Hi @Ledger @Ledger_Support ! I'm using a Nano X strictly for EVM tx signing via Rabby. Considering a Flex/Stax/Gen5:
Do touchscreen devices show on-device tx simulation (ERC balance changes)?
Will DeFi contracts that blind-sign on Nano X still blind-sign on touchscreen models?
Don’t trust supply chains if you can’t verify.
Get a small RISC-V board. Flash an open source signer. 3D print a case for it.
looking forward to a open-source version of this. AI agent self hosted at home.
Say hello to Oki Home, your Personal AI Computer.
We're building a future of limitless, personalized intelligence for every consumer, while keeping all your data in your hands.
Reserve our first batch, shipping mid-december! okihome.ai
0xDarryl.eth retweeted
The cryptographic world computer:
vitalik.eth.limo/general/202…
My attempt to express in somewhat concise terms the true meaning of basically everything planned to happen to Ethereum starting from the fork after Hegota. It's really not just a blockchain anymore. It's a hybrid architecture that combines together blockchains and modern cryptography, to enable much more powerful properties.
FOCIL, EIP-8288, Lean consensus, state management, formal verification, advanced mempool improvements (including privacy), and the longer-term specter of obfuscation all mentioned.
Whatever you wear sends a message. UHNWIs dressing casually says:
"Luxury logos are lame. A low-end millionaire or even some nobody with a $30k net worth can buy big brands thinking it impresses people 🙄. The modern status symbol is being a billionaire on the Forbes list."
0xDarryl.eth retweeted
A proposal to wind down Balancer and distribute the treasury to BAL holders is live on the forum, authored by Marcus Hardt. Discussion is open; a Snapshot vote is expected to happen from 25 to 29 September.
Nothing changes today: pools and withdrawals work as they do now. Any wind-down action waits for the vote.
We can’t trust openai not to train on our data and steal our work. We can’t trust routers not to steal our credentials.
We really need to get ourselves a private local LLM setup.
0xDarryl.eth retweeted
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
0xDarryl.eth retweeted
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
0xDarryl.eth retweeted
GPT-Live-1 is now available in the API.
Bring ChatGPT’s natural back-and-forth to your app, with voice agents that listen while they speak and work with the models and harness you choose.
0xDarryl.eth retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
0xDarryl.eth retweeted
I automated "stealing" viral TikToks.
nano banana pro + kling + n8n analyzes any viral video, extracts exactly why it works, then generates your version with your look
here's the stack:
- @adrian_horning_ gets the tiktok video + transcript
- @GeminiApp (@openrouter) watches the video and breaks down the psychology (hooks, pacing, motion, emotional arc)
creates a "character bible" for visual consistency
- @NanoBanana generates the starting frames
- @Kling_ai animates each 10-second segment (or veo)
@FFmpeg stitches it into one vertical video
- uploads to Google Drive, logs everything
input: 1 viral TikTok URL output: branded alternative in 10 min for ~$2
the viral formula stays. the content becomes yours.
this is how you test 10x more concepts without 10x more creators
want the complete 5-workflow automation?
comment VIRAL + follow (following required for DM)
Right now, the model isn't the bottleneck. It's the harness that needs to be much better.
Harnesses often get dismissed as just scaffolding, just prompt engineering, and not real research. But that couldn't be farther from the truth. The same model weights that score 30% on ARC-AGI score 95% with a better harness.
So we gathered a group of researchers and founders working at the frontier to do a deep dive into the state of harnesses.
We cover how we got to this point, the case for making your harness as expressive as possible, and what YC learned building an agent for every employee in the company.
00:00 - @FrancoisChauba1: Why harnesses matter
04:27 - Building an auto-researcher by accident
07:13 - A five minute history of harnesses
13:56 - Self-improving harnesses
18:35 - @sethkarten: Prime Agent, a self-improving RLM harness
21:50 - Context as an L1, L2, L3 cache
24:51 - From Turing machine to von Neumann computer
28:33 - Messaging between agents
30:04 - ARC-AGI results
33:09 - Emulator Bench and GPU kernels
37:30 - @JonSaadFalcon: OpenJarvis, personal AI on personal devices
38:26 - How far behind are local models
39:21 - The five primitives of a personal AI stack
42:47 - Letting cloud models optimize your local stack
43:53 - 800x cheaper than the cloud
45:58 - @josh__france and @jbellregan: QM, YC's agent harness for work
47:29 - A history of YC's internal agents
49:24 - OpenClaw and a fleet of 50 agents
51:04 - Pulling the brain out of the sandbox
54:43 - Letting the agent choose its own sandbox and model
57:16 - The grind tool: budgets on goals
58:50 - Agents don't understand social context
0xDarryl.eth retweeted
Harnesses often get dismissed as just scaffolding, just prompt engineering, and not real research. But that couldn't be farther from the truth. The same model weights that score 30% on ARC-AGI score 95% with a better harness.
So we gathered a group of researchers and founders working at the frontier to do a deep dive into the state of harnesses.
We cover how we got to this point, the case for making your harness as expressive as possible, and what YC learned building an agent for every employee in the company.
00:00 - @FrancoisChauba1: Why harnesses matter
04:27 - Building an auto-researcher by accident
07:13 - A five minute history of harnesses
13:56 - Self-improving harnesses
18:35 - @sethkarten: Prime Agent, a self-improving RLM harness
21:50 - Context as an L1, L2, L3 cache
24:51 - From Turing machine to von Neumann computer
28:33 - Messaging between agents
30:04 - ARC-AGI results
33:09 - Emulator Bench and GPU kernels
37:30 - @JonSaadFalcon: OpenJarvis, personal AI on personal devices
38:26 - How far behind are local models
39:21 - The five primitives of a personal AI stack
42:47 - Letting cloud models optimize your local stack
43:53 - 800x cheaper than the cloud
45:58 - @josh__france and @jbellregan: QM, YC's agent harness for work
47:29 - A history of YC's internal agents
49:24 - OpenClaw and a fleet of 50 agents
51:04 - Pulling the brain out of the sandbox
54:43 - Letting the agent choose its own sandbox and model
57:16 - The grind tool: budgets on goals
58:50 - Agents don't understand social context
