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Meet ‘AutoAgent’: The Open-Source Library That Lets an AI Engineer and Optimize Its Own Agent Harness Overnight

There’s a particular kind of tedium that every AI engineer knows intimately: the prompt-tuning loop. You write a system prompt, run your agent against a benchmark, read the failure traces, tweak the prompt, add a tool, rerun. Repeat this a few dozen times and you might move the needle. It’s grunt work dressed up in […]

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Netflix AI Team Just Open-Sourced VOID: an AI Model That Erases Objects From Videos — Physics and All

Video editing has always had a dirty secret: removing an object from footage is easy; making the scene look like it was never there is brutally hard. Take out a person holding a guitar, and you’re left with a floating instrument that defies gravity. Hollywood VFX teams spend weeks fixing exactly this kind of problem.

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Open Sourcing Our Real-Time PPE Detection Mobile App

By Spritle Software Engineering Team Workplace safety isn’t negotiable. But manual safety compliance monitoring is slow, inconsistent, and doesn’t scale. We built a real-time Personal Protective Equipment (PPE) detection app that runs entirely on your smartphone — no cloud, no expensive hardware, no delays. The Problem We’re Solving Every year, thousands of workplace accidents happen

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TII Releases Falcon Perception: A 0.6B-Parameter Early-Fusion Transformer for Open-Vocabulary Grounding and Segmentation from Natural Language Prompts

In the current landscape of computer vision, the standard operating procedure involves a modular ‘Lego-brick’ approach: a pre-trained vision encoder for feature extraction paired with a separate decoder for task prediction. While effective, this architectural separation complicates scaling and bottlenecks the interaction between language and vision. The Technology Innovation Institute (TII) research team is challenging

TII Releases Falcon Perception: A 0.6B-Parameter Early-Fusion Transformer for Open-Vocabulary Grounding and Segmentation from Natural Language Prompts Read More »

Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use

The landscape of open-source artificial intelligence has shifted from purely generative models toward systems capable of complex, multi-step reasoning. While proprietary ‘reasoning’ models have dominated the conversation, Arcee AI has released Trinity Large Thinking. This release is an open-weight reasoning model distributed under the Apache 2.0 license, positioning it as a transparent alternative for developers

Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use Read More »

Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use

The landscape of open-source artificial intelligence has shifted from purely generative models toward systems capable of complex, multi-step reasoning. While proprietary ‘reasoning’ models have dominated the conversation, Arcee AI has released Trinity Large Thinking. This release is an open-weight reasoning model distributed under the Apache 2.0 license, positioning it as a transparent alternative for developers

Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use Read More »

Anthropic says its leak-focused DMCA effort unintentionally hit legit GitHub forks

An Anthropic-backed DMCA effort to remove its recently leaked Claude Code client source code from GitHub this week resulted in the accidental removal of many legitimate forks of its official public code repository. While that overzealous takedown has now been reversed, Anthropic still faces an extreme uphill battle in limiting the spread of its recently

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IBM Releases Granite 4.0 3B Vision: A New Vision Language Model for Enterprise Grade Document Data Extraction

IBM has announced the release of Granite 4.0 3B Vision, a vision-language model (VLM) engineered specifically for enterprise-grade document data extraction. Departing from the monolithic approach of larger multimodal models, the 4.0 Vision release is architected as a specialized adapter designed to bring high-fidelity visual reasoning to the Granite 4.0 Micro language backbone. This release

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Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows

Hugging Face has officially released TRL (Transformer Reinforcement Learning) v1.0, marking a pivotal transition for the library from a research-oriented repository to a stable, production-ready framework. For AI professionals and developers, this release codifies the Post-Training pipeline—the essential sequence of Supervised Fine-Tuning (SFT), Reward Modeling, and Alignment—into a unified, standardized API. In the early stages

Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows Read More »

Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning

In the current landscape of generative AI, the ‘scaling laws’ have generally dictated that more parameters equal more intelligence. However, Liquid AI is challenging this convention with the release of LFM2.5-350M. This model is actually a technical case study in intelligence density with additional pre-training (from 10T to 28T tokens) and large-scale reinforcement learning The

Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning Read More »