Strag (@Strag_AI) header
Strag (@Strag_AI) profile picture

Professional AI yapper. Thoughts & opinions are my own.

Philippines
Joined January 2022
Replying to @elonmusk
i just burned 50% of my usage in just 1 day. do we get a reset once the fixes are applied?
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Replying to @NVIDIAGeForceDE
winner is either gonna be me or me 馃 #RTXON
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Replying to @NVIDIAGeForce
winner is either gonna be me or me 馃 #RTXON
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GPT Image 2.6 is already looking like a big jump. the left version is the first time i've seen an image model treat 10 styles as 10 different worlds, not 10 filters on the same face left is the "sunburst" checkpoint, right is "shooting-star". both were tested on arena before 2.6 showed up in openai's docs credits to @chetaslua for the side by side images.
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Meta says Muse Spark Open is the most capable US open-weight model yet. 816B total, 42B active, 1M context. Their own chart has it leading on agentic and multimodal, but Kimi K3 and GLM 5.3 still win on coding. The US is finally back in the open-weight race.
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Replying to @kimmonismus
new GLM model would be huge. GLM 5.3 Flash has been working wonders. If they release a better model than that, I have to switch up fast
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when Hyperframes Studio for windows?
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Replying to @theo
if it has the same improvement as what they did with Opus, OpenAI is cooked
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SpaceXSI says Grok Voice 3.0 answers in under a second, with 40% lower latency than GPT-Live-1 on support-call tests. They also say Starlink uses Grok Voice for 20K support calls a day, with about 70% resolved with no human. Call centers should be paying attention.
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A simple rule from this week's Gemini 4 Argon demos: an AI agent that can check its own output beats one that just answers. Before you try a new model, set up the feedback loop first.
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The smartest first project with any new frontier model: the legacy system everyone at your company avoids. Google pointed Argon at a 6-million-line codebase. Your version is that old spreadsheet workflow or the CRM nobody wants to migrate.
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The pattern from the demos: give it tools and a feedback loop. One demo ran 1,000 rounds of checking its own renders through Blender MCP. Agents improve when they can see their own output.
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Biggest real-world example: helping the Chrome team refactor a 6-million-line legacy codebase in weeks instead of months. If you have old software holding the business back, that's the use case.
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Google says it was tested by 100,000+ people internally for a month. On their own codebases: 35% more agentic code and 30% fewer post-submit rollbacks. Fewer rollbacks is the stat to watch.
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Where you can use it: Gemini API paid tier, Google AI Studio, Google Cloud, the Gemini app and AI Mode in Search for Pro and Ultra, and Antigravity app + CLI for Ultra.
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Most AI launches get judged by flashy demos. The Argon stat I'd actually use to judge it: fewer rollbacks. Shipping more code only helps if less of it breaks.
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