Search Results for "smtp-test"
Sort By:
Showing 19 open source projects for "smtp-test"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
CodiumAI Cover-Agent
CodiumAI Cover-Agent: An AI-Powered Tool for Automated Test Generation
CodiumAI Cover Agent aims to help efficiently increasing code coverage, by automatically generating qualified tests to enhance existing test suites. -
2
Goose
AI coding agent that's more than suggestions - install, execute, edit+
Goose is an open-source, extensible AI agent that enhances the software development process by going beyond traditional code suggestions. It allows developers to install, execute, edit, and test code with any large language model (LLM), facilitating a more efficient and streamlined workflow. Designed to operate locally within a developer's environment, Goose integrates seamlessly with various tools and platforms, providing a customizable and powerful assistant for coding tasks. Its architecture supports extensibility, enabling users to tailor the agent to their specific needs and preferences. ... -
3
AutoBE
AI Vibe Coding Agent of TS backend server
...The project is aimed at moving from prototype to production more quickly while keeping generated code buildable and verifiable. It uses an agentic workflow supported by compiler-friendly checks and test generation, which helps reduce the risk of incomplete AI output. AutoBE can be explored through a local playground where users chat with agents and manage sessions. Its main value is giving developers and non-programmers a structured way to generate backend systems from requirements while still producing documentation and tests. -
4
Expect
Let agents test your code in a real browser
Expect is a developer-focused utility designed to simplify validation, testing, and assertion workflows across software environments by providing a clean and expressive interface for defining expected outcomes. The project likely centers on improving readability and maintainability in testing scenarios, allowing developers to write expectations in a concise and human-readable format. It may support chaining conditions, enabling complex validation logic without introducing unnecessary... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
5
Amazon Q Developer CLI
Chat experience in your terminal
...It blends knowledge of your local workspace with command-line context to suggest commands, explain flags, and scaffold files or workflows. The tool aims to shorten the gap between intent and action by letting you request operations like creating a test, refactoring a function, or drafting a Dockerfile without leaving the shell. It also integrates with common developer flows, offering autocompletion and step-by-step plans before running potentially destructive actions. The CLI targets macOS and Linux and is designed to coexist with standard tools rather than replace them, acting as a smart layer on top. ... -
6
Brokk
Brokk brings code intelligence to AI
...It helps bridge the gap between LLMs and real-world engineering code by offering tooling to index, analyze, query, and augment code context, so that AI can meaningfully reason about existing code, suggest edits, and navigate across projects. Modular build tasks (run, test, build, shadowJar, tidy, etc.) to support development workflows. Integration of front-end + back-end layers (web UI + CLI + internal services). -
7
SWE-agent
SWE-agent takes a GitHub issue and tries to automatically fix it
...GPT-4) into software engineering agents that can resolve issues in real GitHub repositories. On the SWE-bench, the SWE-agent resolves 12.47% of issues, achieving state-of-the-art performance on the full test set. We accomplish our results by designing simple LM-centric commands and feedback formats to make it easier for the LM to browse the repository, and view, edit, and execute code files. We call this an Agent-Computer Interface (ACI). -
8
CodeBurn
See where your AI coding tokens go
CodeBurn is a security-focused tool designed to evaluate and stress-test codebases using adversarial techniques, often leveraging AI to identify vulnerabilities and weaknesses. It simulates attack scenarios against code to uncover potential security risks, helping developers proactively identify issues before they reach production. The system is designed to integrate into development workflows, allowing continuous testing as code evolves. -
9
MiniMax-M2
MiniMax-M2, a model built for Max coding & agentic workflows
...It uses a Mixture-of-Experts (MoE) architecture with 230 billion total parameters but only 10 billion activated per token, giving it the behavior of a very large model at a fraction of the runtime cost. The model is tuned for end-to-end developer flows such as multi-file edits, compile–run–fix loops, and test-validated repairs across real repositories and diverse programming languages. It is also optimized for multi-step agent tasks, planning and executing long toolchains that span shell commands, browsers, retrieval systems, and code runners. Benchmarks show that it achieves highly competitive scores on a wide range of intelligence and agent benchmarks, including SWE-Bench variants, Terminal-Bench, BrowseComp, GAIA, and several long-context reasoning suites. -
AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
-
10
GitHub Copilot CLI
GitHub Copilot CLI brings the power of Copilot coding agent
GitHub Copilot CLI is a command-line interface tool. It brings AI-powered coding assistance directly into your terminal. GitHub Copilot CLI allows you to build, debug, refactor, and understand code via natural language conversations. You can have these conversations within the Active Directory. It integrates tightly with your GitHub context—repositories, issues, pull requests. The Copilot can leverage repository context when making suggestions. The tool is currently in public preview and is... -
