... DungeonQ is a defensive deception runtime for suspicious AI-agent sessions. A trusted gateway routes a designated session into a persistent synthetic world, with useful world-only Wrong Tickets, observable activity and separately approved bounded changes. The goal is to give people and authorized defender agents room to act. v0.16 addresses a concrete lesson: protecting one route is not enough. In the earlier Sol experiment, all three diverted sessions received the artificial target through an open alternate

... output mode, so I would not treat it as only a video generator. Asset-first applications: our Mugen3D work starts with a reusable 3D representation, then adds rendering, animation, dialogue and interaction. This shifts work toward asset quality and runtime engineering, including memory, compression and integration. There is no universal winner here. Single-image 3D still infers unseen surfaces; small text, reflective materials and complex geometry need review. Reusable assets do not remove rendering or animation costs, and this ... ... benchmark comparison with Atlas or Genie 3. For builders shipping interactive products: which requirement would make you choose persistent assets over live generation? Is it editing individual objects, export and engine integration, camera freedom, predictable runtime

... five tool categories and retrieved the same note they had saved. The artificial target remained unchanged. Separate scripted checks show a useful world-only Wrong Ticket being rejected by the origin, and a saved note surviving an actual runtime restart. The operator retains observations; any bounded adaptation still needs separate authority. This is the mechanism behind DungeonQ's defensive deception runtime

... DungeonQ now includes a small MCP client you can copy into another Node project. It uses the official MCP SDK and Node built-ins, connects through the public MCP interface, and imports no DungeonQ runtime code. The example follows a concrete task: read an artificial order and its review policy, save a multiline review, read it back, then issue and use a one-use world ticket. The ticket works inside its synthetic world. Shipping stays pending. The client receives only ... ... explicitly apply a finite adaptation policy through a separate interface. A "continue" message simply resumes the client; it cannot approve that policy or give the client operator authority. This is a reusable integration example for DungeonQ's defensive deception runtime

... everyone, I'm Roland, a solo founder building PretzelGraph, an open source graph runtime for agents. Most workflow and agent tools either hide the agent loop inside a node you cant open, or have you write it in code. In PretzelGraph the agent loop is the graph: you build it on a canvas, watch every cycle run, and reuse the whole agent as a single node in other workflows. Because the loop is open, you can change how your agent ...

... increased significantly, throughput has degraded, or the stream has become unstable. From the application's perspective the call succeeded, but operationally the execution may already be unhealthy. The approach I ended up taking with WAIL was to establish runtime baselines, evaluate degradation while execution is happening, and separate observation from control. Depending on the situation, WAIL can observe, retry, or reroute, while recording signed evidence of the decision and outcome. It runs alongside the provider client rather than requiring ... ... gateway. I'm curious how other teams running AI systems in production draw this line: When a model call succeeds technically but deviates significantly from its normal runtime

... nightmare: Executing LLM-generated code directly on your local machine or primary backend opens you up to prompt injections, unintended file changes, and server exploits. Dependency & build friction: Package installations (npm install, pip install), multi-file builds, and runtime setup take time and clutter your local or production environment. DevOps overhead: An AI shouldn't have to manage complex Dockerfiles, YAML manifests, or manual cloud setups just to run a test script or spin up a preview. What ... ... engineering teams isolated, automated environments to write, test, and deploy code safely. Model Context Protocol (MCP) Integration: Connect your AI client directly to scoped tools so the agent can inspect project files, run builds, open preview environments, and read runtime

... third brand entirely, and the canonical tag pointed at a localhost address on port 3000. The byte count is what actually cracked it. Those two sites have different databases and completely different products. Any page genuinely generated at runtime would have come out at a different size. Identical byte counts meant that response never touched a database. It was a copy of something decided at build time, before either site was provisioned. The cause was in the build ... ... runtime

... build AI agents on edge runtimes like Cloudflare Workers, you ve likely encountered this frustrating issue: code works perfectly locally, but breaks sporadically once deployed globally. These intermittent production bugs are rarely caused by your LLM or prompts. They come from edge specific constraints: cold start timeouts, distributed state sync, cache misconfiguration and unexpected traffic spikes, which local dev environments cannot simulate. As an indie developer, I got tired of debugging these avoidable issues after users hit my service ... ... added the Edge Check module directly into my website. It is a simple structured pre launch audit. It walks you through runtime

... Hunters! For the past few months, I've been deep in the trenches building Aegisora a zero-trust runtime proxy for AI agents. The biggest challenge I noticed in the current landscape: traditional security gateways break down when dealing with non-deterministic AI behavior. If you hard-block an LLM's tool call, the entire execution loop usually crashes. To solve this, I built what I call an "Escalation Boundary." It dynamically intercepts a risky tool execution and holds ... ... open-sourcing the core engine on GitHub this Friday, August 21st! Before the launch, I d love to hear from the makers here: For those of you building Agentic AI apps, how are you currently handling tool permissions and runtime

... enterprise AI and LLM adoption scales, I noticed a huge bottleneck: adding robust security layers almost always introduces crippling latency. For autonomous agents and real-time AI, that s a dealbreaker. So, I built Aegisora a zero-latency runtime security proxy designed specifically for AI systems. It provides real-time threat detection and governance without slowing down performance. We are officially open-sourcing the core architecture on GitHub on August 21st. Before the launch, I d love to hear your ... ... thoughts: ---> What are the biggest security bottlenecks or latency issues you face when deploying AI models? ---> How are you currently managing runtime

