From math and physics to systems that work on real hardware.

We turn mathematical intent into practical products: embedded devices and robotics, AI inference and bounded training, optimized Linux systems, server-class CPUs, accelerators, and measured distributed execution.

Embedded, AI, and high-performance systems engineering

First principles. Real hardware. Measured systems.

How we work

We begin with the behavior the product must produce. Mathematics defines correctness. Physics defines limits on timing, memory movement, power, heat, sensing, communication, and control. Source code exposes the execution path. Profilers and experiments identify the active constraint. Only then do we select or change frameworks, kernels, hardware, and deployment architecture.

From sensors and control equations to reliable embedded C.

Service 01 / embedded systems and robotics

Embedded C and C++, Zephyr RTOS, board bring-up, peripherals, buses, interrupts, timing, state estimation, Kalman filtering, flight-control software, sensor calibration, motors, communications, cross-compilation, and hardware-in-the-loop testing. We work across suitable microcontrollers, embedded Linux processors, heterogeneous compute, and accelerators; Nordic, NXP, ST, and TI platforms are examples of experience, not platform limits.

Make the model fit, run correctly, and produce useful results at the edge.

Service 02 / AI on constrained systems

Model and hardware feasibility, conversion, quantization, memory planning, C and C++ inference, embedded Linux integration, multimodal preprocessing, numerical comparison, first-divergence analysis, and bounded training or evaluation experiments. The target may be a microcontroller, embedded processor, CPU, accelerator, robot, or private edge system; the workload and constraints determine the platform.

Make compute, memory, Linux, and networks work as one measured system.

Service 03 / CPU AI, HPC, and distributed systems

C kernels, AVX2, VNNI, AVX-512, AMX, BF16, quantized execution, packing, tiling, cache reuse, threading, NUMA, Linux tuning, VTune, Advisor, perf, rooflines, numerical parity, model qualification, power, and TCO. Multi-node inference, training, MPI, RDMA, and high-speed networking remain evidence-gated until the distributed laboratory publishes reproducible scaling results.

Decide what can work before committing more hardware, time, or money.

Starting engagement 01 / feasibility and qualification

We evaluate the required behavior, workload, model or algorithm, target hardware, memory, latency, accuracy, privacy, power, lifecycle, regulatory or procurement constraints, and total cost. The result is a measured or assumption-labelled go, no-go, or test-next decision.

Find the first place the real system stops matching the intended system.

Starting engagement 02 / failure investigation

We trace a bounded failure across equations, preprocessing, numerical precision, layouts, state, timing, kernels, operating-system behavior, interfaces, sensors, and hardware. Completion means the issue is corrected or reduced to a named, measured cause with a deterministic reproducer and regression gate.

Build one inspectable path through the system and prove its limits.

Starting engagement 03 / bounded prototype or bring-up

We implement or repair a bounded controller, state estimator, embedded integration, model path, kernel, backend, Linux node, or distributed prototype. The deliverable includes source, reproducible build and tests, agreed correctness and performance gates, documentation, and explicit supported limits.

A decision, working artifact, and evidence your team can inspect.

What you receive

Depending on scope, the result may include a feasibility and TCO model, reproducible benchmark, profiler report, numerical correctness gate, source patch, optimized kernel, configured Linux node, evaluation environment, embedded prototype, or prioritized implementation plan. Assumptions, hardware, software revisions, test inputs, acceptance criteria, and known limits are recorded explicitly.

The methods are visible before you contact us.

Public evidence

C-Kernel-Engine exposes model circuits, numerical contracts, generated C, kernel work, profiler artifacts, and Linux deployment experiments. DroneMath connects equations to state-estimation and robotics code. ShivasNotes documents the mathematics, failures, benchmarks, and engineering decisions. Internal infrastructure developed over fourteen years keeps experiments and evidence organized rather than scattered across unexplained folders.

Optimize the system people can afford to own and operate.

