[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-generate-videos-by-slerp-walking-stable-diffusion-summary":3,"summaries-facets-categories":69,"summary-related-generate-videos-by-slerp-walking-stable-diffusion-summary":6973},{"id":4,"title":5,"ai":6,"body":13,"categories":46,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":51,"navigation":52,"path":53,"published_at":54,"question":48,"scraped_at":48,"seo":55,"sitemap":56,"source_id":57,"source_name":58,"source_type":59,"source_url":60,"stem":61,"tags":62,"thumbnail_url":48,"tldr":66,"tweet":48,"unknown_tags":67,"__hash__":68},"summaries\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary.md","Generate Videos by Slerp-Walking Stable Diffusion Latents",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",10775,1430,16123,0.00284735,{"type":14,"value":15,"toc":39},"minimark",[16,21,25,29,32,36],[17,18,20],"h2",{"id":19},"latent-space-walking-creates-hypnotic-videos","Latent Space Walking Creates Hypnotic Videos",[22,23,24],"p",{},"Sample two random latents (shape 1x4x64x64 for 512x512 images), then use spherical linear interpolation (slerp) across 200 steps from init1 to init2. For each interpolated latent, run diffusion conditioned on a fixed text prompt (e.g., \"blueberry spaghetti\") with classifier-free guidance: concatenate unconditional and conditional embeddings, predict noise with UNet, apply guidance_scale=7.5, and denoise over num_inference_steps=50 using LMSDiscreteScheduler. Decode final latents via VAE to produce one frame per step. Repeat pairs up to max_frames=10000, saving JPEGs at 90% quality. Stitch with ffmpeg -r 10 -f image2 -s 512x512 -i frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p output.mp4. This random walk yields surreal, morphing visuals without prompt changes.",[17,26,28],{"id":27},"custom-diffuse-handles-guidance-and-schedulers","Custom Diffuse Handles Guidance and Schedulers",[22,30,31],{},"Bypass pipeline for fine control: compute unconditional embeddings from empty prompt, cat with conditional (1x77x768). Set timesteps with offset=1 if supported, eta=0.0 for DDIM compatibility. For each timestep, double latents for CFG, predict noise_pred, scale as uncond + guidance_scale*(text - uncond), step scheduler to prev_sample. Scale latents by 1\u002F0.18215 before VAE decode, clamp\u002Fpost-process to uint8 numpy. Supports LMSDiscreteScheduler (multiplies latents by sigmas initially, divides model input by sqrt(sigma^2 +1)). Slerp avoids straight-line artifacts in high-D latent space using arccos(dot) for theta, blending with sin terms if dot \u003C 0.9995.",[17,33,35],{"id":34},"setup-params-and-optimizations","Setup, Params, and Optimizations",[22,37,38],{},"Requires Hugging Face access token for CompVis\u002Fstable-diffusion-v1-3-diffusers (or v1-4), diffusers library, torch, einops, PIL, fire (pip install fire), ~10GB VRAM for 512x512. Run: python stablediffusionwalk.py --prompt \"blueberry spaghetti\" --name outdir --num_steps 200 --num_inference_steps 50 --guidance_scale 7.5 --seed 1337 --max_frames 10000. Wrap diffuse in torch.autocast('cuda') for half-precision speedup. Higher inference steps (100-200) improve quality; guidance 3-10 tunes adherence. Users extended to prompt interpolation, fp16 models (fix dtype mismatches by upgrading diffusers\u002Ftransformers\u002Fscipy), or pipeline simplifications (pipe(prompt, latents=init, ...)).",{"title":40,"searchDepth":41,"depth":41,"links":42},"",2,[43,44,45],{"id":19,"depth":41,"text":20},{"id":27,"depth":41,"text":28},{"id":34,"depth":41,"text":35},[47],"Software Engineering",null,"md",false,{},true,"\u002Fsummaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary","2026-04-08 21:21:20",{"title":5,"description":40},{"loc":53},"9fd1fce56d7f77a1","Andrej Karpathy