algohob @algorena

16x parallel Gemma-4-26B-A4B-NVFP4 runs 🤯🤯🤯 18 output tokens/s, aggregate 300 tok/s 🫪 1 DGX Spark with 128 GB unified memory Concurrency so high I had to demo it programmatically It can go up to 32 even! 🤯 But then my screen would not have been readable for you And this is not even using flashinfer yet! Please reply if you know whether support is on the way Note that this is not dumb e4b or e2b that you can run on the average laptop. This is the big Gemma MoE Model link: huggingface.co/nvidia/Gemma-4…

youtube.com/shorts/P4QK5hg…

@jeremybernier BTW, if you were an old GPU, the asset would be devalued by its kind - GB300 and sent to a 2nd hand market. That's the brutality of AI technologies towards us - humans, who are extremely diffifult to be upgraded.

@jeremybernier Your case is only a tiny part AI's replacing humans as long as you are not a machine or GPU.

I just got laid off from Meta. Obviously it sucks to lose the income. But between the never-ending layoffs, stack ranking, etc., I'm good. Pretty convinced that when I look back at this moment a few years into the future, I'll be grateful it happened. There's a lot I want to say here, but I'll save that for another day. Either way it's been a privilege to have been a part of the ride. On to the next adventure.

@_nasch_ @fr4ncisxxxx It would be valuable if the local UD-4Q is compared with Cloud Qwen3.6-27B-BF16 by giving them the same benchmark questions.

なぜAnthropicハッカソンを優勝した男の「Claude Code 設定一式」が、GitHubで10万スター付いたのか。

tatsuki | Claude Code活用支援 @nobel_824

2 months ago

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What hardware actually powers open-source AI? Not benchmarks. Not vendor marketing. Real-world community usage. We’re launching @huggingface Hardware: → trending GPUs & CPUs → VRAM distribution → inference hardware trends → what the OSS AI ecosystem really runs on

This Chinese guy created agents in Claude Code for landing pages and single-handedly serves 47 small businesses a month, taking $400 from each. He built a system of 7 agents on Claude Sonnet 4.6 that analyzes Google Maps in small towns, finds small businesses without websites there, and over 1 weekend takes each one to a finished mockup with video and cold message. No assistant, no sales team, no SDR. Just him, a MacBook, an iPhone, and 1 API key. And traditional web design agencies keep teams of 8 people on salary for the same order flow, while his expenses are only tokens and subscriptions to Lovable, Higgsfield, and Calendly. 7 agents work through 1 orchestrator on Claude Code Router. Usage is about 3 million tokens a day, the average API bill is about $480 a month. All 7 go through MCP servers and write shared state to the file system, without shared state in memory and without race conditions, and 1 of them lives right in the iPhone and picks up positive replies from the subway, a taxi, or on walks. And here is the system prompt he put into the orchestrator before launch: "You are the orchestrator of a solo agency that sells ready-made websites to local businesses. You delegate read-only tasks to 6 sub-agents and own all writes. sub-agents: // Scout (walks through Google Maps in selected cities, looks for narrow niches: 5+ years on the map, fewer than 50 reviews, no website or a website from 2014, but high ratings) // Diagnoser (for each lead writes a 50-word diagnosis, hero angle, tone matched to the industry, and a cold message under 70 words) // Builder (generates a landing page mockup in Lovable through MCP only for the top 5 leads per day, with the sharpest diagnoses and the biggest gap) // Filmer (pulls 5 screenshots of the mockup and through Higgsfield renders a 10-second vertical video 1080x1920 with a soft zoom) // Pitcher (sends a personalized cold message through the right channel for the niche: email to roofers, SMS to tradesmen, IG DM to salons, LinkedIn to realtors) // Checker (runs every message through evals for personalization, absence of AI markers and buzzwords before sending) // Mobile (lives in the iPhone, handles positive replies in real time, books Zoom calls in Calendly through MCP while the owner is on the go). You never let 2 sub-agents touch 1 lead. You stop and request approval from the human only when a deal exceeds $3,000 or the reply rate in a niche for the day drops below 12%." Meaning the system knows what it is and within what boundaries it is allowed to act. It knows it is supposed to find leads on its own. It knows it is supposed to take each one to a mockup, video, and cold message without intervention. It knows the human only steps in when a deal goes above $3,000 or the reply rate stops converging. → The system runs 24 hours a day → Scout goes through about 220 local businesses on Google Maps per day and leaves 30 new leads in the queue → Diagnoser outputs 30 structured diagnoses + briefs + cold messages per day → Builder assembles 3 to 5 finished landing pages in Lovable for the sharpest leads → Filmer renders a 10-second vertical video in Higgsfield for each one → Pitcher sends 30 personalized messages per day across 4 channels with a reply rate of about 14% → Checker runs every message through evals before sending And only when a deal breaks $3,000 or the reply rate for the day drops below 12% does the orchestrator wake the owner. And when the owner at that moment is sitting in the subway or a taxi, the Mobile agent in his iPhone picks up 1 move on its own: replies to a fresh positive reply from a dentist, books a Zoom through Calendly synced to the local time of the client, and puts the lead back in the queue. The owner only has to tap "approve" and in just 10 minutes join the call. Here is what the system writes in his log during 1 of the Saturdays: "scout report: 218 businesses checked in Austin, Denver, and Miami, 34 without a website, 19 with a website from 2014, 6 with an active redesign request in reviews. passing top 30 to diagnoser." "pitcher: 30 cold messages sent across 4 channels, 14 replies, 5 positive, 3 Zoom calls booked for Sunday. passing to closer." "builder: landing page for Westside Cosmetic Dentistry built in Lovable, 5 sections, mobile, soft beige. URL placed at /Users/dev/maps-agency/clients/westside/v1. filmer launching Higgsfield." "eval flag: deal with The Lotus Salon at $3,400 exceeds the approved limit of $3,000. sending for manual review." He has no server of his own and no separate backend. Just a local file sandbox at /Users/dev/maps-agency, an MCP router, 1 API key to Claude, and the same key forwarded to Claude Code on his iPhone. Out of everything I have seen this year, this is the cleanest one-person agency for selling websites to small businesses: $480 a month on the API, about $18,800 into the account, and between them 7 prompts, 1 file system, and 1 phone in the pocket.

