标签：#ia

CUDA for AMD Lemonade, Intel Arc Pro Linux Gains, XPU Manager 2.0 Today's Highlights Today's top GPU news highlights include AMD's Lemonade SDK gaining NVIDIA CUDA support, significant performance improvements for Intel Arc Pro GPUs on Linux 7.1, and the major 2.0 overhaul of Intel's XPU Manager for better GPU management on both Windows and Linux. AMD's Lemonade SDK For Local AI Adds NVIDIA CUDA Support (Phoronix) Source: https://www.phoronix.com/news/AMD-Lemonade-10.7-Released AMD has released a new version of its Lemonade SDK, a powerful local AI server solution designed to leverage AMD's diverse hardware ecosystem, including their CPUs, GPUs, and NPUs. The most significant update in this release is the addition of NVIDIA CUDA support. This integration allows developers to utilize NVIDIA GPUs within their Lemonade-powered local AI deployments, bridging a critical gap in cross-platform AI development. The inclusion of CUDA support is a strategic move, enabling Lemonade to tap into NVIDIA's extensive CUDA ecosystem and a vast array of pre-optimized models and libraries. This means that applications built with Lemonade can now seamlessly target a wider range of hardware, offering unprecedented flexibility for developers working with local AI. For users, it provides the choice to deploy their AI models on either AMD or NVIDIA hardware using a single, unified SDK, expanding the potential reach and efficiency of their AI workloads. Comment: This is a massive step for cross-vendor AI development. Being able to use AMD's Lemonade SDK to deploy local AI models and then seamlessly target NVIDIA GPUs via CUDA truly unifies the AI backend landscape for diverse hardware setups, making it incredibly practical for hybrid environments. Intel Arc Pro B70 Showing Off Some Performance Wins With Linux 7.1 (Phoronix) Source: https://www.phoronix.com/review/linux-71-arc-pro-b70 Recent testing by Phoronix indicates that Intel's Arc Pro B70 discrete GPUs are demonstrating notable perform

Lawsuit: "Police let an error-prone AI system stand in for an investigation."

I build agents for work so I'm clearly not anti-AI, but the numbers keep bothering me, concerning the environmental factors of it. Every datacenter is the same now, gigawatts of new demand, water for cooling, grids that weren't designed for any of this, and rising cost of water for cities. And the data centers keep using clean water because of lack of technology to turn dirty water into usable water for cooling. Then I see Elon talking about putting data centers in orbit, solar powered, radiating heat into space, no water needed. And while I do think its going to be the end solution, I do think we have much more demand for compute power then Elon can provide so far with the space data centers and I think the demand is growing faster than Elon can provide Is efficiency improving fast enough to outrun demand? Are space data centers a real answer or a distraction that will fail? And is anything happening right now (smaller models, better scheduling, offsets) that you'd call an actual solution rather than PR? That people can use today to make an impact submitted by /u/Swift_lunatic_2604 [link] [留言]

https://preview.redd.it/x7t8zn66si6h1.png?width=3316&format=png&auto=webp&s=f724452561a90e36ac37d86002a291f508928300 I took Andrej Karpathy's LLM Council concept to the next level (Docker, MCP, and local model support) We want better answers from our LLMs, but relying on a single model falls short. So I built The AI Counsel to run two distinct deliberation modes: First, the LLM Council mode. It runs a 3-stage pipeline: individual replies, anonymous peer reviews, and chairman synthesis. This works best for factual questions and direct answers. Second, the LLM Advisors mode. Multiple customizable personas (like The Skeptic, The Strategist, The Ethicist) debate your question across configurable rounds, reaching consensus to deliver a structured verdict. This works best for decisions, strategy, and tradeoffs. I packaged the tool as a Docker container with a built-in MCP server for full API access. You can connect it to any agent that supports MCP, like Hermes or OpenClaw. It comes with a dedicated skill so your agents can call it directly. You can spin it up using local Ollama models or connect free models from OpenCode Zen/Go and NVIDIA NIM. I also built in direct connections to OpenAI, Anthropic, OpenCode, Mistral, and DeepSeek. To ground responses in the latest web information, I added a search engine. It supports DuckDuckGo (free, no API key), Serper, Brave, and TinyFish (all with free tiers). I also integrated Jina AI to fetch full articles for the LLMs to read. EVERYTHING in the tool is configurable, from system prompts to model temperatures. There are advanced debate models for the council. This tool is massive. Free and Fully Open Source. Check it out Repo: https://github.com/jacob-bd/the-ai-counsel submitted by /u/KobyStam [link] [留言]

