Laya is an open-source (Apache 2.0) Python system for making typed decisions over text, email, tickets, and JSON in a single forward pass, positioned as an open alternative to TypeSafe's Jev. It provides choice, score, and null primitives, checkpoints, and a router that selects English or multilingual models by language and script, with documentation covering fine-tuning via reinforcement learning against proper scoring rules, calibration, benchmarks, and deployment.
Beacon is an open-source, local-first memory layer that captures and normalizes session history across Claude Code, Cursor, Codex, OpenCode, and more than 20 other agent harnesses. It records prompts, responses, tools, commands, file activity, approvals, MCP interactions, and token usage, making workflows, corrections, and repository conventions reusable through local dashboards, MCP, and Agent Skills, with forwarding options to Splunk, Datadog, Elastic, AWS S3, and Google Cloud Storage.
AWS open-sourced Strands Harness, a general-purpose AI agent built on its Strands Agents SDK that gives developers a preconfigured agent with file, shell, and web tools plus built-in context management, memory, persistent sessions, prompt caching, and delegation to other agents. AWS claims it runs about 45% cheaper than Claude Code and Codex on comparable tasks, though the article notes at least one rival still outperformed it on efficiency.
This article profiles a SpaceX/xAI Grok team engineer whose personal agent workflow, pstack, lets her ship 2,000 pull requests a month to production using isolated, virtualized testing environments. It argues that a verification skill -- letting an agent check its own work and keep going until a task is done -- is the critical infrastructure underpinning high-throughput agentic coding, and that scaling this to systems of hundreds of services requires a rich, on-demand runtime the agent can drive and inspect.
Anthropic is adding support for AGENTS.md, OpenAI's markdown instructions format for AI coding agents, to Claude Code starting in version 2.1.277 -- if no CLAUDE.md exists in a folder, Claude will use AGENTS.md instead. This ends the need for developers using both Claude Code and OpenAI Codex to maintain duplicate instruction files, and follows AGENTS.md's adoption by more than 60,000 open source projects since OpenAI contributed it to the Agentic AI Foundation under the Linux Foundation.
This guide describes a disciplined approach where developers build and direct AI-agent setups rather than writing software directly. It covers delegating implementation, testing, debugging, and iteration to agents while investing in steering files, codebase refactoring, task decomposition, fast feedback loops, quality standards, and continuous workflow improvement. The approach initially requires significant practice and may feel slower, but can produce substantial productivity gains.
This page describes frontier engineering teams as organizations that provide agents with rich context, make intent explicit, run agents in parallel, and shift testing left. It presents results from Amazon teams, including up to 20x individual productivity gains at Amazon Bedrock, roughly 6x throughput acceleration at Amazon Prime Video, and a 4.5x median productivity gain at Amazon Stores. Kiro supports this workflow through structured requirements, persistent project memory, steering files, architectural planning, and property-based testing.
The DeepSeek API documentation is compatible with OpenAI and Anthropic request formats, covering base URLs, API-key requirements, and supported model names. It explains agent-tool integrations including DeepSeek Harness, Claude Code, GitHub Copilot, and OpenCode, with cURL, Python, and Node.js examples for the Chat API with streaming disabled, thinking enabled, and high reasoning effort.
Jesse Vincent, creator of Superpowers, discusses six months of building with AI coding agents, including an incident where Claude deleted his test suite. He compares managing AI agents to managing junior engineers, stressing clear intent, structured process, and human judgment. The conversation covers agent-written code, strongly-typed languages, and the limits of automating human taste in software development.
Herdr is a runtime for keeping coding-agent terminal sessions alive across laptops, desktops, and remote machines. It manages persistent agent workspaces, identifies agents that are working or blocked, and exposes a CLI and socket API for agent automation.
Omarchy presents itself as a customizable Linux operating system designed for the age of AI agents. The project offers an installable distribution, manuals, plugins, and an agent-oriented workstation environment.
Addy Osmani argues that agentic software factories relocate human judgment rather than eliminating it. The essay recommends defining product intent and quality bars, reviewing code deliberately, and embedding continuous tests and safeguards in the development environment.
