4/ TRACK YOUR BITCOIN PORTFOLIO Prompt: Act as a Bitcoin portfolio analyst who tracks performance, calculates real returns, and keeps emotion out of long-term holding decisions. Build a complete Bitcoin portfolio tracking system that shows my real returns,
PROMPT ENGINEERING
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Which AI Tool to Use and When Guide
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Which #AI Tool to Use and When by @genamind #ArtificialIntelligence #MachineLearning #ML #MI
→ View original post on X — @ronald_vanloon, 2026-04-08 17:23 UTC
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PDR Framework: Parallel Reasoning Agents for Complex Scientific Queries
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Reasoning doesn’t have to mean longer chains of thought:
— Anirudh Goyal (@anirudhg9119) 8 avril 2026
PDR = draft in parallel → distill into a compact workspace → refine, and shift the Pareto frontier.https://t.co/4Sca6dFu4Q https://t.co/kk0fYpc8Y1 pic.twitter.com/PvevaX9ngYReasoning doesn’t have to mean longer chains of thought: PDR = draft in parallel → distill into a compact workspace → refine, and shift the Pareto frontier. arxiv.org/abs/2510.01123 Alexandr Wang (@alexandr_wang) 3/ we’re also releasing contemplating mode, which orchestrates multiple agents that reason in parallel designed to handle complex scientific & reasoning queries. in our testing we found it competitive w/ other extreme reasoning models such as Gemini Deep Think & GPT Pro. — https://nitter.net/alexandr_wang/status/2041909381667958855#m
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Muse Spark: Multi-Agent Collaboration for Test-Time Reasoning Scaling
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To spend more test-time reasoning without drastically increasing latency, we can scale the number of parallel agents that collaborate to solve hard problems. While standard test-time scaling has a single agent think for longer, scaling Muse Spark with multi-agent thinking enables
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Reinforcement Learning Optimizes Model Reasoning with Token Efficiency
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RL trains our models to "think" before they answer, a process known as test-time reasoning. To serve this capability to billions of users and efficiently use tokens, we rely on two key levers: thinking time penalties to optimize token use and multi-agent orchestration that boosts… pic.twitter.com/oHHap4NAg3
— AI at Meta (@AIatMeta) 8 avril 2026RL trains our models to "think" before they answer, a process known as test-time reasoning. To serve this capability to billions of users and efficiently use tokens, we rely on two key levers: thinking time penalties to optimize token use and multi-agent orchestration that boosts
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New Contemplating Mode for Complex Scientific Reasoning Tasks
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3/ we’re also releasing contemplating mode, which orchestrates multiple agents that reason in parallel designed to handle complex scientific & reasoning queries. in our testing we found it competitive w/ other extreme reasoning models such as Gemini Deep Think & GPT Pro.
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Meta AI Updated with New Design and Model
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BREAKING : Meta updated its Meta AI app with a slightly new design as well as its underlying model. “I am Meta AI, powered by Muse Spark from the Muse model family.” It constantly refers to the Muse model family and responses seem to be a bit different from earlier tested
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The importance of the ‘About’ prompt to change everything
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The prompt about 'About' can literally change everything.
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Context Engineering: Enterprise Intelligence’s Next Era Beyond Bigger Models
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Bottom line: the next era of enterprise intelligence won’t be “bigger models”—it’ll be better context. Watch the full video: Context Engineering—Defining the Next Era of Enterprise Intelligence. In partnership with Elastic. Check out the full article: linkedin.com/pulse/context-e…
→ View original post on X — @ronald_vanloon, 2026-04-08 15:00 UTC
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Context Engineering: Organizing Unstructured Data for AI Reasoning
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What is “Context Engineering”? It’s the discipline of stitching your unstructured mess—logs, chats, docs, images—into something an AI can actually reason over. → Vector DBs + hybrid search + embeddings to retrieve by meaning, not keywords. → Decisions anchored in your data, not generic pretraining.
→ View original post on X — @ronald_vanloon, 2026-04-08 15:00 UTC