Building my AI and LLM expertise and sharing everything I learn along the way at https://xitter.cf/t.co/VOeg3DJn8J

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Joined March 2013
Hey everyone! As you can tell from my posts, I’ve been diving deep into large language models (LLMs) lately, and I’d love to connect with anyone who’s curious about their potential. Whether you want to brainstorm the best ways to implement them in your business or simply have a few questions, let’s schedule a call and chat it through. Ready to explore AI / LLMs with me? Drop a comment or shoot me a message, and let’s set up a time to talk! Or use this link to book a 30 minute discussion: calendly.com/-arthurlee/30-m…
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Introducing statistically-lossless Qwen3.8-27B! Three models, 23.7–30.5 GB: up to 57% smaller weights than BF16. 98–99% measured EAR, versus 97.71% for native FP8. EAR measures overlap with BF16’s next-token probabilities. A 99% EAR means 99% average probability overlap, here, measured over the reference’s top-10 tokens after renormalization. The search optimizes EAR directly, assigning more precision to sensitive projections while keeping activations in BF16. The goal is to find the smallest checkpoint while keeping fidelity as high as possible. vLLM + Humming • Apache 2.0 GGUF versions coming next week! Get the models: huggingface.co/collections/k… Based on SLQ (arxiv.org/abs/2605.02404) from ISTA’s DASLab (github.com/IST-DASLab), the group behind GPTQ, GSQ and RCO, with customizations to improve the balance between size and fidelity. More details soon.
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Arthur Lee retweeted
Claude Duet is an early research preview, available through a waitlist on Pro and Max plans in the US. It can still misjudge what’s urgent, and we’ll keep tuning it based on what early users tell us. Join the waitlist: claude.com/product/claude-du… Read more: claude.com/resources/article…
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Okay since Dots is not working as expected. I am still invested in Codex but going to try GrokBot to see if it fits my pattern. Ideally there is an open source version of this so I can sit on top of all the agents. Is there one?
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Decided to drop OpenAI Dots for work. Might use it occasionally to help prep for day but for real work I am back to using Codex. The constant permission issues (yes I did add custom rules to no avail) and lack of will to finish tasks like Codex is just not working for me. I do want to use Dots but it just doesn't do want I need to orchestrate my work.
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OpenAI Dots is geared for consumers (debatable if it is better for Muse for this) and this might be the right direction for OpenAI. For real work, I am finding Dots not as usable as just using Codex and Project folders.
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Arthur Lee retweeted
Next year, I will give one of my SpaceX shares to every kid in America. This will be the first of many.
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Anyone else getting this with OpenAI Dots when trying to read a thread started in Codex? “I tried that thread ID, but it returned a thread-format error before I could read it. I’ll check whether there’s another supported way to open it.”
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I’ll have to test this approach
Replying to @rl_env
Quick to jump to conclusion. This is my OpenAI work dot coordinating with my personal dot. Two entirely separate accounts.
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Arthur Lee retweeted
Day 11/ We've landed a series of changes and more compute to very significantly decrease the occurrence of the disruptive "Selected model is at capacity. Please try a different model". When it does happen, it should also eventually just continue by itself and not just block on it. Let us know if you still see it out there in the wild and we'll take a look.
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Arthur Lee retweeted
Day 9/ We've rolled out optimizations to how we store state on disk for the chatgpt & codex desktop app that should reduce the amount stored on disk by 60%. Small ship but we've heard enough feedback about power users running out of disk space.
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Qwen3.8-27B UD-Q4_K_M vs Liquid LFM2.5-24B-A2B Q4_K_M. 18 runs, 3 tasks. Strict passes: 9/9 vs 2/9. Liquid decoded 2.78× faster but took longer overall. Its RAG misses were formatting. Small test, different settings. Not surprising.
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Whoa - competition love it if these benchmarks translate to real world work!
Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.
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Arthur Lee retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
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Finally!
Haven't heard anyone mention this today but you can set ChatGPT to open directly to your Dot! Huge quality of life improvement imo, thank you @JustinBleuel
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Arthur Lee retweeted
Day 7/ We have increased default context length for GPT-6.1 Sol to 500k when used through Codex & Work, up from 272k or an ~ 80% increase. We will go higher in the future, but it helps reduce occurrence of compactions and with tasks that require keeping a lot of context and nuance over periods of time. It was carefully tested and is the new sweet spot for most of our users. You can still override it if you want to be higher our lower, with maximum context length of GPT-6.1 Sol of 1M.
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Arthur Lee retweeted
TensorFold 1.0.5 is out: - GLM-5.3-Flash runs on one 256 GB M5 Ultra - Nemotron reuses your chat: a 19.8k-token chat's next turn prefills in 0.16 s, not 8.1 s - Fixed: the 27B on macOS 27, and a request that could crash the server I’m re working the Living Weights which will be in the next release. Thank you to everyone who has contributed PR’e and issues. @MiaAI_lab @volatilemarkts @petruspennanen @plotarmordev brew upgrade tensorfold github.com/ashhart/TensorFol…
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Arthur Lee retweeted
I've been building something new for TensorFold. It's not just another quant. It’s a quantization framework designed around how TensorFold actually runs models. I'm calling it Engine-Aware Quantization, or EAQ. First target: GLM 5.3 Flash. Early results vs my current public quant on the same TensorFold version, measured against the original BF16 model: - 41% lower error on what the model actually writes - 54% lower error on the decisions that matter for agents: when to call a tool, which one, when to stop - roughly the same speed. Built for coding, agentic work and tool calling. This isn't a one-off: the framework is meant to carry over to every model and quant I will build in the future. Still validating on real tool-calling and agent benchmarks before anything goes public. More details coming soon. mia-ai.net/lab/projects/eaq
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This iPad Pro + Thunderbolt R9700 is running Qwen3.8-27B at 158.5 tps. Yes… an iPad. The trick 🎮 32GB Radeon AI PRO R9700 🔌 Thunderbolt eGPU enclosure 📱 iPad Pro running LemonSeed Studio 🧠 Qwen3.8-27B Q4 📚 131K context And the speed progression is kind of ridiculous 🐌 baseline → 31.2 tps ⚡ MTP3 → 108.7 tps 🚀 DFlash2 → 158.5 tps Prefill: 📥 4K → 1,636 tps 📥 32K → 1,395 tps LemonSeed actually runs the AMD GPU on iPadOS. It embeds the upstream Linux amdgpu + amdkfd driver as an iPad PCIDriverKit extension, then runs its inference engine on the R9700. Same engine for 🐧 Linux | 🍎 macOS | 📱 iPadOS Same R9700 on macOS reaches 159.4 tps, so the iPadOS result is basically right there with the Mac. ⚠️ Obviously, the iPad's own GPU isn't doing the 159 tps. The iPad is hosting a 32GB desktop AMD GPU over Thunderbolt. 🔗 Link in ALT.
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