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AI Coding Agents for Enterprise: IP Indemnity, Data Residency and 500-Seat Cost Compared

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Our ‘Top AI Coding Agents and Development Platforms‘ guide covered what each AI coding agent does and where it fits. This piece is for a different reader. It is written for the procurement lead, the general counsel and the security reviewer. Those readers ask 4 questions before any rollout. Who pays if generated code triggers […]

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Agentic Context Engineering (ACE): Self-Improving Language Models

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Agentic Context Learning or ACE is a learning paradigm that lets an AI agent improve across tasks by editing the context it reads, while leaving model weights unchanged. The paper outlining the techniques show why full rewrites fail, how the playbook update works, and where the measured gains hold up. This article explores how ACE

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Exa Launches Agent Ultra: A Subagent Swarm Deep Research API Built for Exhaustive List Building

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Exa has released Agent Ultra, the highest effort level of its Exa Agent API. It is built for research that must run to exhaustion: large list building, entity enrichment, and questions that need thousands of sources. Exa team reports that Ultra beats Opus 5.5, GPT-6 Astra, and Perplexity Agent, each at maximum effort, on 4

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Perplexity Trains Its Computer Agent on Real Mistakes With Hint-Guided Self-Distillation

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Perplexity Research published a new post-training study. It trains a model inside Perplexity Computer on real user sessions, including failed ones. The method pairs rejection sampling fine-tuning with hint-guided self-distillation. In a live A/B test, tool-call failures fell from 2.24% to 1.77% between 2 trained checkpoints. Perplexity team reports this as a statistically significant 21.2%

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AI Assurance in Action: Practical Testing Scenarios for AI Systems

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AI Assurance sounds straightforward until an AI system is deployed in the real world. A chatbot may produce a convincing but incorrect answer. An AI agent may follow instructions hidden inside a document. A model upgrade may change previously reliable responses. An AI test-generation tool may create test cases that look correct but miss

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What Is AI Agent Engineering? A Complete Guide to Building Intelligent AI Agents

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A chatbot waits for your question. An automation waits for a predefined trigger. An AI agent can take a goal, figure out what needs to happen, use the right tools, make decisions along the way, and take action. That difference is changing how enterprises think about software. Instead of building applications that only respond to

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Kyutai Releases Voice of Reason: A Speech-Native Model that Solves Spoken Math with Reinforcement Learning

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Kyutai has released Voice of Reason, 2 open-weight speech-to-speech models that solve math problems out loud. Both start from GLM-4-Voice-9B and add supervised fine-tuning (SFT) and reinforcement learning (RL). There is no transcription step and no separate text LLM in the loop. On spoken GSM8K, accuracy climbs from 27.3% for the base model to 77.1%.

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SpeakON Ships a MagSafe AI Voice Button With Its Own Microphone

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Voice input on phones has been solved for years. What has not been solved is the output. Speak into most dictation tools and you get back exactly what you said, fillers and false starts included, in a note you then have to clean up and move somewhere else. SpeakON attacks that gap with hardware: a

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SpeakON Ships a MagSafe AI Voice Button With Its Own Microphone: Turning Your Voice into Polished Communication, and Action across Apps

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What Is AI Native Product Engineering? A Complete Guide

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AI is no longer just a feature you add to software. In a growing class of products, AI is becoming the foundation the entire product is built around. That changes almost everything. The architecture changes. The user experience changes. Data becomes an active product layer. Software needs to handle probabilistic outputs. Agents can perform actions

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