Anthropic leak reveals Claude Code tracking user frustration and raises new questions about AI privacy - Scientific American
Anthropic leak reveals Claude Code tracking user frustration and raises new questions about AI privacy Scientific American
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5 Projects That Put a Fully Customizable AI Assistant on Your Wrist in Under $15
5 Projects That Put a Fully Customizable AI Assistant on Your Wrist in Under $15 Press a button, ask a question, and see the answer on your wrist in 2-5 seconds—not with a bulky phone, but a $15 wrist-mounted AI assistant powered by Claude. Here’s how I did it with an ESP32 microcontroller, and you can too: Project 1: Basic Text-Based Claude on Wrist ($8 Setup) Outcome: Type a question on your computer, get Claude’s answer on a 0.96-inch OLED screen on your wrist. Code to Get You Started: // ESP32_S3_Claude_Basic.ino (simplified for brevity) #include #include const char * ssid = "YOUR_WIFI" ; const char * password = "YOUR_PASSWORD" ; const char * claudeApiKey = "YOUR_CLAUDE_API_KEY" ; void setup () { Serial . begin ( 115200 ); WiFi . begin ( ssid , password ); while ( WiFi . status () != W
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A domestic shortage of electrical equipment such as transformers and switchgear is forcing the US to rely on Chinese imports, delaying data center construction (Bloomberg)
Bloomberg : A domestic shortage of electrical equipment such as transformers and switchgear is forcing the US to rely on Chinese imports, delaying data center construction The struggle to manufacture transformers, switchgear and batteries domestically has forced the US to rely on imports, delaying data center construction.
Semantic Evolution over Populations for LLM-Guided Automated Program Repair
arXiv:2604.02134v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential for automated program repair (APR), particularly through iterative refinement that generates and improves candidate patches. However, state-of-the-art iterative refinement LLM-based APR approaches cannot fully address challenges, including maintaining useful diversity among repair hypotheses, identifying semantically related repair families, composing complementary partial fixes, exploiting structured failure information, and escaping structurally flawed search regions. In this paper, we propose a Population-Based Semantic Evolution framework for APR iterative refinement, called EvolRepair, that formulates LLM-based APR as a semantic evolutionary algorithm. EvolRepair reformula
APITestGenie: Generating Web API Tests from Requirements and API Specifications with LLMs
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TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning
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