ZeroOneRonin Kuroda retweeted
ARK THINKS TOKENIZED ASSETS ARE ACCELERATING AFTER GROWING FROM $20 BILLION TO $35 BILLION
The market has expanded rapidly since ARK published its Big Ideas report in December:
- Total tokenized assets grew from roughly $20 billion to $35 billion
- Tokenized Treasuries now account for about $15 billion, nearly half of the market
- Commodities and active strategies are the next major categories behind Treasuries
DeepSeek’s updated Flash model gained 11.1 points on Terminal-Bench 4.0, rising from 31.2 to 42.3 in the company’s evaluation. Automation-Bench increased from 54.8 to 60.5.
DeepSeek reports that the improvements come with shorter outputs and faster task completion. The V4.1-Flash-1017 weights are available under the MIT license.
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
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ZeroOneRonin Kuroda retweeted
Prior tech platform shifts (web, mobile) have involved: (1) a new technical capability; and (2) a new distribution mechanism
In AI, we've seen a lot of (1) - and little of (2)
Agents are primed to fix this, and will become arguably the best distribution channel of all time
ZeroOneRonin Kuroda retweeted
V4.1 Flash vs 27B coordinating 159 Bonsai 2 4B
With the release of small models lately, wanted to see how a swarm would fare against a large model. Both sides given same VRAM on 3 different tasks
Mailbox Ledger - Swarm failed miserably, for unexpected reason. 27B would not trust chunks extracted by 4Bs, citing in report that “Workers produced inconsistent quality”
Coding - Swarm got demolished again. 27B coordinator never delegated, on one turn thought for 32,768 tokens about float formatting and hit the cap with nothing written
Wide Research - This is where Swarm redeemed themselves. Produced near identical results using significantly less energy. Unfortunately, control run of just 27B did just as well using even less energy
The swarm lost where the task was one coherent piece of work, and tied where the work split cleanly by item. Also coordinator only used less than half as available workers at a time for some reason, although it probably wouldn’t have helped that much.
The bottom line was, quality of the coordinator matters more than how much workers are available. 27B simply wasn’t smart enough to effectively leverage the swarm, but when V4.1 Flash was given a smaller swarm, it produced much better results (need more testing)
ZeroOneRonin Kuroda retweeted
"Human in the loop" assumes the human stays capable. Mitchell et al. show it fails twice: permission prompts degrade review into approval fatigue, sustained AI use atrophies judgment (cognitive debt). Oversight degrades the overseer - and in RLHF, the rater becomes the exploit. Agentic AI is pushing out human oversight. Therefore we need robust digital infrastructure to guardrail agents.
cc @EmanAbio
This paper worths a read. 🧠
arxiv.org/abs/2608.23642
ZeroOneRonin Kuroda retweeted
Teknium just dropped a catalog of 330 open hardware devices you can plug into Hermes 👀
𝗧𝗵𝗲 𝗛𝗲𝗿𝗺𝗲𝘀 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽
• 330 devices across 30 families
• 1,697 worked up project ideas
• Every device has a real local interface: REST, MQTT, MCP, serial, ROS 2
Boards, robots, 3D printers, smart home, retro handhelds, even FPGA chips
And each card shows exactly how Hermes plugs in
A smart plug just reports watts. With Hermes it becomes a rule.
A 3D printer has a camera. With Hermes it cancels its own spaghetti.
Hermes writes the firmware, flashes the board over the air, watches it on cron and pings you when a human is needed
You can start with a $15 ESP32 board and get a pocket Hermes with the same memory and approvals as Desktop
Hermes isn't just on your computer anymore. It's getting hands 🤯
teknium.io/hermes-devices
Another awesome IoT Hermes device!
Check out this page I put up with hundreds of project ideas for Hermes Agent x Open Hardware!
teknium.io/hermes-devices
ZeroOneRonin Kuroda retweeted
what a week for open weights!
we got:
- Bonsai 2 2B/4B/9B
- Nemotron 3.5 Bolt 74B + Super VL 120B
- MiniCPM-V 4.7 1B + 35B-A3B
- LFM2.5-24B-A2B
- Muse Spark Open 816B-A42B
- Step 5 Preview 600B-A27B
- Beam 1.0 501B A23B
- Jais 3 family
- Granite Switch 4.1
- Hy-Memory 30B-A3B
- FLUX 3 Klein
- MOSS-Image
and next week Nemotron 3.5 Bolt E4B lands. my 3060 is not sleeping!
configs/benchmarks for the ones i ran are below 👇
DeepSeek shipped a solid upgrade to V4.1 Flash.