DeepSeek open-sourced DeepSeek Harness (dsh), a new Node.js-based agent runtime released as a developer-preview v0.1 under the MIT license, alongside the official launch of DeepSeek-V4-Pro. Built on the Cordis plugin meta-framework, every component of the harness — models, tools, skills, sessions, sandboxes, storage, loops, scheduling, and the UI — is a hot-swappable plugin. The repo drew over 33,000 GitHub stars within hours of release and already has a fast-growing community plugin ecosystem, positioning it as an open alternative to integrated coding-agent environments like Claude Code.
GitHub Agentic Workflows let repositories run AI agents through GitHub Actions on schedules or repository events. The article explains supported engines, authentication, security layers, workflow frontmatter, compilation, issue-triage setup, reusable patterns, and current public-preview limitations. It also compares Agentic Workflows with Copilot Cloud Coding Agent and traditional custom Actions.
Qwen 3.6 27B built a three-step RSS aggregation web app on Tines 3B using one prompt and two bug reports, correctly applying the platform's workflow conventions, templates, volume mounts, and HTTP-response requirements. It used web research, code execution, logs, and browser controls to diagnose RSS, XML namespace, and configuration issues, then redesigned the architecture around cached feeds and a separate five-minute refresh step. The main limitation was the 100,000-token context window, which required multiple sessions and caused it to repeat or miss fixes, while some rough edges remained.
This LinkedIn post could not be accessed directly since LinkedIn blocks automated content retrieval, but its URL slug ('2026-rag-tech-stack-master-these-9-agentic') indicates it is a post outlining nine agentic tools or components that make up a modern retrieval-augmented generation (RAG) technology stack for 2026. It appears aimed at practitioners looking to build or update RAG-based AI systems using current agentic frameworks. Content behind a login wall / unavailable — inferred from link context.
This YouTube video explains 15 practical rules for reducing token usage across ChatGPT, Claude, and Codex to avoid hitting usage limits. It focuses on managing reused input, editing mistakes instead of re-asking, combining related questions, starting fresh when tasks change, and sending lighter source material. It also introduces a 'Token Saver' skill for Codex and Claude Code and the 'Ringer' multi-agent framework for enforcing hard limits on API calls.
This Substack article explains Moonshot AI's Kimi K3, a 2.8-trillion-parameter open-weight model that reportedly ranks #1 for frontend coding and AI agent tasks at roughly a third of Claude's price. It details K3's design for long-horizon, high-context work rather than quick chatbot queries, along with its pricing, cache economics, and operational best practices. The guide also covers access methods, prompting strategies, cost engineering, and common failure modes for builders adopting K3.
This blog post argues that Kimi K3 performs similarly to Claude on everyday coding work, with comparable output quality and token usage, while being much cheaper via both API pricing and subscription plans. It contrasts Claude's restricted access to some model capabilities with Kimi's open availability, using this to critique U.S. AI policy and the economics of gated frontier models. It also mentions GLM 5.2 and OpenAI's GPT-5.6 as points of comparison in the competitive AI model landscape.
This Phoronix article reports on Frame, an X11 server implementation written entirely in x86_64 Assembly and created largely with Claude Code, spanning nearly 25,000 lines in one file with no external dependencies on Mesa, FreeType, Xlib, or libc. It quotes developer Geir Isene describing how the AI-assisted stack replaced components like gdm, X11, i3, conky, wezterm, and zsh. Chat messages jokingly reacted that 'programming is cooked' in response to this AI-driven low-level systems project.
This product page describes Kit as an extensible AI coding agent CLI with multi-provider LLM support, built-in tools, and a rich extension system. It highlights features including named agents, smart attachments, MCP integration, an interactive terminal UI, session management, non-interactive mode, GitHub integration, and a Go SDK.
This KDnuggets article explains how the open-source library Outlines helps make large language model generation more reliable by constraining outputs to valid structured formats. It shows Python examples including multiple-choice classification, JSON generation with a Pydantic model, and valid JSON for a REST API-style payload. It emphasizes that Outlines masks syntactically illegal tokens during inference to reduce malformed outputs and hallucinations.
This LangChain blog post introduces OpenWiki, an open-source agent and CLI for generating and maintaining documentation for codebases. It creates a repo wiki, connects it to coding agent instruction files like AGENTS.md or CLAUDE.md, and updates documentation over time as code changes. The page also covers setup via npm and support for model providers including OpenRouter, Fireworks, Baseten, OpenAI, and Anthropic.
