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TinyFish Launches Full Web Infrastructure Platform for AI Agents — Search, Fetch, Browser, and Agent Under One API Key

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AI agents struggle with tasks that require interacting with the live web — fetching a competitor’s pricing page, extracting structured data from a JavaScript-heavy dashboard, or automating a multi-step workflow on a real site. The tooling has been fragmented, requiring teams to stitch together separate providers for search, browser automation, and content retrieval. TinyFish, a […]

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How to Hire an AI Development Company in 2026: Architecture Questions You Must Ask Before You Build App with AI

ai, AI (Artificial Intelligence), AI app builders, AI Development 2026, AI Development Strategy, App Architecture, App Scaling & Architecture, Artificial Intelligence, Founder's Guide, Hiring AI Developers, Lovable AI, Production-Ready AI, Refactoring AI Code, Replit AI, SaaS Development, Software engineering, Software Scaling, Spritle Blog, SpritleSoftware, Technical Debt, Technical Leadership & Hiring, v0 Vercel

You’ve already done the hard part—you used an AI app builder like Lovable AI, Replit AI, or v0 by Vercel to build app with AI and get something real off the ground. Maybe it’s a SaaS dashboard, a client portal, or a marketplace MVP. It works… until it doesn’t. Now you’re stuck in that frustrating

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ADLC vs Traditional SDLC: How AI Changes Requirement Gathering From Day One

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Introduction Most software failures don’t happen during deployment—they begin with poor requirements. Recent industry insights show that only around 30–40% of software projects fully succeed, while the majority face delays, cost overruns, or scope issues. Unclear, incomplete, or constantly evolving requirements remain one of the leading causes behind these failures. If you’re a CTO or

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MiniMax Releases MMX-CLI: A Command-Line Interface That Gives AI Agents Native Access to Image, Video, Speech, Music, Vision, and Search

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MiniMax, the AI research company behind the MiniMax omni-modal model stack, has released MMX-CLI — Node.js-based command-line interface that exposes the MiniMax AI platform’s full suite of generative capabilities, both to human developers working in a terminal and to AI agents running in tools like Cursor, Claude Code, and OpenCode. What Problem Is MMX-CLI Solving?

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Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model

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Researchers from Meta AI and the King Abdullah University of Science and Technology (KAUST) have introduced Neural Computers (NCs) — a proposed machine form in which a neural network itself acts as the running computer, rather than as a layer sitting on top of one. The research team presents both a theoretical framework and two

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MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2

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MiniMax has officially open-sourced MiniMax M2.7, making the model weights publicly available on Hugging Face. Originally announced on March 18, 2026, MiniMax M2.7 is the MiniMax’s most capable open-source model to date — and its first model to actively participate in its own development cycle, a meaningful shift in how large language models are built

MiniMax Just Open Sourced MiniMax M2.7: A Self-Evolving Agent Model that Scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 Read More »

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput

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Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput Read More »

NVIDIA Releases AITune: An Open-Source Inference Toolkit That Automatically Finds the Fastest Inference Backend for Any PyTorch Model

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Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces

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Google AI Research Introduces PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing

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Writing a research paper is brutal. Even after the experiments are done, a researcher still faces weeks of translating messy lab notes, scattered results tables, and half-formed ideas into a polished, logically coherent manuscript formatted precisely to a conference’s specifications. For many fresh researchers, that translation work is where papers go to die. A team

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Meet OSGym: A New OS Infrastructure Framework That Manages 1,000+ Replicas at $0.23/Day for Computer Use Agent Research

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Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating

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