Anthropic has made Claude Opus 5.5 the default model on all paid plans and published a guide that does away with old prompting habits. The main takeaway is to drop "think hard" and start at Medium effort.
Amazon Bedrock AgentCore runtime V2 snapshots your initialized environment and restores it for each new session, so session start no longer grows with image size. This post explains how V2 starts a session, how to measure session start time, and four patterns to keep work off the first request.
Coding agents work in tasks, and the number that matters is cost per solved task, not price per token. In this post, you use the AWS CDK to deploy LiteLLM with the NVIDIA NeMo Switchyard routing plugin on Amazon Bedrock, then measure cost per solved task for a routed model group against single-model baselines.
GS Caltex connects Amazon SageMaker Canvas to Amazon SageMaker Pipelines so process engineers' no-code models run in production. This post shows how ML engineers reuse validated preprocessing flows and models, cutting deployment from about a week to a few hours and running batch inference every 30 minutes.
Learn how AssemblyAI runs cost-optimized speech-to-text inference at scale on AWS, using Amazon ECS, Amazon EC2 Spot GPU instances, and an Amazon SQS-based retry architecture to achieve 70% cost savings while maintaining SLA commitments across European Regions.
This walkthrough showcases a beverage can pick-and-place task that uses the UR17G arm , Robotiq 2F-140 gripper , and beverage can to compose a scene in Isaac Sim, check their behavior, and run a pick-and-place example with agent-generated controls.
Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router's production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.
Amazon Bedrock provides built-in, zero-instrumentation cost attribution that traces every inference call back to the IAM principal that made it. This post shows how to attribute per-user Bedrock costs for AI developer and productivity tools using IAM Identity Center, session tags, and CUR 2.0, and how to enforce per-user usage limits.
Agent skills capture repeatable engineering work in files teams can review and reuse, while tools, project context, and permissions remain specific to the host that runs them.
Your Amazon Bedrock inference bill can keep climbing even after you tune prompts and application logic. Four request-level choices (cache duration, service tier, reasoning effort, and maxTokens) change how many tokens the service processes and the rate applied. We show how we tested each and how to verify the effect.
Build a self-service LLM gateway with LiteLLM on AWS Fargate that puts Amazon Bedrock foundation models behind an OpenAI-compatible API. Developers get instant access through a single API key, while platform teams gain per-user cost controls, quota isolation, and observability.
Comparing per-token prices hides the real cost of running a model on Amazon Bedrock. This post shows how to calculate the true cost per task across four hidden cost drivers, reduce it with caching, routing, and output constraints, and prove agent ROI with human baselines, holdout experiments, and a five-dimension scorecard.
Get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features, with no application code for the common case. Attribute spend with IAM principal tags, report with CUR 2.0 or Cost Explorer, and enforce with AWS Budgets actions that apply a Deny policy when a threshold is crossed.
Agents stall between demo and release when teams lack a repeatable way to measure how good an agent is and whether the last change helped.
Learn how to expose an Amazon Bedrock Managed Knowledge Base to AI agents as MCP tools through Amazon Bedrock AgentCore Gateway while keeping per-user access control. A request interceptor injects verified identity, Policy in AgentCore validates it independently, and a resource-based policy restricts direct access to the knowledge base.
Build a composable, interoperable multi-agent system on AWS with the Agent-to-Agent (A2A) protocol. A LangChain orchestrator on Amazon ECS communicates with a Strands agent on Amazon Bedrock AgentCore across organizational boundaries, using bidirectional, OAuth-authenticated calls, with neither side depending on the other's framework or SDK.
TLDR; In this blog, we provide fine-tuning scripts and containers to fine-tune TRELLIS.2, a popular 3D model on your 3D assets, that can run both locally and on Hugging Face Jobs.
Silent data corruption is a GPU fault that returns wrong but plausible numbers with no error, quietly poisoning distributed training. This post adds three checks to a PyTorch DDP loop to detect it, localize the faulty node on Amazon SageMaker HyperPod, and hand the node to automatic recovery for replacement.
A textual diff of two formal policies flags every cosmetic change and hides the one that alters behavior. This post shows how to compute a symbolic, deterministic semantic diff with an open-source anti-unification engine, so reviewers see only the genuine disagreements between two candidate authorization policies.
A practical guide for enterprises rolling out OpenAI Codex to developers on AWS, with Amazon Bedrock as the model backend, covering the access, governance, and cost decisions that platform, security, and finance teams need to align on.
