Writings
Long-form writing across AI infrastructure, memory systems, local agent runtimes, and accelerator architecture. This section is where project ideas and patent-adjacent concepts get room to breathe as essays rather than just landing-page summaries.
· 25 min read
A deep systems-level reference on close-to-metal RL inference at GB300 scale: persistent decode workers, hugepage-backed KV arenas, GPU/NIC command rings, NUMA locality, TLB/IOMMU reduction, cache coherency, and async reward handoff — with full C code.
· 18 min read
A systems-level analysis of GPU-to-GPU hybrid bonding, advanced packaging, chiplet GPUs, AI accelerator fabrics, and the future of multi-die GPU architecture — why the next scaling curve depends less on raw FLOPs and more on how cheaply silicon can talk to silicon.
· 18 min read
A systems-level essay on why long-context attention should not pretend every KV block is equally useful — semantic block metadata, IntentQuant-KV precision policies, and what runtime-intent-aware KV execution looks like in the long-context regime.
· 25 min read
A complete ground-up guide to the world's most essential passive component — from ceramic dielectric physics and manufacturing to a $22B market shaped by AI servers, electric vehicles, and geopolitical supply shocks.
· 13 min read
A systems-level analysis of SK Hynix iHBM, HBM thermal bottlenecks, MR-MUF packaging, HBM5 power density, and why memory cooling is becoming strategic.
· 10 min read
A systems-level analysis of NVIDIA Vera, Rubin, and why the AI factory is becoming an orchestration and memory-movement problem, not just a GPU story.
· 14 min read
A systems-level analysis of Huawei's Tau Scaling Law, LogicFolding, distance-centric computing, verification complexity, and why test and packaging infrastructure may matter more.
· 8 min read
A clear explainer of what tokens are, how tokenization works, why context windows and pricing depend on them, and why they shape model behavior.
· 12 min read
A vendor-neutral reference architecture for standalone DRAM racks serving long-context AI systems, from fabric topology and KV-cache offload to failure modes and deployment criteria.
· 18 min read
A deep technical architecture essay opening up the proposed KV infrastructure ASIC: floorplan, DMA engines, metadata SRAM, compression, prefetching, timing paths, and cluster topology.
· 15 min read
A systems architecture essay on why KV cache has become first-class infrastructure for long-context inference, and why generic memory systems are not enough.
· 11 min read
A ground-up guide to Multi-Query and Grouped-Query Attention, why they shrink KV cache memory, and how they make long-context LLM inference cheaper and faster.
· 16 min read
A systems essay on where CXL fits in the AI memory hierarchy, from GPU HBM and warm KV cache tiers to CPU DRAM, NVMe, and long-context serving pressure.
· 20 min read
A systems architecture essay on why generic CXL memory is the wrong abstraction for transformer inference, and how semantic KV-aware memory control could reduce HBM pressure.
· 20 min read
A systems-level guide to why retimers matter in AI servers, from PCIe 5/6 and CXL signal integrity to vendor positioning, active cables, optics, and the bear case.
· 22 min read
Part 2 extends the memory-timings deep dive into inference performance: Linux runtime tuning, hugepages, NUMA binding, PCIe topology, GPU placement, and verification commands.
· 18 min read
A practical deep dive into how modern systems configure memory timings across BIOS, firmware training, Linux runtime tuning, NUMA placement, CXL tiering, and HBM locality.
· 10 min read
Autonomous agents are starting to design, benchmark, debug, and optimize the low-level CUDA and Triton kernels that determine real AI system throughput.
· 19 min read
A ground-up guide to how transistors become logic, logic becomes stateful machines, and architecture diverges into CPUs, GPUs, ASICs, TPUs, FPGAs, AI accelerators, and brain-like systems.
· 8 min read
A systems architecture analysis of Alibaba Zhenwu M890, explaining why AI infrastructure is shifting from tensor FLOPS to memory orchestration, HBM, KV cache, and rack-scale fabrics.
· 21 min read
A systems engineering guide to Linux board bring-up for x86, Arm, and accelerators, covering firmware, ACPI, Device Tree, drivers, DMA, IOMMU, runtimes, and debugging.