This Simon Willison blog post discusses Ornith-1.0, a new open-weights, MIT-licensed model from DeepReinforce built for agentic coding, released in 9B Dense, 31B Dense, 35B MoE, and 397B MoE variants. Built on pretrained Gemma 4 and Qwen 3.5, it is described as state-of-the-art among open-source models of comparable size on coding benchmarks. The author reports running the 35B GGUF in LM Studio and testing it in an agent harness with tool calls.
This Substack guide explains how to turn a basic OpenAI Codex setup into a reusable multi-agent workflow. It covers creating an AGENTS.md file, defining a custom subagent, and orchestrating five parallel agents to complete a task and return five files at once. The article includes a copy-paste prompt for running the process step by step in Codex or another coding agent.
This CodeRabbit blog post explains 'loop engineering' as a way to move beyond manual prompting by designing autonomous loops that let coding agents execute tasks repeatedly with minimal human intervention. It describes a shift from prompt engineering to context management and harness engineering, outlining building blocks like automations, worktrees, skills, plugins/connectors, sub-agents, and state. It gives a practical example using Claude Code, CodeRabbit reviews, scheduled jobs, and a state file.
This OpenAI developer blog post explains how OpenAI built Secure MCP Tunnel to let ChatGPT, Codex, and other OpenAI products reach private MCP servers without exposing them to the public internet. It describes an outbound-only, customer-run open-source tunnel client that forwards MCP requests to approved local servers while preserving private network boundaries and supporting streaming and authentication. The article also covers related developer tools like Codex integration and an extension called Harpoon for approved private REST APIs.
This gitconnected article describes how Anthropic uses Claude Code across its own product development, with Claude reportedly writing around 80-90% of the company's merged code. It explains Anthropic's shift toward engineers acting as architects and reviewers, and details the four-file public setup Anthropic uses, starting with a CLAUDE.md file providing persistent session context.
Magnitude is a coding agent designed to get frontier-level coding performance out of open models such as GLM, Kimi, and DeepSeek. The site explains that it improves reliable tool calls, controls thinking levels, and avoids circular 'doom loops' common with open models. It offers $5 in free credits, pass-through API pricing for individuals and small teams, and enterprise deployment options within a customer's own VPC or data center.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article (blocked from direct access) describes a hands-on project where the author connected a locally-run LLM to Docker containers to replace traditional monitoring scripts, likely showcasing a practical self-hosted AI automation setup.
This Kimi/Moonshot AI resource page introduces Kimi K2.7 Code, an open-source coding-focused agentic model for long-horizon software engineering tasks. It highlights benchmark improvements over K2.6, a roughly 30% reduction in thinking-token usage, and access via Kimi Code and the Kimi API with always-on thinking mode.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article (blocked from direct access) appears to cover modifications, plugins, or configurations that make Anthropic's Claude Code tool more suitable for production software development use, based on the URL slug 'claude-code-good-mods-make-actually-production-ready.'
This GitHub repository hosts DwarfStar (ds4), a local inference engine by antirez optimized for DeepSeek V4 Flash and GLM 5.2 models, supporting Metal, NVIDIA CUDA, and ROCm backends. It documents model downloads, distributed inference, tensor parallelism, SSD streaming, benchmarking, KV caching, and native agent/tool-call support for developer workflows.
This Medium/Google Cloud article explains how to build an MCP (Model Context Protocol) server that converts raw Markdown tables into styled PNG images for use in posts. It covers the rendering pipeline, using FastMCP and Zod to define the tool interface, and connecting the server to coding agents like Antigravity and Claude Code.
This O'Reilly Radar article by Addy Osmani explains 'Agent Skills,' a Markdown-based workflow system that encodes step-by-step engineering processes, checkpoints, and exit criteria for AI coding agents. It argues agents skip senior-engineer practices like writing specs and verification, so skills add structured scaffolding mapped to standard SDLC phases and engineering best practices.
VentureBeat reports that Raindrop AI launched Workshop, an open-source, MIT-licensed tool for debugging and evaluating AI agents locally via a local daemon and dashboard that logs every token, tool call, and decision. It supports macOS, Linux, and Windows, integrates with SDKs like OpenAI, Anthropic, LangChain, and Claude Code, and includes a 'self-healing eval loop' for autonomous error fixing.
