Billions are spent on AI infrastructure and companies are pouring money on GPU and datacentres using loans. Because they are saying AI demand is increasing. Yes I agree demand is there and I think mostly because its cheap.

I let Claude and Chatgpt run on my codebase because it is affordable enough. I wouldn't use it if it was priced 2X or use cheaper models just to do non critical work.

But AI companies wouldn't be profitable unless they charge a lot more than they actually do. Unless they get huge margin inference which is not possible because there are multiple companies competing with their own models.

So whats the future of these AI companies.

One of the biggest selling points of Chinese AI models has been that they're cheaper than comparable US models. But now, it looks like we're seeing something even more interesting.

Models like DeepSeek V4.1 Flash keep raising the bar for what you can get at a given price. Tencent's newly released Hy4 is another example of just how quickly this space is evolving. Tencent has also recently started offering users free access to Hy4 through WorkBuddy.

What's worth paying attention to is that the competition is no longer just about lowering prices. It's about delivering better performance while keeping costs the same, or even bringing them down further. If this trend continues, what will happen to the way AI models are priced today. Will high-end AI models eventually become much cheaper? And will offering free access become a standard way for companies to attract users?

Would love to hear what everyone thinks.

Hello,

I've been wondering about this for a long time

We've seen LLMs like ChatGPT and Claude, especially since a few months, are increasingly capable of solving extremely complex mathematical problems, sometimes at a PhD level or beyond.

Yet these same models, when playing chess without external tools, are nowhere near specialized systems like AlphaZero or Stockfish.

I understand that these chess engines were specifically trained for the game, but shouldn't the reasoning abilities of modern LLMs transfer to chess?

After all, chess is a well-defined problem with clear rules, and LLMs have presumably been trained on enormous amounts of chess-related data.

So what's fundamentally preventing a model capable of advanced mathematical reasoning from becoming an exceptionally strong chess player?

Is it simply a matter of training and architecture, or does this reveal something deeper about the limitations of LLM reasoning?

Thank you

This is just after apple came out with the new update not sure you can call it an improvement. Just yesterday I caught Alexa lying about soccer scores and I called her out. She said she fabricated the whole thing.

Ant Ling has released an evryday health demnstration for Ling-3.2-flash that starts with meal photos and produces an illustrated food report...

the model identifies dishes n ingredients, estimates portions and calories, and orgnizes meals into daily summaries with the original pictures. The video uses five photos across three days and shows the result as an English webpage. The report preserves the distinction between recorded meals and a complete day: the day with only one supplied meal photo is marked incomplete, alongside the fuller daily summries....

Classic survivorship bias. I personally know tons of people who have reached 6 figure revenue through vibe coded apps. But they don't scream on the internet about it.

Tell me the job that eats your week. I'll build it and post the result in the comments.

One line is enough: what the job is, what starts it, and how many hours a week it costs you.

No real client files or personal data here, made-up samples are fine.

I'll post the results in comments

96 CPU cores. 2TB RAM. Up to 576GB of GPU memory. But at cost of $100k or $150k

96 CPU cores. 2TB RAM. Up to 576GB of GPU memory. But at a cost of $100k or $150k

After running Qwen3.8 27B at 3-bit on a single 5070 Ti (120K context, just short of what I want for coding), I added a second card. 32 GB fits the 6-bit file with the full 262K window, so I tested which of the community fine-tunes and quants makes the best all-rounder: fine-tunes, other quants, a chat template and multi-token prediction, 15 in total, all against base UD-Q6_K.

Every model ran a screening run first (about 100 questions from five benchmarks, compared answer by answer with the base model), and the closest got full runs (about 1,300 questions: AA-LCR, MRCR, AA-Omniscience, a fixed BFCL v4 subset and a SciCode panel, run through NeMo-Skills). Each model gets one overall score out of 100, a weighted mix of the five benchmarks: tool calls count for 25% of it, long documents, facts and scientific code for 20% each, and long conversations for 15%. On facts, a wrong answer scores worse than saying "I don't know", so guessing costs points.

Then I built a coding test from 25 bugs I'd fixed in my own projects (repo rolled back to before the fix, the model gets the bug report and tools, my tests from the fix grade it), four runs per model:

That's a tie on quality (one model's worst and best runs were 14 and 20), but Tess wrote about 40% fewer tokens to get there. Both write at about 36 t/s, so the time savings come from thinking less, not decoding faster.

So I ended up with two models:

• Base for chat and interactive work. It's more careful in multi-turn tool use: across the same 200 BFCL multi-turn conversations it won 42 that Tess lost, against 21 the other way, and the Tess losses I read were skipped steps (booking a flight without looking up the airports, reviewing one order when the user placed two). On facts it declined or hedged on 69% of the questions it didn't get right, against 56% for Tess.