· 12 min read
A systems-level deep dive into HBM, DDR5, SODIMM, LPDDR, CXL, KV-cache pressure, packaging IP, and the future AI memory wall.
· 9 min read
A systems deep dive into the quadrillion-token era, KV-cache memory pressure, prefill/decode disaggregation, token warehouses, and the AI memory wall.
· 16 min read
A technical deep dive into disaggregated memory for AI infrastructure, covering CXL, NVLink, memory pooling, vendor ecosystems, latency, and data-center design.
· 16 min read
A technical essay on why co-packaged optics matters for AI semiconductors, bandwidth density, switch power, optical I/O, and data-center fabric scaling.
· 17 min read
A deep technical explainer on scaling laws, Chinchilla compute-optimal training, data limits, inference economics, and why modern AI progress was predictable enough to plan around.
· 25 min read
A technical concept essay on query-time bounded elimination, semantic witnesses, and reconstruction-first KV memory for long-context transformer inference.
· 22 min read
A deep technical essay on why AI inference is shifting from a compute bottleneck to a memory-bandwidth and orchestration problem.
· 16 min read
A patent-backed essay on attention-sink-aware SRAM placement, explaining how sink-token behavior can reroute the hottest KV state into faster on-chip memory.
· 17 min read
A deep technical essay on why SRAM-heavy accelerators fit decode-side FFN and MoE paths, and how heterogeneous inference reframes the AI chip memory debate.
· 18 min read
A complete guide to model flop utilization, low-precision formats, tensor-core throughput, and how to read AI training efficiency claims without getting fooled by the denominator.
· 19 min read
A systems essay on KV paging, FlashAttention-style tiling, SRAM-sized decompression windows, cold-memory prefetch, and the hardware long-context inference really wants.
· 17 min read
An original technical analysis of FlashDecode covering kernel crossover math, reduction inversion, CXL asymmetry, and runtime switching for long-context inference.
· 19 min read
A deep technical essay on KV cache growth, tiled attention, outliers, and why long-context inference is increasingly a memory systems problem rather than just a compute problem.
· 22 min read
Why runtime dynamism is becoming too expensive for AI inference, and how compilers are evolving into full memory traffic planners across DMA, tensor placement, and deterministic execution windows.
· 18 min read
A systems-level deep dive into KV cache growth, attention memory, and why the next AI architecture war is rapidly becoming a memory war.
· 20 min read
A deep technical essay on coherent fabrics, CXL, NVLink, cache coherence, and why shared-state coordination is becoming the hardware bottleneck for agentic AI systems.
· 18 min read
A deep technical essay on how CXL, coherent memory, SmartNICs, GPUDirect Storage, unified memory, and KV-cache routing all converge on the same core AI infrastructure problem: moving state efficiently.
· 14 min read
Device-local KV, host spill, pinned buffers, DMA paths, and the physical constraints that determine what your serving stack should put where — and why getting it wrong costs 10× on tail latency.
· 18 min read
A technical deep-dive into how CXL, SmartNICs, GPUDirect Storage, and unified memory are solving the memory movement bottleneck in modern AI systems.
· 18 min read
Attention indexing, VRAM, HBM, huge pages, TLB pressure, and pinned memory explained from first principles for modern inference stacks.
· 12 min read
A public-facing note on Indian patent application no. 202641059412 and the case for treating model-weight delivery as its own inference architecture problem.
· 16 min read
A systems-level explainer on how cache misses, branch stalls, and pipeline bubbles shape CPU inference performance in transformer prefill and decode.
· 21 min read
A deep technical primer on /dev/mem_hint, the kernel-mediated hint path linking AI runtimes, PMU classifiers, MSR/MMIO/CXL conduits, and memory PHY policy engines.
· 14 min read
A patent essay on software-defined, workload-aware adaptive memory signaling for AI systems, letting runtimes tune memory timing, power, and latency by phase.
· 8 min read
A technical essay on why HBM is not a magical cache, but a scarce local working tier whose value depends on residency policy, bandwidth budgeting, and orchestration.
