u/mckabue

I'm the developer of  Blober (blober.io), a desktop app for transferring files between cloud providers. pCloud support just shipped, and I'd like honest feedback.

What it does:

• Moves files between pCloud and other providers (Google Drive, OneDrive, Dropbox, Box, S3-compatible storage, Azure Blob, Proton Drive, local drives and NAS), in any direction.
• Uploads over the pCloud API directly in parallel . That solves the "small files crawl" / slow upload threads that show up here.
• It's a transfer tool, not a sync client. Nothing runs in the background and overwrites files. You pick a source and a destination, run it, and verify the result.
• Per-file progress and automatic retry on transient errors; completed files stay done if a task is interrupted.

Connecting takes seconds because the app ships the public rclone pCloud app keys, so there's no need to create your own pCloud app or wait for app review.

If you try it and uploads are still slow, or it doesn't do what you need, please tell me.

DeepSeek released a preview of V4 on April 24, 2026, its biggest model update since R1 shook the AI industry in January 2025. V4-Pro packs 1.6 trillion total parameters (49 billion active per query) and a 1-million-token context window, up from V3’s 128K. The smaller V4-Flash runs 284 billion total parameters with just 13 billion active. Both use a hybrid attention architecture that compresses older context and focuses on nearby text, cutting V4-Pro’s compute to 27% and its memory to 10% of what V3.2 needed at the same context length. V4-Flash is even leaner: 10% of the compute and 7% of the memory. On benchmarks, V4-Pro-Max matches Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.4 on coding tasks like SWE-bench Verified (80.6% resolved) and scores 90.1% on MMLU-Pro. It leads all open-source models and trails leading closed models by only a few percentage points on reasoning. Both models were pre-trained on over 32 trillion tokens and released under the MIT License.

V4 is DeepSeek’s first model optimized for Huawei’s Ascend chips, a deliberate move away from Nvidia. Huawei confirmed its Ascend 950 supernodes will support V4, and DeepSeek gave Chinese chipmakers, not Nvidia or AMD, early access to the model. DeepSeek uses Huawei hardware for inference, though training still appears partially reliant on Nvidia GPUs. The market noticed: domestic chipmakers Hua Hong Semiconductor jumped 15% and SMIC rose roughly 9% on the announcement. Nvidia CEO Jensen Huang put the stakes bluntly on the Dwarkesh Podcast: “The day that DeepSeek comes out on Huawei first, that is a horrible outcome for our nation.” US export controls have cut China off from Nvidia’s best chips since 2022, and Beijing has been pushing firms toward domestic alternatives. V4 is the clearest signal yet that China’s parallel AI stack, from chips to models, is becoming viable. Replacing Nvidia requires not just competitive silicon but a software ecosystem that developers will actually use. DeepSeek’s open-source approach, MIT licensing, and agent-framework compatibility are a direct play for that developer adoption. NVIDIA’s advantage was never just hardware; it was the lock-in from CUDA and the ecosystem built around it. Whether Huawei’s Ascend platform can match that remains the open question, but V4 makes the possibility much harder to dismiss.

Read More: https://toknow.ai/posts/deepseek-v4-huawei-ascend-nvidia-monopoly-open-source-frontier/