Coding agent benchmarks went up, and Terminal-Bench jumped from 31.2 to 42.3.
DeepSeek also says the new model finishes tasks faster with shorter outputs. I care about that more than the scores.
If you run coding agents on long tasks, that could mean less waiting and a smaller token bill.
Curious how much of this holds up in Claude Code and Codex.
🚀 DeepSeek-V4.1-Flash-1017 is here!
Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API.
🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model.
1/4
ZeroOneRonin Kuroda retweeted
Just wow. Hats off to Apple. 🫡
Apple ran an 816B open-weight model locally at 36.7 tok/s across four Mac Studios.
They connected the Macs with Thunderbolt 5 cables.
Apple used the new M5 Ultra Mac Studios + MLX to run Muse Spark Open:
🧠 816B total / 42B active
💾 NVFP4 → ~493GB of weights
🍎 4× M5 Ultra Mac Studios
🔗 RDMA over Thunderbolt 5
📚 8K context
Performance:
🍎🍎 2 Macs, pipeline → 15.74 tps
🍎🍎 2 Macs, tensor parallel → 24.99 tps
🍎🍎🍎🍎 4 Macs, tensor parallel → 36.73 tps
And on four Macs:
⚡ Time to first token → 4.8 sec
Muse Spark Open is 816B parameters.
Even quantized to NVFP4, the weights alone occupy about 493GB.
A maxed-out Mac Studio has 512GB unified memory, but once you add the memory required to actually run inference, the model no longer fits on one.
So Apple splits the model across multiple Macs and uses RDMA over Thunderbolt 5 to make them behave like one local inference system.
Each pair of Macs is connected peer-to-peer with two Thunderbolt cables.
🔗 Apple MLX research link in ALT.
ZeroOneRonin Kuroda retweeted
5 GitHub repos packed with Claude AI agents & skills 🤖
→ github.com/msitarzewski/agen…
→ github.com/wshobson/agents
→ github.com/anthropics/knowle…
→ github.com/anthropics/claude…
→ github.com/addyosmani/agent-…
From coding to marketing, automate smarter with these free resources.
ZeroOneRonin Kuroda retweeted
ChatGPT just got a major voice upgrade! OpenAI is rolling out GPT-Live-1.1, along with the smaller GPT-Live-1.1 Quip. The update introduces 13 new voices with much more natural accents, including new options for German, French, Hindi, Japanese, Korean, and Mexican Spanish, plus a new English voice called "Bubbie".
Voice interactions are also becoming more natural, with better conversational flow and less repetitive phrases when ChatGPT is searching for information. I think voice will become an increasingly important way to interact with AI/SI, and improvements like this bring us closer to the point where talking to an AI feels just like talking to another person.
Have you tried the new voices yet?
ZeroOneRonin Kuroda retweeted
Update on Qwen-Image 2.1 Turbo (Uncensored also)
The 4.2GB image model can drift.
Qwen uses an 8B Qwen3-VL text encoder to tell the image model what to draw.
And apparently quantizing that too hard changes the picture almost as much as quantizing the image model itself. 👀
AtomicChat tested the same 48 prompts against BF16
🟢 Q8 encoder → LPIPS 0.037
🟡 AD-Q4 → 0.087
🔴 ordinary Q4_K_M → ~0.166
Their 4.2GB AD-Q4 image denoiser was ~0.147.
A cheap Q4 text encoder can distort the output more than the 4-bit image model.
And now we can put a better number on the actual compact pipeline
🎨 AD-Q4 denoiser → 4.20GB
🧠 AD-Q4 text encoder → 5.29GB
🖼️ BF16 VAE → 0.68GB
≈10.2GB of model files for text-to-image.
Image editing needs another ~1.16GB vision projector.
So my original 4.2GB number was correct for the denoiser but ~10GB is a much better picture of the compact local stack.
Links to normal and uncensored versions in ALT
Qwen’s new local image model went from 40 denoising steps to just 8. It is now uncensored - but it is fast too (Turbo)
In one RTX 3060 12GB test
🐌 Qwen-Image-2.1 → 49.8 sec
⚡ Qwen-Image-2.1-Turbo → 12.7 sec
About 3.9× faster end-to-end.
Localmaxxers are squeezing the new Turbo model into GGUF.
AtomicChat’s 7B Turbo denoiser is all the way down to
💾 BF16 → 14.23GB
💾 Q8 → 7.59GB
💾 Q5 → 5.40GB
💾 Q4 → 4.20GB
Their custom AD-Q4_K measured 21% closer to the BF16 image than ordinary Q4_K for only ~150MB more.
Best part, it runs through stable-diffusion.cpp.
