About | Christopher Peret

import torch import torch.nn as nn def train(model, loader, epochs=10): opt = torch.optim.AdamW( model.parameters(), lr=3e-4) loss_fn = nn.CrossEntropyLoss() model.train() for epoch in range(epochs): for x, y in loader: x, y = x.cuda(), y.cuda() opt.zero_grad() loss = loss_fn(model(x), y) loss.backward() opt.step() print(f"epoch {epoch} done") class ConvNet(nn.Module): def __init__(self, n_classes=10): super().__init__() self.net = nn.Sequential( nn.Conv2d(3, 32, 3, 1, 1), nn.ReLU(), nn.Conv2d(32, 64, 3, 1, 1), nn.ReLU(), nn.MaxPool2d(2), nn.Dropout(0.25), nn.Flatten(), nn.Linear(64 * 16 * 16, 128), nn.ReLU(), nn.Linear(128, n_classes), ) def forward(self, x): return self.net(x) from torchvision import transforms tfm = transforms.Compose([ transforms.RandomResizedCrop(64), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean, std), ]) train_ds = ImageFolder("train", tfm) loader = DataLoader( train_ds, batch_size=64, shuffle=True, num_workers=4, ) @torch.no_grad() def evaluate(model, loader): model.eval() correct = total = 0 for x, y in loader: x, y = x.cuda(), y.cuda() pred = model(x).argmax(dim=1) correct += (pred == y).sum() total += y.size(0) acc = correct / total print(f"val acc: {acc:.3%}") return acc model.eval() x = torch.randn(1, 3, 64, 64).cuda() traced = torch.jit.trace(model, x) traced.save("model.pt") # ONNX export for deployment torch.onnx.export( model, x, "model.onnx", input_names=["input"], output_names=["logits"], opset_version=17, ) print("exported model.pt")

import torch import torch.nn as nn def attention(q, k, v, mask=None): d_k = q.size(-1) scores = q @ k.transpose(-2, -1) scores = scores / d_k ** 0.5 if mask is not None: scores = scores.masked_fill( mask == 0, -1e9) w = torch.softmax(scores, dim=-1) return w @ v, w class MultiHeadAttention(nn.Module): def __init__(self, dim, heads=8): super().__init__() self.heads = heads self.qkv = nn.Linear(dim, 3 * dim) self.proj = nn.Linear(dim, dim) def forward(self, x): B, N, D = x.shape h = self.heads qkv = self.qkv(x).reshape( B, N, 3, h, D // h) q, k, v = qkv.unbind(2) out, _ = attention(q, k, v) out = out.reshape(B, N, D) return self.proj(out)

#include <Eigen/Dense> #include <array> #include <cmath> using Vec7 = std::array<double, 7>; using Eigen::Matrix4d; // KUKA iiwa DH parameters constexpr double A[7] = { -M_PI_2, M_PI_2, M_PI_2, -M_PI_2, -M_PI_2, M_PI_2, 0}; constexpr double D[7] = { 0.34, 0.0, 0.40, 0.0, 0.40, 0.0, 0.126}; // One DH link transform Matrix4d link(double a, double d, double q) { double ct = cos(q), st = sin(q); double ca = cos(a), sa = sin(a); Matrix4d T; T << ct, -st*ca, st*sa, 0, st, ct*ca, -ct*sa, 0, 0, sa, ca, d, 0, 0, 0, 1; return T; } // 7-DOF forward kinematics Matrix4d fk(const Vec7& q) { Matrix4d T = Matrix4d::Identity(); for (int i = 0; i < 7; ++i) T *= link(A[i], D[i], q[i]); return T; } using Eigen::Vector3d; using Mat67 = Eigen::Matrix<double, 6, 7>; // Geometric Jacobian at q Mat67 jacobian(const Vec7& q) { Mat67 J; Matrix4d T = Matrix4d::Identity(); Vector3d pe = fk(q).block<3,1>(0,3); for (int i = 0; i < 7; ++i) { Vector3d z = T.block<3,1>(0,2); Vector3d p = T.block<3,1>(0,3); J.block<3,1>(0,i)=z.cross(pe-p); J.block<3,1>(3,i)=z; T *= link(A[i], D[i], q[i]); } return J; } using Eigen::Matrix3d; // Damped least-squares IK step Vec7 ik_step(const Vec7& q, const Vector3d& goal) { auto J = jacobian(q).topRows<3>(); Vector3d e = goal - fk(q).block<3,1>(0,3); double k = 0.05; auto Jt = J.transpose(); Vector3d dx = (J*Jt + k*k*Matrix3d::Identity()) .ldlt().solve(e); Vec7 dq; for (int i = 0; i < 7; ++i) dq[i] = (Jt*dx)(i); return dq; }

