World Wire

AWS introduced Pizza Bot , as a self-hosted application for AI tasks that continue while users work elsewhere. It organizes completed results and pending decisions into an email-style inbox. Earlier versions served more than 2,000 people inside Amazon, supporting meeting preparation, email drafting, Slack summaries, CRM logging, and research. The public application was rebuilt as an open source project. Deployable: Yes. Pizza Bot offers macOS, Windows, and Linux desktop builds, and browser and terminal clients connected to a local or standalone backend. Its code is licensed under Apache 2.0. An Inbox for Asynchronous Work Pizza Bot separates tasks into All , the thread history; Unread , completed work awaiting review; and Action , work paused for approval or an answer. Users can organize threads into folders and inspect delegated workers in the Activity panel. Tasks can start manually, through cron schedules, or through webhooks. The server owns scheduling. After down...

An agent, in its simplest form, is an LLM calling tools in a loop. That loop works for short jobs. Give it a task that runs for an hour and 200 tool calls, and it breaks in 2 predictable ways. The AWS Samples design guide for autonomous cloud coding agents names them directly: shallow agents suffer from context overflow, get distracted (goal loss), and do not maintain state over long periods. The layer that fixes this is not the model. It is the harness, which AWS describes as managing everything but the model. This article opens up that layer. Compaction, memory strategy, context budgeting, and todo-state are the machinery that turns a shallow loop into a deep agent. We look at how LangChain Deep Agents , Claude Code , Manus , OpenAI Codex , and Amazon Bedrock AgentCore implement each one, with the actual thresholds they ship. Why a bigger window does not fix it The obvious fix is a larger context window. The evidence says it helps less than expected. Chroma’s Cont...

In this tutorial , we implement NVIDIA cuML as a GPU-accelerated machine learning framework and build a practical workflow that demonstrates how RAPIDS can accelerate familiar data science and machine learning tasks. We begin by configuring the GPU environment and examining cuml.accel, which lets us accelerate existing scikit-learn workloads with minimal code changes, before moving to the native cuML API for direct CuPy and cuDF interoperability. We then benchmark CPU and GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN, while using synchronized timing to obtain meaningful performance measurements. We also build GPU-based manifold-learning and clustering pipelines with UMAP, t-SNE, HDBSCAN, and trustworthiness metrics; explore high-throughput forest inference with FIL; validate GPU-generated SHAP explanations; perform hyperparameter optimization with scikit-learn meta-estimators; and finally serialize trained models while e...

Cognition , the company behind the Devin coding agent, has released SWE-2 , its most capable coding model to date. SWE-2 is post-trained with reinforcement learning from Kimi K3 , Moonshot AI’s 2.8T-parameter open model. Cognition reports a score of 50.0% on FrontierCode 1.1 Main , within 1 point of Fable 5.1 at 64% lower cost. It is also Cognition’s first model with selectable reasoning-effort levels, all trained in a single RL run. Is it deployable? Not on your own infrastructure. SWE-2 has no open weights and no standalone API. It runs only inside Devin: Desktop and CLI today, with Devin Web and Fusion rolling out. What is SWE-2 SWE-2 builds on the infrastructure and recipe behind SWE-1.7 , which was post-trained from Kimi K2.7. This time Cognition scaled RL to the multi-trillion-parameter regime, using a base model with almost 3x the parameters. Cognition says its RL still finds substantial headroom on top of K3, adding 5 to 6 points on many benchmarks. ...