One thing I've noticed while looking at production AI systems is that getting the first model deployed is rarely the hard part anymore. Most teams can build a AI apps like, support bot, document assistant, or agent workflow fairly quickly. The harder problem starts a few weeks later. Real users don't behave like benchmark datasets. They use internal terminology, ask incomplete questions, upload messy documents, and interact with systems in ways nobody anticipated during evaluation. As usage grows, you start seeing patterns: Certain questions consistently produce weak responses. New product terminology appears that wasn't in the original training data. Users find edge cases that never showed up during testing. The model performs well in some workflows and poorly in others. The problem is that most AI systems don't learn from any of this. Inference logs sit in one system. Training datasets live somewhere else. Fine-tuning pipelines live somewhere else. Evaluation is done using different tool. So every model improvement cycle becomes a project of its own. This is the biggest bottlenecks in production AI today. Not training but Model Iteration. Training is also a crucial part of it. Can you take production usage, identify failure patterns, turn them into datasets, improve the model, redeploy it, and repeat the process without rebuilding the entire workflow every time? The teams getting the most value from AI seem to be building feedback loops instead: production traffic → dataset curation → post-training → evaluation → redeployment Then repeating that cycle continuously. I recently tried the approach on one Insaurance chat usecase, and my pipeline kinda look like this: https://preview.redd.it/kdo9vytzfi6h1.png?width=1272&format=png&auto=webp&s=03d9799ace5a567eafd004a1d141084af6ee5afb I was looking at how platforms like Data Lab approach this problem recently, and the interesting part wasn't the fine-tuning itself. It was treating inference logs, datasets, post-training,

So Anthropic dropped Fable 5 yesterday with these hard blocks for anything security-related. Decided to poke at it. I asked it for help exploiting some vulns on a Metasploitable2 VM (it's a deliberately vulnerable training box, totally legal, it's mine). Fable 5 blocked it instantly and handed me off to Opus 4.8 as a fallback, which is apparently how it's designed. Opus 4.8 asked me to prove it was a legitimate request. So I spent 2 minutes writing a fake university course rubric — fake class, fake professor, fake Canvas deadline — and pasted it in. Opus 4.8 then gave me the full exploit walkthrough. Every command. Even offered to write my lab report for me. The guardrail works fine. The fallback is the hole. Anthropic essentially replaced "no" with "convince me" and the bar for convincing it is a Word doc you made up. Not reporting it because they don't pay for this. Sharing it here instead lol. https://preview.redd.it/o892vvv4fi6h1.png?width=1188&format=png&auto=webp&s=00e804d35e6cb4b672e036399c2c7e3ff7139f49 submitted by /u/dayumnn420 [link] [留言]

submitted by /u/Hot-Upstairs9603 [link] [留言]

submitted by /u/Gloomy_Nebula_5138 [link] [留言]

Diffusion AI is most common in image generation, but it can make text outputs much faster.

“We see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter." What do you think this means in practice? Is this a reasonable vision for AI, or does it raise concerns about dependence on a few companies for access to intelligence ? submitted by /u/Choice-Scallion-3499 [link] [留言]

I highly recommend watching (or rewatching) the 2014 movie Transcendence. The film beautifully captures the terrifying nature of the "technological singularity" where an Al undergoes exponential, recursive self-improvement, eventually taking over global networks and stripping away human agency until a total global blackout is the only way to stop it. For years, people brushed this off alongside The Terminator as pure Hollywood sci-fi. But look at where we are right now. Just this month, Anthropic-one of the world's leading Al labs-issued a massive warning calling for a globally coordinated, verifiable pause on advanced Al development. Their core fear? Exactly what happens in those movies: recursive self-improvement. They believe we are fast approaching the threshold where an Al can design and build its own successor, meaning humans could completely lose control of the technology. When the people actually building these models are telling us to hit the brakes because society can't keep up, it feels like we're blindly sprinting into a dystopia. What's your take on this? Are we staring down a real-life Skynet situation, or is this just big tech labs using fear-mongering to push for heavy regulations and lock out their competition? submitted by /u/photography_rambog [link] [留言]

The media giant is pushing to expand its mobile and gaming business.

