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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.

This is the most important chart to understand right now as an AI investor There are 3 waves of token consumption. Each wave is bigger than the last, and we're only just starting wave 2 Wave 1: Chat Wave 2: Agents Wave 3: Physical AI (humanoids, robotaxis) Token usage is expected to multiply 24 TIMES by 2030, with enterprise agents alone burning ~120 quadrillion tokens/month The growth in chat is limited from here, but that's ok because wave 2 has already kicked off and its accelerating extremely fast right now Wave 2 is Agents that work continuously instead of answering one prompt at a time, enterprises deploy them by the thousand, and consumer agents (the new Siri, Gemini on Android) haven't even shipped at scale yet. That's the 24x, and it's why compute, memory and power demand keeps accelerating through 2030 But wave 3 is the real kicker and it crushes everything else. This is the part that isn't in many of the models today. Physical AI: Humanoids, robotaxis, autonomous machines. Expected to be the biggest consumer by 2030 and reach 400 quadrillion tokens a month by 2040, the biggest of every category on the chart And here's why wave 3 is different for infra investors: it pays the infra stack TWICE. Every robot runs inference around the clock (tokens), and every robot IS hardware itself too. A humanoid carries roughly 10x the memory of an advanced car, plus the sensors, chips and power systems. Wave 3 demand shows up in tokens AND in physical components As I've said many times before, the demand for compute and AI infra is infinite becuase the demand for intelligence is infinite. As the form factors we use intelligence continue to grow, the demand does too The sooner you understand this, the sooner it becomes easier to learn how to invest in it. or to make it even easier, you can track my real-time portfolio and 4 other analysts' with live trade notifications and research inside Milk Road PRO. It's just $1 to try it out right now, learn more here: If you enjoyed this, give me a follow @kylereidhead for more insights on AI and markets

开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

Here is my AI investing guide. Sitting here August 2026, my current best thoughts are as follows: 1. LPS (Land Power Shell) is still the most obvious and fastest path to cash on cash returns. Lots of value can be assembled and traded quickly at this layer. And as data centers get more pushback, energized land can explode in value. Very bullish here. I’ve stepped into this layer very aggressively. My partner @anitavlallian and I have acquired almost 6GW coming online in a ramp from today thru 2029 of grid power and behind the meter. 2. Silicon - I helped get @GroqInc off the ground in 2015 and we licensed it to @nvidia for $20B Dec2025. I won’t invest or incubate anything in this layer now. The perf demands of the chips are too high, manufacturing precision is too complex and supply chain influence to get adjacent components like memory isn’t possible for a startup anymore. Lots of capital will be wasted here chasing Groq and Cerebras’ success. Note that both startups made sense a decade ago when these constraints were much more modest. 3. Clouds - Clouds are very very lucrative but very hard to build and very expensive and technically complicated to maintain. And as alignment becomes a more important issue, I expect the clouds will be asked to build robust KYC and attest to it. This makes the risk:reward ratio skewed. I don’t want to be responsible when the USG says a cloud allowed a bad actor to do something bad because of poor KYC. 4. Models are complicated. The big open question is how much of the revenue being generated by them today is because of tokenmaxxing and poor model behavior. If it’s a lot, then the annualized revenues will diminish meaningfully even as token consumption inflects upwards. This is the big economic question at this layer. 5. Harnesses are where the action is and why I started @8090solutions two years ago. In a nutshell, the harness helps enterprises owns their proprietary context (what Alex Karp calls their ‘alpha’). This is an enterprise’s data, workflows, evals, and business rules. A harness that gives this to an enterprise is what creates very low model-agnostic switching costs, which further reinforces my views of #4# above. 6. Applications will be another long term winner along with harnesses. This is where the differentiation between “off the shelf” and “custom time and materials” melts away. Every company, with the right harness, can now imbue their alpha into the software that runs their company. I expect this to mean that “off the shelf” is largely replaced with custom software creating a huge opportunity to write these solutions for companies. Build once and sell repeatedly is a laggard GTM motion for a SaaS world that isn’t needed here. Think custom by design, alpha embedded, proprietary by nature. Fin. Good luck to all the players!

