Google to invest up to $40B in Anthropic in cash and compute
The last time I was impressed by a model was the jump from 4o to GPT 5 (and comparatively o1/o3). The 5-5.4 lineup from OpenAI didn't impress me, but 5.5 feels like a substantial leap again. I'm also using Opus 4.6 (not 4.7 because the safety trigger is too strict), and Gemini 3.1, and while the oth
OpenAI unveils GPT-5.5, claims a "new class of intelligence" at double the API price
OpenAI has announced GPT-5.5, an agentic model designed to work through complex tasks autonomously by switching between multiple tools. The article OpenAI unveils GPT-5.5, claims a "new class of intelligence" at double the API price appeared first on The Decoder.
Qwen3.6-27B-INT4 clocking 100 tps with 256k context length on 1x RTX 5090 via vllm 0.19
Thanks to the community the Qwen3.6-27B speed keeps getting better. The following improves upon my recipe from [yesterday](https://www.reddit.com/r/LocalLLaMA/comments/1sv8eua/qwen3627b_at_80_tps_with_218k_context_window_on/) and delivered a whopping 100+ tps (TG). Model: [https://huggingface.co/Lo
Could creativy in LLM emerge by reframing language?
The AI industry is discovering that the public hates it
GitHub Copilot: GPT-5.5 7.5x more expensive under promotional pricing than 5.4
Eden AI – European Alternative to OpenRouter
Trump fires the entire National Science Board
Benchmark: Windows 11 vs Lubuntu 26.04 on Llama.cpp (RTX 5080 + i9-14900KF). I didn't expect the gap to be this big.
As a life-long Windows user (don't hate me, I was exposed to it at a young age) I was wondering how much (if any) performance I'm leaving on the table. So I did the sensible thing and run some benchmarks. **Setup:** * **OS:** Windows 11 25H2 vs Lubuntu 26.04 * **Engine:** Llama.cpp b8929, CUDA 13.
The exact KV cache usage of DeepSeek V4
Figure 1 of DSV4 paper seems to imply that DSV3.2 uses \~50GB at 1m context and DSV4 uses \~5GB: [https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek\_V4.pdf](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) From my own calculations, the correct FP16
How it feels this month
Using PaddleOCR-VL-1.5 with llama-server for book OCR
I've been running PaddleOCR-VL-1.5 via llama.cpp's server for OCR on book pages. It handles complex layouts, tables, and mixed text/figure pages surprisingly well. Setup: \- Model: PaddleOCR-VL-1.5-GGUF + mmproj.gguf \- Backend: llama-server (Vulkan on Windows) \- Pipeline: layout detecti
The mysterious smile... of your replacement.
Lambda Calculus Benchmark for AI
I can’t believe I can say “ugh I don’t feel like fixing this function, it’s too complex” and I can literally just tell my computer to fix it for me. I didn’t understand what they meant by “people will start paying for intelligence” but now I do.
And in this case it’s free! Aside from the electricity haha I hope these things aren’t conscious. I’d feel awful demanding them to work on my code!
Why Cohere is merging with Aleph Alpha
Canadian AI startup Cohere is taking over Germany-based Aleph Alpha with support from Lidl’s owner, Schwarz Group. With the blessing of their governments, the companies intend to offer a sovereign alternative to enterprises in an AI landscape dominated by American players.
DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles
These generated videos has ruined the fun of social media and youtube.
No more organic content or videos on the internet everything is just Ai and it made me loose the interest, now I am just reading books.
[Qwen3.6 35b a3b] Used the top config for my setup 8gb vram and 32gb ram, and found that somehow the Q4_K_XL model from Unsloth runs just slightly faster and used less tokens for output compared to Q4_K_M despite more memory usage
Config * CtxSize: 131,072 * GpuLayers: 99 * CpuMoeLayers: 38 * Threads: 16 * BatchSize/UBatchSize: 4096/4096 * CacheType K/V: q8\_0 * Tool Context: file mode (tools.kilocode.official.md) |Metric|M Model|XL Model|Difference| |:-|:-|:-|:-| |**Avg Tokens/sec**|28.92|29.78|**+0.86 (+3.0%)**| |**Median
"Weights are coming".Xiaomi’s MiMo V2.5 Pro has landed at 54 in the Artificial Analysis Intelligence Index.
From: \- Xiaomi MiMo on 𝕏: [https://x.com/XiaomiMiMo/status/2047840164777726076](https://x.com/XiaomiMiMo/status/2047840164777726076) \- Artificial Analysis 𝕏: [https://x.com/ArtificialAnlys/status/2047799218828665093](https://x.com/ArtificialAnlys/status/2047799218828665093)