Reminder
🦙 llama.cpp → local LLMs
🎨 stable-diffusion.cpp → local image generation
⚠️ The 4.2GB is the image denoiser, not the entire pipeline. You still need the Qwen3-VL text encoder + VAE.
AtomicChat also released an “uncensored” encoder, but interestingly their own tests found it didn't unlock additional image categories.
40 → 8 steps
14.2GB → 4.2GB denoiser
~50 sec → ~13 sec on a 3060
🔗 Link in ALT.
ZeroOneRonin Kuroda retweeted
Whether the neoclouds (and Coreweave specifically) survives feels like one of the strongest points of contention I've seen in the market recently
ZeroOneRonin Kuroda retweeted
This is how Anthropic uses Claude Code internally.
10 tips from the creator, Boris Cherny:
In January, his top tip was: "Most sessions start in Plan mode." I personally used it (almost) every day.
In June he said new models need no planning step. So he runs auto mode for everything (a second model checks each action for him).
Last week he shared how he prompts instead.
You tell Claude 3 things:
1. What you want it to do
2. How much effort to spend
3. How to check it did the right thing
"There's no secret to prompting," he says.
You talk to Claude like a coworker.
(It's really that simple now.)
Here are all 10, and what I'd type:
1. A loop prompts Claude for him → /loop
2. The app makes his worktrees → /desktop
3. His routines babysit every PR → /schedule
4. He builds from his phone → /remote-control
5. He keeps prompts and tools minimal → /context
6. Mistakes become rules → CLAUDE.md or a skill
7. He skips the plan, runs auto mode → Shift + Tab
8. He prompts like a coworker → goal, effort, check
9. Claude checks itself → "Check it works in Chrome"
10. He runs every agent from one tab → claude agents
But his biggest idea didn't change.
He puts every mistake into CLAUDE.md or a skill.
Then, in his words, Claude "can just run forever".
Which change are you making first?
Until recently, AI agents mostly handled short tasks, like one support call or one email. Now they can stay on a job for weeks or months.
That matters for "relay race" work that runs through a long chain of people.
Every handoff in that chain is a chance for something to fall through: a slow callback, a dropped follow-up, lost context.
Take a personal injury case. Today it moves from intake to investigation to the demand letter, with different people at each step. A single case agent can work it from the first call to settlement, chasing witnesses, records, and bills, and keeping the client and attorney updated along the way.
The same idea applies to processes like mortgages, insurance claims, and enterprise sales.
Because the agent owns the end result, the company selling it can charge on the outcome (a closed loan, a settled case, a signed deal) and move past pricing on the labor it saves (paralegal hours, call center seats).
Full piece from a16z's @dhaber on long-running agents in high-value customer journeys: a16z.news/p/winning-the-rela…
ZeroOneRonin Kuroda retweeted
i always thought ai safety benchmarks gave us a decent idea of how safe a model actually is, but numbers like these make me rethink that
a jailbreak method went from 31% success to just 0.3% under human calibrated evaluation
makes me wonder how much we're missing when we rely too heavily on benchmarks to judge ai safety
We are open-sourcing RISE, our red-teaming algorithm that breaks the safeguards of the latest image models from OpenAI, DeepMind, and others.
RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required.
Read more: whitecircle.com/research/bre…
ZeroOneRonin Kuroda retweeted
Who is making money in consumer AI?
There's really only one category seeing significant spend (so far!) - prosumer AI
The top 1% of U.S. spenders (at $903 / month on average) are overindexed on tools to build products, make content, and get work done 👇
ZeroOneRonin Kuroda retweeted
Grok Voice 3.0 is here from @SpaceXSI 🔥
Looks like a considerable upgrade over the 2.0 version. If you build systems that need voice functionality, this could be very useful:
🥳 Gemini 4 Argon could be just hours away! It was just spotted in Antigravity's code, with pricing listed at $2 per million input tokens, $0.10 per million cached tokens and $10 per million output tokens. That honestly looks very competitive for a frontier model, although I think what really matters is the total cost per task rather than just the price per token, really hoping Google has made some big improvements in efficiency compared to its previous models, because that could make a huge difference in real world usage
I'm also sharing the benchmarks we have so far, alongside Opus 5.5 and GPT 6 Astra, ... so we can get an early idea of how they compare, I think we've all been waiting for this moment, and it genuinely feels like the wait is finally coming to an end. Hopefully Google makes Gemini 4 Argon available on the Pro plan too, so most of us can actually get our hands on it and start testing it ourselves :)








































