Training AI on Amazon Electronic Reviews Using #Python for Natural Language! – by – @gp_pulipaka
! JupyterLab/Jupyter Notebook WordNet, Lexical Semantic Relation Analyzer
Thesaurus, 155,000 Words
Synset 115,000, 205,000 word-Sense Pair. NLTK Library, spaCy, TextBlob
AI
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Training AI on Amazon Electronic Reviews with Python NLP
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The great experience of building with Codex
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it’s great to build with codex https://t.co/PwVdq0WthJ
— Greg Brockman (@gdb) 30 mai 2026it’s great to build with codex
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Illustrated Guide to LSTMs and GRU
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Illustrated Guide to LSTMs and GRU! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Illustrated-Gu
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Theory of Agent: When AI Agents Should Use External Tools
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When should AI agents actually reach for external tools? Researchers from Edinburgh, CUHK, UIUC, Northwestern & Princeton present Theory of Agent (ToA) at ICML 2026. Their answer: only when epistemically necessary — meaning the agent cannot complete the task reliably using its
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10x more completions than Opus in practice
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Yes I know how it works, but in practice it's about 10x more completions than using Opus for the same task, in my experience. You really need to try it for yourself to see. It's night and day.
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Chat tools hit ceiling when context window full; become CI pipeline
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That's a good way to frame it. Chat tools hit a ceiling when the task outgrows what one context window can hold. Once the plan lives in executable code with parallel agents, resumability, and convergence loops, it's closer to a CI pipeline than a conversation.
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Workflows change behavior; reusability via team commands in .claude/workflows
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Exactly right. Benchmarks get attention, workflows change behavior. And the reusability angle is underrated. You can save workflows to .claude/workflows/ as team commands. So once someone figures out the right audit or migration pattern, the whole team gets it without
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Adversarial verification loop prevents organized wandering
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Both, actually. The adversarial verification loop is what prevents organized wandering. Agents don't just fan out and report back. Other agents actively try to refute findings. The system keeps iterating until answers converge, not until agents run out of things to do. So scope
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Claude Code execution plan and JS script diffing for guardrails
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That's a sharp point. Claude Code already does this partially. The first time a workflow triggers, it shows the execution plan and asks for confirmation. But diffing the actual JS script across runs is a different level of control. Especially for teams trying to set guardrails
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Workflow runtime saves progress, agents return cached results
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Both. The workflow runtime saves progress as it goes, so a failed agent doesn't kill the run. Completed agents return cached results on resume. And the convergence loop means other agents can independently cover what a failed one missed. It's less "retry the exact call" and