The ideal is not “store everything,” it’s “keep only what is worth keeping” — more like a really good RAG pipeline than a giant token dump.
TOOLS
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Context Size vs. Quality: When Less Compression Hurts Performance
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Bigger context is useful, but not if it keeps carrying forward noise that should have been compressed away. In some cases that extra clutter can even hurt performance relative to a stronger compaction approach that preserves only the minimum viable context needed for the task.
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Opus to Opus 1M Switch: Daily Limits and Token Efficiency Issues
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This is also why the switch between Opus and Opus 1M is pretty annoying to me: I hit daily limits much earlier, and part of it feels like compaction happens less aggressively, so way more tokens get sent every time (and probably because they keep reducing that daily limit).
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Better Compaction System for Token-Efficient AI Processing
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A much better compaction system would keep the signal and discard the rest, making the whole stack far more token-efficient. That means lower latency, less compute, and lower cost for users.
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ChatGPT App Integration and Disconnection Process Improvements
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Agree, it’s not ideal right now since it requires one to go on two separate surfaces (Codex to uninstall and ChatGPT to disconnect) There’s definitely work going on to make this smoother cc: @edbayes for vis too For the short term, you can go to chatgpt > settings > apps and
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Pre-built Workflows Transform Dashboard Starting Points
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The difference is starting point. Instead of a blank dashboard, imagine proven workflows out of the box: → customer segmentation
→ ad spend optimization
→ demand forecasting As data quality improves, the insights compound.
Security and governance are table stakes, not -
Getting Started with the Codex App
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If you haven’t tried the Codex app yet, you can get started here
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Top Machine Learning Books Recommended for Data Scientists
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Top Machine Learning Books for the Data Scientist to Read!#BigData #Analytics #DataScience #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/Top-ML-DSci -
Building Machine Learning Workflows with Apache Airflow and Kubernetes
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Build #MachineLearning Workflows with #ApacheAirFlow and #Kubernetes. #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Workflow-Kuber
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