Learn how to operationalize Amazon Textract Custom Queries adapters for production: infrastructure as code with AWS CloudFormation and Terraform, a cross-account adapter promotion process, a pre-classification routing pattern for multiple form versions, and production security controls such as VPC endpoints, encryption, and least-privilege IAM.
Tool: Kākāpō Party I presented a closing keynote for the WeAreDevelopers World Congress North America yesterday. As a STAR moment I decided to weave in references to the record breaking kākāpō breeding season we had in 2026.
Describe the interface you need in plain English, then let the agent build a live surface you can both use and update—so you spend less time adapting to tools and more time getting work done.
Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP. This post presents an architecture that combines Amazon EKS, EFA, and Amazon S3 and increased aggregate reinforcement learning rollout throughput by 40% for large-scale RLHF and GRPO training.
Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, launching a Ray cluster from SageMaker Studio, submitting and monitoring the job, and hosting the trained LoRA adapter for inference.
NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, composite evaluation, and data accuracy verification—that reach about 99% numerical accuracy while streaming responses in real time.
Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed, real-time endpoint, and clone a voice from a short reference clip. Cross-lingual cloning preserves the speaker's identity across languages.
Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster's throughput after a brief NeuralCache warmup.
The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynchronous endpoints for word-level, speaker-labeled transcription, plus the production details that matter: the GPU AMI pin, scaling, and cost controls.
Build a multi-account architecture that keeps each team's data in its own AWS account while giving AI agents a unified way to query across them. A central platform account runs the agent using Amazon Bedrock AgentCore Gateway and MCP, while line-of-business accounts expose their data as MCP servers with secure cross-account access and fine-grained authorization.
How we rebuilt the diff surface in the GitHub Copilot app to open a million-line pull request with hundreds of inline review comments.
Tool: Shadow roots, explained with live examples Prompt to Fable 5.1 Medium: Build an artifact to explain shadow roots in CSS with interactive examples
A rewrite this size wasn't affordable before agents. Here's what porting the Copilot agent runtime to 800,000 lines of production Rust actually took.
If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team.
Learn how OpenAI evolved Habitat from a Python library into a globally distributed storage platform serving 1 billion ChatGPT users and 22M requests per second.
Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app.
TRL v1.14's AsyncGRPOTrainer now supports training and synchronising only LoRA adapters to vLLM. A rank-1 adapter is just a few MB and can transfer through a Storage Bucket mounted to each job, without NCCL.
Mistral helped a European energy operator migrate 40,000 lines of Fortran 77 to C++. Learn how it was done, and the lessons to carry forward.
A public low-cost approach fine-tunes LFM2.5-350M with TRL GRPO using around 500 samples and 100 steps, raising IFStruct from 22.6% to 29.7%. Training fits free Colab or Kaggle GPUs, with local llama.cpp evaluation on a MacBook and code open on GitHub.
A Hugging Face blogger reproduced Surya Narreddi's idea of having a language model paint watercolours using TRL and OpenEnv. The model paints through around 150 lines of JavaScript with p5.brush, with datasets, RL environments, training scripts and models all open-source.
Sentence Transformers v6.0 adds a fourth model type, MultiVectorEncoder, for ColBERT-style late-interaction retrieval, with a complete training approach.
Papers with Code uses hybrid retrieval, with Hugging Face Jobs generating embeddings in batches, Storage Buckets providing persistent transfer and Inference Endpoints supplying low-latency query embeddings. It covers over 110,000 papers from arXiv and Daily Papers.
IBM Research published ALTK-Evolve research on the Hugging Face blog. Tests of eight models on 585 multi-step AppWorld tasks found agent memory is not an on/off switch but a dosage requiring model-specific calibration.
Sentence Transformers v6.0 adds a fourth model type, MultiVectorEncoder, for ColBERT-style late-interaction retrieval. It directly loads PyLate, Stanford-NLP ColBERT checkpoints and visual document retrieval models from colpali-engine.
A Hugging Face blog describes a constraint-aware GPU allocator compared with FIFO under identical hardware and workloads. Across five real contention scenarios, GPU utilisation rose from 52–85% to 72–88%, while priority-weighted output improved 24.6–105.1%, averaging 52%.
A Hugging Face blog tutorial demonstrates a streaming data loop for Strands Robots. The same Robot() object records demonstrations, syncs them to a Storage Bucket, streams training data from the Hub and deploys the checkpoint back to hardware, keeping the LeRobot disk format unchanged throughout.
Hugging Face's ALTK-Evolve and ACE both provide agent memory without weight updates, but differ in delivery: ACE injects the full playbook at every step, while ALTK-Evolve retrieves a small number of strongly supported guidelines for each task.
IBM Research shared its experience building model routing into agent systems on the Hugging Face blog, noting that most routers treat model selection as classification when it is actually a systems-optimisation problem.
The third Hugging Face PyTorch profiling instalment traces attention implementation with a profiler. Comparing naive attention with in-place masking shows replacing masked_fill with masked_fill_ removes one GPU Memcpy kernel per forward pass.
Hugging Face's fourth PRX instalment details its data strategy: mixing public and internal pretraining datasets, regenerating long image captions with a VLM and converting them for streaming training. It uses Lance for construction and filtering and MDS for streaming.
NVIDIA's ENPIRE lets coding agents drive autonomous experiments and iteration with real robot arms. Dual YAM arms and an RTX 5090 workstation achieved 99% success on PushT, pin organisation and cutting cable ties. GPT-5.5/Codex and Opus 4.7/Claude Code traded wins, while Kimi-2.6 lagged. Eight agents found higher-scoring solutions faster.
Mistral built an autonomous agent on its open-source coding assistant Vibe to read Rails source files, generate or improve RSpec tests and run within CI/CD without human intervention.
Mistral AI investigated a vLLM memory leak in a disaggregated Prefill/Decode deployment of Mistral Medium 3.1. System memory grew linearly at 400 MB per minute, causing out-of-memory failures within hours. It occurred only on the decode side with graph compilation and KVCache transfer via NIXL. Heaptrack showed stable heap memory; the issue was outside the heap.
Mistral fine-tuned Pixtral-12B with LoRA, substantially outperforming the untuned baseline on Aerial Image Dataset (AID) satellite classification and reducing hallucinated invalid class names. Fine-tuning uses Mistral's API or LaPlateforme UI without extensive hyperparameter tuning, with 8,000 training and 2,000 test samples.
Mistral proposes judge LLMs scoring generator responses on numerical, binary or qualitative scales, then taking weighted averages across evaluation datasets to assess RAG systems.