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Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines 01
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Indian payments chief thinks AI will be heavily involved in next era of digital payment growth
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Liquid AI Ships LFM2.5-230M with llama.cpp, MLX, vLLM, SGLang, and ONNX Support for On-Device Inference
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Instagram is testing more ways to customize ‘Your Algorithm’

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Grayscale’s Pandl Says Technique’s $3B Bitcoin Sale Might Restore Confidence
Grayscale’s Pandl Says Technique’s $3B Bitcoin Sale Might Restore Confidence
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Bitcoin and Gold Are Bleeding
Karachi family comes under attack after losing way in Balochistan

Bitcoin and Gold Are Bleeding

Here’s what seems to be retail investors’ new attraction. Bitcoin has been on an evident downfall since the beginning of the year as it plunged below $60,000 for the first time since late 2024. The largest funds tracking its performance have lost over $8 billion in weeks. Although gold began the year on a positive…

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Sequencer Bug Caused Two Base Network Outages in a Week

A sequencer bug was responsible for two outages of the Coinbase layer-2 network Base last week, according to a post-mortem. The Base engineering team said in a Saturday post-mortem that they identified a bug in sequencer block-building logic that allowed “stale journal state” to persist after a transaction validation failure. “An invalid transaction was received…

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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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