Self-Hosted

MobARK (Mobile Application Reverse Kit) is a self-hosted, open-source mobile appsec platform for Android and iOS that pairs static analysis with a built-in AI Agent. Its defining feature is an AI Agent that chats with your decompiled code through a local LLM (Ollama, LM Studio, or BYOK), so no binary or source data leaves your infrastructure by default. It is Apache-2.0 licensed, requires Python 3.11+ and Node 18+, and runs as a four-container Docker Compose stack. This review covers its features, privacy model, limitations, and how it compares to MobSF and commercial MAST tools in 2026. ...

WebMux is a self-hosted, browser-based remote workspace that tiles persistent SSH, mosh, VNC, and RDP sessions into a single scrollable web interface — effectively “tmux, but for the browser.” It started as a shared jump-box wall of terminals and has grown into a full multi-session terminal and desktop workspace with multi-user accounts, auto-reconnect, and OS service integration. What Is WebMux? WebMux is an open-source TypeScript application that turns your browser into a persistent remote workspace. Instead of running a single terminal in a web page the way tools like ttyd or gotty do, WebMux gives you a 2D tiled grid of sessions — SSH terminals, mosh connections, and even VNC or RDP desktop views — all living in one interface that survives browser closes and server reboots. ...

Simmis is a self-hosted workspace where people and AI agents share a single, versioned, queryable memory — chat rooms, wiki pages, knowledge bases, a double-entry ledger, and a code repository all live on one substrate instead of being scattered across siloed tools. Built in Clojure on the replikativ stack, it treats humans and agents as the same kind of participant, so @mentions, governance, and history work identically across both. It is an early, MIT-licensed “release early” project that is used daily but still has rough edges. ...

An agentic PostgreSQL investigation is an AI-driven workflow that explores a database to answer operational questions, and OpsGraph makes it evidence-first: it discovers an approved read-only schema, validates every SELECT against the PostgreSQL AST and policy bounds, and records a tamper-evident audit chain so no conclusion can be asserted without opening its exact evidence. Instead of giving a model direct credentials and “asking it anything,” OpsGraph constrains the investigation to bounded, read-only, auditable queries that fail closed on unsafe SQL. ...

Costrict Proxy is a self-hosted, OpenAI-compatible gateway that converts a single Costrict account subscription quota into reusable API keys, letting multiple AI agents and clients share one account. Built on CLIProxyAPI v7 and running on port 8317 by default, it exposes standard /v1/models and /v1/chat/completions endpoints with a serialized request queue and automatic token refresh. It is the most direct answer to the “one Costrict account, many agents” problem. What is Costrict Proxy? Costrict Proxy is an open-source gateway that sits between your Costrict account and the AI tools that consume it. Instead of giving every agent its own Costrict subscription — which is expensive and hard to manage — you run one instance of Costrict Proxy, connect it to a single Costrict account, and let it expose that account’s quota as standard API keys. ...

A knowledge inbox for AI agents and Obsidian is a local-first ingestion pipeline that turns raw inputs — links, PDFs, videos, screenshots, and plain text — into structured Markdown knowledge cards inside your own vault. Instead of dumping every snippet into cloud read-it-later apps, it normalizes all sources through a single pipeline, enriches them with OCR, transcription, and auto-tagging, then stores everything as human-readable files plus a queryable SQLite index. Your AI agents read the same local store your notes live in. ...

Local AI coding privacy means running your AI coding assistant entirely on your own hardware — no source code, no prompts, and no context ever leaving your machine. In 2026, with GitHub Copilot changing its training data policy and the EU AI Act entering full enforcement in August, local inference has crossed from niche experiment to production necessity for many developers and teams. Why Your AI Coding Tool Is Leaking Your Code in 2026 Your AI coding assistant is almost certainly sending your source code to a remote server right now. In April 2026, GitHub Copilot updated its policy to train on Free, Pro, and Pro+ user interaction data by default — you must explicitly opt out to stop it. This isn’t an edge case: over 60% of Fortune 500 companies have deployed AI coding assistants, yet 38% have already experienced security incidents related to these tools (Kusari, 2026). The threat model is more complex than most developers realize, and the stakes have never been higher. ...

