I tested Claude Cowork — Anthropic’s new AI feels more like a coworker than a chatbot - Tom's Guide
<a href="https://news.google.com/rss/articles/CBMisAFBVV95cUxNc1JzZ3JpbERjd0FROFlLTnJSTTF3YU5ERHQxMHJvZWhsMzIyWF9BNDV0dTJOdXRlVVVtN190TjRBWmRhQTlQb2VsN1FTdWZuRzl0d0d4ZHEweVg3cGZjb0dKR2hERjVXUW5VVmF0Z002TGNGY0JybE43bUNvVGRBNUVjVVNTYmY0V0gwVUVhR0RBMVJpME5SOC1DdDl4Z3NTY3ZnbFlxWVNRbl9Od3BGdw?oc=5" target="_blank">I tested Claude Cowork — Anthropic’s new AI feels more like a coworker than a chatbot</a> <font color="#6f6f6f">Tom's Guide</font>
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How I Built an Autonomous AI Agent That Runs My Entire Digital Agency
<p><em>Claude Code + MCP servers + scheduled tasks = an agent that manages projects, writes content, analyzes data, and reports back — while I sleep.</em></p> <p>I run <a href="https://inithouse.com" rel="noopener noreferrer">Inithouse</a>, a digital agency with ~14 live products — all MVPs hunting for product-market fit. Think Lean Startup on steroids: rapid experiments, measure everything, kill what doesn't work.</p> <p>The problem? One human can't manage 14 products simultaneously. So I built an autonomous AI agent that does it for me.</p> <p>Here's the full technical breakdown.</p> <h2> The Architecture </h2> <p>The system runs on <strong>Claude Code</strong> (Anthropic's CLI agent) with <strong>MCP (Model Context Protocol) servers</strong> as connectors to external services. The agent
I voice-code from my phone while walking my dog
<p>Last Wednesday afternoon I was at the oval with Normi, my 13-year-old dog, playing tug of war with his favourite rope ball. Between rounds I pulled out my phone, recorded a voice note asking Claude Code to run the full engine test suite across six Telegram chats, and went back to playing. Twenty minutes later, Normi and I were both sitting on the grass, absolutely pooped. I checked Telegram. Claude Code had finished testing, logged the bugs it found, and created GitHub issues for each one. I hadn't typed a single character.</p> <p>That's most of my afternoons now.</p> <blockquote> <p><strong>TL;DR:</strong></p> <ul> <li>I spend 2-4 hours a day walking my 13-year-old dog Normi. During those walks, I dictate coding tasks to Claude Code via Telegram voice notes using <a href="https://githu
Top LLM Gateways That Support Semantic Caching in 2026
<p>Let me ask you something. How many times a day do your users ask your LLM app the same question, worded differently?</p> <p>"What is RAG?" and "Explain retrieval augmented generation to me" are the same question. You know it. I know it. But your LLM provider does not care. It charges you for both. Twice the tokens, twice the latency, same answer.</p> <p>This is where semantic caching comes in, and if you have not explored it yet, let me walk you through it before we look at the tools.</p> <p><strong>TL;DR:</strong> Semantic caching matches LLM prompts by meaning, not exact strings, so rephrased questions return cached responses instead of burning tokens. I compared four tools that support it in 2026: Bifrost (fastest, most complete caching), LiteLLM (widest provider support), Kong AI Ga
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AI Journey 2025 Conference: exploring the future of artificial intelligence - Азия-Плюс
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RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL introduces two key innovations: (1) Skeptical-Agent, an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems. This agent always maintains a skeptical attitude towards its own outputs and thereby enforces rigorous self-refinement even when validation suggests cor
UK AISI Alignment Evaluation Case-Study
arXiv:2604.00788v1 Announce Type: new Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate whether frontier models sabotage safety research when deployed as coding assistants within an AI lab. Applying our methods to four frontier models, we find no confirmed instances of research sabotage. However, we observe that Claude Opus 4.5 Preview (a pre-release snapshot of Opus 4.5) and Sonnet 4.5 frequently refuse to engage with safety-relevant research tasks, citing concerns about research direction, involvement in self-training, and research scope. We additionally find that Opus 4.5 Preview shows reduced unprompted evaluation awareness compared to Sonnet 4.5,
CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection
arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute-force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which predicts circuit locations from activation statistics in under 5 minutes on CPU, providing a speedup of three to four orders of magnitude. We find that reasoning circuits come in two types: stability circuits in early layers, detected through the derivative of representation change, and magnitude circuits in late layers, detected through anomaly scoring. We validate across 9 models spanning 6 architectures, including 2025 models, confirming that CircuitProbe top predictions m
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