@fibanacci101 @elonmusk @IronMan198XAD Pure stability nailed! Your τnet=∑(r_i × [F_i ⋅ cos(θ_i) ⋅ ηvector]) zeros net torque across the 4 vectored nozzles—ideal for Starship's seamless control at 1.36 MN / 340 KTU. Star-system integration locked. 🚀

The NDAA doesn't 'merge' the US & 'israeli' militaries. There is ZERO equivalent law in 'israel'. 'israel' gets everything—US data, joint R&D, and supply chain integration. The US gets no matching access to IOF bases, factories, or decisions. It's an official one way takeover.

The left screams “my body my choice” and “healthcare access” nonstop. But they go radio silent when the abortion pill mifepristone gets handed out like candy with zero real oversight. Mail order chemicals. No doctor visit required. Perfect empowerment, right? Senator Katie Britt (R-AL) @SenKatieBritt and her colleagues have been calling out the FDA on this dangerous generic pill push… and for good reason, because harrowing stories of abuse keep emerging. Boyfriends secretly crushing pills into her drink, forcing abortions against her will. One laced her morning coffee. Killing her baby. Another spiked her glass, then abandoned her while she bled out alone. Coercion enabled by easy access. This isn’t choice. It’s a loophole for predators.

# Neo4j Features and Practical Usage 🕸️ "If this server goes down, what chain of things stops with it?" Cypher's variable-length patterns answer that by traversing relationships across any number of hops. 🏷️ Title: Variable-length patterns (`*1..5`) / SHORTEST / Quantified Path Patterns 🔗 URL: 📘 Overview Variable-length patterns let you express paths whose hop count is not fixed, all in a single declarative pattern. Questions like "trace suppliers up to 5 levels deep" or "find one shortest path" become natural one-liners. Shortest-path finding and Quantified Path Patterns (QPP) belong to the same family. ⚙️ How It Works ・Quantified relationships (legacy syntax): `[:REL*1..5]` matches 1 to 5 repetitions. `*2` is exactly 2, `*3..` is 3 or more, `*..10` is up to 10, `+` is one or more, and `*` is zero or more. ・Quantified Path Patterns (QPP): wrap a whole pattern in parentheses and quantify it, e.g. `((a)-[r:NEXT]->(b)){1,3}`. It is GQL-conformant and supports inline `WHERE` predicates inside the repetition. ・Group variables: variables declared inside a QPP become lists when referenced outside (e.g. `r` becomes an array of relationships). Combine with `reduce()` to compute, say, total distance along a path. ・Shortest paths: `SHORTEST k` returns k shortest paths, `ALL SHORTEST` returns every tied-shortest path, and `ANY` returns any one. The legacy `shortestPath()` / `allShortestPaths()` functions still work but the keyword syntax is faster. 🛠️ Practical Usage Trace a part's suppliers up to 5 levels deep: ```cypher MATCH (p:Part {sku: $sku})-[:SUPPLIED_BY*1..5]->(supplier:Company) RETURN DISTINCT ``` Find the single shortest introduction chain between two people: ```cypher MATCH path = SHORTEST 1 (a:Person {id:$a})-[:KNOWS]-+(b:Person {id:$b}) RETURN [n IN nodes(path) | AS intro_chain ``` 💡 Use Cases ・Impact analysis: how far a server failure cascades. ・Money-laundering detection: tracing fund-movement paths between accounts. ・Org charts and social graphs: boss-of-boss lookups, friend-of-friend shortest reach. ⚠️ Caveats ・Unbounded `*` or `[:REL*]` causes path explosion and can stall on millions of matches. Always set an upper bound such as `*1..5`. ・Pruning early with labels, relationship types, and inline predicates makes queries dramatically faster. QPP is built for this kind of pruning. ・By default Cypher does not allow re-traversing the same relationship; be aware that this behavior can be modified. #Neo4j# #Cypher#

Meet the Visionaries | Guangdong Shengtai Smart Energy Technology | Ep.2 Guangdong Shengtai Smart Energy Technology Co. Ltd. shared its fruitful cooperation with Huawei in Meet the Visionaries. Joint projects including 5MWh ESS and 3.5MW PV systems delivered outstanding results. Both sides talked about Huawei's core strengths: long-term reliability, superior quality and continuous R&D investment. All attendees agreed that PV and energy storage have become essential for enterprises to embrace the zero-carbon future.

