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    Introducing AutoEvals: Find the right model for your task

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    AI engineering & research, product releases and upgrades, and thoughts about the industry from the Inference R&D, Inc. team.

    Introducing AutoEvals: Automatically find the best model for your task

    Introducing AutoEvals: Automatically find the best model for your task

    Automatically discover which model is best suited for your task, with the data you already generate. AutoEvals replays your production traffic across candidate models and tells you which one to run, with real numbers on quality, cost, and latency.

    Aug 5, 2026

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    Amar Singh

    Schematron V2: Frontier HTML-to-JSON extraction at a fraction of the cost

    Schematron V2: Frontier HTML-to-JSON extraction at a fraction of the cost

    Schematron is a family of small, purpose-built models that transform messy HTML into clean, structured JSON, delivering frontier-level extraction quality at a fraction of the cost and latency of large general-purpose LLMs.

    Apr 16, 2026

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    Amar Singh

    Introducing Catalyst: Monitor, train, and deploy self-improving AI models

    Introducing Catalyst: Monitor, train, and deploy self-improving AI models

    Build self-improving AI-systems from production data

    Apr 14, 2026

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    Sam Hogan

    How Inference.net trains Specialized Language Models that cut AI costs by up to 50x

    How Inference.net trains Specialized Language Models that cut AI costs by up to 50x

    Learn how Inference.net trains Specialized Language Models using the NVIDIA NeMO framework to delivery frontier accuracy at up to 50x lower cost.

    Mar 11, 2026

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    Sam Hogan

    Specialized LLMs: The model you need doesn't exist yet

    Specialized LLMs: The model you need doesn't exist yet

    Specialized LLMs trained on your own user data can match frontier quality for a fraction of the cost

    Feb 5, 2026

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    Sam Hogan

    Project OSSAS: Custom LLMs to process 100 Million Research Papers

    Project OSSAS: Custom LLMs to process 100 Million Research Papers

    Project OSSAS is a large-scale open-science initiative to make the world’s scientific knowledge accessible through AI-generated summaries of research papers.

    Nov 11, 2025

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    Sam Hogan

    LOGIC: Trustless Inference through Log-Probability Verification

    LOGIC: Trustless Inference through Log-Probability Verification

    A practical method for verifying LLM inference requests in trustless environments.

    Nov 5, 2025

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    Amar Singh

    Hybrid-Attention models are the future for SLMs

    Hybrid-Attention models are the future for SLMs

    Hybrid attention delivers up to 3x cost reduction compared to traditional transformer models

    Nov 3, 2025

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    Amar Singh

    Announcing our $11.8M Series Seed

    Announcing our $11.8M Series Seed

    We raised $11.8 million in funding led by Multicoin Capital and a16z CSX to train and hosti custom language models that are faster, more affordable, and more accurate than what the Big Labs offer.

    Oct 14, 2025

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    Sam Hogan

    Schematron: An LLM trained for HTML -> JSON at scale

    Schematron: An LLM trained for HTML -> JSON at scale

    Schematron-8B and Schematron-3B deliver frontier-level extraction quality at 1-2% of the cost and 10x+ faster inference than large, general-purpose LLMs.

    Sep 9, 2025

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    Sam Hogan