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ai-models

A collection of 177 posts

Codex’s Windows Computer Use Update Turns Agent UX Into an Ops Surface

Codex’s Windows Computer Use Update Turns Agent UX Into an Ops Surface

Codex’s latest update looks, at first glance, like a Windows checkbox. That undersells it. OpenAI is not merely letting Codex click around another operating system; it is moving the coding agent from “chat window with tools” toward something closer to a managed desktop worker: host-bound execution, remote steering, app

LFM2.5-8B-A1B Makes the Local Agent Pitch Concrete

LFM2.5-8B-A1B Makes the Local Agent Pitch Concrete

Liquid AI’s LFM2.5-8B-A1B is the kind of model release that looks smaller than it is. It is not trying to beat every frontier system at every task. It is trying to make local tool-calling feel fast enough that the model disappears into the workflow. That is a more

Step 3.7 Flash Is the Cheap Agent Executor Thesis With a 198B MoE Attached

Step 3.7 Flash Is the Cheap Agent Executor Thesis With a 198B MoE Attached

StepFun’s Step 3.7 Flash is not subtle about the bet: the next useful coding-agent model is not necessarily the biggest brain in the room. It is the worker that can run most of the trajectory cheaply, call tools reliably, keep enough context in memory, and escalate only when

Entropy-Cut Sampling Makes Reasoning Models Look Less Like RL Magic and More Like Search

Entropy-Cut Sampling Makes Reasoning Models Look Less Like RL Magic and More Like Search

The lazy story about reasoning models is that intelligence lives in the weights and inference is just the receipt printer. Reasoning with Sampling pushes against that. Its Entropy-Cut Metropolis-Hastings method makes reasoning look less like a mystical property unlocked only by RL posttraining and more like a search problem where

LoRA Memory Has a Capacity Law — and a Security Footnote Nobody Should Skip

LoRA Memory Has a Capacity Law — and a Security Footnote Nobody Should Skip

LoRA adapters are usually sold as the cheap fine-tuning trick: fewer trainable parameters, lower VRAM, easier deployment, nice story for teams that cannot afford to retrain the universe every time their product changes. How LoRA Remembers? is useful because it treats adapters as something more specific and more dangerous: parametric

OmniRetrieval Says RAG Should Stop Flattening Every Data Source Into the Same Vector Soup

OmniRetrieval Says RAG Should Stop Flattening Every Data Source Into the Same Vector Soup

Most RAG stacks are built around a convenient fiction: every useful source of knowledge can be flattened into chunks, embedded, and ranked by semantic similarity. That works often enough to become a default, and badly enough to become a recurring production tax. Real organizations do not keep knowledge in one

UI-KOBE Makes the Case for Small GUI Agents With Maps, Not Bigger Brains

UI-KOBE Makes the Case for Small GUI Agents With Maps, Not Bigger Brains

UI-KOBE is a useful reminder that “use a bigger model” is often a lazy answer to an environment problem. Mobile GUI agents spend a depressing amount of runtime rediscovering facts that should be durable. This button opens settings. That screen is a dead end. The account page sits behind two