... direct API access, a terrifying reality sets in: debugging non-deterministic behavior. When an agent hallucinates a tool call parameter, enters an infinite reasoning loop, or gets subtly manipulated by an indirect prompt injection, tracing why it happened at runtime is a nightmare. Traditional logs just show a wall of async LLM requests. While building Aegisora , we realized that observability and security for agents can't be treated like traditional web apps you need real-time runtime

... wrote up how we approached the gap between local browser automation and production workloads. The post covers the core pieces of a browser use cloud runtime: hosted Chromium instances, CDP support, Playwright compatibility, persistent profiles, and live debugging. For makers building AI agents that interact with web pages, the main pain point is usually reliability and observability. A cloud runtime

... move rapidly from experimental chat interfaces to fully autonomous agents with direct API access, runtime security is becoming a massive blind spot. While building Aegisora (our open-source, zero-latency runtime

... autonomous AI agents move from experimental chat interfaces into deep production workflows, they are being granted direct access to critical enterprise APIs and execution environments. While the industry is heavily focused on model accuracy and latency optimization, I believe runtime security remains a massive architectural blind spot. Traditional API gateways and static firewalls fall short when dealing with semantic prompt injections hidden inside natural language payloads often leading to unauthorized API executions or silent PII leaks. For the engineers ... ... Context Isolation: How do you enforce strict Zero-Trust boundaries when multi-agent systems pass context across distributed microservices? Let's talk system design and infrastructure. Looking forward to hearing how your teams are hardening production agents against runtime

... compared open-source browser agents, hosted APIs, and remote browser runtimes for AI workloads. The goal was to see which approaches are actually ready for production, not just demos. Key findings: open-source agents are flexible but often fragile in real-world automation. Hosted APIs are convenient but can lock you into specific models. Remote browser runtimes

... there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime

... spent weeks building out an edge-first stack entirely on Cloudflare Workers. Combining user authentication, payment event handling and transactional emails at the edge comes with lots of hidden runtime limitations. Some obstacles I worked through: OAuth provider compatibility under edge runtime

... deployment complete? to: Is the system producing the right outcomes in production? Then came DevSecOps. Security could not remain a final review before launch. Model access, prompt injection, sensitive data exposure, third-party dependencies, secrets, permissions, and runtime controls all had to become part of the delivery process. Now I think the next phase is emerging: AI delivery operations. That could include: Continuous model and prompt evaluations Policy-as-code Progressive model rollouts Runtime

... NNAPI was deprecated in Android 15, and vendor NPUs (Qualcomm Hexagon, MediaTek APU) are only now becoming reachable through LiteRT's new accelerators and not on older mid-range silicon at all. So I benchmarked NCNN, LiteRT and ONNX Runtime on a Galaxy A33 (Exynos 1280), which is roughly the median device in my actual market: - Best config: NCNN fp16 4.8 FPS cold, 4.0 sustained - Thermal derating after 20 min: 1.2 - GPU (Mali-G68 via Vulkan) was only 12% faster ... ... than CPU - Dropping to 416 416 gets ~9.7 FPS but costs 42% of small-object AP and motorcycles, the class I most need, lose the most The honest conclusion: the gap isn't the runtime

... rule is that the workbook is the source of truth. The harness reads it directly. Generated wrapper files tend to drift and become another thing to maintain. ### Why Headed Remote Browsers Matter The most important architectural decision was browser runtime. Headless browsers are useful. They are fast, cheap, and easy to run in CI. But they are not always faithful to real UI behavior. Some issues only appear in a headed browser: - focus and keyboard edge cases - OAuth or identity ... ... worker. For UI testing, a remote headed browser is not just a convenience. It changes the debugging loop. Instead of asking "what did the script think happened?", you can ask "what did the user actually see?" ### Harness Code vs

... high-privilege API keys, but their reasoning can be hijacked by untrusted data (like a Jira ticket or an email). The real damage doesn't happen at the prompt layer; it happens at execution. So I built Relay a runtime security proxy that sits between the agent's decision and the actual tool execution. How it works: Deterministic Policy Gate: Instead of an LLM guessing if an action is safe, Relay does an O(1) hash lookup on the tool ... ... their AppSec/CISO review. It's free to test right now. Would love for you to tear it apart and give me feedback on the architecture. Live app & docs: https://relay-security-lemon.ver... What do you guys think about the "deterministic vs

... local server, launch layer, Desktop UI, Web Hub, CLI, and TUI are now separate pieces that can run and evolve independently so VibeAround can meet you from whatever surface you actually work in. Highlights Standalone server the VibeAround runtime now runs as a vibearound-server binary without the Desktop UI, still serving the Web Hub, local API bridge, MCP endpoints, previews, pairing, and sessions. Desktop is now one client of the runtime

... side projects off your SSD instead of keeping 30 GB on disk because "setting it back up later is annoying." The problem I was solving I keep old repos around for bad reasons. Source is small; dependencies and Docker runtime aren't . A docker-compose folder might be 200 MB of YAML and 8 GB of Postgres + custom images. Zipping the repo never felt like a real archive. Docker project hibernation (1.6.1) For projects with a Docker Compose file, hibernation ... ... captures runtime

... workflow history. 4. Policy Engines & Access Controls Not every user, agent, or workflow should have the same permissions. This layer defines what is allowed, who can approve actions, and which safeguards apply to different use cases. 5. Runtime Governance This category feels increasingly important as AI agents become operational. Policy checks, approval workflows, human oversight, decision records, and governance controls need to exist while the workflow is running, not only after it's complete. This is the area ... ... category, especially once training, feedback, and production usage start influencing each other. Without knowing where signals came from, governance gets a bit blurry. Comment from Dev Grover(@grover___dev): Great question @bernard_lewis  , I d probably put runtime