· 7 min read
A systems essay on why sparse expert routing becomes a fabric, transport, and queueing problem long before it becomes a pure model-efficiency win.
· 6 min read
A technical essay on why rack power, thermal headroom, and electrical state are becoming live runtime inputs for admission, placement, and useful-throughput control.
· 13 min read
A systems essay on why serving quality is shaped by queue depth, prefill/decode asymmetry, batching policy, and memory-driven service-time variance.
· 11 min read
A technical essay on why the deepest AI infrastructure advantage is moving into placement, admission, routing, and state-management policy.
· 11 min read
A memory-systems essay on how misses really behave in AI infrastructure, from HBM and KV locality to fabric fetch and byte-movement economics.
· 12 min read
A deep technical essay on Rambus, from MRDIMM and interface chips to CXL, security IP, and why AI infrastructure is becoming memory-interface bound.
· 38 min read
A technical essay on DeepSeek V4, from hybrid attention and million-token context to pricing, Huawei inference infrastructure, and the geopolitics of open weights.
· 36 min read
A technical essay on Intel 18A and 14A, from RibbonFET and PowerVia to High-NA EUV, foundry economics, packaging, and the race with TSMC.
· 29 min read
A technical deep dive into Graviton4, Axion, Cobalt 100, AmpereOne, Grace, and Qualcomm across architecture, benchmarks, software, and platform tradeoffs.
· 7 min read
A systems-level primer on SOCAMM2, LPDDR5X server modules, Rambus support silicon, and where modular memory fits in the AI server hierarchy.
· 20 min read
A systems primer on high-power laser arrays, external laser sources, silicon photonics, packaging, and why CPO optics are becoming central to AI data-center networks.
· 22 min read
A deep dive into semiconductor substrates, from ABF and BT to ceramic, glass, fan-out, and the advanced packaging stack underneath modern AI chips.
· 18 min read
A deep technical primer on smart storage controllers, Linux drivers, NVMe queues, DMA, IOMMU, and the full AI server path from read() to GPU HBM.
· 15 min read
A technical primer on AI storage, from NAND and SSD controllers to GPUDirect Storage, HDD economics, HBF, and the memory wall in modern AI systems.
· 17 min read
A technical essay on the next wave of AI CPUs: Intel Clearwater Forest, AMD Venice, Arm AGI, NVIDIA Vera, memory coherency, power, and host orchestration.
· 18 min read
A technical essay on why CPUs are becoming central again in AI infrastructure: memory movement, GPU orchestration, agentic workloads, power, and runtime control.
· 18 min read
A technical guide to the AI power delivery stack from grid to chip, mapping Vicor, MPS, TI, ADI, Renesas, Infineon, Delta, Flex, Murata, Bel, Navitas, and Wolfspeed.
· 22 min read
A technical essay on why NVIDIA and AI data centers are moving toward 800V DC power, and how SiC and GaN semiconductors reshape the infrastructure stack.
· 16 min read
A technical essay on next-generation AI chip floorplans, logic bridges, HBM expansion, on-package NUMA, SRAM locality, and where optical interconnects fit.
· 18 min read
A technical architecture essay on how AI accelerators may evolve from flat HBM layouts into topology-aware on-package NUMA fabrics with chiplets, SRAM layers, and optical links.
· 18 min read
A systems-level explanation of NVLink Switch, NVL72 scale-up fabrics, GPU coherence boundaries, and where scale-up ends and scale-out begins.
· 20 min read
A technical comparison of 800G Ethernet and InfiniBand for AI scale-out, covering congestion control, collectives, latency, cost, and operational tradeoffs.
· 19 min read
A ground-up explanation of CXL.io, CXL.mem, and CXL.cache, and why CXL is really a family of protocols for coherent memory expansion.
· 21 min read
A deep technical walkthrough of NCCL internals, collective communication, rings, trees, channels, topology awareness, and why AI clusters depend on it.
· 16 min read
Why wafer probe, burn-in, ATE, high-speed I/O validation, thermal control, and advanced packaging are turning AI chip test into a first-order cost constraint.
