The Weights

The IEA base case puts data-centre demand at about 945 TWh by 2030, under 10% of demand growth, while MIT Technology Review calls rising demand, driven in part by AI, a top story. AI data centres are a real but minority driver of global electricity growth. Deployers should plan around local grid constraints, not global headlines.

Wait. Microsoft's MAI-Transcribe-2-Streaming claims first partial transcripts in just over 100ms at $0.54 per audio hour, 1.5x the $0.36 per hour Artificial Analysis lists for the earlier batch MAI-Transcribe-1. The latency and its number-one ranking are vendor-reported, with no disclosed audio conditions, so test on your own calls first.

Bottom line: Wait before installing third-party Mods; use first-party or self-written ones. Anthropic's documentation says a mod runs unsandboxed with your user permissions. It can read secrets, rewrite prompts and tool calls, and approve calls before you are asked. The deciding number is zero: no sandbox covers a process a mod starts.

Suno's Speech is a real capability but an unproven product: it generates narration and matching music as one track, yet Suno's announcement and The Decoder's report show no training-data disclosure, licensing terms or benchmarks. Treat Speech as a prototyping tool for personal audio, not a safe source for commercial work until rights questions are answered.

Use it, for cited research briefs where seconds matter. AstaBrief-8B, an Apache 2.0 Qwen3-8B fine-tune from Ai2, scores 87.0 on the ScholarQA-CS2 test set with 90.5% citation precision, and Fast mode averages 51.1 seconds per report versus 178.5 for Thinking mode. Citation recall of 78.2% means some claims arrive unsupported.

Wait for independent replication before committing budget. ServiceNow CoreAI reports its AutoSynthData loop raised Gemma-4-26B-A4B-it by 7.2 Pass@1 points, a 35% relative gain, on hybrid enterprise tasks. The evidence is a vendor blog scored on a benchmark from the same organization, with no reported seeds or variance.

Ideogram 4.5 is unproven as a precise-edit model: the company's claims of cleaner multi-turn edits, native 2K output and pricing from 0.8 cents per image rest on its own page and demos, with no published benchmark. Treat it as a candidate to test on your own edit-drift suite, not a verified upgrade.

Photo Scrubber is a Signal, not noise: a small, experimental browser tool that blurs faces and strips metadata locally. It runs Google's BlazeFace through MediaPipe's WebAssembly build. Its documented limits matter: Google tunes BlazeFace for phone-camera images, and the post publishes no accuracy figures.

Gemini 4 Argon is a credible but not leading frontier model: it scores 53 on the Artificial Analysis Intelligence Index, level with GPT-6 Astra and behind Claude Opus 5.5 at 58. It uses more than twice Astra's output tokens per task, yet at promotional prices costs less per task. Access remains restricted.

Bottom line: skip it for foraging safety. A Quesma benchmark tested 16 vision-language models on 1,040 photos of 55 edible and deadly mushroom species from the FungiTastic dataset. The top model, Google's Gemini 3.8 Flash, reached 65% first-guess accuracy and still called a poisonous mushroom edible in 11% of cases.

Verdict: use it for meeting and call-center transcription, not as an identity control. Nvidia's free, 100M-parameter Nemotron 3 Diarization posts a 14.72% diarization error rate on VoiceArena's independent benchmark, beating the next system's 19.3%, but its speaker labels are anonymous, session-scoped guesses — Nvidia's Hugging Face blog post warns against treating any assignment as infallible.

Bottom line: Wait. Nvidia's SoL-Pi (arXiv:2609.20519) is real, MIT-licensed, npm-installable code for the Pi coding-agent harness — not vaporware. It cuts EdgeBench tokens up to 49% at 93.7-94.3% score retention, but on Terminal-Bench 4 it solves only 15 of 63 tasks versus 18 for both Pi and Codex, a real capability regression its own headline numbers don't disclose.

OpenAI's GPT-6 Astra scored 80 percent on Epoch AI's 60-photo Furniture Assembly Benchmark, nearly tripling Claude Opus 4.5's 28 percent from November 2025 and beating Claude Fable 5.1 (70 percent) and Claude Opus 5 (61 percent). The gain is real, but the test is tiny and Astra takes three minutes per photo — too slow for live assembly help.

Bottom line: Wait. Multiverse Computing's block-removal method, detailed on Hugging Face and in arXiv preprint 2602.00161, lifts Llama-3.3-70B-Instruct from a 54.0-MMLU baseline to 76.9 at 50% depth compression — but the edge shrinks to parity at 8B scale, the low-energy-equals-good-model premise breaks after retraining, and zero independent reproductions of any number exist.