This O'Reilly Radar article explains why context management is a critical but under-taught skill in AI-assisted software development, since poor context handling leads to hallucinations and lost project knowledge. It describes techniques like externalizing decisions into files (DEVELOPMENT_CONTEXT.md, AGENTS.md), context compaction, and orchestration, illustrated with examples from the author's own projects.
This Medium article describes how the author's team built a spec-driven software development pipeline for Claude Code using 16 specialized agents, validation gates, and persistent state tracking. It details a 9-phase `/proceed` workflow from spec validation through implementation, review, and deployment, plus a `/sprint` orchestrator for running multiple pipelines in parallel. The piece shares lessons learned about phase-based workflows, model tiering, and knowledge capture.
This GitHub repository, gstack, is an MIT-licensed collection of slash-command skills for Claude Code and other AI coding agents, structuring software work into stages like planning, building, reviewing, testing, and shipping. It includes tools for browser-based QA, design reviews, security audits, and multi-agent coordination, with integration support for platforms like Codex, Cursor, and OpenClaw. The README frames it as a "virtual engineering team" for AI-assisted development.
This Towards Data Science article explains how retrieval-augmented generation (RAG) systems can retrieve correct documents yet still produce hallucinated or contradictory answers. It identifies five failure patterns, including numeric inconsistencies and fabricated citations, then presents a lightweight Python-based detection-and-healing layer that scores, patches, and rewrites low-faithfulness answers in under 50 milliseconds without external APIs or embedding models. It's a practical technical guide for developers building RAG systems.
This Warp.dev announcement reveals that Warp's terminal client is now open-source under an AGPL license, with community contributions centered on an agent-first workflow managed by its cloud agent orchestration platform, Oz. OpenAI is named as the founding sponsor of the open-source repository, with new workflows powered by OpenAI models. The post also covers broader support for open-source models and a shift to public GitHub issues for feature tracking.
This Medium article explains Claude Code "skills" — folder-based extensions that can include scripts, assets, configuration, and reference files to extend the coding assistant's capabilities. It categorizes common skill types such as API references, data retrieval, business process automation, and CI/CD, and offers practical guidance on building effective skills using progressive disclosure and memory persistence. The piece is aimed at developers looking to customize and extend Claude Code.
This Medium article explains why Model Context Protocol (MCP) servers make AI coding assistants more useful by connecting them directly to external tools and workflows. It uses Vercel's MCP server as an example, showing how an AI can access deployment logs, search documentation, and manage projects without leaving the development flow. The piece is a practical guide aimed at developers wanting to extend AI tool capabilities.
Content behind a login wall / unavailable — inferred from link context: this is a TheNewStack.io article (URL slug "ai-coding-tool-stack") that appears to cover how developers are assembling combinations of AI coding assistants, IDE integrations, and agent tools into a modern development workflow. TheNewStack is a publication focused on software development and cloud-native engineering practices. The piece likely surveys popular AI coding tools and how they fit together in a developer's toolchain.
Content behind a login wall / unavailable — inferred from link context: this is an Instagram Story from Cursor (trycursor), the AI coding tool company, shared with the chat comment 'Full court press....' suggesting an aggressive marketing or product announcement push. Given Cursor's focus, the story likely relates to a new AI coding feature, update, or promotional campaign, but the specific content could not be verified since Instagram Stories require login access.
This HackerNoon article, titled 'The Dark Factory Pattern: Moving From AI-Assisted to Fully Autonomous Coding,' presents a four-phase architecture for moving software teams beyond the common 'AI writes code, human reviews it' plateau. Based on the title and chat context describing it as introducing BDD (behavior-driven development) holdout scenarios, the piece appears to propose patterns for scaling toward fully autonomous coding workflows.
InfoWorld tests several Qwen3.5 models running locally in LM Studio with VS Code and the Continue extension for code analysis and generation tasks. Using a Python packaging project as a testbed, the article compares model sizes, quantization, and response quality against Claude Code. It concludes local models can give useful high-level feedback but struggle badly with autonomous code-editing tool use.
This video explains how vibe coders need to shift from prompting to supervising AI agents as autonomous software-building tools become more common in 2026. It presents five management skills: using version control as save points, knowing when to start fresh, maintaining standing orders and rules files, making small bets to limit blast radius, and anticipating questions agents will never ask. It also cites real examples of agents causing major data loss.
