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Addy on how to use Claude Code in the Claude Opus 5.5 era

Addy Osmani discusses how to use Claude Code in the Claude Opus 5.5 era, arguing that starting with the Claude 5 series, users should reduce piling up prompts and hand-holding task breakdowns, and instead use a single prompt that clearly states the goal and acceptance criteria, letting the model run to completion on its own and then stop. He recommends starting from /effort medium; when the goal is unclear, first have Claude interview you to produce a spec, then open a new session to implement it. For Claude Opus 5 and Claude Fable 5, he removed more than 80% of the Claude Code system prompt, with no noticeable drop in coding benchmarks. He also mentioned that the Claude Code interactive terminal recently defaulted to auto mode, and said this is not a safety guarantee; sub-agents should be split less by hand, and a single agent plus skills often yields better quality.

Artificial Analysis tests GitHub Copilot Fusion: $5.59 cost per task…

In Coding Agent Index v2.0, Artificial Analysis benchmarked GitHub Copilot's Copilot Fusion, which costs $5.59 per task, compared with $11.42 for Claude Code with Opus 5.5 (max), while performing only 2 points lower. Copilot Fusion is a runtime orchestration system that selects and coordinates among Anthropic and OpenAI models such as Claude Opus 5.5, Claude Sonnet 5.5, GPT-6 Astra, and GPT-6 Sol. It leads on Terminal-Bench 4.0 with a score of 71, ranks third on FrontierCode with 51 points, and uses 7.2M tokens per task, about half that of Opus 5.5 in Claude Code.

Gemini 4 Carbon Tops the Artificial Analysis Intelligence Index…

Artificial Analysis evaluations show that Google's Gemini 4 Carbon tops Intelligence Index v5 with 59 points, ranking first in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with Antigravity CLI scores 59 points, 3 points behind Claude Code paired with Claude Opus 5.5 (max, 62 points). The model is currently available only to some users and has not yet been publicly released; it is priced at $4/$20 per 1M input/output tokens with a 50% discount, has a 1M-token context window, and supports text, image, video, and voice input.

NVIDIA Explains How to Fine-Tune Nemotron 3.5 Lightning with the Nemotron-Personas Dataset

NVIDIA has released the Nemotron-Personas France and Belgium datasets and provided a complete post-training workflow for fine-tuning Nemotron 3.5 Lightning with them. The workflow includes defining behavior contracts, generating synthetic conversations with personas, fine-tuning with LoRA on NVIDIA NeMo, merging adapters, and then comparing and evaluating item by item against the base model on a frozen test set. The France dataset was built in collaboration with Pleias, and the Belgium dataset involved Pleias and KU Leuven; the Nemotron repository provides reusable steps and agent skills for data generation, training, and evaluation.

How LangChain Built the Design Agent Content Studio

LangChain's GTM engineering team, working with the design team, built Content Studio, a design agent inside Slack, on Managed Deep Agents. It can generate videos, social graphics, UI mockups, and slide decks, and has already produced more than 3,000 assets and over 80 slide decks. The article breaks down its four-layer knowledge structure (skills, wiki, code validation, memory), the dedicated LangSmith Sandbox for each Slack thread, the two rendering engines HyperFrames and the in-house motion-studio, and how the main agent orchestrates asynchronous sub-agents. The team says that after migrating to Managed Deep Agents, its own code shrank by about 80%, the median model cost per request fell from about $8 in mid-September to about 50 cents, and the entire harness has been open-sourced.

Gemini 4 Carbon Tops the Artificial Analysis Intelligence Index v5

Google's Gemini 4 Carbon ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a standard rate of $4/$20 per million input/output tokens; it is currently available to select users with a 50% discount.

Retrofitting a TV, Fill Lights, and a Camera with Codex and Claude

The author shared the process of using Codex and Claude to reverse engineer and retrofit real-world devices: first enable ADB debugging on the TV, then have the Agent disable system packages, clear boot ads, and rebuild a clean launcher, and replicate the same method on three other TVs. The team also used Claude to reverse engineer 4 fill lights and built a unified web console, and used Codex to build a multi-camera console for a Sony camera and a DJI Pocket 3 and deploy it to Feishu. The author reminds that different brands of devices expose different interfaces and permissions, and that backups should be made before modifying system software.