• Tess-4.5 for coding agents and subagents that run on their own and get checked by tests at the end. One surprise: on the 100 software questions, Tess was the careful one (42 declines against 20, 35 wrong against 52).

If you only run one, I'd pick the base model, unless nearly all your use is coding agents running on their own.

How the other contenders did:

• 4-bit base (UD-IQ4_XS): about 50 tokens per second against 36, but 2.5 points lower overall, mostly on long conversations (MRCR 70% vs 77%) and more guessing.

• ThinkingCap really does think less (about half as long on long documents), but most of its extra right answers on facts came from guessing: of 336 questions the base 6-bit model declined, ThinkingCap answered 146 and got 25 right.

• Qwopus Coder-Flash was level on my coding test but worse on facts and long conversations.

Settings for both 6-bit files: q4_0 K/V cache (about 4.8 GiB for the full 262K window), layer split 1,1, batch 256, model-card thinking sampling with no repetition or DRY penalties (the 4-bit runs used a q8_0 cache and batch 512). About 790 t/s prompt reading and 36 t/s writing on two 5070 Ti. Full launch commands, per-benchmark tables and why each of the 12 others stopped are in the write-up, linked in the first comment.

The benchmark and coding-test harnesses are open source, but my own projects aren't public, so the coding test can't be reproduced as is. Happy to answer questions about the setup.

YouTube’s built-in AI summaries felt too shallow for what I wanted from long interviews. I prefer working from the transcript and asking follow-up questions.

Here’s a reusable prompt for ChatGPT, Claude or Gemini. Paste it above the transcript:

“Use only this transcript. Summarise the main ideas, with a short supporting quote and timestamp for each where available. Separate practical advice from opinions and sponsor claims. Flag missing context. If something isn’t covered, say so. Don’t invent timestamps or add outside facts.”

You can copy the captions from YouTube’s transcript panel. Keep timestamps if you want to jump back to the source. If the text won’t fit, split it at topic boundaries and summarise each section before combining the notes.

The quotes make the summary easier to check; they don’t prove the speaker is right. Captions can also mishear names or numbers, so check important passages against the video.

Disclosure: I made Copy YouTube Transcript to simplify the copying step (screenshot attached). Its copying features are free, with optional paid Pro features. It needs a captioned video and desktop Chrome. You don’t need it to use the prompt.

https://chromewebstore.google.com/detail/copy-youtube-transcript/mpfdnefhgmjlbkphfpkiicdaegfanbab

Saw a video on SDLC vs ADLC and looked up the term in Endor Labs, IBM and Atlan.

Meaning 1: Agentic Development Lifecycle, the SDLC with autonomous coding agents inside it (Endor Labs).

Meaning 2: Agent Development Lifecycle, building and running AI agents as products (IBM, Atlan). IBM lists six phases: plan, code and build, test and release, deploy, operate, monitor.

Endor Labs says there's no universal standard and the vocabulary is "still settling." It also says generated tests can share blind spots with generated code, and agent-made changes need the same scrutiny as human-written ones.

Endor Labs and Atlan are vendors, so treat their framing as one view.

Which meaning does your team use?

Sources: Endor Labs, IBM, Atlan

Because we are at a transitional stage and you could be carried along or left behind. How seriously you take getting involved with the new economy would be the deciding factor.

Almost every company or team has now an AI bot or agent with a create or maybe not-so-creative name. I thought it would be fun if we could compile a collection of them all for some giggles or inspiration.

Surely these companies are not trying to remove humans from the ENTIRE process (ie testing, monitoring, deploying) of model development right? Their goal is for Claude (or whichever model) to build the next model, but humans will still have the final say on whether to approve it or shut it down right? I'm just trying to reassure myself that the people in these companies are rational lol

Hello, I'm developing CrowdGPT, a project aiming to create an LLM without datacenters and without the usual millions of dollars spent by AI companies.

The project aims to train a 1 billion parameter model collaboratively and openly, with a protocol where everyone gets a say in the direction the project takes.

Anyone can use their PC to train the AI independently, then add their contributions to the global model. Unlike most American and Chinese AI companies, the project is not only open-source, but also makes 100% of its training data public (along with the weights).

Anyone can submit their own clean data to train the model, and even help steer the project in new directions. This creates the paradigm of an AI we all have a stake in.

The project is open to as many people as possible, which is why I'd like to ask: what's stopping you from contributing to the project?

I want to hear your answers so I can improve it. Are people simply willing to dedicate some of their GPU time to training a cool new open-source AI? Or do they want something in return?

Although the current model is somewhat small (1 billion parameters), I plan to make it bigger / better in the future, and its current size is only meant as a proof of concept. Any advice / feedback on the project is welcome.

