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How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Staff, Tutorials

In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with

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How to Build an MCP Style Routed AI Agent System with Dynamic Tool Exposure Planning, Execution, and Context Injection

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Model Context Protocol (MCP), Staff, Tutorials

In this tutorial, we build a fully functional MCP-style routed agent system from scratch, combining tool discovery, intelligent routing, structured planning, and execution into a single cohesive workflow. We start by setting up a modular tool server that exposes capabilities such as web search, local retrieval, dataset loading, and Python execution, all defined through structured

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Best 5 Engineering Analytics Platforms of 2026

agentic ai, ai, AI (Artificial Intelligence), analytic models, Analytics, Artificial Intelligence, Cloud Computing, Data Visualization, Predictive Analytics, Real-Time Analytics, Web Analytics

Engineering organizations are operating in an environment that is significantly more complex than it was even a few years ago. Modern software delivery now spans distributed cloud infrastructure, platform engineering initiatives, AI-assisted development workflows, microservices architectures, globally distributed teams, and increasingly fragmented operational tooling ecosystems. As complexity grows, engineering leaders are realizing that traditional reporting

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Claude Code’s product lead talks usage limits, transparency, and the “lean harness”

agentic ai, ai, AI (Artificial Intelligence), anthropic, Artificial Intelligence, Cat Wu, claude, Claude Code, Code with Claude, software development

SAN FRANCISCO—Amid an ever-expanding array of surfaces, growing demand for tokens and compute, and a rapidly evolving user base, Anthropic doesn’t have a long-term road map for Claude Code. However, it’s betting that such a plan would be rendered moot by improvements in model capabilities and new signals from developers on how best to use

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Best AI Agents for Software Development Ranked: A Benchmark-Driven Look at the Current Field

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, New Releases, Software engineering, Staff, Tech News, Technology, Top

The AI coding agent market looks almost unrecognizable compared to 2024 or even early 2025. What started as inline autocomplete has evolved into fully autonomous systems that read GitHub issues, navigate multi-file codebases, write fixes, execute tests, and open pull requests — without a human typing a single line of code. By early 2026, roughly

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Supertone Releases Supertonic v3: On-Device Text-to-Speech Model with 31-Language Support, Fewer Reading Failures, and Expression Tags

agentic ai, ai, AI (Artificial Intelligence), AI Shorts, Artificial Intelligence, Audio Language Model, Editors Pick, Language Model, New Releases, Software engineering, Staff, Tech News, Technology, Text to Audio, Voice AI

Supertone released Supertonic 3, the third generation of its on-device, ONNX-based text-to-speech system. Supertonic 3 ships with 31-language support, improved reading accuracy, fewer repeat and skip failures, and v2-compatible public ONNX assets. It is Lightning Fast, On-Device, Multilingual and Accurate TTS. What Changed from v2 to v3 Compared with Supertonic 2, Supertonic 3 reduces repeat

Supertone Releases Supertonic v3: On-Device Text-to-Speech Model with 31-Language Support, Fewer Reading Failures, and Expression Tags Read More »

How to Build a Django-Unfold Admin Dashboard with Custom Models, Filters, Actions, and KPIs

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Software engineering, Staff, Tutorials

In this tutorial, we build an advanced Django-Unfold admin dashboard. We start by installing Django, Django-Unfold, and the required dependencies, then we create a fresh Django project with a shop application. We configure Unfold with a modern admin theme, custom sidebar navigation, dashboard callbacks, product badges, tabs, filters, actions, and a custom admin homepage. We

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Poetiq’s Meta-System Automatically Builds a Model-Agnostic Harness That Improved Every LLM Tested on LiveCodeBench Pro Without Fine-Tuning

agentic ai, ai, AI (Artificial Intelligence), AI Shorts, Applications, Artificial Intelligence, Editors Pick, For Devs, New Releases, Python, Software engineering, Staff, Tech News, Technology

Poetiq has just published some very interesting results showing its Meta-System reached a new state-of-the-art on LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically building and optimizing its own inference harness — without fine-tuning any underlying model or accessing model internals. The result: GPT 5.5 High with Poetiq’s harness scores 93.9% on LCB

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Cline Releases Cline SDK: An Open-Source Agent Runtime Now Powering Its CLI and Kanban, With IDE Extensions Being Migrated

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Editors Pick, For Devs, Generative AI, Large Language Model, New Releases, Open Source, Software engineering, Staff, Tech News, Technology, Uncategorized

Cline became ‘agentic’ before it was cool, but building on the bleeding edge usually leads to some structural debt. Over time, the agent loop and the VS Code extension became a package deal—making it a headache to maintain or move to new environments. Its tough to just keep layering features on a rigid core. Cline,

Cline Releases Cline SDK: An Open-Source Agent Runtime Now Powering Its CLI and Kanban, With IDE Extensions Being Migrated Read More »

Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models

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Pre-training large language models is expensive enough that even modest efficiency improvements can translate into meaningful cost and time savings. Nous Research is releasing Token Superposition Training (TST), a method that substantially reduces pre-training wall-clock time at fixed compute without touching the model architecture, optimizer, tokenizer, parallelism strategy, or training data. At the 10B-A1B mixture-of-experts

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