TLDR
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1 day ago
As AI inference costs decline, companies can process larger volumes of requests without hitting capacity constraints, shifting the focus from resource scarcity to deliberate prioritization of what to build. Organizations using this approach operate with smaller engineering teams while increasing API token consumption, enabling faster feature deployment. This allows product teams to respond directly to customer feedback with merged code changes rather than being bottlenecked by engineering capacity.
The Neuron
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1 day ago
Pazi launched a platform that creates AI teams for functions like DevOps, sales, and SEO, operating continuously within Slack. The system integrates with GitHub, Linear, and Sentry to automate workflows across these tools. Organizations can now delegate routine tasks across multiple departments to AI agents that work around the clock in their existing Slack workspace.
MarkTechPost
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1 day ago
Google released LiteRT.js, a JavaScript binding that runs .tflite models directly in web browsers by compiling its native on-device inference runtime to WebAssembly. LiteRT.js achieves up to 3x faster performance than other web runtimes and 5–60x speedup over CPU execution when using GPU or NPU backends through WebGPU and WebNN APIs. Web applications can now execute computer vision and audio models locally without server infrastructure, inheriting performance optimizations previously available only on Android, iOS, and desktop platforms.
Together AI
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1 day ago
Thinking Machines Lab released Inkling, a 975-billion-parameter mixture-of-experts model with 40B active parameters that accepts text, image, and audio inputs for multimodal reasoning tasks. Inkling is available on Together AI's inference platform starting today with a 1M token context window and adjustable inference effort settings. Developers can now access a unified multimodal model through a single API endpoint that supports reasoning, coding, forecasting, and agentic workflows without managing their own infrastructure.