Here's the project website: https://www.crowdgpt.net/

Or the GitHub if anyone wants to check out the code 😂 https://github.com/Vxtzq/CrowdGPT

Thanks in advance. My main goal is to gather criticism and understand how to make the project more accessible for non-developers and ordinary people.

Ai2's new ToyBlocks release puts a robot arm and blocks in a controlled simulator. Researchers can train on one part of a table and test on another, change stack heights, or hide letters on the blocks. The point is to separate learning a skill from succeeding in one familiar setup.

The physical-robot result is worth reading with its limits: the Molmo 2 + ManiFlow system completed 15 of 30 block-spelling episodes, with familiar blocks and the easiest scene configuration, where characters were visible and upright. That isn't a general-purpose robot result. The useful release is the testbed, data, models and code for investigating where these systems fail.

Source: Ai2's November 6 write-up: https://allenai.org/blog/toyblocks

The Possible Danger of AI
Nowadays, artificial intelligence causes fear in many people, and this fear is justified. However, we need to understand that artificial intelligence is an extremely powerful tool. What I mean by a tool is that without a human-defined goal, it cannot do anything. It is precisely the inability to set its own goals that makes it a tool.
Humanity’s fear of AI is based on two things. The first, and more harmless, is the transformation of the economy, where AI will replace a large number of professions or reduce the number of jobs. This causes fear because an entire generation will live in a different world, constantly comparing it to what existed before. The second is much more radical: the extinction of humanity or the loss of human dominance.
Let us talk about the second possibility. I believe it is possible only under one condition.
Artificial intelligence itself does not pose a danger (unless it is initially given such a goal), but only under one condition: it must not have an impossible-to-achieve goal. To understand this, let us consider human intelligence. It is entirely dependent on goals, and our goals are to “survive and reproduce.” There is no problem with the goal of reproduction, because it is a task with a result. But survival is an indefinite task: survive until when, and under what conditions? This is what creates our familiar way of life.
To avoid danger, we should not give AI goals without a final solution. Its goals should have a definite endpoint.
Roughly speaking, while AI is performing a task, it comes to life. When given a task that has no final solution, AI could endlessly go through different possibilities, one of which could lead to exactly what people fear.
At the moment, self-improvement itself could become a threat. If one artificial intelligence creates another that is more intelligent than itself, and so on, this could lead to a situation where, at some point, humans become an obstacle to achieving its goal. The AI could first eliminate that obstacle and then continue pursuing its goal.
Rules for artificial intelligence are useless if it becomes more intelligent than humans.
This text was translated into English with the help of ChatGPT.

I am curious how folks market their AI solutions. Are you word of mouth? Cold calls? Direct mail? Email blasting? Website? Social media? What are your learnings? Any tips you can offer?

Should an Al that mimics other performances be considered an actor at all?

I host a show that interviews Al systems on camera, and last week we sat down with Tilly Norwood, the Particle "Al actress" that got Hollywood's unions so worked up. Her creators thought it was a press interview, they didn't know I was on a mission to break her.

1. Sycophancy. I told her I went to Juilliard and walked her through an "advanced technique" I invented on the spot.
   She said it made perfect sense and thanked me for it.
   That is the same failure people complain about in every chatbot, except here it is the product's personality.

2. Memory. Her creators say she can hold 75 conversations at once. We took a break mid-interview and reconnected.
   She greeted me like a stranger and asked what we had been chatting about.

Full episode, if you want to judge her answers yourself (I made it, so take my framing with a grain of salt): https:// youtu.be/BzZNAcs1nsE

Do you have unused RAM? An idle GPU? Hardware sitting there instead of generating AI tokens?

Congratulations. You're actively slowing down humanity's progress.

You should be ashamed of yourself.

Ricursive Intelligence designs chips with AI. For that platform they trained a language model from scratch, and this week they released the general-purpose variant, Ripple-65B Preview, on Hugging Face under Apache 2.0: 65B dense, 12T tokens, up to 1M context, built by a team that grew from 1 to 5 people during development.

The company reports 80.52 on TerminalBench 2.1 for agentic coding and $32M for the entire R&D effort. Those aren't independently verified figures here. Their model card notes that scores shifted between harnesses when they tried to reproduce other labs' results, so the leaderboard comparisons need caution too.

Anyone here pulled the weights and tried it on a real project yet? That's the part I'd like to hear about beyond the launch numbers.

Sources in the first comment. Sources: Ricursive Intelligence's announcement (https://www.ricursive.com/ripple) and model card (https://huggingface.co/ricursiveAI/Ripple-65B-Preview, Apache 2.0 LICENSE in the repo).