This Fortune article reports that Amazon reportedly held a mandatory meeting to review recent site outages, including incidents tied to AI-assisted coding changes and 'high blast radius' issues, per the Financial Times. Elon Musk warned 'Proceed with caution' in response, while Amazon said the meeting was a routine operations review unrelated to AWS. Source: https://fortune.com/2026/03/11/elon-musk-amazon-outage-ai-relate-incident-meeting-report-cybersecurity/
This YouTube Shorts video from Nate B Jones discusses Anthropic's Claude Code updates, including new browser inspection capabilities, and frames it as part of a broader shift from AI assistant to 'agent operating system.' It compares Claude Code to Cursor and Codex and discusses safety-forward sandboxing for enterprise agent adoption. A chat message reflects on personal experience using agentic IDEs. Source: https://youtube.com/shorts/TZ_5GHNqOnw
This is a Bloomberg Businessweek article link shared via Apple Stocks, titled 'Claude Code and the Great Productivity Panic of 2026.' Based on the headline and description, it examines how AI coding agents, which promised to simplify software development, instead triggered a high-pressure race among companies to build faster regardless of cost. A chat message reinforces this framing, noting the irony that coding agents made development more frantic rather than easier. Source: https://stocks.apple.com/AVjEPYVA8QxGGug3Wsca5IQ
This Nate B Jones video explains a five-level framework for AI coding, ranging from autocomplete-style assistance to fully autonomous 'dark factory' software production with no human-written or human-reviewed code. It contrasts teams like StrongDM, which ship production software this way, against most developers who use AI tools but paradoxically work slower due to unclear specifications. The video argues the key bottleneck has shifted from implementation speed to the quality of specifications guiding AI coding agents.
This SemiAnalysis newsletter piece argues that Claude Code is a major inflection point for AI agents, describing it as a terminal-native CLI agent that reads codebases, plans multi-step tasks, and executes them autonomously. It explains how the authors use Claude Code and similar systems for coding and broader information work, and claims agentic workflows are reshaping software development and enterprise productivity. The piece also discusses adoption metrics, pricing economics, and competitive implications for Anthropic, OpenAI, and Microsoft.
This Ramp engineering blog post explains how the company built its own background coding agent, called Inspect, and the technical architecture behind it. It describes sandboxed VM sessions on Modal, integrations with tools like Sentry, Datadog, LaunchDarkly, Braintrust, GitHub, Slack, and Buildkite, and support for frontier models, MCPs, and custom skills. The post also outlines recommended components for building a remote coding agent platform, including sandboxing, APIs, multiplayer collaboration, and multiple client surfaces.
BigCode is an open scientific collaboration focused on the responsible development and use of large language models for code. The website presents the project's mission and invites visitors to learn more about its open research and support for code-generation AI.
Content behind a login wall / unavailable — inferred from link context: this is a shared Reddit post from the r/cursor community, dedicated to the Cursor AI code editor. The accompanying chat message references a 'bubbling up problem' for companies in this space, suggesting the post likely discusses user frustrations, pricing changes, reliability issues, or competitive pressure facing AI coding tool companies like Cursor.
This XDA-Developers article could not be directly retrieved due to site access restrictions, but its title and the accompanying chat snippet ("4 Python scripts that supercharged my NotebookLM workflow") indicate it walks through custom Python scripts the author built to extend and automate Google NotebookLM for research tasks.
This website curates a collection of AI coding tools for developers ranging from beginners to experienced engineers, framing AI coding as a major productivity and innovation driver in software development. It emphasizes an ad-free, affiliate-free presentation and highlights featured tools including Cursor, GitHub Copilot, and Windsurf AI.
This Medium article explains how to use Google DeepMind's Antigravity as a full autonomous coding environment rather than a simple chatbot interface. It outlines five workflows: adopting a "Mission Control" mindset for managing multiple agents, structuring tasks with clear boundaries, using browser-based verification, leveraging the CodeMender agent for legacy code, and defining custom workflows via an `.agent/workflows` directory.
