AI

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(), “time”]) t_kinase = float(df_signal.loc[df_signal[“kinase_active”].idxmax(), “time”]) t_tf = float(df_signal.loc[df_signal[“tf_active”].idxmax(), “time”]) final_state = df_signal.iloc[-1].to_dict() summary = { “peak_receptor_activity”: round(peak_receptor, 4), “peak_kinase_activity”: round(peak_kinase, 4), “peak_tf_activity”: round(peak_tf, 4), “time_to_peak_receptor”: round(t_receptor, 4), “time_to_peak_kinase”: round(t_kinase, 4), “time_to_peak_tf”: round(t_tf, 4), “final_state”: {k: … Read more

AI chatbots are the new trend. What used to be “ask Google” has now become “ask Claude”. And that’s not just a change of platforms. The new approach to negotiation goes much deeper than trying to find you the best car or looking for a skills development course. It is now spreading into almost every … Read more

Now actors and writers must be human. As the Academy released its rules for the 99th Academy Awards, the organization announced that any movies with “AI-generated actors” or “AI-written screenplays” would not be eligible for acting or writing awards (but otherwise still eligible). So what do you do, exactly, in a time when we can … Read more

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 conversations. We start by loading and examining the dataset, examining its structure, categories, and dialog format to get a clear view of the available information. We then developed simple parsers to extract important components such … Read more

If you’ve been using reinforcement learning (RL) in a mathematical reasoning language model, code generation, or any realizable task, you’ve probably stared at the progress bar while your GPU cluster fired up generating output. A team of researchers from NVIDIA proposes fine-tuning by integrating predictive modeling into the RL training loop itself, and doing it … Read more

The bottleneck in building better AI models has never been computing alone – it’s always been data quality. Meta AI’s RAM (Reasoning, Alignment, and Memory) team is now addressing that problem directly. Meta researchers are silent Default dataa framework that uses AI agents in the role of an independent data scientist, tasked with iteratively building, … Read more

Large language models are incredibly capable, but frustratingly subtle. When a model misbehaves — it generates answers in the wrong language, iterates endlessly, or rejects safe requests — AI devs have very few diagnostic tools. why occurs at the internal accounting level. That’s the problem Qwen-Scope was built to solve. Qwen Team has just been … Read more

The team behind Kimi.ai (Moonshot AI) recently made a significant contribution to the open AI infrastructure space. The research team has made significant contributions to the open AI infrastructure space. They let go FlashKDA (Flash Me Delta Attention), high performance CUTLASS based kernel Kimi Delta Attention (KDA) way. I FlashKDA the library is available on … Read more

Video base models can paint a nice frame. They are still notorious for missing it. Push the camera down the hall in Wan 2.1 or CogVideoX and walls warp, objects morph, and details disappear — a giveaway that these models are suited to 2D pixel correlation rather than 3D scene simulation. A team of researchers … Read more

DeepSeek’s latest AI model was ready for a big launch. However, the markets did not react as expected to the release of DeepSeek’s V4 preview, despite the Chinese startup making technological advances with its latest software. Investors are less likely to panic when a more powerful, more efficient, and less expensive AI model is announced. … Read more