Why most agents are just flowcharts in disguise, and what to build instead.

The post Is Agentic AI Just Automation? appeared first on Towards Data Science.

Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes

The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.

How DFlash trades spare compute for saved memory bandwidth, and why its gains shrink as concurrency rises

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.

The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.

A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training

The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.

Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes

The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.

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 acceptance metrics, and show what determines whether speculation pays off.

The post Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash appeared first on Towards Data Science.

LoRA fine-tuning solved our under-labeling problem. Whether it makes sense for you depends on three questions.

The post Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) appeared first on Towards Data Science.

A walkthrough of and the maths behind using low-capacity networks to acquire fine-grained scoring when only categorical labelling is available for training

The post Estimating from No Data: Deriving a Continuous Score from Categories appeared first on Towards Data Science.