Earth’s spin can hold a 100000 km cable taut once the system’s centre of mass sits at geostationary orbit. The effective potential U(r) = −μ/r − ½ω²r² pulls inward below GEO and outward above it. A counterweight beyond GEO keeps the tether in tension. Carbon nanotubes remain the leading candidate for the specific strength this taper requires. Climbers then ride the cable instead of rockets. The rotation that flattens Earth can also hold a staircase into space.

Deriving cosmic expansion from Einstein: The slide shows how Einstein's field equations R_μν − ½ g_μν R = 8π G_N T_μν + Λ g_μν, for a perfect fluid T_μν = −p g_μν + (p + ρ) u_μ u_ν, yield the Friedmann-Lemaître equation H² ≡ (Ṙ/R)² = 8π G_N ρ/3 − k/R² + Λ/3. Here H(t) is the Hubble parameter and Λ the cosmological constant. The equation powers models of universe evolution, helps estimate its age and density from observations, and underpins studies of dark energy with telescopes like Hubble and JWST.

China's air and water quality continued to improve in the first seven months of 2026 amid strengthened efforts to curb air pollution and protect water resources, the Ministry of Ecology and Environment said on Thursday: - Average density of PM2.5—a key indicator measuring air pollution—dropped by 1.8 μg per m³ - Proportion of days with good air quality up 3.9 percentage points YoY - Nationwide proportion of surface-water bodies classified as "good quality" up 2.4 percentage points YoY to 86.5%

Numbers rarely sit exactly at their average - the standard deviation formula shows the typical distance they stray from it. Population: σ = √[∑(xᵢ − μ)² / N] xᵢ = elements in population μ = population mean N = population size Sample: s = √[∑(xᵢ − x̄)² / (n − 1)] xᵢ = elements in sample x̄ = sample mean n = sample size The sample version is applied by bank risk analysts to measure day-to-day swings in currency exchange rates.

🚨 Claude Code made me 6 trading bots in 15 mins In the US alone, emotional retail traders lost more than $1.8 billion on liquidations While billions of amateur traders were staring at charts, overtrading, and getting wrecked on fees, a quiet group of algorithmic traders treated prediction markets like a hyper-liquid data engine They didn't guess outcomes -> they knew the structural price gaps in advance Here is how they did it, and why manual trading is completely dead: It's all about removing emotion and deploying cross-market statistical arbitrage Linear Spread Cointegration Formula: S_t = P_P,t - β * P_K,t - μ Ornstein-Uhlenbeck Continuous Dynamics Formula: dS_t = θ(μ - S_t)dt + σ dW_t Euler-Maruyama Discretization (MLE Calibration) Formula: S_t_i = S_t_i-1 * e^(-θΔt) + μ(1 - e^(-θΔt)) + ε_t Level 1 Order Book Imbalance (OBI) Formula: I_t = (V_b(t) - V_a(t)) / (V_b(t) + V_a(t)) Volume-Weighted Micro-Price Prediction Formula: P_micro(t) = P_mid(t) + I_t * (Δspread / 2) Cross-Venue Predictive Signal Optimization Formula: ΔP_Kalshi(t + δ) = f(I_Polymarket(t), P_micro,Polymarket(t) - P_micro,Kalshi(t)) In the era of advanced AI, the winner is not the one who guesses the score, but the one who lets automated systems execute with absolute patience and discipline. AI does the hard parts now -> you don't even need a CS degree to build this The full behind-the-scenes live system build is now available to the public 📝

The most underrated math theorem but Google secretly used it to change the world. Perron-Frobenius Theorem (positive matrix version): Let A be an n×n matrix with every entry a_{ij} > 0. Then there exists a unique positive real number λ > 0 (the Perron root) and a unique (up to scaling) positive vector x > 0 such that: A x = λ x Moreover: λ = ρ(A) (spectral radius of A) λ > |μ| for every other eigenvalue μ λ is simple (algebraic multiplicity = 1) Your search results aren't just a list; they are the coordinates of a high-dimensional vector pointing toward the most authoritative nodes on the web.