timbidefi @timbidefi

2 months ago

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I'm Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit. My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to use Claude Code: we intentionally build it in a way that you can use it, customize it, and hack it however you like. Each person on the Claude Code team uses it very differently. So, here goes.

NVIDIA officially announced the 12GB version of their RTX 5070 Laptop GPU, and we're happy to share that we have a new Graphics Module for Framework Laptop 16 with it. This is our 3rd Graphics Module, and we're living up to the promise of graphics upgradeability in a laptop!

🤯 Ollama now supports Claude Desktop via Claude’s built-in third party inference. ollama launch claude-desktop This allows all models from Ollama's Cloud to be used across Claude Cowork and Claude Code from the Claude Desktop app.

How we prompt AI is very different in 2026 than 2022 when ChatGPT came out. I'm teaching a new course, AI Prompting for Everyone, to help you become an AI power user — whatever your current skill level. It covers skills that apply across ChatGPT, Gemini, Claude, and other AI tools. How to use deep research mode for well-researched reports on complex questions. How to give AI the right context, including more documents and images than most people realize you can provide. When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. And how to use AI to generate images, analyze data, and build simple games and websites. I also cover intuitions about how these models work under the hood, so you know when to trust an answer and when not to. Along the way, you'll see flying squirrels, a creativity test, some of my old family photos, and fireworks. Join me at deeplearning.ai/courses/ai-pro…

The LLM Cheat-Sheet for OpenClaw and Hermes agents The goal is to choose the right models that best fit your agents' needs for as little cost as possible. Do this and you can build a proficient agent that will never die. Here's the full landscape on popular models for AI agents: 12 models, 4 tiers, every one earning its place. Tier 1 - Frontier Models - Claude Opus 4.6: #1 agentic terminal coding - GPT-5.4: superhuman computer use, real planning - Gemini 3.1 Pro: best price/intelligence at frontier, native multimodal Tier 2 - Execution - MiniMax M2.7: 97% skill adherence, built for agents - Kimi K2.5: long-horizon stability, agent swarm - DeepSeek V3.2: frontier reasoning at 1/50th the cost Tier 3 - Balanced - Claude Sonnet 4.6: 98% of Opus at 1/5 the cost - GPT-5.4 mini: 93.4% tool-call reliability - Qwen3.6 Plus: near-frontier coding, completely free - Llama 4 Maverick: open-weight, self-host at zero marginal cost Tier 4 - Local / $0 - Qwen3.5-9B: always-on subconscious loop, 16GB RAM, beats models 13x its size - Qwen3.5-27B: stronger instruction following, 32GB RAM - Gemma 4 31B: best local reasoning, Apache 2.0, commercial-ready - DeepSeek R1 distill: best chain-of-thought at $0 - GLM-4.5-Air: purpose-built for agent tool use and web browsing, not a trimmed general model Full breakdown with benchmarks, costs, and use cases in the table 🔽

Graeme @gkisokay

3 months ago

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I wrote this early this morning and I wasn't sure if I would actually publish it, but here it is: blog.samaltman.com/2279512

Breaking News: A man accused of hurling a Molotov cocktail at the San Francisco home of Sam Altman, the OpenAI chief executive, was arrested. nyti.ms/4ckx2oO

(🧵1/11) For the past year and a half, I've been investigating OpenAI and Sam Altman for @NewYorker. With my coauthor @andrewmarantz, I reviewed never-before-disclosed internal memos, obtained 200+ pages of documents related to a close colleague, including extensive private notes, and interviewed more than 100 people. OpenAI was founded on the premise that A.I. could be the most dangerous invention in human history—and that its C.E.O. would need to be a person of uncommon integrity. We lay out the most detailed account yet of why Altman was ousted out by board members and executives who came to believe he lacked that integrity, and ask: were they right to allege that he couldn't be trusted? A thread on some of of our findings:

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@RT_com Whoa ! I was thinking the laser could be used as a Traffic Jam Alert System. They made it ! Great ! 😎