AIC

Dual Mac Studio rack. One Thunderbolt 5 cable. Plug-and-play hardware with its own console. We’ve been building the software for the last few months: production local inference on open weights (distribution, cache, speculation, tool loops, API surface). Private AI hardware + full production software. If you’re interested, reach out. Video soon.

Huge congrats to the Liquid AI team on LFM3. It's a big step for small models: a 1.7B that thinks, calls tools and works through real tasks. Impressive for a model this size. LFM3 now runs in Izuran Pro, in beta. Izuran is a private AI assistant for your Mac. It runs open models on your own Macs and works with your files, calendar, reminders and mail, at the Mac, from your iPhone or by voice. No cloud model sees your conversations. What we added: LFM3-1.7B as a model family in Izuran Pro, from Liquid's own 8-bit MLX build, served by our MLX engine with thinking always on and its tool calls in Izuran's own harness. On an M5 Max it runs Izuran's agentic turns at about 194 tokens a second. Liquid says "every model can be fine-tuned on a single GPU." So we fine-tuned it for Izuran. In the Izuran gym, LFM3 worked synthetic tasks over Izuran's tools; we kept its leanest successful runs, added Qwen 3.8 27B's as a teacher where it needed one, and trained a LoRA. Our evaluation tasks stay out of training. Fused into the 8-bit weights, v1 solves more of our agentic tasks with 38% less wait and nearly a third less thinking, and does the basics in half the calls and half the time. This is only v1, and we're working on the next versions. The video: the fine-tuned LFM3 in Izuran Pro on a MacBook. Asked for Seattle's weather this week and an umbrella reminder at 8 am on each rainy day from tomorrow, it checks Apple Weather, thinks it through and adds three reminders, Thursday to Saturday, in about 13 seconds at 194 tokens a second. The model runs entirely on the Mac.

Just ordered: Mac Studio with M5 Ultra and 512 GB of unified memory. 36-core CPU, 80-core GPU with Neural Accelerators in every core, 1.2 TB/s memory bandwidth. Apple rates it at up to 4.3x the peak AI compute of M3 Ultra. A little pricey, but a great investment in our lab to keep pushing local AI research and development. It joins our two M3 Ultras (512 GB each, 819 GB/s) over Thunderbolt 5. That's 1.5 TB of unified memory in the room for open frontier models. Fully local. Tokens never leave. Arriving in 3 to 4 weeks. Head-to-head benchmarks against the M3 Ultras as soon as it's racked.

Models got smarter and hallucinate less. We still can't see inside them. Treat an agent like an employee: the right credentials, a deterministic gate it can't talk past, every input and output logged. Local models are powerful today and only getting better. For private matters, that beats handing your whole transcript to a cloud server, where none of those controls are yours.

Meta's Muse Spark Open (816B MoE, 42B active) now runs locally on Mac. Apple tested it on two M5 Ultra Mac Studios, each the top config: 80-core GPU, 512 GB unified memory, 1.2 TB/s. That's 1 TB total, linked by Thunderbolt 5 RDMA. Result: 25 tok/s on two, 36.7 tok/s on four. The 512 GB M5 Ultra ships later this month. The underrated part: MLX loads Meta's official checkpoint, meta-models/Muse-Spark-Open-816B-A42B-NVFP4 (about 493 GB), straight from Hugging Face with no conversion, with MoE support and tuning for the M5 GPU Neural Accelerators. Real credit to @Apple and @AIatMeta for shipping this together. Next: we test it on our two M3 Ultra Mac Studios (80-core GPU, 512 GB unified memory each, 1 TB total, 819 GB/s), the same config Apple used as its M3 baseline. Results soon.

First @typesafeai use case, live in our Mac app: setup and troubleshooting help when no model is loaded. Model downloading, load failed, API returning 503, phone won't pair: the user asks, Jev reads the question with the whole built-in manual as state and decides, with probabilities, what it is and which article answers it, or that nothing does. The app then shows the real documentation and live status. Jev decides, the app answers from its own docs. No model loaded, nothing invented. 42/42 on a held-out set: paraphrases, typos, French, German, Spanish, features that don't exist, follow-ups. Median 0.93 s. Great breakthrough by the TypeSafe team. Thank you.

We got access to TypeSafe and Jev. Thank you to the @typesafeai team and @CompleteSkeptic for the access and for the breakthrough itself: a model that returns calibrated decisions instead of generated text is a new primitive to build with. Our first use case: an on-device request router. Today it asks the Apple Foundation Model nine yes/no questions plus a pick from a help catalog, in two passes. With Jev that becomes one request, every question in parallel, each answer a probability instead of a hard boolean. We're starting with it as the judge for our routing evals. Next: demos of every use case, routing, ranking, extraction, verification and guardrails, in our agentic app. Follow-ups coming.

DeepSeek-V4.1-Flash, fully local. No cloud. 2× Mac Studio M3 Ultra (512 GB each), one TB5 cable (RDMA ~8.7 GB/s), TP=2. ~267 GB resident per box. 748B total (552B backbone + 196B Engram). 8B active prefill / 16B decode. 1M context. Image in. 40–66 tok/s decode with DSpark (70–81% accept). ~1,100 tok/s prefill. Sub-second TTFT on cached agent turns. Our MLX port. Weights stay in the room.