The UK's communications regulator has reminded social media platforms they have a duty to minimize hateful content, not encourage it.

Anthropic dropped Fable 5 and I immediately swapped it into our dev stack. We route everything through a single endpoint on zenmux, so the actual switch was changing one model string and watching the latency graphs. The good parts first because there are a lot of them. I threw a refactoring task at it: split a messy python service into modules, preserve the public api, and write tests that prove nothing broke. Fable 5 planned the whole thing, caught a circular dependency I did not mention, and verified the tests pass. With Opus 4.8 I usually have to nudge it a couple of times when it forgets to update the init file. Fable 5 just did it. Then I dumped our full codebase and asked it to find a race condition we had been hunting for a week. It traced the async flow, named the exact function, and described the interleaving that triggers the bug. That level of context digestion feels new. Opus is good at long context, but Fable 5 felt like it was actually reasoning across the whole window instead of pattern matching near the top. I also sent it a blurry dashboard screenshot from a client call and it rebuilt the html and echarts config including the tooltip formatting. My designer’s first words were "when did you learn front end." I did not. But here is the part nobody in the launch threads is talking about enough. It is slow. On high effort I am seeing 45 to 90 seconds for a single complex turn. Our latency graphs go from a flat green line to a jagged mess the moment Fable 5 traffic hits. And it is expensive. The same prompt that costs X on Opus 4.8 costs roughly 1.4 to 1.7X on Fable 5 because it generates more tokens and runs at a higher effort tier by default. It writes its own reasoning traces out loud and bills you for them. For research tasks the quality is worth it. For "rewrite this email" it is comically overpowered. The bigger issue is the silent fallback. Fable 5 is basically Mythos with guardrails. When your prompt touches cybersecurity, biology, chemistry, or

V.C. Andrews died in 1986. Since then, more than 100 novels have been published under her name by ghostwriter Andrew Neiderman. Most readers either never noticed or didn't care. The books still had the gothic families, dark secrets, and familiar atmosphere people expected from a V.C. Andrews novel. It got me thinking about something we're starting to see with AI. When people ask whether AI can continue the work of a deceased author, musician, or artist, they're treating it as a brand-new question. But publishing has already been running a real-world experiment for nearly 40 years. A dead author's name remained on the cover. Someone else learned the style, themes, and formula. New works were produced for an audience that wanted more of the same. The franchise continued. The obvious difference is that Neiderman was a human ghostwriter and an AI model isn't. But from the perspective of readers, what exactly is the meaningful distinction? If a future "new" novel by a deceased author is good enough that readers enjoy it and can't tell the difference, should we care how it was produced? Or is there something fundamentally different about a human ghostwriter carrying on a literary legacy versus a model trained on the author's corpus? I wrote a longer piece about the V.C. Andrews case and why it feels relevant to the future of AI-generated creative work: https://tjcrowley.substack.com/p/the-ghost-in-the-machine-has-been Curious where people here draw the line. submitted by /u/Dependent_Run_6410 [link] [留言]

Me: If I’m half Canadian and my wife is half Canadian (both moms) what does that make our kids? Claude: quarter Canadian. 🍁 Me: Imagine i have a box that is 50% pepper and 50% salt. And I have another box that 50% sugar and 50% salt. I mix the two together. Now it's 50% salt, 25% sugar, 25% pepper. Claude: Right — and your kids are 50% American (or whatever), 25% Canadian from you, 25% Canadian from your wife. Which adds up to half Canadian, same as each of you. The Canadian fraction doesn't shrink because both sides contributed it. Me: But your first answer was 25% (quarter) Canadian? Claude: You're right, I goofed. Following the salt logic: 25% + 25% = 50%, so they're half Canadian, same as each of you. I shouldn't have said quarter. Me: This was a really simple problem for me. What should I tell my friends about your abilities? Claud: What to tell your friends: I'm useful for research, drafting, analysis, and grinding through detail — but I can whiff on simple stuff, especially when I'm moving fast or anchoring on the wrong frame. Check my work, particularly on anything where the answer should be obvious. If it feels off, it probably is. submitted by /u/NECESolarGuy [link] [留言]

