与「OneStep」相关的搜索结果

🚨Claude code users!! Your 5-hour and weekly limits will stretch much further. Anthropic published a new official documentation page for experimental Code Mode in Claude Code v2.1.323+. Instead of sequential tool calls, Claude writes JavaScript to run parallel tool calls in one step.

Project Suncatcher is our moonshot exploring whether we can one day host machine learning infrastructure in space 🚀 To do so, we need to know whether our AI hardware can operate in orbit. Can our TPUs handle the physical stress of spaceflight and the radiation and thermal extremes of space? These are the questions Google researchers have been exploring for the past few years. Last week, we launched our first test satellite carrying four TPUs into orbit — putting us one step closer to getting answers.

And yeah @fermah_xyz started with a problem that sits deep inside the ZK stack. Zero knowledge applications can prove that a computation was done correctly, but producing those proofs at scale takes serious infrastructure: specialised hardware, several proving systems, workload scheduling, and reliability and cost controls. A team building a ZK rollup, zkVM, bridge or coprocessor cannot simply ask for "a proof" and move on. Fermah's first answer was a universal proof generation layer. Provers with GPUs and other hardware supply capacity, applications that need proofs create the demand, and Fermah matches the two. That marketplace is now called Froben. The problem behind the proof Running this in production exposed something bigger. A proof is one step in a longer sequence. Something has to trigger the work, the work has to run, the result has to be verified, and then the result usually has to start the next action. Fermah's argument is that blockchains are good at recording state and weak at starting what happens next. Take a lending position that crosses its liquidation threshold. The chain records the fact, but nothing forces the liquidation. A keeper, bot or backend still has to notice the condition, build the transaction and submit it. Consensus settles what happened, and a human run service still decides what happens after. Fermah calls the missing piece protocol agency: a protocol's ability to act on a verifiable condition without depending on an outside operator. What Kernel does Kernel is Fermah's Protocol Agency Engine, and its pitch is short: Define. Execute. Attest. A developer defines a workflow, Kernel runs it, and the result is published onchain with a cryptographic proof. Three design choices matter here: -Typed nodes. Workflows are built from a library of verified building blocks such as HTTP calls, onchain reads, price feeds and math functions. The type system validates how they connect before anything runs. -Sandboxed execution. Workflows run in an isolated environment built for untrusted code, and the same inputs always produce the same output. - AI where it helps, code where it counts. Fermah says AI agents can compose workflows, but deterministic code executes them, with no LLM in the execution path. Kernel can work as an oracle too. A smart contract requests a workflow, Kernel runs it, and the contract receives the result with a proof that the workflow ran exactly as written. Froben and Flashcast Fermah did not walk away from ZK. Froben still runs, now on top of Kernel, and its paying customers include ZKsync Era and Abstract. In Froben, a workflow defines the proving pipeline and Kernel schedules the work across the prover fleet. Flashcast Social shows a different use of the same engine. A user posts a question, and within seconds it becomes a live prediction market. Kernel then runs the resolution workflow and settles the market automatically when the event happens, with no committee and no dispute window. Proof generation and market resolution have little in common on the surface. What they share is a condition that has to produce an action, and that is the workload Kernel is built around. Fermah also lists data aggregation, pricing oracles and cross chain coordination as things Kernel can run. Where agents fit Fermah's latest thinking extends this to autonomous software agents. An agent working onchain needs to find available actions, check whether it is allowed to run them, pay, execute, and use the output as the input to its next step. Fermah describes this as action manifests, which make actions machine readable across discovery, eligibility, execution, payment and output. The shift in one line The old question was how to generate ZK proofs efficiently at scale. The new one is how a protocol can turn a verifiable condition into a complete, executable sequence. Proving is still part of the system, but it is now one workload among several that Kernel coordinates.

I find it funny that some people got hung up on Starship not having gone orbital before Flight 14 when in the grand scheme of things it was completely arbitrary in terms of performance. The 19ish second burn only added about 91 m/s of velocity (a 1.2% increase in velocity) This capability has been physically capable since Flight 6 which demonstrated a single Raptor relight in space. Heck, Flight 13’s burn was about 14 seconds, but intentionally oriented to primarily do a plane change instead of a prograde insertion burn for continued safety. Any of the relights COULD have done the orbital insertion burn, the duration was not of any special technical capability. The reason Starship didn’t go orbital until Flight 14 is probably what we should applaud them for the most… SAFETY. Their methodical approach to getting good data and going one step at a time while hucking the single most massive object ever put into space proves they weren’t just doing things haphazardly, despite the occasional narrative from the outside. While it’s easy to compare Starship to vehicles like SLS or Vulcan or New Glenn, the reality is the focus hasn’t been on orbit, it’s been all on the “downhill” side of that. Safe and targeted de-orbit, clean reentry, targeted and soft splashdown. That’s the part that has (almost) never been done before. And here I do have to add a little Space Shuttle admiration for figuring out a lot of these things on its first flight in 1981 (although it came a bit close to not being successful). Pretty amazing there was a vehicle of similar scale capable of such precise deorbit and reentry that it landed on a runway with no go-around capabilities. Crazy. But Starship is well on a path to absolutely leap-frog the Space Shuttle in terms of performance (already over double and someday 6 fold) and hopefully in terms of full and rapid reuse. I hope we see the latter part speed up even more now that orbit has been demonstrated. Exciting times ahead!!! Keep going @spacex @elonmusk

One step closer to 4-8x faster Ethereum finality! It took some time and lots of tokens, but we now have a formally verified proposal for a decoupled consensus protocol in I* (a future Ethereum upgrade)! Not yet a full spec (up next), but it includes all the key consensus-relevant details to become one. Since Ethereum aspires to be live without most of the stake online, the protocol involves many more components than a normal BFT protocol, and its correctness involves much more than standard safety and liveness. Those nuanced properties are now verified! What's more, I came away convinced that all protocol design will involve AI-assisted Formal Verification in the future, both for correctness and iteration speed. The work wasn't limited to just: Design the protocol -> Formally verify it Instead, the loop became more like: Design -> Formal Model -> Find exactly what breaks and why -> Redesign it. For a fairly complicated protocol like this one, I think having the Lean model be part of the design loop played a big role in accelerating the process. A future with agents paired with formal models is a superpower for Ethereum development, because they can then use those models to find exactly where an argument breaks down, formalize counterexamples, test proposed fixes, iterate on the protocol. Many details that would slip under the radar when asking agents (and indeed, humans) can now be specified exactly and checked by the Lean kernel. This then forces agents to be more precise and lets them make verifiable progress on their own. It's been incredible to see this play out, seeing agents find gaps and propose protocol changes to fix them. In other words, autoresearch can speed up protocol design, formal verification is here to stay, and Ethereum Finality will get faster.

Our unboxed process for Cybercab assembles vehicle modules in parallel & frames the car in one step at the end. This streamlines automation & reduces the line size by half, unlocking much greater production capacity. The first real revolution in automotive manufacturing in over a century

🌊 Another milestone for the East Anglia THREE offshore wind farm in the UK. All 95 foundations have now been successfully installed, together with our partner @Masdar. One step closer to delivering clean, homegrown electricity to 1.3 million homes, strengthening energy security and advancing electrification.