Tag: Reddit

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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submitted by /u/esporx [link] [comments]

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...

I ran a small real-world test today with two kids (ages 8 and 11) using a long-running AI story world I’ve been experimenting with. Instead of one-shot story generation, the system maintains a persistent world state where choices carry over and shape future events. I let them pick the setting — they chose a Minecraft × Harry Potter mashup where they play wizards trying to defeat the Ender Dragon. One thing that made a huge difference: I used their real names as the characters, and the story started in their actual school. The engine generated story text and illustrations each round. They made all the choices. After about 10 rounds, they were constantly laughing, debating which option to pick, and building on each other’s ideas. It felt much more like co-creating a world than listening to a...

submitted by /u/tekz [link] [comments]

Ran a real-world test this week: Gemma 3 12B vs paid frontier models across actual business workflows. The honest assessment? 90% of tasks: no meaningful difference. 5%: frontier models worth it (pay-per-use). 5%: neither quite there yet. This matches the data - open models are catching up fast. The article explores: - Why the "gasoline doesn't matter" - only if it powers your task - The shift from "one model to rule them all" to specialized local models - Why even AGI will eventually be open-sourced (historical precedent) - The water company future: infrastructure > model quality https://www.linkedin.com/posts/azizme_activity-7424774668034842624-v1-2?utm_source=share&utm_medium=member_desktop&rcm=ACoAACX_HOcBcpTEWJ3cXyVbVqKJsi39tDHJLFY Curious what others are seeing in their do...

submitted by /u/app1310 [link] [comments]