Products & Apps
Mac Studio (M5 Max / M5 Ultra)
Apple announces Mac Studio with M5 Max and M5 Ultra plus a new Mac Mini
Apple announced a new Mac Studio with M5 Max and M5 Ultra chips, plus a refreshed Mac Mini with higher-spec options. Starting at $2,499 but configurable to roughly $22K, the panel framed it as home AI infrastructure: Wolfram's 'central heating' theory — financed over two years it costs about the same as a Pro AI subscription, with unlimited local tokens.
$2,499 Mac Studio starting price (up to ~$22K configured)
Dev ToolsOpen weights
Pipette
Liquid AI releases Pipette, an open-source on-device eval suite
Liquid AI released Pipette, an open-source evaluation suite for on-device models. Noted in the newsletter TL;DR; the segment didn't make the published episode cut.
New ModelsOpen weights
Qwen3.8-27B
Qwen3.8-27B ties GPT-5.6 Luna and runs on a 4090
Alibaba's overnight community darling: a 27B-parameter Apache 2.0 model scoring 52 on the Artificial Analysis Intelligence Index — the same as GPT-5.6 Luna at max reasoning — and 51 on the Agentic index. It runs at ~68 tokens/sec on a 4090, ~40 on Macs via MLX, and even 11 tok/s in-browser on WebGPU kernels. The Hugging Face hub exploded with 152 fine-tunes, 650 quantizations and close to 10 million quant downloads, and Unsloth's 1-bit quants run it on 8GB of RAM at roughly 77% of BF16 quality.
52 AA Intelligence Index, tying GPT-5.6 Luna at max reasoning68 tok/s on a single RTX 4090152 fine-tunes on Hugging Face
New ModelsOpen weights
LFM2.5 QAD checkpoints
Liquid AI ships LFM2.5 QAD 4-bit checkpoints for edge devices
Liquid AI released quantization-aware-distilled 4-bit checkpoints for LFM2.5 models from 230M to 2.6B parameters, retaining roughly 97% of BF16 quality with 3x faster decode on edge devices.
~97% of BF16 quality at 4-bit3x faster decode on edge
New ModelsOpen weights
S1-mini
Superwhisper S1-mini cleans up dictation fully on-device
Superwhisper — the dictation app Karpathy made famous when he coined vibe coding — released its first open-weights model: S1-mini, a 0.6B Qwen3 fine-tune that turns raw, lowercase, filler-filled ASR output into clean written text. Apache 2.0, English-only for now, about 450MB in GGUF, and it runs entirely on-device behind Whisper or Parakeet.
0.6B parameters, Qwen3 fine-tune~450MB in GGUF
New ModelsOpen weights
North Micro Vision
Cohere North Micro Vision: 2.4B VLM under Apache 2.0
Cohere released North Micro Vision, a 2.4B-parameter vision-language model under Apache 2.0 scoring 92.1% on DocVQA. Weights are on Hugging Face.
2.4B parameters92.1% DocVQA
New ModelsOpen weights
LFM2.5-VL-3B
Liquid AI LFM2.5-VL-3B runs 228 tok/s on M5 Max in ~3GB
Liquid AI released LFM2.5-VL-3B, a small vision-language model that runs at 228 tok/s on an M5 Max in roughly 3GB of memory. Weights are on Hugging Face.
228 tok/s on M5 Max~3GB memory footprint
New ModelsOpen weights
LFM2.5-2.6B
Liquid's LFM2.5-2.6B: agentic RL trained inside real harnesses, running in 1.7GB on a phone
A 2.69B-parameter hybrid model pre-trained on ~34T tokens whose post-training ran agentic RL inside real harnesses (Hermes Agent, OpenClaw, Pi), so tool calling was learned where tool calling happens. It beats Qwen3.5-9B, three times its size, on ToolSandbox and instruction following, runs 220 tok/s on an M5 Max CPU and fits in ~1.7GB at Q4 on a phone. Liquid's own model card honestly scopes it away from agentic coding and knowledge-heavy work: this is for private, on-device agents.
2.69B parameters, 128K context77.83 ToolSandbox, above Qwen3.5-9B220 tok/s on Apple M5 Max CPU
