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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.

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Nasdaq was at @RAISESummit in Paris, France — a city built on grand engineering. We asked top leaders across the industry: What's the Eiffel Tower of AI infrastructure? From the bottlenecks slowing down progress to the breakthroughs that could unlock what's next, here's what @NelsonGriggs [@Nasdaq], @NetAppCEO [@NetApp], @AndrewdFeldman [@Cerebras], Travers Nisbet [@p0], Gareth Davies [@Okta], Tim Davis [@Modular], @RodrigoLiang [@SambaNovaAI], @theduncanclark [@Canva], @pirroh [@Replit], @May_Habib [@Get_Writer], @jainarvind [@Glean], and @RobertWachen [@Etched], had to say.

Ethereum is for shipping. Here are some of the things the Ethereum ecosystem launched, upgraded, and announced over the past month. 0/ @RobinhoodApp launched Robinhood Chain (@RobinhoodCrypto) on mainnet, an Ethereum Layer 2 built on the @arbitrum stack, enabling 24/7 trading of tokenized stocks and ETFs for users in over 120 countries through Robinhood Wallet. The network surpassed $1B in DEX volume in just over a week. 1/ @aztecnetwork achieved Stage 2 rollup decentralization under @l2beat's framework, removing governance control over its core protocol and taking another step toward trust-minimized infrastructure. 2/ @VitalikButerin shared updates to Ethereum's evolving technical roadmap, outlining the next phase of Lean Ethereum and the protocol's long-term direction. 3/ @ethlabs_org launched as a non-profit R&D lab for Ethereum and ETH. Their mission is to make Ethereum the settlement layer of the global economy. 4/ @ethereuminsti launched as an independent non-profit dedicated to accelerating the institutional adoption of Ethereum, its L2s, applications and overall ecosystem. 5/ @aave introduced Stable Vaults, fixed-rate stablecoin yield vaults designed for seamless integration into consumer applications. 6/ @zama launched the First DeFi Yield Venue for Confidential USDC (cUSDC) in partnership with @Morpho and @SteakhouseFi, bringing private stablecoin lending to Ethereum. 7/ @swissknifexyz launched a new Privacy Protocol Tracker, making it easier to compare fees, wait times, supported chains, and other metrics across Ethereum privacy protocols. 8/ Global tickets for @EFDevcon 8 went live, alongside speaker applications for this year’s conference in Mumbai, India. 9/ @Optimism and @toss__official signed an agreement to explore bringing the Korean Won onchain, expanding blockchain-based financial infrastructure for one of South Korea's largest fintech platforms. 10/ @OctantApp completed Epoch 12, its first full quadratic funding round using a zero-knowledge vote coprocessor and new voting application, distributing 88.1 WETH to Ethereum public goods projects. 11/ @0xprivacypools launched the trusted setup ceremony for Privacy Pools V2 ahead of its next protocol deployment. 12/ Gwei Name Service launched an ownerless, immutable Ethereum naming system built without administrative ownership. 13/ @ammalgam launched on Ethereum mainnet, introducing oracle-free lending without impermanent loss. 14/ @lodestar_eth added support for Fast Confirmations, allowing Ethereum node operators to confirm transactions in a single slot using validator attestations. 15/ @PrivacyBoost introduced preconfirmations for private transfers, allowing transfers to be treated as usable in around one second before final settlement. 16/ The @ethereumfndn published Ethereum Basics for Governments and Institutions, a new primer introducing Ethereum as a credibly neutral digital public utility for policymakers and enterprise leaders. 17/ @OndoFinance launched @OndoPerps, enabling tokenized stocks to be used as collateral for perpetual futures across equities, commodities, and indices. 18/ @base introduced Base Privacy, giving enterprises infrastructure to trade, pay, and settle onchain with built-in confidentiality and compliance features. 19/ @BGDA_UK launched BAGEY, a natively tokenized UK regulated fund on Ethereum where the blockchain serves as the legal register of record. 20/ @sparkdotfi launched the Stablecoin FX Layer on @Uniswap v4, introducing shared liquidity infrastructure that lets stablecoins access a common liquidity layer. 21/ @Trueo_ launched user-created prediction markets, allowing anyone to create a market by asking a question and letting participants trade on the outcome through a fully onchain prediction market. 22/ @kpk_io integrated @OpenCover Covered Vaults, allowing depositors to add opt-in onchain insurance to curated vaults, with coverage underwritten by @NexusMutual. 23/ @PropellerSwap launched Turbine on Ethereum mainnet, enabling large trades to settle with lower slippage by sourcing liquidity across onchain, offchain, and peer-to-peer markets. 24/ Hosted by @web3privacy, the Neocypherpunk Summit brought together around 1,000 builders and researchers in Berlin to advance privacy, open-source infrastructure, and human rights. 25/ @eth_systems launched as a company building modular privacy infrastructure for institutional Ethereum.

