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Most end-to-end OCR models slow down as output grows. Each generated token adds to the KV cache. Memory rises and generation drags. Parsing dozens of pages becomes impractical. Baidu’s Unlimited OCR addresses this directly. It swaps the decoder’s attention for a design that keeps memory constant. TL;DR Unlimited OCR is a 3B-parameter Mixture-of-Experts model, with […]
Gradium today released two real-time speech translation models: stt-translate and s2s-translate. Both run across five languages and stream results live in the browser. Gradium claims a better accuracy-latency tradeoff than gpt-realtime-translate and gemini-3.5-live-translate. It also adds output voice control, including cloning, that gpt-realtime-translate lacks. TL;DR Gradium launched two real-time speech translation models: stt-translate (speech →
In this tutorial, we build OpenHarness from scratch to better understand how a practical agent harness works. We recreate the major building blocks that make an agent system useful, including tool use, typed tool schemas, permissions, lifecycle hooks, memory, skills, context compaction, retry logic, cost tracking, and multi-agent coordination. Instead of treating an agent framework
In this tutorial, we build a fully offline Graphify workflow that turns a realistic multi-module Python application into a knowledge graph. We start by installing Graphify and supporting graph libraries, then generate a small but connected sample application with configuration, database, authentication, service, API, cache, model, and SQL layers. We extract the graph locally using
Nous Research has expanded the Skills System inside Hermes Agent, its open-source self-improving agent. The new addition is /learn, a command that writes a reusable skill for you. Point it at a document page, a local SDK, a past conversation, or pasted notes. The live agent gathers the material, then authors a SKILL.md on your
Generative AI has reshaped how software gets built. What began as line-by-line autocomplete now spans full application generation, multi-agent build pipelines, and natural-language interfaces to entire codebases. Large language models trained on code can read context, follow intent, and produce working frontends, backends, and infrastructure with little manual setup. For early-level AI engineers, software engineers,
16 Best Generative AI Coding Tools in 2026 Compared: Features, and Best Fit Read More »
Autoregressive large language models generate text one token at a time. Each token waits for the one before it. This serial loop leaves modern GPUs underused and keeps inference slow. The cost grows worse with long Chain-of-Thought reasoning models. Their lengthy outputs make latency the dominant part of generation. Speculative decoding is the standard fix.
Today, Mistral AI released OCR 4, its latest document-understanding model. This new release adds bounding boxes, block classification, and inline confidence scores alongside extracted text. It supports 170 languages across 10 language groups and runs in a single container for fully self-hosted deployments. OCR 4 also serves as an ingestion component for enterprise search, RAG,
Datalab has released lift, a 9B open-weights vision model for structured extraction. You pass it a JSON schema, and it returns a JSON object that matches. The model reads PDFs and images directly, then decodes against your schema. This is Datalab’s first model built purely for extraction. The team already ships open-source OCR tools: chandra,
In this tutorial, we build a speech recognition and translation workflow using NVIDIA Canary-1B-v2. We begin by setting up the required audio, NeMo, NumPy, and SciPy dependencies, then load the Canary model on a GPU-enabled runtime for efficient inference. From there, we prepare audio into a clean 16 kHz mono format, perform English ASR, translate
