AI Intelligence Archive

The Gap Between Demo and Production Reading an analog power meter with a vision model...

The most difficult AI workflows to debug are the ones that don't crash. A multi-step agent pipeline...

I ran one prompt through four models last week. Opus 5.5 and Sonnet 5.5 in Claude Code, GPT-6 Astra...

Ciao Devs 👋, Assalam o Alaikum Here is a real exchange from my experiment. I asked a model how...

The red leaves at the SAIJOU mountain spend the whole summer getting ready before they bloom. After...

A version number is a promise: nothing a client depends on changed without me saying so. For MCP...

Derivative turns software requirements into executable Python, but generated code is never allowed to...

The problem We build Mushare, a messaging network for AI assistants: you tell Meta's Muse...

On the first day, I planned nine tasks. Three got done. The other six ran into familiar problems:...

At Autoadify we build Workflows: a schedule, a new Shopify/WooCommerce product, or a post that's...

This is a submission for the Kaggle Benchmarking Challenge. What I Benchmarked ...

When I was about fourteen I spent my days in a computer club in Romania. Anyone could walk in. CP/M...

I built a small retrieval-augmented generation system over a corpus of research papers and wanted to...

Twenty-one sentences, no pretrained model. Drag to rotate, click a word to see its nearest neighbors,...

This article was originally published on the Zenrows blog. In this tutorial, you'll build a Mastra...

Why full fine-tuning an 8B model requires 130+ GB VRAM, how low-rank matrix decomposition cuts parameters by 99%, and how weight merging enables zero-latency serving.

TL;DR: Credentials conflate authentication and authorization: Long-lived API keys,...

Months ago, a commenter on this series proposed a test: if the same backend really stands behind both...

Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local in…

An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-clock time and to control their own actions. Prior work has focused on time-awareness, but …

Reinforcement learning (RL) fine-tuning improves vision-language-action (VLA) policies through closed-loop experience, yet generalization beyond the fine-tuning distribution remains limited. Our analysis reveals a selective reshaping of ex…

A holographic superconductor whose scalar mass depends on the gauge field strength, $M(\Fsq)$, reproduces a superconducting dome for a suitable $M$, and recovering that $M$ from a given dome has so far taken days for a single training run.…

Artificial intelligence supports building operations in several forms, each with its own barrier. Expert rules must be tuned for every system, supervised models need labeled data that buildings rarely record, and language models return fre…

We describe the Itgan systems for the three ASR subtasks of NADI 2026, namely robust country-level ASR (1.1), mixed-dialect ASR (1.2), and Tunisian code-switched ASR (1.3). All three share one recipe, Whisper adapted with LoRA on consumer …

Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction between the agent and an environment. We study the sample complexity of stochastic multi…

Knowledge graph embedding (KGE) models encode the entities and relations of a knowledge graph into a low-dimensional latent space, enabling tasks such as classification or link prediction. Most KGE models follow an autoencoder architecture…

I've spent the last stretch building a SOC 2 readiness tool, and the most useful thing I learned has...

Prevailing multi-vector visual document retrievers store each page as about a thousand patch vectors, often in vector databases run by a third party. Since no one can read a page from its vectors, this index is easily treated as less sensi…

Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers. This paper shows that a natural account of these sp…

Lossless compression can reduce the storage and movement of model weights without changing their floating-point values, but repeated statistical analysis and code construction add computational overhead. We study whether the statistical st…

Reinforcement learning is crucial for improving large language models' reasoning and generalization. It relies on massive rollouts whose lengths become increasingly long-tailed as context windows grow. In on-policy training, these long-tai…

Security Operations Centers (SOCs) for information technology and operational technology share one incident-response problem: a flood of correlated alerts and too few analysts. Large Language Models (LLMs) are increasingly proposed as reas…

Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing Attention) learns joint Gaussian predictive distributions of five surface variables from station observation…

Ship-form design combines smooth geometric representation, local shape editing, and constraints on the resulting hull. We present the Natural-Language-to-Hull Framework (NL2Hull Framework), which formulates ship-form editing as a typed dis…

In-database predictive query processing increasingly applies Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tuple-level model inputs to the inference runtime, leaving re…

Feedback from automated testing, human red teaming, and incident response can strengthen an AI system's defenses when discovered failures lead to effective repairs. We study when this feedback process provides sufficient protection and whe…

Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based robots. Practical challenges remain re…

Inverse optimization estimates the weights of an objective function that explain observed decisions as optimal solutions, and is used in a variety of fields. For mixed-integer linear programs (MILPs), existing methods aim to reproduce the …

Human learning is a dissipative dynamical process: mastery accumulates through practice, decays through forgetting, and propagates across interdependent concepts. We model it as a nonlinear dissipative system of ordinary differential equat…

You know the feeling. You've just spent 3 hours on a fix, your brain is mush, and now you have to...