class AgentAnalyzer: @staticmethod def plot_response_distribution(result: Dict): fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle(‘Agent Response Analysis’, fontsize=16, fontweight=”bold”) responses = result(‘all_responses’) scores = result(‘critic_scores’) uncertainty = result(‘uncertainty’) selected_idx = result(‘selected_index’) …
Tag:
Build
-
-
Generative AI
How to build a stable and efficient QLoRA fine-tuning pipeline using Unsloth for large language models
In this tutorial, we demonstrate how to efficiently fine-tune using a large language model tasteless And QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common …
-
AI News
How to build an interpretable AI analysis pipeline using SHAP-IQ to understand feature importance, interaction effects, and model decision breakdown
INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc(INSTANCE_I).values y_true = float(y_test.iloc(INSTANCE_I)) pred = float(model.predict((x))(0)) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects = extract_main_effects(iv, feature_names) pair_df = …
-
AI News
A coding implementation to build a hierarchical planner AI agent using open-source LLM with tool execution and structured multi-agent reasoning
def executor_agent(step: Dict(str, Any), context: Dict(str, Any)) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”: context.get(“goal”), “assumptions”: context.get(“assumptions”, ()), …
-
AI Tools
How to build an elastic vector database with persistent hashing, sharing, and live ring visualization for RAG systems
def draw_ring(ring: ConsistentHashRing, dist: Dict(str, int), title: str): node_ids = sorted(ring.nodes.keys()) plt.figure(figsize=(8, 8)) ax = plt.gca() ax.set_title(title) if not node_ids: plt.text(0.5, 0.5, “Ring is empty”, ha=”center”, va=”center”) plt.axis(“off”) plt.show() return …
-
Generative AI
Composio open source agent orchestrator will help AI developers build scalable multi-agent workflows beyond traditional React loops
Over the past year, AI developers have relied on the React (Reasoning + Acting) pattern – a simple loop where an AI thinks, chooses a tool, and executes. But as …
-
Generative AI
4 AI Security Alignment Approaches: How to Build AI That Won’t Lie, Harm, or Manipulate
Last updated on February 21, 2026 by Editorial Team Author(s): Tanveer Mustafa Originally published on Towards AI. Understanding RLHF, Constitutional AI, Red Teaming and Value Learning You ask Chatgpt how …
-
Generative AI
How to Build Transparent AI Agents: Making Traceable Decisions with Audit Trails and Human Gates
In this tutorial, we create a glass-box agentive workflow that makes every decision traceable, auditable, and clearly controlled by human approval. We design the system to log every thought, action …
-
AI Tools
How to build an advanced, interactive exploratory data analysis workflow using PyGWalker and feature-engineered data
In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a truly interactive exploratory data analysis workflow using them directly. PyGWalker. We start by preparing the …
-
Startups are under constant pressure to ship products that are simple, affordable, and scalable without compromising performance or user trust. for years, AI system To meet these demands there has …
Newer Posts