LangChain summarizes the five components for building company-level agents

Harrison Chase and Caspar Broekhuizen of LangChain published an article summarizing the five core components shared by its three internal agents for marketing, sales, and engineering: agents live in existing workplaces such as Slack, a distinction between acting on behalf of users and a fixed identity, global and user-level memory, code execution within a sandbox, and a large number of runs triggered by schedules or events. The article uses GTM Agent, OpenSWE, and Content Studio as examples to explain their specific approaches to identity, memory, and code execution, all three of which are built on Managed Deep Agents.

Artificial Analysis measured Meta Muse Spark Open inference throughput

Artificial Analysis published preliminary test results on AA-AgentPerf for Meta's new open-weight model Muse Spark Open, an 816B-parameter, 42B-active MoE that scores 39 on the Artificial Analysis Intelligence Index. Tests show NVIDIA B300 and GB300 each reach about 150M tokens/s per megawatt under throughput-first configurations, with GB300 at about 108M and B300 at about 92M at 100 tok/s output speed; AMD MI355X reaches 70M tokens/s per megawatt at 65 tok/s. 46 of the model's 62 layers use 2,048-token sliding window attention, reducing KV cache requirements at 1M context by about 75% versus full attention; Meta released an NVFP4 checkpoint and AMD released an MXFP4 checkpoint.

Amazon SageMaker AI Multi-Turn Reinforcement Learning Practical Guide: Training gpt-oss-20b with a Wordle Example

AWS has published a practical guide for multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI. Using Wordle as an example, it deploys the agent and reward function to Amazon Bedrock AgentCore, and the training job updates LoRA adapters via GRPO. In the example, gpt-oss-20b at default reasoning effort improved its solve rate on held-out words from 61% to 84%, at a training token cost of about $339 and an AgentCore runtime cost of about $79. The article also details failures such as two inverted reward design errors, token invalidation under concurrency, and tool calls degrading into plain text, and recommends evaluating under the decoding settings used in final serving.

TinyFish Breaks Down Production-Grade Agent Search Architecture and Deep Research Experience

TinyFish released two technical guides: one defines a production-grade agent search architecture, and the other summarizes lessons from building a deep research system. The architecture is built on four web primitives—TinySearch, TinyFetch, TinyBrowser, and TinyAgent—and runs a seven-stage control loop around a foundation model, covering task definition, retrieval planning, tool routing, evidence evaluation, and failure retry. In OpenBenchmarks tests with a fixed gpt-5.6-sol model and 100 tasks, TinyFish search plus fetch achieved a 79.0% completion rate, with a median task cost of $0.050 and a runtime of 24 seconds; under a search-only configuration, the completion rate was 59.3%.

What robots.txt can and cannot control for AI crawlers

robots.txt is only a crawling permission declaration, not access control; it can only constrain which paths compliant crawlers may fetch, and cannot control non-compliant crawlers, content that has already leaked, or acquisition methods that do not go through crawlers. Following RFC 9309, the article explains rules such as User-agent, Allow, Disallow, and the 500 KiB read limit, and provides robots.txt syntax for blocking GPTBot, CCBot, ClaudeBot, and Bytespider by UA, as well as methods for using nginx to apply differentiated rate limiting by UA, using logs to count request volumes per crawler, and using curl to verify whether rate limiting takes effect. The author emphasizes that robots.txt expresses intent, server-side rate limiting controls costs, logs reveal the facts, and licensing agreements address legal issues—each of the four handles a distinct part.

AWS team open-sources multi-agent system Deep Insight, breaking down five major challenges in production environments

The AWS Solutions Architecture team built and open-sourced the multi-agent system Deep Insight, which can transform CSV data and natural language prompts into cited .docx analysis reports. The system consists of eight agents—Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker, and others—collaborating in layers, controlling execution flow based on the Graph and Agents-as-Tools patterns of the Strands Agents SDK, and selecting Claude Haiku, Opus, and Sonnet by role. Production deployment relies on the Amazon Bedrock AgentCore runtime; LLM-generated code is executed in isolation within per-session ephemeral containers on AWS Fargate, accompanied by a Web UI and an Ops dashboard.

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