· 18 min read
A practical map of wafer probe, burn-in, ATE, high-speed I/O validation, handlers, and system-level test for modern AI chips.
· 20 min read
Why MLIR, Triton, and XLA are becoming the control layer between AI hardware, kernel generation, memory policy, and inference efficiency.
· 22 min read
Why fat-tree assumptions break under MoE routing, prefill-decode disaggregation, KV transfer, and topology-aware inference scheduling.
· 24 min read
A first-principles look at token pricing, the physical memory-and-cooling cost floor, and which AI infrastructure moats survive.
· 35 min read
A ground-up technical guide to co-packaged optics, pluggable transceiver limits, silicon photonics, and AI data-center fabric power.
· 12 min read
A technical essay on the semiconductor manufacturing stack beyond lithography: deposition, etch, CMP, implant, metrology, clean, and subsystems.
· 12 min read
A technical deep dive into ASML lithography systems, from DUV and immersion to Low-NA EUV, High-NA EUV, and why manufacturable EUV took three decades.
· 28 min read
A technical deep dive into the equipment stack behind advanced nodes: how Lam, Applied Materials, ASML, KLA, and TSMC fit together from etch and deposition to EUV and yield.
· 6 min read
GPU:CPU planning is becoming workload-specific again. For long-context, retrieval-heavy, and orchestration-heavy inference, CPU capacity, memory bandwidth,...
· 10 min read
From a 70-year algorithm to medical AI: how quantum computing flips the resource game for molecules, materials, and drugs
· 12 min read
The inference memory essays in this series talk about KV cache, weight residency, and HBM pressure. Training has its own version of all three — and the...
· 13 min read
Mixture-of-Experts models are sold as a 10× efficiency win: 10× more parameters, same per-token compute. That math is correct. But it omits the router — the...
· 14 min read
The KV cache gets all the attention. But for large models at low batch sizes, loading transformer layer weights from HBM dominates decode latency just as...
· 13 min read
A thesis-driven essay on why VQE and QAOA hit barren plateaus, routing overhead, and noise walls long before meaningful NISQ advantage.
· 9 min read
A technical essay on surface codes, fault-tolerance thresholds, and the brutal redundancy required to turn noisy physical qubits into useful logical ones.
· 11 min read
A technical essay on which cryptographic systems quantum computers actually threaten, which survive, and how post-quantum migration should be understood.
· 12 min read
A technical essay on quantum computing as structured interference, covering superposition, entanglement, amplitude amplification, error correction, and where speedups actually come from.
· 10 min read
A technical survey of large language model architectures covering transformer backbones, attention variants, sparsity, MoE, normalization, positional encoding, and systems tradeoffs.
· 15 min read
A ground-up technical primer on quantum computing covering qubits, gates, entanglement, circuits, error correction, and the path from theory to useful machines.
· 17 min read
A synthesis essay connecting transistor physics, memory hierarchies, inference systems, agents, and economics into one view of the AI stack.
· 20 min read
A technical essay on three systems ideas shaping frontier models: sparse MQA, fused MoE mega kernels, and hyper-connections as a throughput and efficiency stack.
· 14 min read
A technical essay on the host-side copy tax in inference systems, why PCIe crossings burn usable bandwidth, and how GPUDirect RDMA plus DPUs recover MoE throughput.
· 16 min read
A technical essay on how HBM is physically constructed, from stacked DRAM dies and TSVs to silicon interposers, and why packaging physics sets the AI bandwidth ceiling.
· 17 min read
A technical essay unpacking SMs, warps, tensor cores, register files, and why GPU design choices make sense for parallel workloads but strain during autoregressive decode.
· 17 min read
A technical essay on what modern node names really mean, how FinFET and GAA devices extend scaling, and why Moore's Law is slowing for AI chip design.
· 12 min read
A technical essay on turning KV cache into rack-scale infrastructure across HBM, CXL DRAM, and NVMe, with DPUs acting as the metadata control plane.
