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124 long-form pieces on architecture, training, inference, agents and safety.

All Model Architecture19 Training & Alignment22 Inference & Serving18 Agents & Orchestration11 Reasoning & Evaluation26 Safety, Security & Governance7 Platforms & Practice21
Safety, Security & Governance 11 min

What the EU AI Act enforcement phase actually changes for startups in 2026

The Act passed in 2024, the GPAI rules went live in August 2025, and enforcement powers arrive in August 2026. Most Series-A startups need a handful of specific changes, not a compliance department.

regulation governance eu-ai-act compliance
Training & Alignment 27 min

What the Model Remembers: Extraction, Memorisation, and the Price of a Privacy Guarantee

Two hundred dollars of API calls pulled more than ten thousand verbatim training examples out of ChatGPT. Memorisation is not a defect that better engineering removes; it scales log-linearly with everything the field is scaling. …

privacy safety security memorisation ∑ ◫
Reasoning & Evaluation 3 min

What the bake-off taught us: classical ML is not dead, it is just under-attended

We pitted twelve sklearn algorithms head-to-head on a tabular dataset. The winner was not the most expensive one. It was not the most modern one. It was the one whose assumptions matched the data.

machine-learning tabular benchmarks evaluation
Reasoning & Evaluation 24 min

When Did the World Change? Changepoint Detection From Page's CUSUM to Bayesian Online Inference

CUSUM has been provably optimal since 1986, yet on the first human-annotated changepoint benchmark a detector that never reports a change beat most of the field under default settings. Seventy years of changepoint theory, from Pa…

anomaly-and-changepoint changepoint-detection anomaly-detection statistics ∑ ◫
Reasoning & Evaluation 20 min

When the Judge Is Also a Player: LLM-as-Judge, Contamination, and Why Leaderboards Drift

A strong model grading other models looks like a free lunch for evaluation. It is not. Position, verbosity, and self-preference biases plus quietly leaked test sets mean a leaderboard number can move several points without any mo…

evaluation llm-as-judge benchmarks contamination ∑ ◫
Agents & Orchestration 10 min

Why 'agents' is the wrong frame for most workflows you actually want

The industry frames every LLM feature as an agent. Most production systems that work are pipelines with one or two LLM steps and clear handoffs. Default to a workflow, reach for an agent only when the problem demands it.

agents workflows orchestration architecture
Model Architecture 24 min

Why GANs Lost Image Generation, and Why Adversarial Losses Are Everywhere Anyway

In 2021 a diffusion model beat BigGAN-deep on ImageNet while nearly doubling its recall, and GANs stopped being the default way to generate images. Yet latent autoencoders, SDXL-Turbo, HiFi-GAN and the neural audio codecs behind …

adversarial-generative-models gans image-generation diffusion-models ∑ ◫
Agents & Orchestration 6 min

Why LangGraph? A Case for Control Over Convenience in Production Agents

The frameworks that demo best are rarely the ones that survive in production. LangGraph wins where it matters by refusing to hide the thing every other framework tries to hide: the control flow.

langgraph agents orchestration production
Training & Alignment 25 min

Why Policy Gradients Need a Baseline: Variance, Trust Regions, and the Road to PPO

Add 1,000 to every reward in an environment. The optimal policy is unchanged, and the expected policy gradient is unchanged — but the variance of the estimator you actually compute goes up by four orders of magnitude. Every advan…

rl policy-gradients reinforce ppo ∑ ◫
Training & Alignment 21 min

Why Token-Level RL Collapses: GSPO and Sequence-Level Importance Sampling

GRPO weights every token by its own importance ratio, and on long responses that single-sample estimator quietly poisons the gradient until the model collapses. GSPO moves the ratio up to the whole sequence, and Qwen3's largest m…

reinforcement-learning rlhf gspo grpo ∑ ◫
Safety, Security & Governance 24 min

You Cannot Have All Three: COMPAS, Calibration and the Impossibility Theorems of Fair Classification

In 2016 ProPublica showed that COMPAS wrongly flagged 44.9% of Black defendants who never reoffended against 23.5% of white ones, and its vendor showed the scores meant the same thing for both groups. Both were right, and a one-l…

fairness-and-bias fairness bias calibration ∑ ◫
Inference & Serving 24 min

Your Answer Depends on Who Else Is in the Batch

A thousand identical requests to the same model at temperature zero produced eighty different completions. The cause is not random seeds, not sampling, and not GPU atomics. It is that reduction kernels change their summation orde…

inference determinism numerics kernels ∑ ◫
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