Apple scales categorical flow maps to a 1.7B-parameter model

MG Siegler argues Google should have built Meta's Muse personal assistant agent, since Muse depends on Gmail, Google Calendar, and Search hooks. Google's rival, Gemini Spark, is still in testing, limited to US users on Ultra and now Pro tiers.

A Reddit user in r/StableDiffusion describes spending a day generating AI videos from a single image plus a highly detailed prompt covering camera movement, character actions, and timing, and finding most attempts frustrating. The poster concludes the missing piece was the right inputs rather than more prompt detail.

Across 11 open-weight and 8 closed models on the Artificial Analysis Intelligence Index, the cheapest open-weight model completes a task at roughly one fifth the cost of a comparable closed model. Self-hosting on rented hardware drops to $0.12-$0.35 per million output tokens at full utilization, and on gpt-oss-120b the A100 beats the H100 on output cost.

GPT-6's prompt caching adds explicit breakpoints, higher cache hit rates, and new diagnostics. OpenAI says the controls cut latency and costs for developers.

The Extreme variant runs a 30B-parameter mixture-of-experts model locally, while new sensing hubs handle models up to 200M parameters for local scribe and voice agents. Apple's WWDC 20B MoE model is the comparison point.

A Reddit r/Singularity post claims GPT-6 Astra composed a complex four-part Bach-style piece with no harmony-rule errors, using techniques no previous AI managed. No benchmark, audio sample, or independent verification accompanies the claim.