Cost is an engineering constraint

Peak throughput is not the same as useful value. We evaluate the workload against response time, concurrent users, memory capacity, utilization, privacy, power, cooling, operations, replacement, and cloud or API alternatives. Hardware recommendations and total-cost models are dated, assumption-driven, and tied to the actual workload rather than a generic CPU-versus-GPU claim.

Fourteen years of building the tools behind the work.

Experience across the complete path

Antsand has evolved since 2014 into the internal system used to organize data, experiments, technical content, and publication. C-Kernel-Engine turns transformer mathematics into generated C and CPU kernels. DroneMath and Antshiv Robotics connect physics and estimation equations to robotics software. Linux CPU nodes, Intel profiling, Xeon research, and TI TDA4VM deployment keep the work connected to real machines. The complete engineering record is published through ShivasNotes, GitHub, and YouTube.

First principles before frameworks. Evidence before claims.

What makes our work different

We begin with the real behavior the system must produce. Mathematics defines correctness. Physics defines limits on compute, memory movement, power, heat, sensing, and communication. Source code shows how the work is actually performed. Profilers and experiments reveal which constraint matters now. Only then do we choose or change frameworks, kernels, hardware, and deployment architecture. This depth lets us move from model equations to Linux, CPU instructions, networks, embedded devices, and total cost without treating any one tool as the answer.

The person responsible for the work is visible.

Principal engineer

Engagements are led by Anthony Shivakumar. Antsand provides the internal systems used to organize project data, evidence, review, and publication, while C-Kernel-Engine, ShivasNotes, DroneMath, and the relevant repositories provide inspectable engineering artifacts. Specialist collaborators may be introduced when a scope requires them, but responsibility, assumptions, review points, and acceptance criteria remain explicit.

Qwen3-VL attention: finding the execution contract beneath the formula.

Current investigation / numerical correctness

A current CKE investigation traces model divergence through named tensor boundaries and compares attention dtype, storage, tiling, and reduction semantics against an authoritative runtime. The final case will include hardware, model, context, threads, both commits, benchmark command, before-and-after result, date, and known limitations. Material improvement will not be described as full parity unless the end-to-end gate closes.

CPU prefill: equivalent settings and inspectable measurements.

Benchmark in preparation / performance

This case will compare CKE with an established runtime using the same model, quantization, context, thread count, CPU, power policy, and measurement window. It will include commit identifiers, warm-up policy, raw outputs, profiler evidence, date, and limitations. This remains intentionally unclaimed until the controlled benchmark artifact is ready.

Work that can begin with bounded inputs and acceptance criteria.

Capability maturity / available now

Model and hardware feasibility; CPU kernel profiling; first-divergence and numerical analysis; Linux CPU investigation; and embedded feasibility. Availability means the investigation method and deliverables exist, not that every model, processor, or device is already supported.

Evidence lanes currently being hardened.

Capability maturity / being built

A public benchmark dashboard, broader model qualification, a Whisper and audio lane, and native Xeon BF16 and AMX evidence. These are active engineering targets, not services represented as complete today.

Directions that require measured closure before promotion.

Capability maturity / research roadmap

Distributed CPU execution, multi-node scaling, and broader embedded deployment remain research roadmap items. They become demonstrated capabilities only after reproducible hardware, communication, synchronization, correctness, and performance evidence is published.

Use an established product or runtime when it already solves the problem.

Not a fit

We are not the right fit for generic chatbot integration without a systems constraint, frontier-scale GPU pretraining, unbounded research without measurable acceptance criteria, or performance claims that cannot be reproduced. We will not replace a mature framework when it already satisfies the workload. The useful engagement begins when model output is wrong, a target is unsupported, performance is unexpectedly poor, or correctness, privacy, hardware behavior, and total cost cannot be validated through the default abstraction.

Tell us what is failing, slow, expensive, or impossible today.

Send the model or system, available hardware, required outcome, what has already been tried, and any real deadline. We will explain what we would investigate first, whether we can help, and what a useful initial engagement would produce. If the problem is outside our useful scope, we will say so directly.

ShivasNotes

Independent notes on software, systems, AI workflows, robotics, and building Antsand in public.

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