· 22 min read
A technical essay on why AI inference latency is usually constrained by memory bandwidth and hierarchy design, not raw arithmetic throughput.
· 12 min read
Why the 78% CPU / 31% GPU problem in agentic inference isn't a software bug — it's an architecture problem. Using a BlueField-3 DPU to bypass the host and feed the GPU directly.
· 12 min read
The center of gravity for AI inference is shifting from dense matmuls to stateful orchestration, context movement, and memory capacity. For three years, "AI scaling" meant buy more GPUs. Agentic AI breaks that model.
· 8 min read
RISC and CISC aren't processor brands — they're philosophies. One says make the hardware simple and push work to software. The other says make the hardware clever to make software easier. Modern chips use both ideas, but understanding the trade-off explains almost everything about why your phone's processor looks nothing like your laptop's from 1995.
· 23 min read
Agentic AI workloads — orchestrators calling planners calling retrievers calling verifiers — are not well-served by inference infrastructure designed for isolated single-model requests.
· 25 min read
OpenAI, Anthropic, Google, and a dozen open-source providers all quote $/1M-token prices. Nobody explains what those numbers are made of. This essay derives the cost of an output token from first principles.
· 12 min read
KV cache bandwidth must be partitioned like network bandwidth. With compiler contracts, token buckets in hardware, and page coloring, multi-tenant tail latency improves 3.6x and throughput improves 28 to 31 percent.
· 14 min read
Power is not a limit to hit and then throttle. Power is a budget to allocate. By making joules-per-token a first class SLO, we can turn blind firmware throttling into global, proactive contract scheduling.
· 6 min read
The physical limits of the motherboard have been reached. HBM is fast, but the moment an AI workload spills to host memory over PCIe, performance falls off a 75x cliff. Here is why CXL memory disaggregation is dismantling the definition of a server.
· 9 min read
AI is powered by billions of approximate numbers. Once you understand floating point constraints—from fp32 to fp8—a lot of AI hardware design, quantization, and training stability suddenly clicks.
· 44 min read
A comprehensive guide to the global semiconductor supply chain — from EDA tools and chip design through foundries, equipment, materials, memory, packaging, CPO, and test. 100+ companies across 7 countries, with detailed chokepoint analysis.
· 7 min read
No hardware caches. No L1/L2. The TPU exposes raw scratchpad SRAM to the XLA compiler — and that changes everything about memory determinism vs. generality.
· 7 min read
GPU clusters route packets through switches. TPU pods route light through MEMS mirrors. The physical network reshapes itself to match the computation.
· 6 min read
NVIDIA bets on fat nodes with maximum per-GPU memory. Google bets on thin nodes with maximum inter-chip bandwidth. Both are rational — they optimize for different cost functions.
· 7 min read
The GPU gets the credit. The SSD does the lifting. A systems essay on the five roles NAND flash plays — from checkpoint absorption to KV-cache offloading and weight staging.
· 7 min read
8–16× HBM capacity, within 2.2% of HBM read performance. How NAND-based HBF is designed to close the 300× bandwidth gap in the AI memory hierarchy.
· 7 min read
A bottom-up accounting of flash capacity in hyperscale AI — from 15 TB per node to 3 exabytes per cluster. The numbers, the form factors, and the supply-chain pressure.
· 8 min read
When a 128K-context request shares a GPU with forty 2K requests, the scheduler sees equal slots. The memory system sees a 64× asymmetry — and the minnows pay the price.
· 7 min read
Batch size is a number. Batch geometry is a distribution. Two batch-32 configurations can differ by 10× in memory cost — and the memory system sees the distribution.
· 9 min read
For some KV pages, recomputing attention from scratch costs less than fetching from host RAM. A systems essay on the fourth action in eviction policy design.
· 15 min read
A trace-driven research harness for studying KV-cache eviction, quantization, and prefetch tradeoffs — featuring a regret-aware eviction policy with multi-seed ablation results.
· 29 min read
A technical and industrial essay on the full power ecosystem behind AI data centers, from generation and transmission to UPS, switchgear, and GPU delivery.
