newsletterJuly 2, 2026·Towards Data Science

Tokenminning: How to Get More from Your Chatbot for Less

Tokenmaxxing is out. Real patterns for reducing costs without sacrificing AI effectiveness The post Tokenminning: How to Get More from Your Chatbot for Less appeared first on Towards Data Science.

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•Tokenmaxxing is out
•This story was reported by Towards Data Science, covering developments in the newsletter space.
•AI advancements continue to reshape industries — read the full article on Towards Data Science for complete coverage.

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Design Loops, Not Prompts

But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science.

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Time-Series LLMs, Explained with t0-alpha

t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast. The post Time-Serie

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The Untaught Lessons of RAG Question Parsing: Structure Before You Search

Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science.

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Why Powerful ML Is Deceptively Easy — Part 2

The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science.

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Tokenminning: How to Get More from Your Chatbot for Less

Key Takeaways

Related Articles

Design Loops, Not Prompts

Time-Series LLMs, Explained with t0-alpha

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

Why Powerful ML Is Deceptively Easy — Part 2

Discussion

Tokenminning: How to Get More from Your Chatbot for Less

Key Takeaways

Related Articles

Design Loops, Not Prompts

Time-Series LLMs, Explained with t0-alpha

The Untaught Lessons of RAG Question Parsing: Structure Before You Search

Why Powerful ML Is Deceptively Easy — Part 2

Discussion

Related Articles

Towards Data Science
Design Loops, Not Prompts
But don't let the model check itself The post Design Loops, Not Prompts appeared first on Towards Data Science.

Towards Data Science
Time-Series LLMs, Explained with t0-alpha
t0-alpha is a decoder-style patch transformer for probabilistic time-series forecasting. Raw series are split into 32-step patches, embedded, processed through causal time-attention and group-attention layers, and decoded into future quantiles rather than a single point forecast. The post Time-Serie

Towards Data Science
The Untaught Lessons of RAG Question Parsing: Structure Before You Search
Enterprise Document Intelligence [Vol.1 #6ter] - Six positions on the question-parsing brick that contradict the mainstream RAG playbook The post The Untaught Lessons of RAG Question Parsing: Structure Before You Search appeared first on Towards Data Science.

Towards Data Science
Why Powerful ML Is Deceptively Easy — Part 2
The next leakage problem is not only temporal. It is spatial, structural, and coverage-related. AI-generated illustration created with DALL·E The post Why Powerful ML Is Deceptively Easy — Part 2 appeared first on Towards Data Science.