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If Jensen gave me his word about compute delivery or I asked for components to be accelerated, he always came through. Sure, we’d put together a purchase order at some point later, but I never for a second thought it wouldn’t happen.

Today, @llama_index is an applied research lab for document OCR and document work. Our core focus is investing in building the best-in-class models+ agents at the pareto frontier of accuracy/cost/latency for document processing. To do this, we have to invest in all the different components around the research infra stack: 1. Building comprehensive and fair benchmarks. Today we have ParseBench, ExtractBench, and ExtractBench Pro. They're already the most popular benchmarks in parsing and extraction today, and we're just getting started. 2. Investing in data curation and synthetic data generation. 3. Investing in compute and our training pipeline. This has produced models like OCRia-1. 4. Investing in agent optimization - tune everything around the models to further hillclimb cost and accuracy. 5. Closing the loop based on feedback - whether from active usage of our product, implicit/explicit customer feedback, or automated verification. We are accelerating our capabilities at a crazy pace. Today, our work is documented in our research page: There is a lot more to come!

🚨 BREAKING: President Trump OFFICIALLY announces @anduriltech is investing $3.7 BILLION in Arsenal-2, a TWO MILLION sq. ft. shipbuilding facility in Maryland It'll create 3,100 new permanent jobs, and support over 14,000 jobs overall. The LARGEST US shipyard since WWII "You'll build the next generation of American submarines, including autonomous subs, torpedo tubes, and critical components for our cutting edge Virginia class attack submarines, the greatest and quietest ever built." America has really slipped in our shipbuilding capacity over recent decades, but luckily, it's coming back strong 🔥 Great job, @PalmerLuckey!

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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!

Build AI infrastructure end-to-end with Supermicro’s Data Center Building Block Solutions®, combining validated components and sub-systems into a modular architecture that supports flexible deployment from individual systems to full data center scale.

Supermicro’s Data Center Building Block Solutions® deliver modular AI infrastructure built from validated components and sub-systems, enabling flexible end-to-end deployment from individual GPUs and networking to complete data center infrastructure, software, and services.