Gists","article","https:\u002F\u002Funknown","summaries\u002Fgenerate-videos-by-slerp-walking-stable-diffusion-summary",[63,64,65],"python","ai-tools","machine-learning","Interpolate random latents with slerp under a fixed prompt to create smooth, hypnotic videos from Stable Diffusion frames (50 inference steps, 7.5 guidance, 200 steps per 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60-Min ASR with Speakers, Timestamps, Hotwords",{"provider":7,"model":8,"input_tokens":6978,"output_tokens":6979,"processing_time_ms":6980,"cost_usd":6981},8981,1739,13836,0.00215885,{"type":14,"value":6983,"toc":7056},[6984,6988,7015,7022,7026,7049,7053],[17,6985,6987],{"id":6986},"unified-long-form-transcription-in-single-pass","Unified Long-Form Transcription in Single Pass",[22,6989,6990,6991,6995,6996,6999,7000,7003,7004,6995,7007,7010,7011,7014],{},"VibeVoice-ASR handles 60-minute audio within 64K tokens without chunking losses, maintaining speaker consistency and semantics. It jointly performs ASR, diarization, and timestamping, outputting JSON-like structures with Start\u002FEnd times, Speaker IDs, and Content. Load via Transformers >=5.3.0: ",[6992,6993,6994],"code",{},"AutoProcessor"," and ",[6992,6997,6998],{},"VibeVoiceAsrForConditionalGeneration.from_pretrained(\"microsoft\u002FVibeVoice-ASR-HF\")",". Use ",[6992,7001,7002],{},"processor.apply_transcription_request(audio)"," for inputs, then ",[6992,7005,7006],{},"model.generate(**inputs)",[6992,7008,7009],{},"processor.decode(generated_ids, return_format=\"parsed\")"," for list of dicts or ",[6992,7012,7013],{},"\"transcription_only\""," for plain text. Example on podcast audio yields segments like {\"Start\":0,\"End\":15.43,\"Speaker\":0,\"Content\":\"Hello everyone...\"}, preserving multi-speaker flow.",[22,7016,7017,7018,7021],{},"Custom hotwords via ",[6992,7019,7020],{},"prompt"," parameter fix misrecognitions: on German-accented \"VibeVoice\" audio, without prompt it transcribes \"Revevoices\", but \"About VibeVoice\" prompt corrects to exact match, ideal for names or terms.",[17,7023,7025],{"id":7024},"flexible-inference-and-optimization-techniques","Flexible Inference and Optimization Techniques",[22,7027,7028,7029,7032,7033,7036,7037,7040,7041,7044,7045,7048],{},"Batch process lists of audio\u002Fprompts for efficiency. Adjust ",[6992,7030,7031],{},"tokenizer_chunk_size"," (default 1440000 samples\u002F60s at 24kHz, multiples of 3200 hop length) to fit memory, e.g., 64000 for shorter segments with cached states. Chat templates enable role-based inputs: ",[6992,7034,7035],{},"[{\"role\":\"user\",\"content\":[{\"type\":\"text\",\"text\":\"prompt\"},{\"type\":\"audio\",\"path\":\"url\"}]}]",", processed via ",[6992,7038,7039],{},"apply_chat_template",". Torch.compile speeds up by 2x+ on benchmarks (e.g., batch-4 German audio: ~0.2s uncompiled to ~0.1s compiled). Pipeline mode works but requires custom parsing of raw JSON strings. For training, use ",[6992,7042,7043],{},"model.train()"," with ",[6992,7046,7047],{},"output_labels=True"," in chat templates, computing loss on JSON-like targets.",[17,7050,7052],{"id":7051},"proven-performance-across-benchmarks","Proven Performance Across Benchmarks",[22,7054,7055],{},"Achieves average 7.77% WER on Open ASR Leaderboard (e.g., 2.20% LibriSpeech clean, 13.17% earnings22, RTF 51.80x real-time). Technical report shows low DER, cpWER, tcpWER on long-form datasets. Supports 50+ languages without ID specification, handling code-switching; distribution chart emphasizes English-heavy training with broad coverage. MIT-licensed, deployable on Foundry or Gradio playground.",{"title":40,"searchDepth":41,"depth":41,"links":7057},[7058,7059,7060],{"id":6986,"depth":41,"text":6987},{"id":7024,"depth":41,"text":7025},{"id":7051,"depth":41,"text":7052},[],{"content_references":7063,"triage":7081},[7064,7069,7074,7078],{"type":7065,"title":7066,"url":7067,"context":7068},"paper","VibeVoice-ASR