JuicyTalkBuild a Multi-Agent AI Workflow for Biological Network Modeling, Protein Interactions, Metabolism, and Cell Signaling Simulation
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…
A Coding Implementation to Parsing, Analyzing, Visualizing, and Fine-Tuning Agent Reasoning Traces Using the lambda/hermes-agent-reasoning-traces Dataset
In this tutorial, we explore the lambda/hermes-agent-reasoning-traces dataset to understand how agent-based models think, use tools, and generate responses across multi-turn conversations. We start by loading and inspecting the dataset,…
A New NVIDIA Research Shows Speculative Decoding in NeMo RL Achieves 1.8× Rollout Generation Speedup at 8B and Projects 2.5× End-to-End Speedup at 235B
If you have been running reinforcement learning (RL) post-training on a language model for math reasoning, code generation, or any verifiable task, you have almost certainly stared at a progress…
A Coding Implementation of End-to-End Brain Decoding from MEG Signals Using NeuralSet and Deep Learning for Predicting Linguistic Features
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”: [], “r”: []} def pearson(a, b): a, b = a…
Meta Introduces Autodata: An Agentic Framework That Turns AI Models into Autonomous Data Scientists for High-Quality Training Data Creation
The bottleneck in building better AI models has never been compute alone — it has always been data quality. Meta AI’s RAM (Reasoning, Alignment, and Memory) team is now addressing…
A Coding Guide on LLM Post Training with TRL from Supervised Fine Tuning to DPO and GRPO Reasoning
import subprocess, sys subprocess.check_call([sys.executable, “-m”, “pip”, “install”, “-q”, “-U”, “torchao>=0.16”, “trl>=0.20”, “transformers>=4.45”, “datasets”, “peft>=0.13”, “accelerate”, “bitsandbytes”, ]) import sys as _sys for _m in [m for m in list(_sys.modules) if…
Qwen AI Releases Qwen-Scope: An Open-Source Sparse AutoEncoders (SAE) Suite That Turns LLM Internal Features into Practical Development Tools
Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe requests — AI devs…
A Coding Deep Dive into Agentic UI, Generative UI, State Synchronization, and Interrupt-Driven Approval Flows
In this tutorial, we build the entire Agentic UI stack from the ground up using plain Python, without relying on external frameworks to abstract away the core ideas. We implement…
Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks
The team behind Kimi.ai (Moonshot AI) just made a significant contribution to the open-source AI infrastructure space. The research team has made a significant contribution to the open-source AI infrastructure…
Microsoft Research’s World-R1 Uses Flow-GRPO and 3D-Aware Rewards to Inject Geometric Consistency Into Wan 2.1 Without Architectural Changes
Video foundation models can paint a beautiful frame. They are still notoriously bad at remembering it. Push the camera through a corridor in Wan 2.1 or CogVideoX and walls warp,…