newsletterJuly 14, 2026·Towards Data Science

Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs

How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science.

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•How to stop parsing JSON by hand and start trusting your model's output The post Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs appeared first on Towards Data Science.
•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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Put Your Own Logic Inside the Codex Agentic Loop

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10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong

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Can an LLM Forget the Right Things?

Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things?

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Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash

Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acc

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Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs

Key Takeaways

Related Articles

Put Your Own Logic Inside the Codex Agentic Loop

10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong

Can an LLM Forget the Right Things?

Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash

Discussion

Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs

Key Takeaways

Related Articles

Put Your Own Logic Inside the Codex Agentic Loop

10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong

Can an LLM Forget the Right Things?

Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash

Discussion

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Towards Data Science
Put Your Own Logic Inside the Codex Agentic Loop
Understanding Codex hooks The post Put Your Own Logic Inside the Codex Agentic Loop appeared first on Towards Data Science.

Towards Data Science
10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong
Enterprise Document Intelligence [Vol.1 #M3] - The ten positions the series argues from, and the map of every article that argues them The post 10 Positions for Enterprise RAG That Mainstream Tutorials Get Wrong appeared first on Towards Data Science.

Towards Data Science
Can an LLM Forget the Right Things?
Most LLM inference runtimes have no idea a physical deadline exists. This one refuses admission rather than miss a 33ms robot control cycle, evicts KV cache by meaning instead of age, and is written entirely in hand-written CUDA — no cuBLAS, no libtorch. The post Can an LLM Forget the Right Things?

Towards Data Science
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash
Speculative decoding can turn underused CPU compute into faster token generation, without changing the model's output. In our vLLM tests, DFlash delivered 3.92x the autoregressive throughput with Qwen3.5-9B on Intel Xeon 6 at concurrency 1. We break down where the speedup comes from, explain the acc