I want that switch

Local AI psychosis : If you buy one DGX Spark, you'll be adding a second one to your cart within a few weeks

The vote to activate ENSv2 on Ethereum mainnet is now live. This is a major step toward launching the next generation of ENS, with the decision in the hands of the DAO. Read the proposal and vote ⤵ ens.gov.blockful.io/proposal…

I like what DeepSeek is doing. - They have a top of the line open-model. - Top of the line harness. - And now top of the line inference engine. All open-source and available out of the open. Thumbs up for them, I love it.

a few more things i’ve learned replacing Muse Spark 1.3 in OpenCode: 1. DeepSeek V4.1 Flash can last me ~8 hours of work before i hit the daily free limit, as long as i’m not constantly spawning subagents. 2. the 200K context cap is manageable, but i usually hit compaction several times during a long session. for bigger repos, that matters. 3. when context starts becoming the bottleneck, i switch to Space Bunny for the 1M window instead of forcing DeepSeek through repeated compactions. 4. for planning, i still prefer GLM 5.3 Flash, but i don’t use it for everything. on simple build tasks, it can take 2–3x more turns than DeepSeek. 5. if DeepSeek starts making weird implementation choices, MiMo 2.6 Flash is my fallback before i jump to a paid frontier model. right now my free stack is basically: GLM → plan DeepSeek → build Space Bunny → long context MiMo → backup

Run DeepSeek V4.1 Flash 1017 on 2x DGX Sparks ✨ - 1M total KV pool, 512K context - 75 tok/s on prose, single stream - 130 tok/s on prose, 4 concurrent streams - Prefill 2000-2175 tok/s - EXL3-EAQv1, 2.9 bpw The quant in this recipe got about the same calibration as my GLM 5.3 Flash, with some extras. Feedback would be appreciated! Special thanks to @bertholomusai and all the contributors for the engine work, and to @ashxhart for TensorFold. Get it here: github.com/MiaAI-Lab/DeepSee…

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case). I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves. We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them: "/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started" I imagine we'll have some backend services that could be replaced entirely by this new architecture.

Saying software is dead because anyone can build it is like saying photography is dead now that everyone in the world has a camera in their pocket Not only is it not dead it's the beginning of the Kodak Brownie moment for software, and we will see an absolute explosion of it It will be the first generation for who building software will feel no different than using it. This is crazy! The quality and bar of what we expect from our everyday apps will be raised dramatically, we'll have personal software, we'll have disposable software, and agents will be the foundation for most of it Tech has always been chaotic and evolving, the people who have been able to adapt and move in front of new opportunities have always excelled, this is nothing new, I'm seeing it now and we'll see it 10 years from now Find the opportunities in the chaose and disruption, there are countless of them

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.

Quote tweet this with your .eth validator.eth

Vitalik: AI Will Become the New UI On October 6, 2026, Ethereum co-founder Vitalik Buterin @VitalikButerin said at OKX NOW @okx in Singapore that AI will become the new user interface in more and more scenarios. He shared how a local AI agent wrote a script to update his ENS hash in about five minutes, and said performing complex blockchain operations without a traditional UI will become normal. This could reduce security risks associated with traditional frontends, while introducing new challenges such as prompt injection, AI safety, and whether agents correctly understand onchain operations.