Generative AI

The tension in the AI ​​debate has always been a binary choice: respond quickly or respond intelligently. Real-time speech-to-speech (S2S)…

words = [p[1] for p in pairs] ids_ws = [tokenizer.encode(" " + w, add_special_tokens=False)[0] for w in words] ids_nws =…

Mistral AI has been quietly building one of the first open source/heavyweight AI coding agent systems, and is shipping its…

class CellSignalingSimulationAgent: def run(self, df_signal: pd.DataFrame) -> AgentResult: peak_receptor = float(df_signal["receptor_active"].max()) peak_kinase = float(df_signal["kinase_active"].max()) peak_tf = float(df_signal["tf_active"].max()) t_receptor = float(df_signal.loc[df_signal["receptor_active"].idxmax(),…

In this lesson, we examine the lambda/hermes-agent-reasoning-traces dataset understanding how agent-based models think, use tools, and generate responses across multi-curve…

If you've been using reinforcement learning (RL) in a mathematical reasoning language model, code generation, or any realizable task, you've…

EPOCHS = 15 opt = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4) sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=EPOCHS) loss_fn = nn.MSELoss() hist = {"tr": [], "va":…

The bottleneck in building better AI models has never been computing alone – it's always been data quality. Meta AI's…

Large language models are incredibly capable, but frustratingly subtle. When a model misbehaves — it generates answers in the wrong…

In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on…