--- title: Best Hugging Face posts — April 2026 | daily.dev description: The most upvoted Hugging Face posts from April 2026, curated by the daily.dev community. canonical: https://daily.dev/sources/huggingface/best-of/2026/04 twitter:card: summary_large_image twitter:site: @dailydotdev og:url: https://daily.dev/sources/huggingface/best-of/2026/04 og:type: website og:site_name: daily.dev og:title: Best Hugging Face posts — April 2026 | daily.dev og:description: The most upvoted Hugging Face posts from April 2026, curated by the daily.dev community. og:image: https://media.daily.dev/image/upload/s--VAY5ToZt--/f_auto/v1724209435/public/daily.dev%20-%20open%20graph --- # Best of Hugging Face — April 2026 1. 1 [](https://daily.dev/posts/deepseek-v4-a-million-token-context-that-agents-can-actually-use-uczd4kivx "DeepSeek-V4: a million-token context that agents can actually use") Article Hugging Face · 19w DeepSeek-V4: a million-token context that agents can actually use DeepSeek-V4 is a new frontier open model designed specifically for long-running agentic workloads. It introduces a hybrid attention architecture combining Compressed Sparse Attention (CSA, 4x compression) and Heavily Compressed Attention (HCA, 128x compression), reducing KV cache memory to roughly 2% of standard grouped query attention. V4-Pro requires only 27% of the single-token inference FLOPs of V3.2, and V4-Flash drops to 10%. Key agent-specific improvements include preserved reasoning traces across tool-call boundaries and user turns, a new XML-based tool-call schema with dedicated tokens to reduce parsing failures, and a Rust-based sandbox infrastructure (DSec) used for RL training against real tool environments. On agent benchmarks, V4-Pro-Max reaches 80.6 on SWE Verified, 73.6 on MCPAtlas, and 67.9 on Terminal Bench 2.0, placing it at parity with frontier closed models. Four model checkpoints (Pro and Flash, instruct and base) are available on Hugging Face Hub. 27 2 2. 2 [](https://daily.dev/posts/granite-4-1-llms-how-they-re-built-luubflwrn "Granite 4.1 LLMs: How They’re Built") Article Hugging Face · 19w Granite 4.1 LLMs: How They’re Built IBM releases Granite 4.1, a family of dense decoder-only LLMs (3B, 8B, 30B) trained on \~15 trillion tokens using a five-phase pre-training pipeline. Key highlights include long-context extension up to 512K tokens, SFT on \~4.1M LLM-as-Judge-curated samples, and a four-stage RL pipeline using on-policy GRPO with DAPO loss. Notably, the 8B instruct model matches or surpasses the previous Granite 4.0-H-Small (32B-A9B MoE) model. FP8 quantized variants are available for vLLM inference. All models are released under Apache 2.0. 23 3. 3 [](https://daily.dev/posts/any-custom-frontend-with-gradio-s-backend-0v2eu317s "Any Custom Frontend with Gradio's Backend") Article Hugging Face · 20w Any Custom Frontend with Gradio's Backend gradio.Server is a new Gradio class that extends FastAPI, allowing developers to pair any custom frontend (React, Svelte, plain HTML/JS) with Gradio's backend infrastructure including queuing, concurrency management, ZeroGPU support, and gradio\_client compatibility. The @app.api() decorator wraps Python functions with Gradio's queuing engine, preventing GPU contention when multiple users hit an endpoint simultaneously. A working demo builds a 'Text Behind Image' editor: a \~50-line Python backend loads a BiRefNet segmentation model, while a 1300-line vanilla HTML/CSS/JS frontend handles canvas layering, drag-and-drop, and client-side PNG export. The frontend communicates via the Gradio JS Client to benefit from queue management. Future posts will cover MCP tool registration, SSE streaming, batch processing, and multi-page app patterns. 10 1 [See all Hugging Face archives](/sources/huggingface/best-of) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@graph":[{"@type":"CollectionPage","@id":"https://daily.dev/sources/huggingface/best-of/2026/04#page","url":"https://daily.dev/sources/huggingface/best-of/2026/04","name":"Best Hugging Face Posts — April 2026","description":"The most upvoted Hugging Face posts from April 2026, curated by the daily.dev community.","isPartOf":{"@type":"WebSite","url":"https://daily.dev"}},{"@type":"ItemList","@id":"https://daily.dev/sources/huggingface/best-of/2026/04#items","numberOfItems":3,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://daily.dev/posts/deepseek-v4-a-million-token-context-that-agents-can-actually-use-uczd4kivx","name":"DeepSeek-V4: a million-token context that agents can actually use"},{"@type":"ListItem","position":2,"url":"https://daily.dev/posts/granite-4-1-llms-how-they-re-built-luubflwrn","name":"Granite 4.1 LLMs: How They’re Built"},{"@type":"ListItem","position":3,"url":"https://daily.dev/posts/any-custom-frontend-with-gradio-s-backend-0v2eu317s","name":"Any Custom Frontend with Gradio's Backend"}]},{"@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Sources","item":"https://daily.dev/sources"},{"@type":"ListItem","position":3,"name":"Hugging Face","item":"https://daily.dev/sources/huggingface"},{"@type":"ListItem","position":4,"name":"Best of","item":"https://daily.dev/sources/huggingface/best-of"},{"@type":"ListItem","position":5,"name":"April 2026"}]}]} ```