· 23 min read
A systems essay on how Vera Rubin NVL72 changes AI data center cooling with 45°C supply temperatures, fan-free trays, hose-free design, and liquid-cooled busbars.
· 22 min read
A systems essay on the five-layer direct liquid cooling architecture of Blackwell GB300 NVL72 racks and the suppliers behind each thermal layer.
· 13 min read
A systems essay on where thermal control actually belongs inside vLLM, from scheduler decisions and swap behavior to tensor-parallel batch cutting.
· 22 min read
A technical guide to detecting silent HBM thermal throttling on H100 and H200 clusters when standard GPU temperature dashboards look deceptively healthy.
· 18 min read
A systems essay on HBM thermal telemetry, KV fetch stalls, and why hot memory dies turn thermal debt into an inference scheduling problem.
· 19 min read
A systems essay on why co-packaged optics matters because it makes disaggregated memory and expert movement schedulable under tight latency budgets.
· 11 min read
A technical essay on thermal-safe KV admission, HBM backpressure, reuse prediction, and production-serving policy design for H100 and H200 inference clusters.
· 30 min read
A technical primer mapping the AI optical networking stack across fiber, lasers, transceivers, DSPs, switches, and test infrastructure through the companies building each layer.
· 17 min read
A systems essay on attention sink tokens, structural KV cache waste, and why long-context serving needs memory-policy-aware treatment of hot-but-low-utility tokens.
· 19 min read
A systems essay on how dense GPU racks accumulate thermal debt, why point-in-time observability misses it, and what thermally-aware control planes should measure.
· 9 min read
A detailed product and architecture essay on TechDemoForge, its workflow, repo structure, and why local-first technical demo generation is useful.
· 17 min read
A systems essay on why prefill and decode should be split across different hardware pools and why the real engineering challenge becomes memory orchestration.
· 17 min read
A systems essay on draft/verify KV pressure, rollback fragmentation, and why speculation pays off only when memory policy is designed for it.
· 8 min read
A systems essay on sparse attention serving, hierarchical KV residency, predictive prefetch, and why long-context wins increasingly come from memory policy.
· 8 min read
A systems-first essay on why the next optical contest in AI infrastructure is shifting inward toward dense rack-scale and scale-up fabrics.
· 6 min read
A deeper systems-first essay on why AI networking will be built from a layered materials stack rather than a single optical winner.
· 7 min read
A deeper systems essay on why the next constraint is the power, heat, and topology cost of moving bits across clusters.
· 15 min read
A systems essay on how optics is moving into the scheduler, topology planner, reliability model, and power logic of next-generation AI infrastructure.
· 15 min read
A practical hardware-focused companion essay on optical architectures, power budgets, serviceability, and the real tradeoffs hyperscalers optimize for.
· 12 min read
A systems essay on explicit data movement, KV cache management, tiered memory placement, DMA orchestration, and why scheduler quality now directly determines inference efficiency.
· 18 min read
A long-form systems argument for selective coherency, explicit tensor movement, CXL.mem over universal coherence, and schedule-first design in large-model infrastructure.
· 16 min read
A systems primer on HBM as a bounded working set, KV cache dominance, PagedAttention, weight offload, and why scheduled weight streaming is the next step in inference architecture.
· 11 min read
A systems essay on 48-bit virtual addressing, mmap-heavy designs, storage density, 5-level paging, and why explicit data orchestration becomes the long-term answer.
· 16 min read
A systems essay on compiler-emitted memory intent, object semantics, workload phases, reuse confidence, and why hardware orchestration needs structured plans instead of blind guesses.
· 14 min read
A technical essay on explicit memory intent, residency maps, regret-aware eviction, recomputation-vs-transfer arbitration, atomic doorbells, and DPU embodiments for AI memory fabrics.
· 6 min read
A systems essay on why software-only memory orchestration hits a ceiling and why the real future is hardware-resident movement control near the fabric, the tiers, and the accelerators.
· 8 min read
A technical essay on memory placement, movement, residency, reuse, admission, and eviction as first-class scheduling decisions rather than passive implementation details.
