Where the AI market is talking

Show HN: Pacific Slate: a self-hosted, model-agnostic multi-agent AI assistant

I didn't want to buy a standalone computer or repurpose a laptop to run constantly so I could maintain a system to sync my LLMs, so I built this. It's a simple overview of my system, laid out in a way easy to unpack and replicate for yourself. The project is meant to be configured individually, and uniquely, since one solution might not be what's best for another. If anything, maybe it gives you some ideas on how to implement things for your own project. Best wishes, Ryan.

FCC moves to ban Lidar-equipped foreign drones from US

That doesn't seem to address the prior comment ? > over time it allows other players (inside of that country) to fill that void .. and develop their own home grown solutions .. By eliminating competition, it forces Americans to subsidize second-rate manufacturers, and to pay more to get less. What incentive do local manufacturers have to improve? Also, the US only has a fraction of world output; they can't replicate everything everyone else does.

Ask HN: Who wants to be hired? (August 2026)

Remote: Yes (UTC+1) Willing to relocate: Yes Technologies: TypeScript, AWS, React, React Native, Node.js, Express.js, Typescript, Next.js, Fastify, CSS, Tailwind, Vue.js, SQL, NoSQL, Supabase, MongoDB, PostgreSQL, FastAPI, LangChain, Firebase, Lambda, Auth0, Docker Portfolio: https://sx-portfolio.com Résumé/CV: https://sx-portfolio.com/resume.pdf Email: emads14@gmail.com LinkedIn: https://www.linkedin.com/in/emad-kheir/ Hi, I'm Emad Khair, a senior software engineer with 10 years of experience. I've worked for top-tier clients from all over the world ranking from freshly started startups to full scale enterprises. I'm currently based in the GMT+1 timezone and I'm looking for a fully remote position. I have a strong background in both …

The OpenAI and Hugging Face incident, explained in simple terms

Every fast write moves work somewhere else

2 angles I think DB designers don’t often think about: 1. Durability extends to the client. Replicated db might ack a write to client, but what if that ack gets lost on the way back over network? If client talks to the DB over simple HTTP, the write might first look like a failure. Can the client retry? 2. Human perception times are biological and don’t change much. But everything in the tech stack has gotten so so much faster since the 80s. Throughput matters, sure, but latency (relatively speaking), is much less of a constraint now than it was.

My server is a phone now

No worries at all, I don’t mind the question. But it could be a mix! My writing process involves bouncing ideas off LLMs about wording and sentence structure whenever I feel like I’ve reached a checkpoint in the writing process. For a long time, even though I consider myself fluent in English and primarily speak English every day, both at home and at work, I’ve still felt like I’m not quite where I’d like to be in terms of proficiency. That feeling actually kept me from writing until fairly recently, because I was always worried that whatever I put down wouldn’t come across as coherent (or maybe polished?) enough. I used to write a lot in my native language back in the day, so I think part of that hesitation came from knowing how naturally writing used to come to me and feeling like I cou…

Shopify replaced Redis with MySQL for inventory reservations–and it scaled

The LinkedIn “techbro” writings are the worst. They talk like a cryptobro “APPLE JUST PUBLISHED A CODE REPOSITORY ON THEIR HAND CRAFTED LLM ANYONE CAN USE THIS TO COMPETE WITH OPENAI AND ANTHROPIC” and its some random simple LLM on github that handles very little compared to OpenAI and Anthropic.

70% of AI revenue comes from OpenAI and Anthropic

One interpretation is that so called profitability is one 15 minute phone call between OpenAI and Anthropic to increase prices. If you think that antitrust is the reason that this won't happen, you've stated a speculation. Not a certainty.

Why Wall Street Is Ignoring Big Tech's Debt

And capitalism also works with crashes taking unprofitable companies down. And frauds, eventually, gets companies valuation like Enron and FTX to zero. And people go to jail. Now I'm not saying OpenAI or Anthropic are frauds. What I'm saying is that, eventually, things revert to what is just. The late 90s SV tech-bros behind pets.com or webvan for example faked it for 18 months to 36 months or so. At some point when the expenses outnumber the revenues, capitalism does its thing.

Why Wall Street Is Ignoring Big Tech's Debt

anthropic and openai are a part of the ai economy, but not the only. the most valuable company in the world is nvidia, a massive player that benefits from the existance of the ow models. what im trying to say is that we focus a bit too much on the labs, but the biggest part is in the infra that makes everything possible.

