MacPaw launched Eney for Mac and iPhone, an assistant that says it spots what needs attention and asks for approval before it acts. 9to5Mac credits local models now capable of complex tasks, though demanding work still goes to the cloud.
Perspective Coverage
3 publishers
- Builder
- Builder 30%
- Operator
- Operator 47%
- Investor
- Investor 23%
Reality
- Evidence40
- Adoption
- Not scored
- Hype gap+20
- Incentives65
- Confidence55
buildConfirmed4 publishers Liquid AI says its LFM2.5-24B-A2B scores 48.09 on LiveClaw against 38.19 for the larger Qwen3.6-35B-A3B. The 184 tok/s it reports on an Apple M5 Max is a decode rate, so agent latency depends on how many tokens the model spends thinking.
Perspective Coverage
4 publishers
- Builder
- Builder 45%
- Operator
- Operator 33%
- Investor
- Investor 22%
Reality
- Evidence42
- Adoption
- Not scored
- Hype gap+30
- Incentives65
- Confidence62
Microsoft claims its Decision-1 scoring model answers 35 times faster than GPT-6 Sol
Microsoft released Decision-1, a model that scores fixed answer options and, by its own benchmarks, runs 35 times faster than GPT-6 Sol. Whether routing work moves off general-purpose models now depends on independent tests and on pricing that confirm those self-reported figures.
Reality
- Evidence30
- Adoption15
- Hype gap+35
- Incentives65
- Confidence35
buildConfirmed2 publishers Liquid AI's open-weight d1-3B answers classification questions in a single forward pass, taking 16 ms on a Jetson AGX Thor and 50 ms on an Orin Nano. That puts routing and intent tagging on edge boards without a large model, on figures Liquid measured itself.
Perspective Coverage
3 publishers
- Builder
- Builder 55%
- Operator
- Operator 30%
- Investor
- Investor 15%
Reality
- Evidence50
- Adoption
- Not scored
- Hype gap+20
- Incentives70
- Confidence60
buildOne report1 publisher Liquid AI's 279.5M drafter turns a 3.13x decode gain into 2.62x end to end
Liquid AI shipped a 279.5-million-parameter draft model for LFM2.5-VL-3B on September 24, reporting 2.30x to 3.13x faster decoding on an Apple M5 Max. Image encoding and prompt prefill are unchanged, and the company's tests did not cover quantized deployments.
Reality
- Evidence55
- Adoption
- Not scored
- Hype gap+15
- Incentives70
- Confidence60
buildOne report1 publisher On-device continual learning gets an artifact: 230M model, rank-4 LoRA, one frozen teacher
Online-SDFT fine-tunes a 230M model on an Android phone from delayed, unlabeled interactions. The teacher is the same frozen base weights with the adapter off, and the write-up reports no measurements.
Reality
- Evidence38
- Adoption12
- Hype gap+15
- Incentives65
- Confidence35