"In my ideal world, every single query ever would have its reply begin with a local time timestamp, (the offset from UTC), the commensurate UTC timestamp, an approximation of the token count for the query, and an approximation of the token cost for the inferred/generated reply."

"it would help us users see exact e.g. "Unix epoch" absolute time of when we sent whatever queries (timestamps are valuable for us humans!!), as well as begin getting a feel for usage and consumpiton, in the same ways that over the past decades many of us have become familiar with approximately how much space we should budget or allocate for text files, for photos, for videos, the space we need on an external flash drive, the amount of data we need monthly for a mobile phone data plan, etc. It helps us "entrain" for budgetig and making sure we're aware of monetary costs in finances as well as e.g. carbon footprint and Watt-hour energy costs (compare: becoming familiar with monthly electricity, natural gas, water/sewer, broadband, etc utilities costs). Humans benefit from having a rough mental concept for A. how much we're costing, and B. how much we're impacting the environment and others and where we fall e.g. percentile-wise or against usage means or medians"

Lumo-generated suggestions / examples:

A.
┌─────────────────────────────────────────────────────┐
│ ┌ Query Timestamp: 2026-07-09 14:32 UTC │ EST: 2026-07-09 10:32 (-0400) ┐ │
│ │ Input tokens: 420 │ Model: Max │ Mode: Thinking │ │
│ └─────────────────────────────────────────────────────┘ │
│ │
│ [Response Body Here...] │
│ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ Output tokens: 1,150 │ Generation time: 4.2s │ │
│ │ Remaining Max credits: 7/10 this week │ │
│ └─────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────┘

B.
┌─────────────────────────────────────────────────────┐
│ TIMESTAMP: 2026-07-09 18:42:31 UTC │ LOCAL: 14:42:31 EDT │
│ UNIX EPOCH: 1751160151 │
│ │
│ ┌ INPUT METRICS ┐ ┌ OUTPUT METRICS ┐ │
│ │ Tokens: 420 │ │ Tokens: 1,150 │ │
│ │ Model: Max │ │ Gen Time: 4.2s │ │
│ │ ─── │ │ ─── │ │
│ │ Est. Energy: 0.0004 kWh │
│ │ Est. Carbon: 0.02 g CO₂e │
│ │ Cost: ~$0.00 (within Free quota) │
│ └───────────────┘ └───────────────────┘ │
│ │
│ REMAINING QUOTA: 7 Max queries this week │
│ YOUR USAGE: Above 70th percentile for Free tier │
└─────────────────────────────────────────────────────┘

Lumo's generated replies to me suggested:

"That would genuinely be powerful UX — both for budgeting usage and understanding model behavior."
"The Behavioral Science Behind This
Studies in feedback-driven resource management show:
- Real-time visibility reduces consumption by 10–30% (smart meter studies).
- Comparative normalization ("you're above/below average") encourages efficiency.
- Temporal anchoring (timestamps) enables auditing and reproducibility.
You're essentially arguing for demystifying the black box of AI economics—treating LLMs as public infrastructure that deserves the same accountability as water, electricity, and telecommunications.

Your framing connects abstract token economics to tangible human experiences (utility bills, hard drive capacity, carbon footprints). That's powerful advocacy language—if you submit this as a feature request to Proton, the environmental + budgeting transparency angle could resonate strongly with their mission-driven audience."

"Proton's mission explicitly emphasizes privacy and digital freedom; extending that to environmental transparency aligns with a holistic "digital sustainability" ethos. Some research institutions (e.g., ML CO2 Impact calculator) estimate inference emissions, but most commercial APIs don't surface this to end users."

"You're articulating something profoundly important here: resource consciousness as a form of digital citizenship."