The #1 mistake that kills progress. “I know enough for now” is a trap. The moment you stop learning, you start falling behind. Knowledge for later—keep feeding it now, and your growth will never stop.

A novel prompting technique called "Highlighted Chain of Thought" (HoT) significantly improves large language models' ability to explain reasoning while making their answers more verifiable for humans. The two-step approach: - AI reformulates questions and marks critical facts using XML tags - Model generates answers referencing these highlighted elements, creating clear logical connections - Color-coded highlights enable faster human verification of AI reasoning - Structured approach forces more careful consideration of presented facts, potentially reducing hallucinations Performance gains: - Up to 15% improvement across various benchmarks and models - Compared to traditional CoT methods, HoT showed gains of 1.6 percentage points for arithmetic tasks, 2.58 for question-answering, and 2.53 for logical reasoning - Most substantial improvements on AQUA (+14.64) and StrategyQA (+15.07) benchmarks - Tested across five major models including GPT-4o, Gemini-1.5-Pro, and Llama-3.1 variants across 17 different task types Important limitations: - Reasoning models showed minimal or negative benefits from HoT techniques - Smaller models struggled with tagging instructions, often producing incorrect tags - Moving tags to random phrases significantly impacted accuracy Human verification paradox: - Human testers completed verification 25% faster with highlighted answers - However, highlighting increased trust in AI responses—even incorrect ones - Humans correctly identified accurate answers 84.5% of the time (vs 78.8% without highlighting) - Ability to spot errors dropped from 72.2% to 54.8% when highlighting was present Future directions: Researchers plan to train models to generate HoT answers directly rather than using prompt examples, potentially making the method more effective and broadly applicable.

Le Chat - Mistral’s revamped AI assistant platform with 10x response speed and new iOS and Android apps GitHub Copilot - Microsoft’s coding assistant with new agentic features Pikadditions - New video-to-video feature that enables users to integrate any subject or object into existing footage Tough Tongue - Multimodal AI agent for navigating difficult conversations Krea Chat - Use natural language commands to create and edit images Mistral Small 3 - Open-source, 24B model with enhanced speed and accuracy Talo - Real-time voice translator for video calls Tana - Put your notes to work with voice and AI Zonos-v0.1 - Open-source, real-time TTS models with voice cloning Luma Ray2 - New image-to-video with enhanced realism and motion Cursor - Coding agent with new custom tool integration and upgraded semantic search Readdy - Generate professional designs in seconds with ready-to-use code Deepgram - Introducing Nova-3, the first voice AI model with real-time multilingual speech-to-text Perplexity Sonar - New upgraded model with 10x faster response speed DeepScaleR - Open-source 1.5B model with exceptional math reasoning 3D AI Studio V3 - Generate, organize, and edit 3D models Firefly Video Model - Adobe’s IP-safe AI video generation tool

Perplexity has launched its own Deep Research tool, an AI-powered research assistant that generates comprehensive reports within minutes, directly challenging similar offerings from OpenAI and Google with the same name. The details: - The autonomous tool performs dozens of searches, analyzes hundreds of sources, and compiles findings into structured reports in just 2-4 minutes - In benchmark testing on Humanity's Last Exam, the tool scored 21.1%, outperforming Gemini Thinking (6.2%) and Grok-2 (3.8%), though falling behind OpenAI's 26.6% score - Unlike OpenAI's Deep Research feature that requires a $200 monthly subscription, Perplexity offers 5 free daily uses for regular users, with expanded access for Pro subscribers - Perplexity CEO Aravind Srinivas publicly taunted OpenAI CEO Sam Altman on X, claiming he had "mogged" him with their latest product release Why it matters: The timeframe between premium AI features and their free alternatives continues to shrink, with Perplexity directly undercutting OpenAI's pricing (despite mixed initial reviews). With major AI companies pushing similar research tools, the transformation of research workflows isn't a matter of if but when it becomes standard practice.

The NBA has unveiled several innovative AI and robotics technologies being implemented by teams like the Golden State Warriors during its 2025 All-Star Technology Summit, featuring specialized robots for training, coaching, wellness, and equipment management. The details: - A.B.E. (Automated Basketball Engine) serves as an advanced rebounding and passing assistant during shooting practice, with Stephen Curry already incorporating it into his regular training sessions - M.I.M.I.C. robots execute offensive and defensive plays under coach direction, providing consistent practice opponents that can replicate specific formations - K.I.T. (Kinematic Interface Tool) enhances player wellbeing by offering companionship and motivation both in the locker room environment and during workout sessions - B.E.B.E. (Bot-Enhanced Basics & Equipment) streamlines organizational tasks and handles repetitive duties such as basketball inflation and equipment management Why it matters: Professional sports is experiencing a robotics revolution, with the NBA offering a fascinating glimpse into specialized robots that provide competitive advantages for teams seeking every possible edge. While these robotic assistants may appear unusual now, their presence throughout the sporting world will soon become commonplace.