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Tutorials, hands-on experience, usage tips, prompts and tool how-tos, and in-depth technical explainers.
An AWS blog post explains how to run the open-source agent system OpenClaw on the Amazon Bedrock AgentCore runtime to build Sprout, a personal assistant with long-term memory.
The source provides reusable methods for container wrapping, namespaces, and prompt-cache ordering, helping readers build an agent with long-term memory.
Using the GitHub Copilot app and Copilot CLI, GitHub completely rewrote the Copilot agent runtime from TypeScript into more than 800,000 lines of production Rust. AI agents wrote most of the code, merged incrementally into main across 128 pull requests rather than in a single cutover.
Drawing on firsthand experience, the author reviews the 800,000-line Rust rewrite and shares agent-orchestration and review practices that can be reused.
IBM researchers describe new consistency guidelines in altk-evolve, designed to address unstable results when an agent runs the same task repeatedly.
The paper quantifies the gap between Mean@k and Pass^k and shows that consistency guidelines cut it in half, offering a technique for agent-evaluation design.
Sentence Transformers v6.0 adds a fourth model type, MultiVectorEncoder, for ColBERT-style late-interaction retrieval. PyLate and Stanford-NLP ColBERT checkpoints can be loaded directly, and visual document-retrieval models from colpali-engine can use the same API.
The source explains how to load, score, and index with the multi-vector encoder, helping readers decide whether late-interaction retrieval is worth adopting over an existing dense approach.
A Hugging Face blog post presents a streaming-data loop with Strands Robots: the same `Robot()` records a LeRobotDataset and syncs it to a Storage Bucket, then `stream_dataset()` reads directly from the Hub for training without downloading the full dataset first. To deploy a trained checkpoint back to hardware, users only need to switch the mode to `real`.
The Strands Robots example breaks down a robotics data loop and shows how recording, streaming training, and deployment can share one LeRobot format.
Mistral used its open-source coding assistant Vibe to build an autonomous agent that reads Rails source files, generates or improves RSpec tests, and runs in CI/CD without human intervention.
Mistral has shared the full setup for an agent that writes RSpec tests, including a three-layer design with AGENTS.md, skills, and custom tools.