Learn how Java developers can build production-ready autonomous AI agents using Amazon Bedrock and Spring AI. This post walks through tool calling, conversation memory, resilience patterns, and multi-agent orchestration, then deploys the agent to AWS with Amazon Bedrock AgentCore.
This post shows how to keep the AWS Agent Registry current automatically. Using AWS CloudTrail, Amazon EventBridge, AWS Lambda, and AgentCore Identity, an event-driven pipeline detects MCP server changes and pushes updated tool metadata to the registry with no stored secrets, across single- and multi-account environments.
Every generative AI team keeps rebuilding the same components: RAG pipelines, document processors, and agent scaffolding. This post shows how to access a curated catalog of 23 production-ready, open source reusable assets from the AWS Generative AI Innovation Center, with real delivery results from DoorDash, Lyft, and Ricoh.
Learn how to build an AI-driven product development lifecycle (AI-PDLC) with a suite of six AWS Quick agents that take you from scattered customer signals to a validated prototype across Discover, Decide, Design, and Prototype, with human approval at every gate and a specification ready to hand to coding agents like Kiro.
Multi-vector embedding models give each query token its own opportunity to find a match in a document. With pool_factor=4 , my normally trained model retains 94.6% of its unpooled NanoBEIR nDCG@10, with a 4x reduction in document vectors. Token pooling-aware training raises that retention to 97.8% at the same storage budget!
The Web Search API in Amazon Bedrock AgentCore gives agents current, cited web results signed with the AWS credentials they already use. In this post, build a daily dependency watcher as a LangGraph graph that researches package ecosystems in parallel, keeps its rules in code, and checks every finding against its cited sources.
When you deploy an agent, whether it is working is really several questions: did it finish the task, stay on topic, leak data, or skip steps? This post shows how to evaluate agents on Amazon Bedrock AgentCore with built-in, DeepEval, and AutoEval evaluators, and how to match each behavior that matters to the right evaluator at the right level.
Running Amazon Bedrock across many accounts shows you the bill but not what happened inside each call. This post builds a centralized observability data lake that aggregates Bedrock invocation logs from every linked account into one queryable table, so you can see prompts, token counts, model choice, and per-invocation cost across your organization.
Large Language Models (LLMs) are exceptionally good at generating text. But what if the model could directly evaluate the available options and return a probability distribution over them, without generating any text?
Anthropic's documentation notes that Claude Fable 5.2 is highly sensitive to how a prompt describes code ownership. In tests on small coding tasks, when a prompt said nothing about ownership or presented the codebase as someone else's, the model tended to follow the existing structure, even when that repeated existing code. When told explicitly that the codebase is its own, the model merged repeated code in about half of tries, against under a tenth without that prompt.
Our house has a box room, full of boxes that we still haven't unpacked from our last move. A couple of weeks ago something in that room started intermittently beeping .
CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.
A seven-layer composable reference architecture for taking enterprise agentic AI from prototype to production on Amazon Bedrock AgentCore. Each layer has a stable contract, so you can swap technologies at any layer without redesigning the rest of the stack.
Most of the changes I make to my projects these days come in the form of PRs opened for me by Claude Code or Codex. The PR is my unit of work - I use them to bundle commits together, iterating on them until a feature is complete, document decisions and then merge the finished result.
My comment on The Web Is More Than Enough, by Paul Ford — Hacker News. OK, this comment convinced me to give that a shot. Here's a thing I made in Three.js this morning - a Nudibranch themed Faberge Egg: https://static.simonwillison.net/static/2026/Hidden-Reef-Egg...
Research: parse_number() as a SQLite extension: C vs Rust I was out walking the dog when I got curious about how easy/difficult it would be to write a SQLite C extension that adds a parse_number() function for turning strings like -$1,234.56 or '12.5%' into numeric values that could be summed and sorted.
What do you assign when a coding agent can finish last year’s homework in one prompt? In the second run of Stanford’s CS146S, Mihail Eric brought open-source maintainers into the course.
What happens when students can one-shot last year’s homework? Inside CS146S and the engineering team at Monaco.
Writing code got faster. The work around it didn't. See how you can plan, write code, review changes, and investigate production issues with GitHub Copilot, and what work still needs your judgment.
Run Claude Code as a remote agent on Amazon Bedrock AgentCore. Each coding task runs in its own isolated session in your AWS account, every model call goes through Amazon Bedrock, and the result comes back as a GitHub pull request you review and merge.