Translate to Simplified Chinese Language: r/LlamaFarm %% • %% • %% Suggested for you %% Follow post %% Show fewer posts like this %% Save %% Hide %% Report %% NVIDIA’s monopoly is cracking — Vulkan is ready and “Any GPU” is finally real %% Join %% Share %% I’ve been experimenting with Vulkan vis Lemonade at LlamaFarm this week, and… I think we just hit a turning point (in all fairness, it’s been around for a while, but the last time I tried it, it has a bunch of glaring holes in it). %% First, It runs everywhere! %% My M1 MacBook Pro, my Nvidia Jetson Nano, a random Linux machine that hasn’t been updated since 2022 — doesn’t matter. It just boots up and runs inference. No CUDA. No vendor lock-in. No “sorry, wrong driver version.” %% Vulkan is finally production-ready for AI. Here’s why this matters: %% Vulkan = open + cross-vendor. AMD, NVIDIA, Intel — all in. Maintained by the Khronos Group, not one company. %% NVIDIA supports it officially. RTX, GeForce, Quadro — all have Vulkan baked into production drivers. %% Compute shaders are legit. Vulkan isn’t just for graphics anymore. ML inference is fast, stable, and portable. %% Even ray tracing works. NVIDIA’s extensions are integrated directly into Vulkan now. %% So yeah — “Any GPU” finally means any GPU. A few caveats: %% Still a bit slower than raw CUDA on some NVIDIA cards (but we’re talking single-digit % differences in many cases). %% Linux support is hit-or-miss — Ubuntu’s the safest bet right now. %% Tooling is still rough in spots, but it’s getting better fast. %% After years of being told to “just use CUDA,” it’s fun to see this shift actually happening. I don’t think Vulkan will replace CUDA overnight… but this is the first real crack in the monopoly. %% Share %% r/AI_Agents %% • %% • %% Because you’ve shown interest in this community %% Join %% Follow post %% Show fewer posts like this %% Save %% Hide %% Report %% Agents vs. Workflows %% So I’ve been thinking about the definition of “AI Agent” vs. “AI Workflow” In 2023 “agent” meant “workflow”. People were chaining LLMs and doing RAG and building “cognitive architectures” that were really just DAGs. In 2024 “agent” started to mean “let the LLM decide what to do”. Give into the vibes, embrace the loop. It’s all just programs. Nowadays, some programs are squishier or loopier than other programs. What matters is when and how they run. I think the true definition of “agent” is “daemon”: a continuously running process that can respond to external triggers… What do people think? %% r/AIAssisted %% Happy Weekend! %% • %% • %% Suggested for you %% Follow post %% Show fewer posts like this %% Save %% Hide %% Report %% My Deep Research workflow %% Brainstorm with an agent first (if it’s coding then gpt-5-codex) , use pen and paper, basic web search. %% [I try to find context hanging around in my life, can be call transcript, client messages, articles, gitingest of my past work] %% 2. At the end of the brainstorm, I ask the agent to draft this into a detailed, lengthy response, documenting everything we have discussed, but framing it into research questions and being extremely critical. %% 3. I copy-paste this into all Deepresearch providers (Gemini, ChatGPT, Claude, Perplexity, Grok) %% [Saw an empirical research citing Gemini to be the best, source: https://deepresearch-bench.github.io/, does feel like it tbh.] %% [From experience, Claude Deepresearch runs the longest, 25-30 mins on long detailed queries like mine] %% 4. I vibe-select the best response, or go back to my brainstorming agent to compare the different responses. %% 5. Next is actionables, decisions, plans, tasks. %% I do this before working on a ‘big task’ , that’s worth this much effort. %% While comparing the different Deepresearches, I am looking at commonalities. %% Maybe even diversions and then try to think on these for a while. Get some tables. %% Interested to know about yours, is there anything that can be improved? %% For context: I am an AI Engineer. %% Join %% u/wecasa %% • %% Promoted %% Vote %% Share %% Share %% Hide