I've been discussing Huawei's τ scaling (temporal scaling) with people recently, and noticed the conversation tends to stay at the surface level without reaching its substance — likely because many participants don't come from an EE background and aren't familiar with the classical meaning of τ in circuit theory. The very first time constant you learn in a circuits course is τ = RC: the resistance of a wire multiplied by its capacitance gives the order of magnitude of the time a signal needs to traverse that wire. The longer the wire, the greater the resistance and capacitance, and the slower the signal. Within this framework, the past sixty years of geometric scaling are reinterpreted as one particular implementation of temporal scaling. Transistors were shrunk to shorten switching delay; circuits were packed more tightly to shorten metal interconnects and reduce signal propagation delay. Geometric scaling was only ever the means — compressing delay was always the end. Huawei's thesis is that once geometric scaling stalls, you find other ways to keep compressing delay. As it happens, He Tingbo's τ scaling paper released its v2 a couple of days ago, expanding from 16 to 23 pages. I compared the two versions: the data and conclusions are unchanged. The additions are essentially responses to several points of criticism the industry raised about v1. Three are worth discussing. The most important addition is the test evidence now backing the previously bare claim of "41% energy efficiency improvement." In v1, that number had no baseline and no test conditions — the most obvious target for scrutiny. V2 supplies a full comparison table. The baseline is the 2025 Kirin 9030 Pro. Both chips use the same mature process node; the key difference is that the baseline uses a conventional planar design, while Kirin 2026 folds critical paths across two vertically bonded wafers. Folding shortens interconnects and reduces interconnect delay. The timing margin freed up on the critical path translates directly into a higher maximum clock frequency: 3.1 GHz at 1.1 V supply, 13% above the baseline. The "41% energy efficiency improvement" comes from a separate operating point specifically configured for an iso-performance comparison: voltage scaled down to 0.9 V, frequency scaled down to 2.5 GHz, with measured power at 25°C coming in at 0.59× the baseline. A back-of-the-envelope estimate checks out: dynamic power scales roughly with the square of supply voltage, so an 18% voltage reduction contributes about one-third of the power drop from the square term alone. Factor in the 9% frequency reduction and the interconnect capacitance eliminated by folding, and you land right around 0.59×. So the precise meaning of "41% energy efficiency improvement" is power reduction at iso-performance. In essence, the timing margin gained from folding is traded for lower power consumption; the efficiency gain comes from logic folding. As a side note, v2 also reports that power density after dual-layer stacking is actually 5.6% lower than the baseline. The second addition addresses the question peers are most likely to ask: 3D stacking has been around for years — AMD's 3D V-Cache and Intel's Foveros are both in volume production — so what's new about LogicFolding? To understand the paper's answer, you first need to know how two layers of silicon communicate. They rely on inter-layer bond pads, which function like elevators connecting the upper and lower floors. In prior production 3D stacking, bond pad pitch ranges from 9 μm to tens of micrometers, yielding roughly ten thousand connections per square millimeter — enough to attach a bus to an entire cache block. So the established design approach has been to move complete functional blocks wholesale onto the upper tier. AMD, for example, stacks an entire cache die on top of a processor die; the two tiers are designed independently and connected through an interface. But inside a chip, a single square millimeter contains hundreds of millions of transistors. If you want adjacent logic gates to sit on different tiers — one on top, one on the bottom — that connection density falls far short. Kirin 2026 brings bond pad pitch down to 1.5 μm, yielding 440,000 connections per square millimeter. That approaches the density of the top-level metal wiring inside a chip. Routing a signal across tiers costs roughly the same as routing it across metal layers within a single die. At this point, the two silicon layers merge into a single entity in the circuit sense. EDA tools can decide at the individual logic-gate level which gate goes on which tier, handing the problem to algorithms for global optimization — a completely different degree of design freedom from what came before. The paper also explains why they didn't take the more aggressive route of fabricating a second device layer directly on top of the first. That approach offers the finest inter-layer connectivity, but manufacturing the second layer requires high temperatures that damage the already-completed first layer. It isn't production-viable today. The third addition is thermal management. Vertical stacking significantly increases thermal density per unit area, and the lower die's heat dissipation path is blocked by the upper die. This is the first objection anyone raises about 3D stacking, and v1 did not address it in depth. V2 openly acknowledges that thermal management remains a key challenge for the LogicFolding architecture. The countermeasure is thermally-aware partitioning and floorplanning: during the design phase, high-power circuits are excluded from folding candidates, and the floorplan avoids placing high-power blocks in vertical adjacency to prevent hotspot superposition. Whether this strategy is a set of manually imposed engineering constraints or has already been codified into an automated flow within their internal EDA tools, the paper does not say. It only identifies a multi-physics tool chain as the single most important investment for the next decade. Combined with the measured data showing power density 5.6% below the baseline at the iso-performance operating point, the thermal concern has at least received a direct response. That said, this approach is fundamentally avoidance-based. As stacking grows to three or four tiers, the design space eligible for folding will be progressively squeezed by thermal constraints — a boundary the paper does not explore. Additionally, v2 includes a cross-sectional micrograph of the bond interface between the two wafers and explicitly states that wafer-on-wafer hybrid bonding is used. This spec is worth benchmarking against the industry: 1.5 μm pitch wafer-to-wafer hybrid bonding on a production logic chip has no precedent. TSMC's SoIC is currently in production at 6 μm pitch; Intel's Foveros Direct is at 9 μm. Impressive, to say the least. After comparing the two versions, I'm left with two questions. One is about equipment: who supplied the bonding tools capable of this spec? The paper says only that it is the result of years of process development across a multi-vendor ecosystem. The other is about EDA: designing two wafers as a single chip is beyond what any commercially available EDA tool can do today. The paper acknowledges this, stating only that methodological details will be "published within months." Yet the frequency table shows that the 2027-generation Kirin at 3.39 GHz is already tagged as having physical silicon, meaning this toolchain was up and running inside Huawei long ago — and has been validated on at least two product generations. My personal guess is that this EDA capability was built in-house by Huawei. If anyone has insight on this, I'd welcome the discussion.