11
Cody
Type less, code more: Cody is an AI code assistant
Cody is an AI coding assistant that uses advanced search and codebase context to help you understand, write, and fix code faster. Generate code on demand using AI. Cody also unblocks you when you’re jumping into new projects or trying to understand legacy code. Run Cody's one-click prompts or create your own custom prompts to execute AI workflows. Cody generates single lines, or whole functions, in any programming language, configuration file, or documentation. Cody uses your code graph plus... -
12
Codeflash
Optimize your code automatically with AI
Codeflash is a general-purpose optimizer for Python that uses advanced large language models (LLMs) to automatically generate, test, and benchmark multiple optimization ideas, then creates merge-ready pull requests with the best improvements for your code. Optimize an entire existing codebase by running codeflash --all. Automate optimizing all future code you will write by installing Codeflash as a GitHub action. Optimize a Python workflow python myscript.py end-to-end by running codeflash optimize myscript.py. ... -
13
AutoMaker
Start directing AI agents
Automaker is an autonomous AI development studio designed to transform how software is built by allowing developers to describe features, then watching AI agents implement code, tests, commits, and more with minimal manual typing. Instead of writing every line of code by hand, users add feature cards to a Kanban board with natural language descriptions, and AI agents powered by the Claude Agent SDK handle multi-step tasks such as planning, generating code, running tests, and committing to an... -
14
Aden Hive
Outcome driven agent development framework that evolves
Hive is an open-source agent development framework that helps developers build autonomous, reliable, self-improving AI agents by letting them describe goals in ordinary natural language instead of hand-coding detailed workflows. Rather than manually defining execution graphs, Hive’s coding agent generates the agent graph, connection code, and test cases based on your high-level objectives, enabling outcome-driven agent creation that fits real business processes. Once deployed, agents can capture failure data, evolve automatically to meet their success criteria, and redeploy without constant manual intervention, delivering continual improvement over time. The framework also includes human-in-the-loop nodes, credential management, cost and budget controls, and real-time observability so teams can monitor execution and intervene as needed. ... -
15
Codex Autoresearch
A codex plugin for running optimization loops inside a codebase
...The system operates in a loop where the agent modifies code, evaluates results against measurable metrics, and either keeps or discards changes based on performance. It generalizes the concept of autoresearch beyond machine learning, allowing optimization of test coverage, latency, lint errors, and overall code quality. Developers define a goal and verification command, and the agent continuously runs experiments to reach the desired outcome. The framework supports multiple operational modes, including debugging, planning, security auditing, and release validation. It can run unattended for extended periods, producing logs of experiments and improvements. ... -
16
CoStrict
Strict AI coder for enterprises, quality first
...Unlike typical AI coding tools that prioritize speed over rigor, CoStrict introduces a “strict mode” methodology that enforces disciplined processes such as requirements analysis, architecture planning, task decomposition, and test generation before producing code. This makes it particularly suitable for organizations that require consistency, auditability, and reliability in AI-assisted development. The system integrates repository-wide analysis using retrieval-augmented generation, allowing it to understand large codebases and provide context-aware suggestions, reviews, and modifications. ... -
17
Visual Studio Code client for Tabnine
Visual Studio Code client for Tabnine
...Whether you call it IntelliSense, intelliCode, autocomplete, AI-assisted code completion, AI-powered code completion, AI copilot, AI code snippets, code suggestion, code prediction, code hinting, content assist, unit test generation or documentation generation, using Tabnine can massively impact your coding velocity, significantly cutting down your coding time. -
18
Kite
Primary Kite repo, private bits replaced with XXXXXXX
...We used a variety of infrastructure, on a mix of cloud platforms, depending on what was most economical, though it was mostly on AWS. You should be able to develop, build, and test Kite entirely on your local machine. However, we do have cloud instances & VMs available for running larger jobs and for testing our cloud services. We bundle a lot of pre-computed datasets & machine learning models into the Kite app through the use of a custom filemap & encoding on top of go-bindata. The data, located in kite-go/client/datadeps, is kept in Git-LFS. -
19
lastest
AI-supported visual verification and tests you can actually trust.
...An AI agent records you clicking through your running app and generates Playwright tests with multi-selector fallback. Replays are deterministic and token-free, so your CI/CD bill doesn't scale with your test suite. Lastest ships three diff engines side-by-side — pixel (pixelmatch), structural (SSIM), and perceptual (Butteraugli) — so flaky pixel diffs stop crying wolf. A review dashboard tracks baselines, approvals, and audit history. Run it via Docker on your own infrastructure for unlimited screenshots and full data residency, or run on Lastest Cloud for managed CI runners. ...
- Previous
- You're on page 1
- Next