Tabby AI delivers 85–90% of GitHub Copilot’s completion quality with complete data sovereignty — no telemetry, no cloud routing, no vendor access to your code. For teams of 25+ developers, the hardware investment pays for itself in under seven months compared to Copilot’s $19/seat/month pricing. What Is Tabby AI? The Self-Hosted Coding Assistant in 2026 Tabby AI is an open-source, self-hosted AI code completion server built with 92.9% Rust for performance and memory safety. Unlike plugin-only tools such as Continue.dev or Cline — which rely on external Ollama instances or commercial APIs — Tabby ships its own inference server, multi-user management dashboard, SSO integration, and repository context indexing out of the box. Released under the Apache 2.0 license, it runs entirely on your infrastructure: on-premise hardware, your own cloud VMs, or air-gapped environments with zero outbound internet required after initial model download. ...

대부분의 llama-stack vs Ollama vs vLLM 비교 글은 핵심을 놓칩니다. 이 세 가지 도구는 서로 경쟁하는 게 아닙니다. llama-stack은 오케스트레이션 API 레이어이고, Ollama와 vLLM은 추론 엔진입니다. 올바른 질문은 “무엇을 선택할까?“가 아니라 “어떻게 조합할까?“입니다. 2026년 권장 스택은 셋 모두를 사용합니다. What Is Each Tool? (Clearing Up the Confusion) llama-stack, Ollama, vLLM은 로컬 LLM 생태계에서 각각 다른 레이어를 담당하는 도구입니다. llama-stack은 Meta가 2026년 4월 8일에 릴리스한 OpenAI 호환 API 서버로, Ollama·vLLM·Fireworks 같은 여러 추론 제공자를 플러그인 방식으로 연결하는 오케스트레이션 레이어입니다. Ollama는 개발자 로컬 환경에 최적화된 추론 엔진으로, 한 줄 명령어(ollama run llama4)로 모델을 실행할 수 있습니다. vLLM은 PagedAttention 알고리즘을 기반으로 한 프로덕션 급 추론 엔진으로, GPU 서버 배포에 최적화되어 있습니다. ...

Qwen 3.5 Coder is Alibaba’s latest open-weight code generation model family, spanning 0.5B to 72B parameters, and it is the first open-source coding model to come within 3-5% of GPT-5 on production benchmarks while carrying an Apache 2.0 license. For engineering teams burning $5–30 per million tokens on frontier API calls, that gap is closing fast enough to demand a hard look at the numbers. Qwen 3.5 Coder 2026: The Open-Weight Model Closing the Gap on GPT-5 Open-source AI coding model adoption grew 140% in 2025, reaching 2.3 million developers worldwide, and Qwen models alone accumulated 4.7 million downloads from Hugging Face in Q1 2026. That level of adoption is not driven by enthusiasm — it is driven by benchmark results that are forcing enterprises to reassess proprietary API spend. The Qwen 3.5 Coder 72B scores 61.8% on LiveCodeBench 2026, compared to GPT-5’s 64.2%, a gap that narrows further on domain-specific tasks like web development and data science pipelines. Alibaba’s release strategy is deliberate: the full model family ships under Apache 2.0 with no per-user fees, no usage caps, and no vendor lock-in. The architecture builds on Qwen2.5-Coder’s proven transformer base, adding deeper code understanding through expanded training on GitHub repositories, competitive programming datasets, and documentation corpora across 90+ languages. For most engineering teams, the choice between Qwen 3.5 and GPT-5 is no longer a quality question — it is a cost and control question, and Qwen is winning on both dimensions for a growing share of workloads. ...