🧠 Hot Chips 2026: AI's Memory Wall Is Becoming a Money and Packaging Wall Hot Chips 2026 is still producing the usual charts of larger chips and higher peak compute. But this year, memory vendors are becoming central to the AI hardware story. The reason is simple: adding more arithmetic is getting easier. Keeping those units fed is not. Zhihu contributor 乱序摸鱼 reviewed the conference's memory talks and traced a larger shift across Samsung, Micron, SK hynix, and emerging architectures such as HBF. His main takeaway: the boundary between compute and memory is starting to dissolve. 1️⃣ The Memory Wall has become a Money Wall AI is not merely consuming more memory. It is pushing the DRAM industry toward a far more silicon-intensive product. For the same capacity, HBM requires roughly 3× the DRAM die area of DDR. A wafer redirected to HBM can therefore deliver fewer total bits, even before packaging and yield are considered. At the same time, new DRAM capacity can take more than two years to become productive. AI accelerator demand changes much faster. The article cites a revealing mismatch: AI compute can grow around 3× every two years, while HBM bandwidth grows by less than 2× over the same period. HBM solves the bandwidth problem through massive parallelism: wider interfaces, more channels, more banks, TSVs, and advanced packaging. But the bandwidth is not free. Its cost appears elsewhere as silicon area, power, packaging complexity, and yield risk. 2️⃣ The HBM base die is turning into a SoC Traditional HBM had a clean division of labor. The upper DRAM dies stored data, while the base die handled interfaces, testing, and basic routing. HBM4 changes the economics. Once the base die moves to an advanced logic process, using it as an expensive wiring layer becomes increasingly difficult to justify. Samsung's roadmap therefore expands its role in stages. The shorter-distance PHY comes first. Then the memory controller moves down from the xPU, followed by telemetry, repair, testing, RAS, and potentially lightweight processing. This turns HBM from a passive device into something that understands its own banks, temperature, failure state, and access scheduling. But integration creates new tradeoffs. A shorter interface may reduce energy per bit while concentrating the same throughput into a smaller area, raising local power density and creating thermal hotspots. The base die is becoming smarter, but every new function brings more RTL, validation, thermal design, and software work. 3️⃣ Near-memory compute should be measured in bits avoided Putting compute near memory sounds attractive, but available silicon is not enough to justify doing it. The more useful question is: How much data movement disappears after this operator moves closer to memory? Filtering, compression, copying, search, and simple reductions can make sense near memory. They may scan a large dataset and return only a small result, eliminating enormous amounts of traffic. Dense matrix multiplication is different. Data moved into a GPU may be reused many times by Tensor Cores. Moving that computation into a constrained base die could create a weaker accelerator while adding a new compiler and runtime target. The author's rule is practical: move operations that significantly reduce data volume, not operations that merely fit into spare logic area. Samsung's zHBM takes this idea further by vertically stacking HBM above the xPU. The modeled design removes much of the millimeter-scale horizontal path between memory and compute. Samsung's public targets suggest around 70% lower I/O power and roughly 100 W saved in a modeled 1,200 W GPU system. Those are architectural targets rather than proven production results. The shorter electrical path also introduces harder problems in bonding yield, thermal design, power delivery, repair, and cross-die ownership. 