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Just wanted to say thanks for the thoughtful discussion and feedback on my previous post. I did not expect that level of interest, and I appreciate how constructive most of the comments were. Based on a few requests, I put together a short demonstration showing the system applied to a deliberately difficult street-level image. No obvious landmarks, no readable signage, no metadata. The location was verified in under two minutes. I am still undecided on the long-term direction of this work. That said, if there are people here interested in collaborating from a research, defensive, or ethical perspective, I am open to conversations. That could mean validation, red-teaming anything else. Thanks again to the community for the earlier discussion. Happy to answer high-level questions and ...

Every AI API provider gives you a snapshot of current usage. None of them show you trends over time, project when you will hit your limit, or let you compare across providers. I built onWatch to solve this. It runs in the background as a single Go binary, polls your configured providers every 60 seconds, stores everything locally in SQLite, and serves a web dashboard. What it shows you that providers do not: Usage history from 1 hour to 30 days Live countdowns to each quota reset Rate projections so you know if you will run out before the reset All providers side by side in one view Around 28 MB RAM, no dependencies, no telemetry, GPL-3.0. All data stays on your machine. https://onwatch.onllm.dev https://github.com/onllm-dev/onWatch submitted by /u/prakersh [link] [comments]

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I have been working solo on an AI-based project called Netryx. At a high level, it takes a street-level photo and attempts to determine the exact GPS coordinates where the image was taken. Not a city guess or a heatmap. The actual location, down to meters. If the system cannot verify the result with high confidence, it returns nothing. That behavior is intentional. Most AI geolocation tools will confidently give an answer even when they are wrong. Netryx is designed to fail closed. No verification means no output. Conceptually, it works in two stages. An AI model first narrows down likely areas based on visual features, either globally or within a user-defined region. A separate verification step then compares candidates against real street-level imagery. If verification fails, the r...

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It happened again. A 13-person team in Shenzhen just shipped a browser-based version of Claude Code. No terminal, no setup, runs in a sandbox. Anthropic built Claude Code but hasn't shipped anything like this themselves. This is the same pattern as Manus. Chinese company takes a powerful Western AI tool, strips the friction, and ships it to a mainstream audience before the original builders get around to it. US labs keep building the most powerful models in the world. Chinese teams keep building the products that actually put them in people's hands. OpenAI builds GPT, China ships the wrappers. Anthropic builds Claude Code, a Shenzhen startup makes it work in a browser tab. US builds the engines. China builds the cars. Is this just how it's going to be, or are Western AI companies eventuall...

Anthropic shipped Opus 4.6 and OpenAI shipped GPT-5.3-Codex on the same day, 27 minutes apart. Both claim benchmark leads. Both are right -- just on different benchmarks. Where each model leads Opus 4.6 tops reasoning tasks: Humanity's Last Exam (53.1%), GDPval-AA (144 Elo ahead of GPT-5.2), BrowseComp (84.0%). GPT-5.3-Codex takes coding: Terminal-Bench 2.0 at 75.1% vs Opus 4.6's 69.9%. The pricing spread is hard to ignore Model Input/M Output/M Gemini 3 Pro $2 $12.00 GPT-5.2 $1.75 $14.00 Opus 4.6 $5.00 $25.00 MiMo V2 Flash $0.10 $0.30 Opus 4.6 costs 2x Gemini on input. Open-source alternatives cost 50x less. At some point the benchmark gap has to justify the price gap -- and for many tasks it doesn't. 1M context is becoming table stakes Opus 4.6 adds 1M tokens (beta, 2x prici...