Simple but clear example of how to vary the volume of a sound as it plays. Use your imagination and put it to use in any more complex patch! #korg# #volca# #volcamodular# #modular# #sounds#

A few thoughts on the very near future First of all, what had previously been little more than a rumor has now been confirmed: GPT-5.6 had already been fully trained for two months and was available to selected users in early access. The obvious question is why it was not rolled out earlier. I do not think this was because OpenAI feared that the model might be overshadowed by Fable 5 or Mythos 5. Instead, OpenAI likely began working with government and regulatory authorities at a very early stage to ensure that the model could be released at all. Even after it had been previewed and announced, it still took some time before it could be rolled out publicly. That said, OpenAI clearly handled the rollout far better than Anthropic, which apparently did not have the same level of cooperation with government and regulatory authorities. Conversely, however, this also clearly means that future delays and increasingly strict model reviews will probably force us to wait longer for official releases. The next widely discussed rumor is that, within a few weeks, most likely no more than six, we will see either a preview or even the release of GPT-6. (Andrew Curran @AndrewCurran_ is one of the most reliable sources here on X, so I think that's very realistic.) The model has undergone entirely new pretraining, and the pace of releases is accelerating. The numbers are clear: Frontier labs are releasing more and better models at an increasingly rapid pace. Whereas we once had to wait months, quarters, or even half a year for major new releases, they are now arriving almost weekly. The latest frontier models may be more efficient in terms of intelligence per token, but they are also being deployed with much larger reasoning budgets. In practice, models such as Fable 5 and GPT-5.6 often consume considerably more tokens during complex or agentic tasks. This is not necessarily a sign of declining efficiency. Rather, it suggests that improvements in efficiency are being reinvested into deeper reasoning, longer trajectories and more capable agentic behavior. The result is that total compute consumption per task can continue to rise even as the underlying models become more efficient. Fable 5 and GPT 5.6 demonstrate just how intensive token usage has become. Although Sam Altman explicitly stated that GPT-5.6 is 54% more token-efficient (via CNBC), the fact remains that compute demand continues to increase, requiring more powerful and efficient computing infrastructure. Inference chips will probably become even more important as well. In summary, my initial conclusion from the latest releases is that compute demand will not merely continue to grow, but will probably exceed the available supply. This naturally means that energy demand will also increase, and, based on my initial assessment, probably more sharply than previously expected. This is likely to remain the largest bottleneck in the very near future. And this is important to me: there are bottlenecks. Not the training of the models, but besides compute, above all energy. This needs to be taken seriously! The US power grid, for example, is a major bottleneck, and the obvious question is how the necessary expansion can be achieved. Capital expenditure on data centers in the United States continues to rise sharply. This year, it exceeds 800 billion. It is not yet clear what the situation will look like in 2027, but I can hardly imagine investment declining or less CapEx being required. The reason lies precisely in the developments already mentioned: Demand is growing, particularly demand for energy. China clearly has an advantage here, a genuine moat, and I believe the West must be extremely careful not to fall behind because of the energy advantage China already possesses in practice. This could also help explain why, according to a recent Reuters report, China is considering restricting Western access to its frontier models. It may have concluded that it will win the long-term race. Unless there is a genuine breakthrough, whether in small modular nuclear reactors or fusion energy, I expect major problems to emerge over the coming years, for example by 2030. So far, I do not see any viable solutions. We can therefore clearly establish two points: Models are becoming larger, better, and increasingly useful for all users. There is no end to this development in sight. At the same time, the bottleneck appears to be growing increasingly severe, and this is already visible in practice. Regulation, energy demand, and compute demand could mean that, in the very near future, the release cadence will not accelerate as quickly as hoped or desired. This creates a clear contradiction. Thank you for coming to my TED Talk.

Before a robot can change the world, it needs software that builders can trust. NVIDIA robotics engineer Jaiveer Singh leads the team behind Isaac ROS, an open source platform helping developers build autonomous mobile robots, manipulation systems and humanoids with modular, CUDA-accelerated libraries and AI models. “When more people can build robots,” he says, “the future gets here faster.”