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Oct 242026Saturday

01:48
AWS Machine LearningOct 24 01:48
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.
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
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#lab #Tutorials / practice #Agents #Reasoning

Oct 232026Friday

16:58
MarkTechPostOct 23 16:58
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%.
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,
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#ai-vertical #Papers/Research #Agents #Search

Oct 222026Thursday

07:36
AWS Machine LearningOct 22 07:36
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.
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 d
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#lab #Tutorials / practice #Agents #English text is being prepared.

Oct 212026Wednesday

17:22
HuxiuOct 21 17:22
Developers use AI reverse engineering to run iOS 16 on Android phones such as Redmi
Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bluetooth, camera, and speaker are all unavailable; GPU hardware acceleration has not been made to work, causing lag; booting takes 4 minutes; code signing verification and sandbox restrictions have also been relaxed; it is only suitable for early-adopter demonstrations.
Developer dipa-studio successfully booted and ran iOS 16.7.16 on a Redmi Note 10 Pro, able to enter the home screen, operate Settings and Control Center, and subsequently similar demonstrations appeared on devices such as the Redmi Note 12, Nothing Phone, and OnePlus 12. The porting process extensively used Claude and Codex to write driver adaptation code, and a reverse-engineering tool REA that automatically analyzes hardware interfaces also emerged. In the current version, cellular network, Bl
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#hot-signal #English text is being prepared.#Open-source ecosystem #Agents
03:34
Simon WillisonOct 21 03:34
Simon Willison built Battle Drones, an audio-networked RTS game, with Claude Code
Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchronize game state via microphone audio, and because the audio caused headaches, he added online multiplayer based on a WebSockets relay server.
Simon Willison released Battle Drones, a real-time strategy game developed entirely through vibe coding with Claude Code. It went through 12 major versions and 321 commits and runs in mobile and desktop browsers. The game includes 9 building types, 7 ground vehicles, 6 air vehicles and 3 infantry units, and supports fog of war, random map generation, AI versus AI, and an Agent battle mode in which two LLMs fight each other. He also implemented a multiplayer method in which two devices synchroniz
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#newsletter #Tutorials / practice #Agents #English text is being prepared.