What Happened: OpenAI and Hugging Face

Why Wall Street Is Ignoring Big Tech's Debt

You will spend thousands of dollars on hardware to run those at lower quality (quantized) than the benchmarks where they match March SOTA performance. Source: I have the hardware to run those locally. I would never recommend it to anyone trying to save money. It’s so much cheaper to pay even Anthropic or OpenAI.

Why Wall Street Is Ignoring Big Tech's Debt

I spent $18k in credits last month, I have 3x 20x Anthropic accounts (for Fable to bypass limits) which runs me another $600ish p/m and then a chatgpt pro account another $200 p/m. The value I got out of it is easily 1-200x what I paid.

Show HN: DeepSeek-V4 Latent Reasoning – moving "thinking" into latent space

LLMs already think in latent space. The generated reasoning tokens are only the surface of what's happening internally. An LLM may write one thing in the traces but decide differently in the latent space. The whole token-based "reasoning" thing was just a clever hack to extend the existing architecture without completely redoing it. In one of Anthropic's recent papers, they added an additional subnetwork trained to map internal states to readable text, so that's probably the vector of further development.

Why Wall Street Is Ignoring Big Tech's Debt

You can now, as of this week, run locally models with the same performance of a SOTA model of March this year: https://news.ycombinator.com/item?id=49214008 https://news.ycombinator.com/item?id=49229621 The FAFO day of Anthropic and OpenAI arrived.

Why Wall Street Is Ignoring Big Tech's Debt

But the question is how much are people willing to pay for AI. I use AI every day at work, but I only pay $20. That's the most I will ever want to pay. And, so far, it gives me everything I need. If OpenAI or Anthropic suddenly said "Sorry, the game is up. You'll have to pay $100/month now", I would 100% look into cheaper Chinese solutions. I suspect the AI subscription (or API) economy is whale economy. You have a small, small percentage of users that are happy to pay whatever it takes - while the vast majority will either use the free tier, and then the next group will pay for the cheapest or next cheapest subscription. EDIT: And I'll echo what another user wrote here. The VAST majority of office users around the world don't work for tech companies flush in cash. Even adding someth…

70% of AI revenue comes from OpenAI and Anthropic

>> When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing. All that is discussed in the video, plus those distinctions. And most important, that AI revenue would not exist...if OpenAI and Anthropic would not be funded, by the same Amazon, Google and Microsoft they are buying it from! We now have several voices saying the same: "Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981 "Why Wall Street Is Ignoring Big Tech's Debt" - https://news.ycombinator.com/item?id=49230630

70% of AI revenue comes from OpenAI and Anthropic

Ai revenue or datacenter/compute revenue? There's a lot of circular financing right now and there's a pretty obvious bubble for sure but I haven't been able to figure out what exactly this guy's argument is after seeing it a few times recently. Like yeah most ai datacenter spend is those two companies - that's pretty standard monopoly (or monopsony for the cloud providers) dynamics. If you're saying some major percentage of all ai related spending is openai and anthropic spending money on compute - well that's not exactly right because Google etc are also spending money on building datacenters (that's not ai spend in this definition? WTF is ai revenue exactly?) - and that's just pretty much indicating it's a frothy market with two unprofitable companies at the center with huge cogs? …

70% of AI revenue comes from OpenAI and Anthropic

Ai revenue or datacenter/compute revenue? There's a lot of circular financing right now and there's a pretty obvious bubble for sure but I haven't been able to figure out what exactly this guy's argument is after seeing it a few times recently. Like yeah most ai datacenter spend is those two companies - that's pretty standard monopoly (or monopsony for the cloud providers) dynamics. If you're saying some major percentage of all ai related spending is openai and anthropic spending money on compute - well that's not exactly right because Google etc are also spending money on building datacenters (that's not ai spend in this definition? WTF is ai revenue exactly?) - and that's just pretty much indicating it's a frothy market with two unprofitable companies at the center with huge cogs? …

Improving GPT‑5.6 Sol in ChatGPT, expanding GPT‑5.6 Luna access for free users

It's funny because > Take away the huge R&D and they become hugely profitable with a robust ad business like Google Search. Is refuted by the sentence that comes immediately before : > They have a billion weekly active users, almost all free (Not Google search free. Free free) with about ~50M subscriptions. Of course they're not profitable even without R&D. Low cost isn't no cost. Please explain why have OpenAI been actively not trying to “hugely profitable with a robust ads business” for the past 3 and a half year then? Or at least make a profit that can cover some of there R&D expenses, instead of losing money from their operations? > Just admit you're wrong about inference costs and move on. Unfortunately I'm not. And again, none of your links above says otherwise.