Eric Wang
@ericw_ai
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Following
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Prev @Stanford | Research @ AI lab | I play chess in my spare time (Elo 2300+)
San Francisco, CA
Joined September 2025
This guy literally breaks down how to learn faster than 99% of people
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Enjoy GPT 5.2 and Gemini 3 Pro on Perplexity!
GPT-5.2 is now available for all Perplexity Pro and Max subscribers.
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Google literally published a 69-page prompt engineering masterclass
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GPT-5.2 Pro is SOTA on ARC-AGI, with two orders of magnitude efficiency improvement over the past year:
A year ago, we verified a preview of an unreleased version of @OpenAI o3 (High) that scored 88% on ARC-AGI-1 at est. $4.5k/task Today, we’ve verified a new GPT-5.2 Pro (X-High) SOTA score of 90.5% at $11.64/task This represents a ~390X efficiency improvement in one year
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This person literally shows how to make your brain remember more with zero effort
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This guy literally dropped the best practical AI roadmap you’ll ever see
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This guy shared a solid guide on structuring your AI project directory
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This guy connected Claude with Blender, and now it’s generating wild 3D scenes https://t.co/WnSDOE5R3F
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This guy literally shows how to build your own MCP server in 14 mins https://t.co/mcd6NoLSJn
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This guy shared a solid guide on structuring your AI project directory
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Today we’re shipping 3 more updates for Claude Code: - Claude Code on Android - Hotkey model switcher - Context window info in status lines
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Today we’re introducing GPT-5.2 in ChatGPT, our most advanced model series for professional work. GPT-5.2 Thinking is designed to help with real, economically valuable tasks — the kind of work professionals do every day: building spreadsheets and presentations, writing and
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Elon Musk literally dropped a 1-hour masterclass on life, beliefs, and AI https://t.co/Wv6OQXYUSL
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To measure these capabilities, we’re open-sourcing DeepSearchQA, a new benchmark to evaluate agents on complex web search tasks. Deep Research achieves state-of-the-art performance on this benchmark, as well as on the full Humanity's Last Exam set (reasoning & knowledge), and
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A senior Google engineer just dropped a 424-page doc called Agentic Design Patterns. Every chapter is code-backed and covers the frontier of AI systems: → Prompt chaining, routing, memory → MCP & multi-agent coordination → Guardrails, reasoning, planning This isn’t a blog
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