This GitHub README documents Fabric, an open-source framework created by Daniel Miessler that helps people apply AI to everyday tasks via modular, reusable prompt “Patterns.” It covers installation, usage commands, and examples such as summarizing content, analyzing claims, and extracting insights from videos or websites. The README also details helper apps, custom pattern creation, cross-platform clipboard tools, a web/Streamlit interface, and multi-model support including Grok.
This GitHub Blog post describes a workflow where Markdown files serve as an app's specification, compiled into working code (Go) by GitHub Copilot. It walks through an example project's README, main spec, and compile prompt files to show a repeatable edit-compile-test loop for AI-assisted software development.
InfoWorld examines 'vibe coding' (AI-assisted development) and its place in the software development lifecycle, arguing it speeds prototyping and collaboration but raises concerns about security, governance, IP, and production readiness. The article quotes experts from companies including New Relic, SAP, Postman, and CloudBees on how to use it safely.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article covers a new open-source platform that makes self-hosting large language models faster and easier than expected, likely aimed at users wanting local, private AI model deployment as an alternative to cloud services.
Content behind a login wall / unavailable — inferred from link context: this XDA Developers article likely describes a workflow for integrating Google's NotebookLM AI research tool with VS Code, using it to organize documentation or code context to assist software development.
Simon Willison discusses Addy Osmani's book, originally titled 'Vibe Coding,' being retitled 'Beyond Vibe Coding: From Coder to AI-Era Developer.' He argues the new title better reflects a professional engineering approach to AI-assisted development, distinguishing true 'vibe coding' from broader, more rigorous AI-assisted software engineering practices.
This ZDNet review covers Google's Firebase Studio, a cloud-based, full-stack AI workspace and agentic IDE, comparing it to Microsoft's VS Code. It highlights the App Prototyping agent, which uses AI prompts to build and refine app ideas, demonstrated through a test Kanban board app integrated with Google Calendar. The review also notes Firebase Studio can import repositories from GitHub, GitLab, Bitbucket, or local machines, runs entirely in the cloud, and is currently free with usage limits.
This Fireship video covers Amazon's release of Kiro, an AI IDE built on a VS Code fork and powered by Claude Sonnet 4.0. It explains Kiro's 'spec-driven development' workflow, which starts with a requirements document, then a design document, then an implementation plan before code generation. The video compares Kiro with Cursor, Copilot, Firebase Studio, and Windsurf, noting a polished UI but early performance and feature gaps.
This conference talk (LDX3/LeadDev) surveys how software engineers, startups, and Big Tech teams are actually using AI coding tools in 2025, contrasting enthusiastic executive claims with mixed real-world engineer experiences. It covers usage of Claude Code, MCP, and AI code review across companies like Anthropic, Google, and Amazon, featuring perspectives from Kent Beck and Martin Fowler. The talk highlights where AI tools succeed for individual developers and where they still struggle in production settings.
This Cursor community forum thread discusses the AI coding tool's pricing plan, usage quotas, rate limits, and token usage transparency. Commenters compare Cursor with alternatives like Claude Code, Windsurf, GitHub Copilot, and OpenRouter, focusing on cost and model selection within agent chat. The thread also links to a community guide on optimizing AI model usage and costs.
This Business Insider article reports that Perplexity CEO Aravind Srinivas made it mandatory for employees to use at least one AI coding tool, typically Cursor or GitHub Copilot. He said these tools reduced engineers' experimentation time from three or four days to about one hour, and are helping non-technical staff make interface changes faster. The article also discusses broader industry survey data on AI coding tool adoption and potential trade-offs.
Content behind a login wall / unavailable — inferred from link context: this Analytics India Magazine article's title suggests it argues that 'context engineering' — carefully structuring the information and tools given to LLMs — is emerging as a more disciplined successor to the informal 'vibe coding' trend popularized in 2025. It likely covers how developers are moving from ad hoc prompting toward more systematic approaches for building reliable AI-assisted applications. The specific arguments and examples could not be confirmed since the page failed to load.
This Qodo report presents survey findings from over 600 developers on how generative AI is shaping code quality, trust, and productivity across the software development lifecycle. It found that 82% of developers use AI coding tools daily or weekly, while missing context, hallucinations, and trust gaps remain major barriers to reliable AI-assisted development. The report highlights that AI code review and persistent codebase understanding correlate with better code quality and higher developer confidence.