California regulation focusing on loud streaming advertisements takes impact on July 1

California regulation focusing on loud streaming advertisements takes impact on July 1

Streaming advertisements could be getting rather a lot quieter this week. A California regulation banning streaming companies from exhibiting advertisements “louder than the video content material” that they accompany is about to take impact on Wednesday, July 1. (Present laws already imposes comparable quantity restrictions on broadcast and cable TV commercials.)  Ars Technica notes that…

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Writer Ian Bogost says ‘The Small Stuff’ can help us reclaim our lives from too much convenience

Has Silicon Valley been building the wrong things? Despite its self help-y title, writer/designer/academic Ian Bogost’s forthcoming book “The Small Stuff: How to Lead a More Gratifying Life” asks some pointed questions about how technology has transformed our experience of the physical world. Using Bogost’s popular article in the Atlantic about the decline of stick…

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Writer Ian Bogost says ‘The Small Stuff’ can help us reclaim our lives from dematerialization

Has Silicon Valley been building the wrong things? Despite its self help-y title, writer/designer/academic Ian Bogost’s forthcoming book “The Small Stuff: How to Lead a More Gratifying Life” asks some pointed questions about how technology has transformed our experience of the physical world. Using Bogost’s popular article in the Atlantic about the decline of stick…

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OCRmyPDF Tutorial: Convert Scanned Documents into Searchable PDF/A Files with Sidecar Text Extraction and Batch Processing

def _purge(*prefixes): for name in [m for m in list(sys.modules) if any(m == p or m.startswith(p + “.”) for p in prefixes)]: del sys.modules[name] def _load_ocrmypdf(): _purge(“PIL”, “ocrmypdf”) import ocrmypdf return ocrmypdf try: ocrmypdf = _load_ocrmypdf() except ImportError as e: if “_Ink” in str(e) or “PIL” in str(e): print(“Repairing an incompatible Pillow (reinstalling pillow<12)…”) sh(f'”{sys.executable}”…

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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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