This makes $100M training runs obsolete. Why spend months training a massive model when you can deploy a smaller one and scale intelligence dynamically per query? Hard math problem? Give it 60 seconds to think.
Simple question? Answer instantly. Intelligence becomes elastic
LLMS
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Elastic Intelligence: small model, dynamic per-query scaling
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Economics flip: small models with more inference match GPT-4
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The economics just flipped completely. Training GPT-4: $100M+ in compute
Inference scaling: $0.10 per complex query You can make a 7B model as smart as GPT-4 by letting it think 100x longer at inference. Smaller models + more thinking time = beats bigger models at fraction of -
All frontier AI labs discover identical reasoning breakthrough
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The evidence is everywhere: – OpenAI o1: Extended reasoning at inference
– DeepSeek-R1: Self-verification loops
– Gemini 2.0 Thinking: Dynamic compute allocation
– Claude Opus: Multi-path exploration Every frontier lab independently discovered the same breakthrough. This isn't -
Test-time compute scaling: models think harder during inference
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Test-time compute scaling is simple but revolutionary: Instead of making models bigger, you make them think harder during inference. The model generates multiple reasoning paths, verifies answers, backtracks when wrong, and improves solutions in real-time. It's thinking, not
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OpenAI’s o1 demonstrates smarter models via inference-time compute
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OpenAI's o1 proves you can make models smarter by making them "think longer" at inference not training bigger models. DeepSeek, Google, Anthropic all pivoting to test-time compute. Training wars are over. The inference wars just started. Here's the paradigm shift happening
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OpenSearch Agentic Memory: Building Context-Aware Agents
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OpenSearch as an agentic memory solution: Building context-aware agents using persistent memory https://
buff.ly/OtZ3ZMe
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
OpenAI Releases GPT-5.2-Codex Advanced Coding Model
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New: GPT-5.2-Codex! OpenAI's most advanced coding model is optimized for complex engineering tasks, recommended for long‑horizon, agentic coding workflows. You can try it in the Poe app on all platforms and in the Poe API at https://
poe.com/GPT-5.2-Codex. -
Google adds interactive steps to Opal agent builder
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Google is working on new interactive steps for its Opal agent builder, which will allow builders to choose between Chat and Interactive UI options. This will allow Opal and Gemini agents to request additional context from users when needed.
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OpenAI Releases GPT-5.2-Codex Model
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OpenAI released GPT-5.2-Codex model on the Responses API, and it is now available on OpenAI's Platform.
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GPT-5.2 Writes 3M Lines of Code to Build Browser in Cursor
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Truly incredible how fast the frontier of agentic coding is moving GPT-5.2 wrote 3M+ lines of code, nonstop for three days, to build a browser from scratch in Cursor!