Engineering & DevTools

Goose is an on-machine AI agent that can automate development tasks from start to finish. It runs locally, works with any LLM, and connects to MCP servers or APIs for broader tool access.

Zencoder links with 20+ developer tools so you can turn issues, alerts, and tasks into code changes faster. It’s built for teams that want to reduce context switching across Jira, GitHub, Sentry, CI/CD, and observability tools.

TestGrid CoTester is an AI-assisted testing platform for web and mobile apps. It helps teams generate, execute, and troubleshoot tests across real devices, browsers, and private cloud infrastructure.

An API platform for building AI applications with Kimi’s K2.5/K2.6 models. It supports long context, tool calling, vision input, and autonomous agent workflows for developers.

Qodo helps teams review code in the IDE and pull requests with deep codebase context. It focuses on catching issues earlier, enforcing rules, and making reviews more consistent across complex repos.

Roo Code is a VS Code extension that helps you generate, refactor, debug, and document code from natural language. It also supports multiple working modes and can connect to MCP servers for tool use.

Mintlify helps teams build and maintain product documentation with an AI-native workflow. It also adds an assistant for users and supports llms.txt and MCP for AI discovery.

ReadMe helps you build and maintain a developer hub with API docs, versioning, analytics, and built-in AI features. It’s aimed at teams that want docs that stay in sync with their product and API.

MySpec is a cloud-based spec-driven development tool for software builders using AI coding agents. It interviews you about a project, then produces a structured specification bundle that can be exported to tools such as Cursor and Claude Code or accessed through MCP.

Continue runs source-controlled AI checks on pull requests and reports results as GitHub status checks. It’s aimed at engineering teams that want automated review for specific standards, with humans still deciding whether to accept suggested fixes.

Amazon Q Developer helps you write, review, test, refactor, and upgrade code, with extra support for AWS operations and data/AI tasks. It runs in IDEs, the command line, AWS console, and chat tools like Slack and Microsoft Teams.

CodeRabbit reviews pull requests, supports IDE and CLI workflows, and can pull in context from connected MCP servers. It’s aimed at teams that want more context-aware review comments and code suggestions without changing their existing review process.

BCMS is a headless CMS that plugs into frontend frameworks and deployment platforms like Vercel, Netlify, DigitalOcean, and Docker. It helps you manage content in BCMS and deliver it through your app using environment variables and the BCMS client.

IFTTT provides a fully automated tool to test your service API before publication. It sends sample requests, validates responses, and checks OAuth setup and trigger/action behavior.

Req2Ops turns client briefs, emails, and PDFs into structured project plans. It helps teams capture missing details, organize work, and identify next actions before development starts.

AIReiter is a cloud API platform that gives developers a unified way to access image, video, and language models from several AI providers. You can submit generation jobs, monitor their status and usage, and switch models without rebuilding each integration.

CurlShot is a cloud screenshot service for capturing full web pages or viewports as PNG, JPEG, WebP, or PDF files. You can use its no-install web interface or automate renders through a simple HTTP API.

GPT4All lets you run large language models on everyday desktops and laptops without API calls. It includes a desktop app and Python bindings for local inference, plus support for chatting with your own data.

Nano Collective builds privacy-respecting AI tools meant to run locally or under your control. It’s aimed at developers who want automation without handing data to a hosted SaaS stack.

LatticeFlow AI helps machine learning teams inspect vision datasets and model behavior to find quality issues. It appears aimed at ML engineers, data analysts, and quality control teams working on computer vision systems.