AI Intelligence Archive

“OpenAI-compatible” is useful shorthand. It is not a portability guarantee. Two APIs may accept...

What a four-major-version Angular migration taught me about supervising AI agents on...

Bringing TypeSafe's System One model into a real Java application with The Pipeline...

Giving an AI agent unrestricted access to your entire computer is probably not a great...

Google published a new open source project, and within a day it was the most discussed AI submission...

Model Context Protocol has changed the way AI applications interact with external tools. Instead of...

Hi Guys!!! As you know I wasn't good for well, a week and Now..... Let's Dive In!!! I never...

An AI chatbot creates a new data pathway through your website. A visitor submits text, retrieval...

Claude Code talks too much, and its status bar tells you nothing. So I built two open-source plugins...

I built an evaluation suite for an MCP server — the kind that hands a product's data to an AI agent...

Your AI agent works on your laptop. You ran it a hundred times in development. It gives reasonable...

The discovery of novel inorganic materials drives technological breakthroughs in critical fields such as computing and energy storage. Generative AI has promised to accelerate the materials discovery pipeline, but state-of-the-art flow and…

Scene safety understanding plays a life-or-death role in situational awareness in various critical domains. Traditional methods that rely on learning direct mappings between scenes and safety levels often lack interpretability, limiting th…

Conventional wisdom in deep learning holds that overparameterization---having more parameters $p$ than training samples $n$---is benign: larger models generalize better and, even without regularization, interpolating models generalize well…

How I went from "Trust me, bro" to "boomer with receipts" using Jev By separating...

Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (TSLMs) remain inherently static. They either receive complete sequences for offline proc…

Recent advances in large language models (LLMs) have made them widely used for code-related tasks. Identifier names are statistically informative in naturally occurring code, but their information is not always reliable. We investigate whe…

Adapted for DEV Community from an article originally published on the Oimi blog. The engineering...

Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries potential safety concerns and low specificity may in practice lead to increased clinician …

Transforming scholarly PDFs into machine-readable fulltext remains a bottleneck for large-scale information systems. Recent vision-based parsers improve accuracy, but need GPUs and may introduce noise into the extracted text. GROBID, a mod…

Successful mobile manipulation requires coordinated base and arm motion while maintaining accurate spatial positioning. However, demonstration-trained policies can struggle to realise the intended base motion reliably, leading to spatial m…

Fairness-aware distributed learning prioritizes clients with large losses to reduce performance disparities, but label poisoning can create large losses, thereby inducing a fairness--robustness conflict. We propose FairMean to manage this …

I had the opportunity of trying Devin and its suite for the first time. By suite I mean its desktop...

We spent years trying to make coding easier. Better editors. Better frameworks. Better libraries....

Long-horizon and multi-turn agents typically generate short actions and process long observations from tools and environments. This growing context demands efficient prefill, compact KV-cache storage, and accurate long-context retrieval. T…

Read-only exploration: letting a coding agent look without letting it touch I build...

Read on: measuring the noise floor before trusting a delta · 繁體中文版 Do this first — it is...

With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, quickly identifying impactful work is essential, yet manuall…

Originally published at cchinchilla.dev. Part 2 of From code to weights, a 12-part series on ML...

Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclea…

Quick answer: As of 4 days ago, September 18, Claude Code natively reads AGENTS.md, the open,...

Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation strategies are often reactive, responding only after performance has dropped and imposing …

The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using excl…

Political discourse is widely perceived to be growing more hostile, yet robust evidence remains scarce. This study examines blame attribution in the Danish Parliament from 1997 to 2026, combining a purpose-built classifier, BlameBERT (F1: …

Residual Vector Quantization turns continuous representations into discrete, multi-level token sequences. Yet most methods operate in Euclidean space, despite the coarse-to-fine structure of the resulting codes and the latent hierarchies p…

Argument Mining (AM) transforms natural language into its underlying argument structures. This transformation is typically realized through a sequence of AM tasks that form an end-to-end AM pipeline. However, AM approaches often differ in …