection of headlines and navigation menus. America's top companies are discussing AI, but lack explanation of its benefits, according to one of the article headlines. The page also offers subscription options to access premium content.

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Context Engineering for AI Agents: Lessons

The author, Yichao 'Peak' Ji, and his team at Manus faced a key decision at the beginning of their project: whether to train an end-to-end agentic model or build an agent on top of the in-context learning abilities of frontier models. They chose the latter, which allows them to ship improvements in hours instead of weeks, and have since developed principles for context engineering, including optimizing the KV-cache hit rate and using a context-aware state machine to manage tool availability.

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AI won't use as much electricity as we are told (2024)

Predictions that AI will consume a large percentage of the world's electricity, potentially hindering the transition to renewable energy, are likely exaggerated and follow a pattern of similar overestimates made about the internet and personal computers in the past. In reality, the IT sector's share of electricity use has remained relatively small, around 1-2%, and efficiency gains are likely to offset any increased energy demand from AI growth.

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Greatest irony of the AI age: Humans hired to clean AI slop

The rise of AI has created a paradox where it is both consuming millions of jobs and creating new ones, specifically in the area of "cleaning up" the low-quality content, or "AI slop", that it generates. Humans are being hired to fix the mistakes and flaws in AI-generated content, such as articles, images, and videos, which is becoming a unique category of employment for hundreds of thousands of people, including designers, writers, and digital artists.

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The illusion of diminishing returns in LLM progress

Continued scaling of large language models yields significant improvements in their ability to complete long tasks, as marginal gains in single-step accuracy compound into exponential improvements in task completion length. Larger models can execute tasks for many more turns than smaller models, even when the smaller models have perfect single-turn accuracy, and this is due to the larger models' ability to avoid self-conditioning errors that cause mistakes to compound over time.

ArXiv

Learn Your Way: Towards an AI-Augmented Textbook, Google Research

Textbooks have a limitation as a one-size-fits-all medium that cannot be easily adapted to individual needs. The Learn Your Way system uses generative AI to transform and augment textbooks, adding personalized representations and maintaining content quality, with evaluations showing advantages over traditional textbook usage.

ArXiv

Are elites meritocratic and efficiency-seeking? Evidence from MBA students

Ivy League MBA students, a group of future elites, were found to implement more unequal earnings distributions and be highly responsive to efficiency costs in their redistributive choices. Their preferences differ from the broader population, with a lower tendency towards strict meritocracy, providing insight into how elites' views may contribute to high levels of inequality in the US.

ArXiv

Hyb Error: A Hybrid Metric Combining Absolute and Relative Errors (2024)

The Hyb Error metric, given by $\frac{|x-y|}{1+|y|}$, combines the advantages of absolute and relative error while mitigating their limitations, approaching absolute error for small $|y|$ and relative error for large $|y|$. This metric has a useful property where a Hyb Error of $\epsilon$ is equivalent to the condition for the "isclose" floating-point equality check function, making it a pragmatic choice for measuring error in sequences.

ArXiv

OpenFake: An Open Dataset and Platform Toward Large-Scale Deepfake Detection

Deepfakes, created using advanced AI techniques, have made it increasingly difficult to distinguish between real and synthetic media, particularly in politically sensitive contexts, and are often used to spread misinformation. To combat this, a new comprehensive dataset of real and synthetic images has been created, paired with a crowdsourced platform that encourages the submission of challenging synthetic images to help develop more effective deepfake detection methods.

ArXiv

Getting AI to work in complex codebases

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Repo

The Little Book of llm.c – friendly explaining llm.c in plain English

The Little Book of llm.c is a companion guide to Andrej Karpathy's llm.c repository, a reference implementation of GPT-2 in C, which provides a step-by-step explanation of the code for beginners. The book is available in various formats, including PDF, EPUB, and online, and covers topics such as data tokenization, model definition, CPU and CUDA inference, and training loops, as well as extending the codebase and debugging techniques.

Repo

Show HN: RapidFire AI: 16–24x More Experiment Throughput Without Extra GPUs

RapidFire AI is a new experiment execution framework that enables rapid and intelligent workflows for fine-tuning and post-training of large language models (LLMs) and deep learning (DL) models. It features hyperparallelized training, dynamic real-time experiment control, and automatic multi-GPU system orchestration, allowing users to compare many configurations concurrently and optimize model performance.

Repo

Show HN: Apples2Oranges. Ollama with hardware telemetry.On device LLM playground

Apples2oranges is a desktop app developed by bitlyte.ai that allows users to run and compare small language models (LLMs) side by side, providing hardware and inference telemetry, and direct model comparisons. The app is built with a range of technologies, including Rust, Tauri, and React, and offers features such as dual chat interface, smart memory management, and comprehensive telemetry data, making it a useful tool for developers, hobbyists, and students working with on-device AI and LLMs.

Repo

Lobe Chat: open-source, modern design AI chat framework

Lobe Chat is an open-source, modern design UI/framework for ChatGPT and other large language models, offering features such as speech synthesis, multi-modal support, and a extensible plugin system. It allows for one-click deployment of private chat applications and supports various technologies, including model visual recognition, text-to-image generation, and local large language model support.

Repo