· 8 min read
A technical essay on why local direct-storage acceleration and network direct-memory acceleration still do not add up to one universal GPU-native end-to-end storage fabric.
· 8 min read
A technical essay on why eliminating host-side bounce buffers shifts the real bottleneck inward, toward deterministic HBM↔SRAM orchestration inside the accelerator.
· 16 min read
A technical essay on hidden staging buffers, GPUDirect-era dataflow, and why eliminating unnecessary copies matters across storage, network, memory, and accelerator paths.
· 10 min read
A detailed technical essay on RDMA, zero-copy realities, and why "RDMA exists" still does not mean true end-to-end zero-copy in disaggregated inference.
· 7 min read
A technical essay on policy above transport for KV movement, workload-aware admissibility, swappable glue layers, Scenario E and F, and experiment-backed results.
· 12 min read
A technical essay on gray failures, checkpoint economics, cooling-compute seams, and seam-aware control planes for modern AI cluster reliability.
· 10 min read
A technical essay on seam failures across facilities, fabrics, heterogeneous inference pools, checkpoint economics, and why component dashboards miss the most expensive cluster incidents.
· 5 min read
A technical explainer on authenticated power contracts, alternative execution plans, safe switching boundaries, and runtime enforcement for edge inference systems.
· 4 min read
A technical deep dive into compiler-scheduled DMA, explicit fences, bounded SRAM, and why deterministic buffer legality changes the inference memory system.
· 4 min read
A technical essay on latency-aware policy selection across model variants, DMA strategy, memory residency, and accelerator performance-state control on edge systems.
· 8 min read
A technical essay on HBM pressure, predictive multi-tier placement, precision state transitions, MoE router-history signals, and bandwidth-aware runtime scheduling.
· 8 min read
A systems essay on Android-first operations, FastAPI supervision, runtime adapters, websocket telemetry, and safe control of local coding agents across machines.
· 5 min read
An introduction to ChromeLens, deterministic CDP tracing, interactive flow profiling, and the hydration penalty behind complex modern web applications.
· 11 min read
A detailed technical essay on larger L2 caches in AI systems, miss-rate reduction, average access time, bandwidth relief, energy tradeoffs, and where bigger L2 stops being enough.
· 12 min read
A detailed technical essay on AI-native residency fabrics, class-aware memory, weights, KV cache, experts, and why bigger generic caches are not the full answer.
· 13 min read
A technical essay on residency-first decode acceleration, distributed on-package SRAM, HRM/HBM crossover behavior, and why autoregressive inference is fundamentally memory-bound.
· 9 min read
A practical systems essay on rolling-window low-utilization metrics, sampled idle behavior, power telemetry, and what point-in-time GPU utilization misses.
· 7 min read
A technical essay on privacy-first local portfolio tooling, Schwab CSV analysis, options workflows, IV crush scenarios, and practical risk reporting without platform theater.
· 8 min read
A technical essay on dynamic multi-tier weight residency orchestration across HBM, DRAM, and NVMe, with scoring, guardrails, state transitions, and simulation-first evaluation.
· 8 min read
A technical essay on runtime-agnostic, policy-governed, structure-guided experimental prioritization using AlphaFold-derived data, explainable scoring, and decision memory.
· 7 min read
A technical essay on predictive context region orchestration, region-level attention and residency control, speculative promotion, reversible demotion, and coherence-aware long-context inference.
· 8 min read
A technical essay on bandwidth amplification, repeated refill across the hierarchy, and why better AI machines need stronger movement discipline, not just more compute and memory.
· 13 min read
A deep technical essay on the bandwidth cost of repeatedly reloading hot weights during autoregressive inference, and why a wired on-chip residency primitive changes the machine rather than merely nudging the policy.
· 6 min read
A laptop-first systems write-up on shared execution units, bounded admission windows, and why local multi-agent workflows waste more backend work than most people realize.
· 23 min read
A systems essay on why LRU is the wrong default for HBM residency under LLM serving pressure, and how confidence gating, thrash budgets, and safe-window compaction change allocator behavior.