Bernstein Sees Prediction-Market M&A Wave as Platforms Integrate

Prediction-market operators are bringing trading infrastructure in-house, a rapid shift that could trigger a wave of acquisitions across crypto platforms, sportsbooks, brokerages and standalone exchanges, according to analysts at Bernstein. In a research report on Monday, Bernstein said the industry is going through “operational consolidation,” with major platforms moving to control more of the prediction-market…

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Meta’s Astryx Brings a CLI and MCP Server to an Open-Source React Design System Agents Can Read

Meta released Astryx this week. It is an open-source design system, currently in Beta. The project grew inside Meta’s monorepo over eight years. Astryx is built on React and StyleX. StyleX is Meta’s compile-time CSS engine. TL;DR Astryx is Meta’s open-source, agent-ready React design system, now in Beta. It pairs StyleX styling with a CSS-variable…

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Trade Development Authority of Pakistan Internship Program 2026

Are you a dynamic graduate or final-year student looking to kickstart your professional career within a premium public sector organization? The Government of Pakistan’s Trade Development Authority of Pakistan (TDAP) has officially announced its latest training initiative, bringing a remarkable opportunity for young professionals to apply for the TDAP Internship Program in July 2026. This…

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Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines

rprint(Panel.fit(“[bold]Baseline 1: Predict output_type from context using pure Python Naive Bayes[/bold]”)) model_artifacts = {} classifier_df = df.dropna(subset=[“output_type”]).copy() classifier_df = classifier_df[ classifier_df[“output_type”].astype(str).str.len() > 0 ].copy() if classifier_df[“output_type”].nunique() >= 2 and len(classifier_df) >= 30: X_text = ( classifier_df[“context”] .fillna(“”) .astype(str) .map(lambda text: text[:12000]) .tolist() ) y = classifier_df[“output_type”].astype(str).tolist() train_indices, test_indices = stratified_train_test_indices(y, test_size=0.2, seed=SEED) X_train = [X_text[i]…

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