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Dot keeps surfacing missed Gmail emails

@flarb

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Tabs in GMail have become useless. I'm constantly missing miscategorized emails that are rather important. I only casually check the tabs other than inbox--there's just too much stuff in the other ones. @openai 's Dot has repeatedly surfaced very important emails in my Gmail I've missed due to being stuffed in tabs instead of inbox. I'm not sure why @google can't use Gemini to add this functionality to GMail.

Ralph Barbagallo (@flarb, Threads, Oct 5, 2026) — first-hand: his OpenAI Dot repeatedly surfaces very important emails he missed because Gmail's tabbed inbox stuffed them into tabs he only casually checks. A concrete ongoing result: his Dot catches the important mail Gmail misfiling would otherwise bury. Text-only post; media_type=none.

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My Dot burns 1.6B tokens a day designing full engineering packages

​ TL;DR: One Dot, running autonomously on its cloud computer since launch day, has been consuming \~1.6B Astra tokens per day (\~48B/month). My rough API-equivalent estimate is $15-20k per day, or $450-600k per month (about $540k central estimate), all inside a subscription. It designs full engineering packages (CAD, calculations, BOM, electrical docs, drawings) by running design-calculate-verify loops for hours without me. Some of the parts have already been cut on my laser cutter from its program and came out fine. I doubt these economics will last, so I'd use them while they do. And no, I would not ship any of it to manufacturing without human engineering review. I've been running a single Dot since the day Dots launched, and it has been burning roughly 1.6 billion Astra tokens every day since. That's about 48B tokens a month. I give the Dot a large engineering task, answer a few clarifying questions, and then it goes off and works on its own cloud computer for the rest of the day, sometimes longer. Later I just put doc with tasks to my Google Drive and ask it to do them one by one. Here's what that has looked like in practice. 1. A complex physical structure, designed from scratch. It built the CAD model, iterated on the construction, produced manufacturing geometry and assembly documentation, wrote the BOM and electrical documentation, and ran the engineering calculations. One of the resulting packages contained: • 1,000+ unique components • 6,000+ solids in the complete STEP assembly • almost 300 pages of assembly drawings and documentation • hundreds of documented electrical connections • manufacturing parts and assemblies • procurement/BOM spreadsheets • machine-readable manifests and verification data • structural/load calculations and sensitivity cases 2. A complicated engineering product with strength and thermal analysis. It ran strength calculations, thermal analysis and thermal deformation cases, changed the design based on the results, and then repeated the whole cycle until the numbers made sense. 3. A detailed 3D combine harvester for a real-time 3D application. It analyzed the model, built several optimization levels, and cut geometry dramatically while keeping the important parts. The source model had about 200k active triangles, and the candidates came out at roughly 152k, 84k, 56k and 50k. It also tested visibility from the actual cameras, generated thousands of verification renders across different machine states, and packaged everything with scripts and integrity checks. What impressed me most is that it doesn't just produce a pretty model and say "looks good". It does the boring engineering loop over and over: design, calculate, find a problem, change geometry, recalculate, verify, document. "One-shot" doesn't mean one inference. I give it a task once, and it executes thousands of internal cycles: inspect files, reason, modify CAD/code, run tools, check results, run simulations, revise, verify, repeat. About the token numbers 1.6B tokens per day averages out to about 18.5k tokens per second. Obviously that is NOT generation speed. In workloads like this, the overwhelming majority of the volume is almost certainly repeated context processing. So this is aggregated tokens from my profile page. About the cost I tried to estimate what the same workload would cost through the API. It depends heavily on the input/output/cache mix, long-context pricing and reasoning tokens, so this is not an exact billing calculation. But my rough estimate is $15,000-$20,000 per day. Taking about $18k/day over 30 days gives $450-600k per month, with a central estimate around $540k. And that's happening inside a 100$ subscription product. That's the part I find kind of crazy. I don't know whether OpenAI intends launch-period economics to stay this generous I remember Tibo posted, that this is one month promotion period), and I strongly suspect limits or pricing will change a lot. But right now, for workloads where you hand an agent a complicated objective and let it grind for hours or days, Dots are ridiculously cost-effective compared with buying the same amount of usage through the API. Some of the metal parts for first project have already been made. The Dot wrote the cutting program for my laser cutter, and the parts came out successfully. I only had to tweak the cutting speed a little: it was being too careful, and the machine could safely run faster than it programmed. Yes, I know. An AI designs the parts, writes the program for a laser cutter and I press the start button. This is exactly how Skynet starts. For now the Terminator factory consists of one laser cutter in my workshop, and the Terminators are mostly brackets and other simple parts, but I'm keeping an eye on it. If I stop posting updates, you know why. But the first pass at the structural design was a different story. It calculated the thing as if it had to survive a nuclear war (Skynet, hello again), with something like a 5x safety margin on everything. I had to restart it with clearer constraints, and the second run came out sensible. So it still needs a human who knows what the real requirements are, but the corrections were usable. The obligatory disclaimer Before anyone says it: I would not send an AI-generated safety-critical design directly to manufacturing without human engineering review. To its credit, the Dot doesn't pretend otherwise and makes clear disclaimers through all the documents. The better packages explicitly separate calculated results from assumptions, unresolved material properties, required physical tests, certification and acceptance criteria. That distinction matters a lot. But as an engineer's assistant that can effectively spend billions of tokens attacking one problem, this is probably the most extreme value I've ever gotten from an AI subscription in my life.
r/DarthSilent