Most large language model (LLM) inference framework comparisons begin with a leaderboard. One framework posts the highest tokens per second on a standard benchmark, and that number quietly becomes the reason a team adopts it. The trouble is that the conditions that produce a clean benchmark result rarely resemble the conditions a model faces in production. Synthetic benchmarks tend to use fixed prompt lengths, steady request rates, and a single model on familiar hardware. Production traffic ...
Mark Cuban said something at the RAISE Summit that cuts through the AI hype cycle. His argument: if AI were actually "done," you wouldn't see Microsoft hiring 6,000 people. You wouldn't see Anthropic and OpenAI deploying forward-deployed engineers to enterprise clients. The fact that they need humans to implement it tells you AI is hard. He gives a concrete example. Ask Claude or ChatGPT to pull a specific search, generate a report, and email it to you weekly. It can't do i...
Kling, Veo 3.1, Sora 2, Hailuo, Seedance, the rankings change every week depending on whose demo went viral. For a solo creative shop, none of that ranking matters as much as one thing: can you get the same character or product to look consistent across ten shots. A model can nail one gorgeous four-second clip and still be useless for a real campaign. Client work isn't one shot. It's a sequence that has to hold together. The tools that actually make the cut for me aren't always the ones wi...
Read this today, meta employees suing over AI picking them for layoffs, judge basically said they can't prove it since they "weren't in the room" when it happened. It feels like the real problem with AI firing you isn't whether it's happening, it's that nobody outside the room can actually prove it either way.
Built a small tool to see how far I could push AI as a research + reasoning partner rather than a chatbot: type a company name, it pulls recent news and context, then structures a strategy analysis using the Value Stick (WTP/WTS) framework. Free, no login. I'd genuinely like feedback on where the reasoning breaks down or feels shallow — not just "cool tool" feedback. https://value-stick-wizard.lovable.app/ [link] [...
The headline numbers make Gemini 3.6 Flash look like a straightforward upgrade. Google says it uses 17% fewer output tokens than 3.5 Flash on the Artificial Analysis Index. It also reports gains on DeepSWE (49 vs. 37), MLE-Bench (63.9 vs. 49.7), OSWorld-Verified (83.0 vs. 78.4), and GDPval-AA v2 (1421 vs. 1349). The output price is also lower at $7.50 per million tokens. That is a solid aggregate story. https://preview.redd.it/ddq9bmd41qeh1.png?width=1600&format=png&auto=webp&...
3.6 Flash is the current Flash now, it replaced 3.5 this week. Now that everyone's basically on it, is anyone actually noticing it got better? On the Artificial Analysis index it scores the exact same 50 as 3.5, so the intelligence didn't really move. It's faster and a bit cheaper ($7.50/M output vs $9), which is nice for heavy agent use, but for normal chat or coding it feels like the same model with a new number on it. The price is $1.50/$7.50 when DeepSeek V4 Flash runs like $0.14/$0.28. If...
