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Emisar.dev
Emisar is the last MCP server youโll need to install: a Zero-Trust gateway connecting Claude, Cursor, ChatGPT, and any AI agent to your infrastructure. One server handles production access, debugging, alerts, and internal operations, with new capabilities added as packs. Agents can inspect real production state, debug what they shipped, and help resolve incidents. Safe reads run automatically; policy allows, blocks, or routes risky actions for approval. No SSH keys, VPNs, remote shells, or standing shell access โ and every call is recorded.
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Emisar.dev's answer:
emisar is for SRE, DevOps, platform engineering, infrastructure, and security teams that want AI agents to inspect and operate production systems. It is especially relevant to teams managing multiple Linux hosts, clusters, databases, cloud services, or regulated environments where unrestricted shell access and incomplete audit records are unacceptable.
Emisar.dev's answer:
The hosted control plane and operator interface use Elixir, Phoenix, LiveView, PostgreSQL, and Tailwind CSS. The host runner and MCP bridge are written in Go. Action packs use YAML and JSON Schema, while production infrastructure is managed with Terraform on Google Cloud. The system communicates through MCP, OAuth 2.1, TLS, and WebSockets.
Emisar.dev's answer:
Emisar.dev's answer:
Founder Andrii Dryga spent a decade working as a CTO, full-stack engineer, SRE, and DevOps engineer. He experienced the cost of running the wrong command on the wrong cluster, while also seeing AI solve operational problems in seconds. emisar grew from the need to preserve both truths: AI agents are useful, and production access must remain bounded. Its answer is to give agents a reviewed catalog of operations instead of a blank terminal.
Emisar.dev's answer:
emisar lets AI agents work on real infrastructure without giving them a shell. Agents choose from a finite catalog of typed, versioned actions. Policy decides what runs, what requires approval, and what is denied, while an outbound-only runner verifies the action again on the host. New capabilities arrive as packs behind the same MCP integration, and every request is recorded in both a searchable audit trail and a tamper-evident host journal. [
Emisar.dev's answer:
Choose emisar when you want an agent to keep investigating and handling routine operations without handing it SSH credentials or supervising every call. Compared with raw shell access, copy-paste workflows, or one-off MCP servers, emisar provides reviewed action contracts, host-level enforcement, risk-based policy, scoped access, approvals, pack integrity checks, and a durable audit trail. It is built specifically for governed infrastructure access rather than generic automation.
Based on our record, PyTorch seems to be more popular. It has been mentiond 144 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
PyTorch: A popular deep learning framework for Python. - Source: dev.to / about 1 month ago
Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 3 months ago
Install PyTorch with GPU support: Go to the official PyTorch website (pytorch.org) and use their configurator to get the correct pip or conda command for your specific CUDA version. It will look something like this:. - Source: dev.to / 3 months ago
Open source contributions to democratize AI capabilities represent one of the most direct ways individual developers can impact AI inequality. Contributing to projects like Apache MXNet, PyTorch, or specialized tools for underserved communities multiplies your impact beyond individual projects. - Source: dev.to / 4 months ago
What's particularly intriguing is how NemoClaw integrates with Nvidia's broader AI ecosystem. Unlike standalone HPC libraries, it's designed to work seamlessly with frameworks like PyTorch and TensorFlow, enabling researchers to combine traditional numerical methods with machine learning approaches in ways that weren't practical before. - Source: dev.to / 4 months ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
NumPy - NumPy is the fundamental package for scientific computing with Python
CUDA Toolkit - Select Target Platform Click on the green buttons that describe your target platform.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.