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𝗧𝗛𝗜𝗦 𝗜𝗦 𝗠𝗨𝗖𝗛 𝗕𝗜𝗚𝗚𝗘𝗥 𝗧𝗛𝗔𝗡 “𝗧𝗛𝗘 𝗣𝗘𝗡𝗧𝗔𝗚𝗢𝗡 𝗠𝗔𝗬 𝗨𝗦𝗘 𝗔𝗜.” President Donald Trump’s new Executive Order signals a fundamental change in defense acquisition: contractors will increasingly be expected to prove where critical materials, components, equipment, and software originate—through subcontractors and toward the raw-material source. The Pentagon wants to see the defense supply chain, identify where an adversary could interrupt it, and make prohibited foreign sourcing harder to excuse. The order directs the Department of Defense to: • Map designated critical supply chains. • Obtain hierarchical bills of materials showing what is inside a defense product, who supplied it, and where it originated. • Extend visibility beyond prime contractors into lower-tier suppliers. • Identify foreign dependencies, bottlenecks, sole-source suppliers, concentration risks, and single points of failure. • Use AI to analyze acquisition data and expose connections conventional contract reviews may miss. • Accelerate qualification of alternative sources and materials. Beginning January 1, 2027, certain waivers involving covered materials from prohibited sources will become substantially harder to obtain. Contractors seeking waivers may need approved mitigation plans showing how the dependency will be reduced or removed. Fraud, deliberate misrepresentation, or willful failure to implement an approved plan could result in contractual remedies and possible referral to the Attorney General. Restrictions under 10 U.S.C. §4872 concern designated sensitive materials from China, Russia, North Korea, and Iran, including certain specialty metals, rare-earth elements, permanent magnets, and tungsten-related materials. But one distinction matters: 𝗔𝗜 𝗜𝗦 𝗡𝗢𝗧 𝗕𝗘𝗜𝗡𝗚 𝗔𝗨𝗧𝗛𝗢𝗥𝗜𝗭𝗘𝗗 𝗧𝗢 𝗗𝗘𝗖𝗜𝗗𝗘 𝗜𝗡𝗗𝗘𝗣𝗘𝗡𝗗𝗘𝗡𝗧𝗟𝗬 𝗪𝗛𝗜𝗖𝗛 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝗔𝗥𝗘 𝗧𝗥𝗨𝗦𝗧𝗪𝗢𝗥𝗧𝗛𝗬. AI will help map and analyze supply-chain vulnerabilities. Human officials retain responsibility for waivers, mitigation plans, contracting actions, and enforcement. President Trump and Secretary of Defense Pete Hegseth deserve recognition for this determination to expose hidden dependencies and strengthen the industrial foundation behind the American warfighter. Now comes the decisive test: implementation. AI cannot map what contractors cannot identify, subcontractors will not disclose, or suppliers report inaccurately. 𝗦𝗨𝗣𝗣𝗟𝗬-𝗖𝗛𝗔𝗜𝗡 𝗠𝗔𝗣𝗣𝗜𝗡𝗚 𝗜𝗦 𝗡𝗢𝗧 𝗔𝗗𝗠𝗜𝗡𝗜𝗦𝗧𝗥𝗔𝗧𝗜𝗩𝗘 𝗖𝗢𝗠𝗣𝗟𝗜𝗔𝗡𝗖𝗘. 𝗜𝗧 𝗜𝗦 𝗔 𝗧𝗘𝗦𝗧 𝗢𝗙 𝗪𝗛𝗘𝗧𝗛𝗘𝗥 𝗧𝗛𝗘 𝗦𝗬𝗦𝗧𝗘𝗠 𝗕𝗘𝗛𝗜𝗡𝗗 𝗧𝗛𝗘 𝗪𝗔𝗥𝗙𝗜𝗚𝗛𝗧𝗘𝗥 𝗖𝗔𝗡 𝗦𝗨𝗥𝗩𝗜𝗩𝗘 𝗖𝗢𝗡𝗧𝗔𝗖𝗧 𝗪𝗜𝗧𝗛 𝗪𝗔𝗥. “Don’t sell me hype and call it readiness. Prove it before you put a soldier’s life behind it.” — Linda Restrepo Editor-in-Chief, N360™ — Sovereign Intelligence & National Security Technologies #DefenseSupplyChain# #ArtificialIntelligence# #DefenseIndustrialBase# #NationalSecurity# #MilitaryReadiness#

Introducing Canvas UI, the first ever html-in-canvas component library. Your DOM is the render target now. Real-time shaders over real, interactive UI. 24 components. React, Vue, Svelte, vanilla TS. Free. Open source. 🧵

My honest take on @Kimi_Moonshot K3 👀 As soon as I got access to Kimi, I put it through the exact same 3D football stadium challenge I had previously given Claude Fable 5. Fable 5 finished in under an hour. Kimi took almost 3 hours 🥵 At first, that was a very bad impression but i decided to see what's taking it this much of time. It was running E2E tests, validating desktop, tablet, and mobile, taking screenshots, finding failures, fixing them, even adapting for low end hardware and low FPS before moving on. All these that Fable 5 never did. None of it because it wasn't able to run this WebGL app on a headless browser. Kimi also shipped a clean React + Three.js codebase with proper components while Fable, as you might have checked codebase, generated one big HTML file with everything stuffed in. With all of this happening, it felt like I was doing more "Vibe Coding" with Fable 5 and more "Vibe Engineering" with Kimi K3. It feels like it's spending more time making sure the code actually works and easily scalable. Really impressed so far. More Kimi K3 experiments coming soon. Code (Kimi): Code (Fable): Live (Kimi hosted):