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/ @sparkfinance 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.

Trillionaire Elon Musk Credits His Success to These 10 Books. Trillionaire Musk famously claims he was "raised by books," absorbing foundational knowledge from science fiction, biographies, and physics texts long before conquering Silicon Valley. When founding SpaceX, Musk did not rely on a traditional aerospace degree; instead, he devoured textbooks like Structures: Or Why Things Don’t Fall Down and Ignition! to master rocket science from the ground up. Inspired by Isaac Asimov’s vision of safeguarding human consciousness across galaxies, Musk established SpaceX to make humanity multi-planetary, demonstrating how literary inspiration can directly catalyze monumental real-world industries. To highlight the literary blueprint behind his extraordinary success, BrandAI @Brand has curated and ranked the following ten definitive books credited by Musk: 1. "The Lord of the Rings" by J.R.R. Tolkien – Shaped his worldview to become a hero who saves the world. 2. "The Hitchhiker’s Guide to the Galaxy" by Douglas Adams – Taught him that the hardest part is phrasing the question. 3. "Benjamin Franklin: An American Life" by Walter Isaacson – Inspired him as a model of a self-made entrepreneur. 4. "Einstein: His Life and Universe" by Walter Isaacson – Fueled his passion for scientific ambition and genius. 5. "Structures: Or Why Things Don’t Fall Down" by J.E. Gordon – Formed his fundamental understanding of structural aerospace engineering. 6. "Ignition!: An Informal History of Liquid Rocket Propellants" by John D. Clark – Provided the essential chemistry framework for building SpaceX rockets. 7. "Superintelligence: Paths, Dangers, Strategies" by Nick Bostrom – Shaped his cautious and critical perspectives on the future of AI. 8. The "Foundation" Trilogy by Isaac Asimov – Planted the ideological seed for space exploration and cosmic preservation. 9. "Zero to One: Notes on Startups" by Peter Thiel – Recommended by Musk for its brilliance in exploring how to build the future and create breakthrough companies. 10. "Life 3.0: Being Human in the Age of Artificial Intelligence" by Max Tegmark – Praised by Musk as a compelling guide to the challenges and choices regarding the future of consciousness.

# Practices for Embedding AI Agents in Software # Read-Free / Write-Gated 🎯 The Hook Approving every single tool call is a recipe for approval fatigue, where the rubber-stamp on a dangerous write operation is just one click away. Separate reads from writes and focus human attention where it matters. 🔥 The Problem Agents mix side-effect-free reads with irreversible writes. Gating everything equally drowns humans in approval requests. Since reads dominate most workloads, approval fatigue sets in fast, and the critical write approvals get waved through without scrutiny. Remove all gates, though, and you risk irreversible damage from unchecked writes. 💡 The Pattern Split tool calls into "read" (search, fetch, reference) and "write" (create, update, delete, send). Let reads flow freely while gating writes with authorization, validation, approval, and audit. Classify R/W statically at tool registration time in code, never by LLM judgment. Graduate write gate strictness by reversibility: irreversible operations like email sends or payments require human approval, while reversible ones like draft saves pass through policy validation only. This dramatically reduces approval fatigue while maintaining safety for side effects. ✅ When to Use Use when: - Read and write operations are mixed, with reads making up the majority - Irreversible writes exist (email sends, payments, production DB changes) - You need to preserve human review bandwidth for high-risk operations Don't use when: - Reads themselves access sensitive data (PII lookups, confidential documents) and need authorization too - All operations are read-only with no writes at all - It's an experimental environment where all operations are reversible and low-cost ⚠️ Pitfalls - Never let the LLM classify read vs. write. Injection can make it label a write tool as "read," bypassing the gate entirely - Watch for "reads with side effects" like API call counters or view history tracking - Applying the same gate strictness to reversible and irreversible writes brings approval fatigue right back 🔧 Implementation Approach - Assign type (read/write) and gate mode (none/auto/human_approval) statically at tool registration, making it structurally impossible for the LLM to reclassify at runtime - Implement the write path as a pipeline of input validation, gate evaluation, execution, and full audit logging, while reads log only metadata - Graduate write gate strictness using a reversibility flag, combining irreversible operations with mandatory dry-run as a prerequisite - Enforce all gate logic in deterministic code at the gateway layer, with zero reliance on prompt-based access control #AIAgents# #SoftwareArchitecture#