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Added web search to Papers with Code's upcoming chat interface This runs on Hugging Face Inference Providers via Baseten (new DeepSeek-V4 Flash), using Exa's MCP server, running inside a Modal container Drop your HF username, and I'll add you, eager for feedback :)

Niels Rogge

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17,577 görüntüleme • 4 gün önce •via X (Twitter)

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Initial DeepSeek v4 and v4 Flash support added to anemll-flash-llama.cpp! 🚀 M5 MAX 128GB can run full DeepSeek-V4 with 1.6T params in original FP8/FP4 weights from SSD without requantization! One-click scripts to convert HF safetensors to dense + MoE sidecar (no GGUF needed) Inference and server examples Prefill and decoding benchmarks Branch and docs here: This is WIP and I plan to add cleanup and more testing this week. Feedback welcome!

Initial DeepSeek v4 and v4 Flash support added to anemll-flash-llama.cpp! 🚀 M5 MAX 128GB can run full DeepSeek-V4 with 1.6T params in original FP8/FP4 weights from SSD without requantization! One-click scripts to convert HF safetensors to dense + MoE sidecar (no GGUF needed) Inference and server examples Prefill and decoding benchmarks Branch and docs here: This is WIP and I plan to add cleanup and more testing this week. Feedback welcome!

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this is a [deep breath] M5 Stick S3 acting as a frontend for Gemini Flash 3.1 Live, running via a CloudFlare worker, with tools to set device settings (brightness, volume, power), web fetch / search, and vector search (of tldraw docs) via cf's Vectorize. crazy low latency, push to talk on this hardware is great too

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I paired Cloudflare OS with self hosted deepseek-v4-flash and the result is freakin amazing Imagine running this setup inside DGX spark, you get fully private work companion solving all your work stuff The thing about this type of software is it already have a good harness and it probably can work out of the box with every open weight model Cant wait for the future

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Evernote Search just got a serious glow-up ✨ We made several visible improvements to the search modal, including: 👉 A centered search modal with scrollable suggestions 👉 Higher priority for results with title matches 👉 A new default section view for recent notes, grouped by when they were last updated We also made some behind-the-scenes performance boosts to speed up loading time and give you smarter, faster suggestions while typing. All these changes add up to a faster, smarter, more seamless search experience. Available in v10.143 and up on Desktop and Web. Happy searching! 💚

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Gamma

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Researchers spend 2 hours daily just scanning papers. What if you can directly find the exact papers you need? Imagine you are working on a research project. ↳ You can go Google Scholar ↳ You hit a search query ↳ Google Scholar return 2000+ papers ↳ You keep opening new papers in new tabs ↳ The scanning process goes on ↳ After an hour, you are still not sure ➤ Which papers are relevant? This is the problem faced by almost all researchers. 🎁 The good news? Meet WisPaper - the perfect paper finder. This tool deploys AI agents to find the exact papers, 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 𝐰𝐨𝐫𝐤𝐬 1. Go to 2. Enter your search query 3. For example, I entered the following 𝐹𝑖𝑛𝑑 𝑝𝑎𝑝𝑒𝑟𝑠 𝑜𝑛 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠 𝑖𝑛 𝑏𝑖𝑔 𝑑𝑎𝑡𝑎 𝑠𝑦𝑠𝑡𝑒𝑚𝑠 4. 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 will start the search process. 5. The search process is broken down into 3 steps. ✓ Analysing questions ✓ Validating criteria ✓ Searching and validating 6. After completion, you will see a list of papers. 7. You can add papers to your library 8. If you need specific search, you can add criteria. In addition to searching, 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 also helps in: ➝ Organizing papers in the form of a library ➝ Send you alerts about papers relevant to you 🎗️ Try 𝐖𝐢𝐬𝐏𝐚𝐩𝐞𝐫 today for free:

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Just launched #CES2026, the new open-source NVIDIA Nemotron Speech ASR model is here to solve latency drift and redundant compute. Its cache-aware streaming architecture eliminates the need for buffered inference, giving you stable, sub-100ms latency (24ms median T-T-F) and up to 3x more throughput on your GPU. 🤗 Read the technical blog with real-world results from Daily and Modal on Hugging Face:

Just launched #CES2026, the new open-source NVIDIA Nemotron Speech ASR model is here to solve latency drift and redundant compute. Its cache-aware streaming architecture eliminates the need for buffered inference, giving you stable, sub-100ms latency (24ms median T-T-F) and up to 3x more throughput on your GPU. 🤗 Read the technical blog with real-world results from Daily and Modal on Hugging Face:

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