Technical Report","https:\u002F\u002Farxiv.org\u002Fpdf\u002F2601.18184","cited",{"type":7070,"title":7071,"url":7072,"context":7073},"other","GitHub Repo","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FVibeVoice","mentioned",{"type":7075,"title":7076,"url":7077,"context":7073},"tool","Live Playground","https:\u002F\u002Faka.ms\u002Fvibevoice-asr",{"type":7070,"title":7079,"url":7080,"context":7068},"Open ASR Leaderboard","https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fhf-audio\u002Fopen_asr_leaderboard",{"relevance":7082,"novelty":7083,"quality":7083,"actionability":7083,"composite":7084,"reasoning":7085},5,4,4.35,"Category: AI & LLMs. The article provides a detailed overview of the VibeVoice-ASR tool, which is highly relevant for developers looking to integrate advanced ASR capabilities into their AI products. It includes practical examples of how to implement the tool, making it actionable for the target audience.","\u002Fsummaries\u002Ff783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary","2026-04-14 14:33:41",{"title":6976,"description":40},{"loc":7086},"f783931b642bec27","__oneoff__","https:\u002F\u002Fhuggingface.co\u002Fmicrosoft\u002FVibeVoice-ASR-HF","summaries\u002Ff783931b642bec27-vibevoice-asr-60-min-asr-with-speakers-timestamps-summary",[64,65,63],"Process up to 60 minutes of audio in one pass for structured transcripts (speaker IDs, timestamps, content) across 50+ languages, with custom hotwords boosting accuracy on proper nouns.",[],"k5A7DIi-WA3Sto8YpRe2VuLWOEJvNSRD9YjVEHARqp4",{"id":7099,"title":7100,"ai":7101,"body":7106,"categories":7134,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7135,"navigation":52,"path":7141,"published_at":7142,"question":48,"scraped_at":7143,"seo":7144,"sitemap":7145,"source_id":7146,"source_name":7147,"source_type":59,"source_url":7148,"stem":7149,"tags":7150,"thumbnail_url":48,"tldr":7152,"tweet":48,"unknown_tags":7153,"__hash__":7154},"summaries\u002Fsummaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary.md","Python Conquers AI, Data, and Backend via Libraries",{"provider":7,"model":8,"input_tokens":7102,"output_tokens":7103,"processing_time_ms":7104,"cost_usd":7105},4487,1297,19308,0.00152325,{"type":14,"value":7107,"toc":7129},[7108,7112,7115,7119,7122,7126],[17,7109,7111],{"id":7110},"pythons-shift-from-slow-to-essential","Python's Shift from 'Slow' to Essential",[22,7113,7114],{},"Python overcame early criticisms of slow performance and high memory use compared to C++ or Java by prioritizing simplicity and ecosystem growth. This focus made it ideal for complex tasks: data scientists analyze patterns and build ML models faster with less code, avoiding hours of boilerplate. Backend devs deploy AI models via clean APIs. By 2026, Python powers advanced tech across industries, proving simplicity scales better than raw speed for most real-world problems—Instagram handles massive traffic with Django for this reason.",[17,7116,7118],{"id":7117},"data-science-and-ai-libraries-for-rapid-prototyping","Data Science and AI Libraries for Rapid Prototyping",[22,7120,7121],{},"Use Pandas and NumPy for efficient data manipulation and numerical computing, Matplotlib and Seaborn for visualizing patterns, Scikit-learn for traditional ML models, and PyTorch for deep learning. These cut development time, letting you predict outcomes or build generative AI instead of wrestling syntax. For AI specifically, TensorFlow, Transformers (for NLP), and OpenCV (image recognition) handle massive datasets and algorithms in healthcare, finance, and cybersecurity. Deploy recommendation systems or chatbots quickly—Python's flexibility turns complex data into actionable insights without performance