Key points
- Character.AI — AI, the chatbot platform, formally declared bankruptcy this week.
- MS care — Whether the diagnostic ability really surpasses doctors is still an open comparison to verify, not a demonstrated breakthrough.
- A 16GB AMD card — The local LLM crowd got a genuinely hardcore port: a developer moved llama.
- No BIOS flashing required — The author's own testing covers mainstream cards such as the RTX 5090/5080/4090, though laptop Ada is not modifiable yet.
This is the Daily AI Stew, brought to you by Robin on 咸话咸说.
Disney buys Character.AI, but leaves the lawsuits outside
Character.AI, the chatbot platform, formally declared bankruptcy this week. It sounds like a story of a product in decline, but the court filings tell a much more complicated plot: this is in fact a key part of a Disney deal. Through the bankruptcy Disney hired away most of Character.AI’s staff, including the recently departed CEO Karandeep Anand, and may also gain most of the company’s technical assets while sidestepping its heavy legal baggage. Over the past few years Character.AI has faced several private lawsuits over chatbot safety issues and has been investigated by regulators in multiple jurisdictions. In a statement related to the bankruptcy case, the new CEO described the goal as reaching a mutually agreed settlement with personal-injury claimants and the states that opened investigations, while preserving asset value for shareholders. The cleverest part of the deal structure is that the remaining Character.AI entity keeps carrying the lawsuits that have not finished. Source: Ars Technica
Microsoft’s “healthcare super-intelligence”: a loud claim, evidence still lagging
Microsoft executive Mustafa Suleyman says Microsoft’s AI matches or even beats doctors on difficult case diagnosis, and he calls that goal a “healthcare super-intelligence”. For now, though, what is visible is mostly the company’s own claim and other outlets’ secondhand headlines — TechCrunch, The Verge and Gizmodo all reported it, but the material contains no specific tasks, no evaluation set, no sample size, no doctor comparison and no third-party validation. Whether the diagnostic ability really surpasses doctors is still an open comparison to verify, not a demonstrated breakthrough. Source: Google News
A 16GB AMD card: running Qwen3-27B’s full 262k context
The local LLM crowd got a genuinely hardcore port: a developer moved llama.cpp’s adaptive KV cache streaming from NVIDIA/CUDA onto AMD’s ROCm/HIP, letting Qwen3-27B run its full 262,144-token context window on a single 16GB RX 9070 XT. The approach keeps the complete roughly 7.4GB KV cache in host memory and streams it asynchronously over PCIe so the transfer overlaps GPU compute, while quantization and vocabulary trimming bring the weights down to about 13.6GB. In the measurements, decoding speed runs at 71.8 to 86.5 tokens per second for 1k to 4k contexts, dropping to roughly 31 once the context is pushed to the full 262k — slower, but in a framework that used to run out of VRAM at 65k, that is an order-of-magnitude extension. The interesting detail is that cutting power from the default 304W down to 231W costs only about 3.6% in prefill latency. The code is published on the feature/adaptive-kv-stream-rocm branch of github.com/achyudh/llama.cpp. Source: Reddit r/LocalLLaMA
No BIOS flashing required: cross-platform VBIOS tools for RTX 30/40/50
Developer Panchovix released three open-source tools at once: a VBIOS editor spanning Linux and Windows, a custom VBIOS loader based on the Windows registry, and a Fedora-based Linux loader that boots by patching the GSP firmware in memory, generates a standalone GRUB boot entry, and rebuilds itself automatically after kernel updates. They can adjust power limits, voltage, NVVDD/MSVDD offsets, fan curves and a pile of other parameters — an RTX 5090’s power limit can be pushed below 400W — without touching the physical VBIOS at all, which sidesteps NVIDIA’s falcon signature check. The backstory is that since the Maxwell generation NVIDIA has integrated a Falcon security processor into the GPU to verify firmware signatures, so directly flashing a custom VBIOS has basically not been viable for a decade. For players running multi-GPU inference under a single-card power budget, this hands back the freedom to overclock and unlock without risking the hardware. The author’s own testing covers mainstream cards such as the RTX 5090/5080/4090, though laptop Ada is not modifiable yet. Source: Reddit r/LocalLLaMA
Tyger: a 0.6B small model takes charge of “when to speak” in a full-duplex voice agent
The open-source community published Tyger’s open weights: a 14-million-parameter causal GRU stacked on top of the NVIDIA Nemotron 3.5 ASR Streaming 0.6B base, dedicated purely to full-duplex turn control for voice agents. Every 80 milliseconds, with no look-ahead, it emits one of three states — the user has finished speaking, the user has interrupted and the agent should stop, or the agent should offer listening feedback such as “mm-hmm” or “really?” — while listening to both the microphone and the agent’s own playback so it does not mistake echo for user input. The accompanying tyger-s2s speech stack integrates Gemma 4 E4B FP8 with VoxCPM2, going from the end of the user’s turn to the first audio output in about 0.7 seconds on an RTX 5090, and it runs fully local by default so the audio never leaves your machine. The cost is not small either: the whole stack needs 28GB of VRAM, supports only FP32, and has so far only been tested on the 5090. Source: Reddit r/LocalLLaMA
ORL: a diffusion language model hits 2,788 tokens per second on a single 5090
A research team published ORL (On-Policy Residual Learning), a residual learning method for diffusion language models, together with their own inference engine dlmrt. The numbers look impressive: the NVFP4-quantized version of LLaDA-2.1-mini with ORL reaches 2,788 tokens per second on a single RTX 5090; QwenDLM-4B gets roughly a 6.9× end-to-end throughput gain, and HumanEval pass@1 rises from 82.9% to 89.6%; post-training used only 4.2M self-generated tokens and 36 H100 hours. dlmrt captures the entire denoising loop with CUDA Graphs, running 1.2 to 1.6× faster than SGLang/vLLM under the same settings. Worth keeping in mind that these are single-request peaks rather than averages, and that NVFP4 is only available on Blackwell cards. The model, the training code, the evaluation pipeline and the inference engine are all open source, and the paper is on arXiv. Source: Reddit r/LocalLLaMA
Read together, today’s items are interesting: Disney is using bankruptcy proceedings to operate on an AI deal, Microsoft has made a big claim about medical diagnosis that still awaits verification, and in the open-source world one person pushed the long-context limit of a 16GB card up by an order of magnitude, another pulled the right to modify VBIOS back out of signature checking, and yet another made a diffusion language model’s speed look properly close to the autoregressive approach for the first time. The big companies’ narratives keep getting louder while open-source engineering progress keeps moving forward quietly — and as a developer, it is the latter that actually lands on your machine.