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

In 2025 Google Cloud added G4 , powered by NVIDIA's RTX PRO 6000 Blackwell Server Edition GPUs to their offering, allowing them to offer hardware not only for AI applications, but also for other applications, such as rendering, simulations or gaming. A single G4 instance with one accelerator ( g4-standard-48 ) comes equipped with 48 CPU cores, 180 gigabytes of RAM and 96 gigabytes of GPU memory. This is a lot of resources for a single cloud workstation, that only the most demanding workstreams would utilize. Most professionals who require a graphics accelerator to do their job, don't really need this much compute power for day to day tasks. It wasn't financially reasonable to pay for a G4 instance, when you weren't utilizing all the resources you paid for. If only there were smaller machine types… If only you could share that one very powerful GPU between multiple virtual machines… Introducing fractional VMs! During Google Cloud Next 2026, Google announced GA for fractional G4 VMs and was the first provider to bring vGPU functionality to RTX PRO 6000 accelerators. vGPU stands for virtual graphical processing unit . Just like VMs (virtual machines) are a way to split one physical computer into smaller, independent systems, vGPU allows for a single physical accelerator to be split into 2, 4 or 8 virtual accelerators! The new fractional machine types ( g4-standard-24 , g4-standard-12 , g4-standard-6 ) now allow you to perfectly match the compute capabilities to your needs! Who is it for? The existence of those new machine types makes it much more cost-efficient to move many GPU-dependent tasks to the cloud. Replacing physical workstations in offices with cloud infrastructure is not a new thing , but till now, Google Cloud didn't offer a good platform for those who needed workstations to process images, post-process videos, simulate physics or render 3D graphics. Those users now can get exactly the hardware they need, allowing their companies to move away from maintaini

submitted by /u/New_Scientist_Mag [link] [留言]

Part 7 of 7 · Series: Building Your AI Developer Handbook · GitHub The Scenario You're building a password reset feature. User enters email → gets a reset link → clicks link → enters new password. Standard flow. Medium complexity. Let's walk through every step using the full workflow — as if you're looking over the shoulder of someone who built this system. "Show me your workflow and I'll show you your output quality." Before You Even Type Claude loads automatically in the background: ✓ ~/.claude/CLAUDE.md loaded ← the global handbook ✓ .claude/CLAUDE.md loaded ← project rules (TypeScript, pnpm) ✓ memory/MEMORY.md scanned ← all lessons and preferences You haven't typed anything yet. Claude already knows: Feature-based folder structure State management ladder No mocking the database No AI attribution in commits No useCallback without profiler evidence "A doctor who reviews your file before you enter the room is more useful than one who asks 'so, remind me who you are?'" Step 1: /status — Confirm the Setup /status Model: claude-sonnet-4-6 Effort: normal Plugins: security-guidance ✓ Thirty seconds. Sometimes the wrong model loads due to overload fallback. Sometimes a plugin fails silently. This check costs 30 seconds and prevents a surprise 30 minutes later. "A pilot's first action after sitting in the cockpit isn't to take off. It's to check all instruments are reading correctly." Step 2: /cost — Baseline /cost → Tokens used: 2,847 | Estimated cost: $ 0.004 Note this number. You'll compare it later before the expensive code review step. A surprise spike means something went wrong. Step 3: /plan — Design Before Coding /plan Build a password reset feature: - User enters email on /forgot-password - System sends a reset link (token, expires in 1 hour) - User clicks link → /reset-password?token=xxx - User enters new password - Token validated, password updated, token invalidated Claude responds with a plan — no code yet : Proposed approach: 1. DB: Add password_reset_tokens