bottlenecks halting progress.",[17,7123,7125],{"id":7124},"backend-and-data-engineering-frameworks-for-scale","Backend and Data Engineering Frameworks for Scale",[22,7127,7128],{},"Build scalable web apps with Django (Instagram's choice for high traffic), FastAPI for AI APIs, Flask for lightweight services, or Streamlit for ML dashboards and prototypes. These frameworks emphasize readability and speed, reducing code volume while managing databases, auth, and servers. In data engineering, integrate Python with Apache Spark and Hadoop for distributed processing, or Airflow for ETL pipelines and real-time streaming. Automate workflows on cloud infrastructure efficiently—Python's clean syntax simplifies massive data flows, making it indispensable for production systems.",{"title":40,"searchDepth":41,"depth":41,"links":7130},[7131,7132,7133],{"id":7110,"depth":41,"text":7111},{"id":7117,"depth":41,"text":7118},{"id":7124,"depth":41,"text":7125},[47],{"content_references":7136,"triage":7137},[],{"relevance":7083,"novelty":7138,"quality":7083,"actionability":7083,"composite":7139,"reasoning":7140},3,3.8,"Category: Software Engineering. The article discusses Python's role in AI, data science, and backend development, addressing the audience's need for practical applications of AI tools and frameworks. It provides specific libraries and frameworks like Pandas, PyTorch, and Django, which are actionable for developers looking to integrate AI into their products.","\u002Fsummaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary","2026-05-14 09:31:57","2026-05-14 11:00:22",{"title":7100,"description":40},{"loc":7141},"37ea158d3a7e0a74","Python in Plain English","https:\u002F\u002Fpython.plainenglish.io\u002Fpython-for-everything-5d889172897d?source=rss----78073def27b8---4","summaries\u002F37ea158d3a7e0a74-python-conquers-ai-data-and-backend-via-libraries-summary",[63,7151,65,64],"data-science","Once mocked for slowness, Python now dominates data science, AI, backend development, and data engineering through libraries like Pandas, PyTorch, Django, and Airflow, enabling efficient analysis, model building, scalable apps, and pipelines.",[],"lsn69aHaiZQnUR0phjkMTCANxdbzUtdkDJ1l6zWyxhU",{"id":7156,"title":7157,"ai":7158,"body":7164,"categories":7210,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7211,"navigation":52,"path":7215,"published_at":7216,"question":48,"scraped_at":7217,"seo":7218,"sitemap":7219,"source_id":7220,"source_name":7147,"source_type":59,"source_url":7221,"stem":7222,"tags":7223,"thumbnail_url":48,"tldr":7225,"tweet":48,"unknown_tags":7226,"__hash__":7227},"summaries\u002Fsummaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary.md","Building Real-Time Industrial Digital Twins with AI",{"provider":7,"model":7159,"input_tokens":7160,"output_tokens":7161,"processing_time_ms":7162,"cost_usd":7163},"google\u002Fgemini-3.1-flash-lite",3909,369,2758,0.00153075,{"type":14,"value":7165,"toc":7206},[7166,7170,7173,7177,7180,7203],[17,7167,7169],{"id":7168},"from-observation-to-active-simulation","From Observation to Active Simulation",[22,7171,7172],{},"Traditional industrial digital twins often function as passive dashboards, merely visualizing historical or current machine metrics. To be effective in modern environments, digital twins must transition into active systems that understand, simulate, and predict reality. This requires moving away from static reporting toward architectures capable of processing thousands of sensor events per second to mirror the dynamic state changes of production lines and supply chains.",[17,7174,7176],{"id":7175},"architecting-for-real-time-predictive-intelligence","Architecting for Real-Time Predictive Intelligence",[22,7178,7179],{},"Building a digital twin that 'thinks' requires an infrastructure that integrates