4️⃣ HBF could add a cold tier, but software must make it work High Bandwidth Flash proposes another route: use stacked NAND to provide much more capacity at a lower cost than HBM. The tradeoff is severe. HBF may offer cheap capacity, but its bandwidth per gigabyte is roughly an order of magnitude lower. That makes it unsuitable as a drop-in replacement for HBM. Its strongest use cases are large datasets with consistently low access rates: 🔹 Cold MoE experts 🔹 Sparse KV cache 🔹 Prefix cache 🔹 Model state that must remain nearby but is not read every token Even MoE is not automatically a good fit. A single token activates few experts, but a larger batch combines expert requests from many users. The supposedly cold expert pool can become hot surprisingly quickly. The harder problem is system software. An HBM-HBF hierarchy needs placement, allocation, prefetching, request coalescing, cache policy, wear management, and runtime telemetry. The article's verdict is cautious: the architectural need is real, but HBF's path from an attractive model to a dependable product remains largely unproven. 5️⃣ HBM scaling is becoming a packaging problem SK hynix's presentation shows why adding more DRAM layers is no longer a simple capacity upgrade. Moving from 12Hi to 16Hi increases the number of layers by 33%, while the package height rises from roughly 720 μm to 775 μm, an increase of less than 8%. The remaining option is to compress everything. Dies become thinner, inter-die gaps narrower, and bump pitches denser. Warpage, underfill, thermal resistance, and bonding yield all become harder to control. Yield is also cumulative. A defect introduced early may only appear after many expensive processing and stacking steps have already been completed. For 20Hi and beyond, hybrid bonding becomes less of a futuristic option and more of a practical necessity. By removing conventional bumps and thick underfill gaps, hybrid bonding can reduce interconnect pitch and thermal resistance. It can also return part of the height budget to the DRAM itself. SK hynix estimates that, within the same overall stack height, core dies could be up to 24% thicker than with MR-MUF while using an interconnect pitch below 18 μm. That extra silicon thickness matters for mechanical strength, wafer handling, warpage, and manufacturability. 6️⃣ The real boundary being redesigned The most important Hot Chips 2026 memory story is not another increase in TB/s. It is the rising cost of the physical distance between data and compute. As arithmetic moves from FP16 to FP8 and FP4, chips can contain more MAC units than the system can consistently feed. Performance is increasingly lost to memory access, synchronization, interconnect power, thermal limits, and data placement. The optimization target is therefore expanding from one chip to the entire task path. HBM base dies are becoming logic devices. Memory controllers are moving closer to DRAM. Flash may become another managed AI memory tier. Compute and memory may eventually be vertically integrated. None of this eliminates complexity. It decides where that complexity should live so the whole system becomes more efficient. The author's final insight is a good one: Sometimes the best architecture does not make the road faster. It discovers that the trip never needed to happen. 🔗 Full analysis: #HotChips2026# #HBM# #AIInfrastructure# #Semiconductors# #MemorySystems# #AdvancedPackaging# #AIHardware#