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Testing communication with an NPC to get a voice response, tutorial by Marvelmaster #b3d #greasepencil #UnrealEngine #LiveLink #Face2DLive #LipSync #runtime #Unreal2D #paper2D #flipbook #paperZD #WIP

2 Kommentare

Profilbild von Slav
Slavvor 1 Jahr

Tooker limping walk 😅 Time to learn animations for flip books and paperZD blueprints. #b3d #greasepencil #UnrealEngine #Unreal2D #paper2D #flipbook #paperZD #WIP

Profilbild von Sketchy
Sketchyvor 1 Jahr

@marvelmaster87 Well done 👏🏻 looking good

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this is f*cking beyond comprehension. Google engineers just shipped the entire agent lifecycle in one release: build, scale, govern. and every piece answers a specific way agents die in production > context layers (Static, Turn, User, Cache): you decide what the model carries between turns, so token spend stops being a mystery > a self heal plugin: the agent notices a tool call failed and retries it a different way instead of dying mid run > adk deploy: one command from your laptop to the managed runtime, no packaging, no infra ticket > Go joins Python and Java, with its own A2A SDK then the part nobody builds for themselves: > a dashboard on token consumption, latency, error rates and tool calls: the four things that actually kill an agent > a traces tab that opens the real sequence of actions the agent took, step by step > a playground wired to the deployed agent, past sessions included, so debugging is not a redeploy loop > an Evaluation Layer with a User Simulator, because you cannot unit test a non deterministic system and the part that decides whether it ever ships: > agents get native identities as first class IAM principals: least privilege applies to them like it does to people > Model Armor screens prompt injection, tool calls and responses, inline for Gemini or over REST > Security Command Center inventories every agentic asset and flags data exfiltration by an agent ADK is already at 7 million downloads. the runtime has a free tier, and express mode runs off a Gmail address. the prototype was never the hard part.

NO1ennn

24,801 Aufrufe • vor 1 Monat

My favorite AI workflow lately is my thought-to-post pipeline. I just go on walks, have a good content idea, ramble it, and have an optimized post in my writing style without typing. It's super simple: 1. Download an AI-powered voice dictation app to your phone (I use Wispr Flow) 2. Go on long walks and let ideas flow - when you get a good one, open Wispr Flow and ramble your thoughts (doesn't need to be perfect) 3. Notes auto-save. These become the core ideas for posts later 4. Open Claude and create a new Project called "Post generator" 5. Use this prompt: "I’m going to provide you with my own written material, and your task will be to understand and mimic its style. You'll start this exercise by saying "BEGIN.” After, I'll present an example text, to which you'll respond, "CONTINUE". The process will continue similarly with another piece of writing and then with further examples. I'll give you unlimited examples. Your response will only be "CONTINUE.” You're only permitted to change your response when I tell you "FINISHED". After this, you'll explore and understand the tone, style, and characteristics of my writing based on the samples I've given. Finally, I'll prompt you to craft a new piece of writing on a specified topic, emulating my distinctive writing style" 6. Now's the fun part: Go to Twitter Analytics and download your top posts (Premium → Analytics → Content → Download button) 7. Paste your best-performing tweets into Claude repeatedly until it says "FINISHED" 8. Take your voice notes, paste them into your trained Claude Project, prompt "make a post in my writing style" 9. Post is ready to go. Polish and edit slightly *if* needed. The AI is trained on how you actually write, not generic content. Your voice notes capture your real, raw thoughts without the friction of typing. I have my best ideas while walking. If I try to write them in my notes app mid-walk, I forget halfway through. Voice dictation captures everything as I ramble. Game changer for turning scattered thoughts into polished posts!

Rowan Cheung

129,419 Aufrufe • vor 1 Jahr

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 Aufrufe • vor 2 Monaten