streaming data with predictive modeling. The core objective is to shift from reactive monitoring to proactive decision-making. By utilizing Python-based AI pipelines, engineers can ingest high-velocity industrial data to:",[7181,7182,7183,7191,7197],"ul",{},[7184,7185,7186,7190],"li",{},[7187,7188,7189],"strong",{},"Simulate Reality:"," Create virtual representations that adapt to demand fluctuations and operational shifts in real-time.",[7184,7192,7193,7196],{},[7187,7194,7195],{},"Predict Failures:"," Use continuous data streams to identify anomalies before they result in downtime, moving from scheduled maintenance to predictive, condition-based maintenance.",[7184,7198,7199,7202],{},[7187,7200,7201],{},"Enable Autonomous Decisions:"," Empower the digital twin to trigger automated responses or optimize production parameters without human intervention, effectively closing the loop between data ingestion and operational action.",[22,7204,7205],{},"This approach transforms the digital twin from a visual aid into a core component of industrial automation, allowing systems to adapt to complex, shifting operational environments.",{"title":40,"searchDepth":41,"depth":41,"links":7207},[7208,7209],{"id":7168,"depth":41,"text":7169},{"id":7175,"depth":41,"text":7176},[85],{"content_references":7212,"triage":7213},[],{"relevance":7082,"novelty":7083,"quality":7083,"actionability":7083,"composite":7084,"reasoning":7214},"Category: AI Automation. The article discusses how to build real-time industrial digital twins using AI, which directly addresses the audience's interest in AI-powered product development. It provides actionable insights on integrating streaming data with predictive modeling, making it relevant and practical for engineers and product builders.","\u002Fsummaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary","2026-06-30 10:57:32","2026-06-30 12:57:12",{"title":7157,"description":40},{"loc":7215},"93e96619473c5ad7","https:\u002F\u002Fpython.plainenglish.io\u002Fbuilding-industrial-digital-twins-that-think-in-real-time-with-python-and-ai-49bb8668a050?source=rss----78073def27b8---4","summaries\u002F93e96619473c5ad7-building-real-time-industrial-digital-twins-with-a-summary",[63,64,65,7224],"automation","Modern digital twins must move beyond static dashboards to active, predictive systems that simulate and anticipate factory operations using real-time streaming data.",[],"LJpDjCRDU3uH1uBOSaJlJ3768B6Epw08opD4hPrI6fs",{"id":7229,"title":7230,"ai":7231,"body":7236,"categories":7315,"created_at":48,"date_modified":48,"description":40,"extension":49,"faq":48,"featured":50,"kicker_label":48,"meta":7316,"navigation":52,"path":7327,"published_at":7328,"question":48,"scraped_at":7328,"seo":7329,"sitemap":7330,"source_id":7331,"source_name":7332,"source_type":59,"source_url":7333,"stem":7334,"tags":7335,"thumbnail_url":48,"tldr":7336,"tweet":48,"unknown_tags":7337,"__hash__":7338},"summaries\u002Fsummaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary.md","Spatial Graph Neural Networks for Urban Function Inference",{"provider":7,"model":7159,"input_tokens":7232,"output_tokens":7233,"processing_time_ms":7234,"cost_usd":7235},11411,611,3048,0.00376925,{"type":14,"value":7237,"toc":7310},[7238,7242,7253,7257,7260,7292,7299,7303],[17,7239,7241],{"id":7240},"building-a-spatial-graph-pipeline","Building a Spatial Graph Pipeline",[22,7243,7244,7245,7248,7249,7252],{},"This tutorial demonstrates an end-to-end workflow for urban function inference, where the goal is to classify Points of Interest (POIs) based on their spatial context. The pipeline leverages ",[6992,7246,7247],{},"city2graph"," to bridge geospatial data processing with graph-based machine learning. The process begins by collecting real-world POI and street network data from OpenStreetMap (OSM) via ",[6992,7250,7251],{},"OSMnx",". To ensure reproducibility and robustness, the workflow includes a synthetic data fallback that generates clustered POIs if live OSM data is unavailable.",[17,7254,7256],{"id":7255},"feature-engineering-and-graph-construction","Feature Engineering and Graph Construction",[22,7258,7259],{},"Spatial features are engineered by calculating local POI density and proximity to the nearest street segments. The core of the spatial analysis involves constructing various proximity graph families to represent urban structure, including:",[7181,7261,7262,7267,7272,7277,7282,7287],{},[7184,7263,7264],{},[7187,7265,7266],{},"K-Nearest Neighbors (KNN)",[7184,7268,7269],{},[7187,7270,7271],{},"Delaunay Triangulation",[7184,7273,7274],{},[7187,7275,7276],{},"Gabriel Graphs",[7184,7278,7279],{},[7187,7280,7281],{},"Relative Neighborhood Graphs (RNG)",[7184,7283,7284],{},[7187,7285,7286],{},"Euclidean Minimum Spanning Trees (EMST)",[7184,7288,7289],{},[7187,7290,7291],{},"Waxman Graphs",[22,7293,7294,7295,7298],{},"These topologies are compared to evaluate how different connectivity strategies capture urban relationships. The data is then converted into ",[6992,7296,7297],{},"PyTorch Geometric"," formats, supporting both homogeneous graphs (for standard classification) and heterogeneous graphs (to model relationships between different urban function categories).",[17,7300,7302],{"id":7301},"model-training-and-inference","Model Training and Inference",[22,7304,7305,7306,7309],{},"For classification, the tutorial implements a two-layer ",[6992,7307,7308],{},"GraphSAGE"," model. The model learns node representations by aggregating features from local graph neighborhoods. The training process uses a 60\u002F20\u002F20 split for training, validation, and testing. Performance is evaluated using accuracy and macro-F1 scores. Finally, the learned embeddings are visualized using PCA, and predictions are mapped back to geographic space, providing a clear view of how the model interprets urban functions based on spatial structure.",{"title":40,"searchDepth":41,"depth":41,"links":7311},[7312,7313,7314],{"id":7240,"depth":41,"text":7241},{"id":7255,"depth":41,"text":7256},{"id":7301,"depth":41,"text":7302},[123],{"content_references":7317,"triage":7325},[7318,7321,7323],{"type":7075,"title":7247,"url":7319,"context":7320},"https:\u002F\u002Fgithub.com\u002Fc2g-dev\u002Fcity2graph","recommended",{"type":7075,"title":7251,"url":7322,"context":7320},"https:\u002F\u002Fosmnx.readthedocs.io\u002F",{"type":7075,"title":7297,"url":7324,"context":7320},"https:\u002F\u002Fwww.pyg.org\u002F",{"relevance":7083,"novelty":7138,"quality":7083,"actionability":7083,"composite":7139,"reasoning":7326},"Category: AI & LLMs. The article provides a practical pipeline for urban function inference using spatial graph neural networks, which directly addresses the audience's need for actionable AI engineering content. It includes specific techniques and tools like `city2graph`, `OSMnx`, and `PyTorch Geometric`, making it relevant for developers looking to implement AI features.","\u002Fsummaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary","2026-06-13 12:56:20",{"title":7230,"description":40},{"loc":7327},"1eaf4aab7431c0b6","MarkTechPost","https:\u002F\u002Fwww.marktechpost.com\u002F2026\u002F06\u002F12\u002Fa-coding-implementation-on-spatial-graph-neural-networks-for-urban-function-inference-using-city2graph-osmnx-and-pytorch-geometric\u002F","summaries\u002F1eaf4aab7431c0b6-spatial-graph-neural-networks-for-urban-function-i-summary",[63,65,7151,64],"A practical pipeline for urban function inference using city2graph, OSMnx, and PyTorch Geometric to classify POIs based on spatial relationships and graph topology.",[],"NgedcdoA-nvXxSX0y0M6IsoB3fqq-EzowtGYVGVd-XQ"]