AI Agent2026
Routes code-based AI agents through repeatable reverse-engineering and pentesting workflows and orchestrates local and remote tools (jadx, Frida, IDA, BurpSuite) so agents can triage APKs, binaries, JS, firmware, and CTFs without guessing the toolchain. Includes master routing rules, tool-index detection, MCP integration, and a field-journal for reusable lessons.
AI Infra2026
Runs a Kubernetes-native runtime that multiplexes many stateful agent-like actors onto a small pool of sandboxed worker pods via full-state snapshots and pre-warmed workers, enabling sub-second suspend/resume and 30x+ oversubscription.
AI Coding2026
Reviews Git diffs or entire files via a CLI that combines deterministic pipelines (precise file selection, bundling, rule matching) with an LLM agent to produce line-level review comments. Includes built-in security rules and integrations for multiple LLM providers, designed for CI and large changesets.
MCP Server2026
Provides long-term memory for AI coding agents by compiling sanitized lifecycle observations into a git-versioned Markdown wiki that enables cross-agent handoffs, per-project isolation, and optional vector-backed retrieval.
AI Agent2026
Orchestrates, composes, and governs multiple AI agents (Claude Code, Codex, Cursor, Pi, and custom agents) via a meta-harness that enforces policy-based sandboxing, spend caps, and live collaborative sessions. Agent behavior is declared in YAML and can run locally or in managed cloud sandboxes.
AI Model2026
A 35B mixture-of-experts LLM specialized for agentic coding and tool-enabled code generation, fine-tuned with self-scaffolding reinforcement learning. Supports very long contexts, OpenAI-compatible tool calls, and multiple serving runtimes under an MIT license.
AI Model2026
Provides an open-source Mixture-of-Experts coding LLM (397B) optimized for agentic, tool-enabled coding workflows with a 262,144-token context window, OpenAI-compatible API, serving recipes (vLLM/SGLang), and published coding-benchmark results.
AI Model2026
A self-improving, agentic coding LLM tailored for terminal-style coding agents and tool-calling, provided as 35B MoE GGUF weights with very large context support. Trained with reinforcement learning to jointly generate task scaffolds and solutions; designed for local inference and OpenAI-compatible tool endpoints.
AI Agent Papers2026
Matches detection paradigms to four stratified attack-surface layers of AI agents — infrastructure, protocol/tool, agent behavior, and model — and presents AI-Infra-Guard: an open-source red-teaming framework with rule-based infra scanning, LLM-driven audits of MCP servers and skill packages, and a jailbreak/attack-operator harness.
AI Agent Papers2026
Turns natural-language PLC requirements into verified, runnable IEC 61131-3 Structured Text by driving a closed loop of generation, compilation, deployment, and behavioral verification on a live OpenPLC runtime. The verification-gated harness forces inputs, traces execution, repairs failures, and renders ladder diagrams plus process simulation to raise dynamic runtime pass rates.
AI Dataset2026
Evaluates whether tool-using LLM agents reliably complete stateful business workflows via 507 executable agent–tool–user tasks across retail, travel, auto insurance, neobank, and IT/HR consulting. Provides browsable Parquet tables for tasks, scenarios, and agent instructions; v1.0 is intended for evaluation-only.
AI Agent Papers2026
Provides a self-evolving ontology layer that enables LLM-based data agents to query and interact with heterogeneous data via an MCP server; it auto-builds and iteratively refines schema, content, and tool layers based on agent interactions.