use std::collections::BinaryHeap; use std::collections::HashMap; use std::cmp::Ordering; #[derive(Copy, Clone, Eq, PartialEq)] struct Node { cost: u32, pos: (i32, i32), } // min-heap: reverse the ordering impl Ord for Node { fn cmp(&self, other: &Self) -> Ordering { other.cost.cmp(&self.cost) } } impl PartialOrd for Node { fn partial_cmp(&self, other: &Self) -> Option<Ordering> { Some(self.cmp(other)) } } fn heur(a: (i32, i32), b: (i32, i32)) -> u32 { let dx = (a.0 - b.0).abs(); let dy = (a.1 - b.1).abs(); (dx + dy) as u32 } // A* shortest path over a grid fn astar( start: (i32, i32), goal: (i32, i32), adj: impl Fn((i32, i32)) -> Vec<(i32, i32)>, ) -> Option<u32> { let mut open = BinaryHeap::new(); let mut best = HashMap::new(); open.push(Node { cost: 0, pos: start }); best.insert(start, 0u32); while let Some(cur) = open.pop() { if cur.pos == goal { return Some(cur.cost); } for nb in adj(cur.pos) { let g = best[&cur.pos] + 1; let seen = best.get(&nb) .copied() .unwrap_or(u32::MAX); if g < seen { best.insert(nb, g); let f = g + heur(nb, goal); open.push(Node { cost: f, pos: nb, }); } } } None }

about

From field automation to intelligent software

My background combines 16 years of hands-on systems work, 13 years in automation and controls, and software projects across agentic AI, robotics, full-stack development, and simulation. Alongside my own products, I take on consulting and advisory contracts.

2026 - Present

Advisory Board Member

Boardy

Member of the Boardy Pro Advisory Board, working closely with the Boardy team to shape the direction of Boardy Pro.

▸ boardy_pro

2025 - Present

Founder

AgentLoader · AxisForge · Top Quark Studios

▸ ventures

AgentLoader — cloud-hosted autonomous coding agents and AI infrastructure: agent fleets running in sandboxed Firecracker microVMs, bring-your-own repo, keys, and model.
AxisForge — robotics and semiconductor research and development, with patent filings in preparation.
Top Quark Studios — independent game studio crafting bold, playful worlds.

2025 - Present

Independent Research & Engineering

Self-directed

Building software and AI projects across architecture, prototyping, and product development.
Selected work includes robotics visualization and simulation, machine-learning experiments, and authentication infrastructure.
Current focus includes software architecture, agentic AI, applied ML, robotics, and automation.
Available for consulting and advisory contracts across agentic AI, automation, and software architecture.

2024 - Present

Speaker & Organizer

Sin City Programmers

Volunteer public speaker on software development and AI advancements.
Organize and lead hackathons to foster innovation and collaboration in the Las Vegas tech community.
Advocate for machine learning and deep learning through local educational initiatives.

2024 - Present

Inception Program Member

NVIDIA

Member of NVIDIA Inception, NVIDIA's program supporting AI startups.

▸ inception_program

2024 - 2025

Founder & CEO

NeuralMech

Led a cross-functional team of 11, including Berkeley software engineers and an ex-CERN PhD; secured a $3.3M term sheet.
Directed applied-AI product work spanning robotics visualization and simulation, machine-learning experiments, and a pricing engine.
Specified and acquired AI training hardware to support in-house model training and deployment.

▸ neuralmech

2021 - 2022

Building Automation Controls Instructor

Steamfitters Local 601

Taught HVAC controls in the state apprenticeship program.
Developed course material grounded in practical field experience.

2009 - 2022

Automation Controls & Instrumentation Specialist

Steamfitters Local 601

Built 13 years of hands-on field experience in automation, controls, and instrumentation.
Worked across HVAC controls and automation programming.
Developed a systems-first approach to integration, troubleshooting, and technical delivery.

▸ field_projects: 9 sites

Fiserv Forum[arena]
Northwestern Mutual Tower[high-rise]
Miller Brewery[brewery]
Miller Brew House[brewery]
Children's Wisconsin[hospital]
Columbia St. Mary's[hospital]
Port Washington Generating Station[power-gen]
Elm Road Generating Station[power-gen]
Riverside Energy Center[power-gen]

education

Professional education and training

Formal training across software development, machine learning, and automation.

Professional Certificate

Full-Stack Web Development Certificate

University of California, Berkeley

Continuing and Professional Education program focused on full-stack software development.
Coursework included JavaScript, React, Node.js, databases, APIs, testing, deployment, and full-stack application development.

▸ credentials

Seal of the University of California, Berkeley

Specialization

Machine Learning Specialization

DeepLearning.AI / Stanford Online

▸ credentials

Coursework taught by Andrew Ng.

Five-Year Program

Automation Apprenticeship

Steamfitters Local 601

Completed a five-year apprenticeship in automation and controls.
Earned journeyman qualification in automation and controls.

▸ credentials

▸ field_employers: contractors worked with, 2009 - 2022

Bechtel
AECOM
Day & Zimmermann
J.F. Ahern Co.
Butters-Fetting Co.
Grunau Company

▸ whoami

$ loading…

Founder

AgentLoader · AxisForge · Top Quark Studios

▸ ventures

AgentLoader — cloud-hosted autonomous coding agents and AI infrastructure: agent fleets running in sandboxed Firecracker microVMs, bring-your-own repo, keys, and model.

AxisForge — robotics and semiconductor research and development, with patent filings in preparation.

Top Quark Studios — independent game studio crafting bold, playful worlds.

Independent Research & Engineering

Self-directed

Building software and AI projects across architecture, prototyping, and product development.

Selected work includes robotics visualization and simulation, machine-learning experiments, and authentication infrastructure.

Current focus includes software architecture, agentic AI, applied ML, robotics, and automation.

Available for consulting and advisory contracts across agentic AI, automation, and software architecture.

Founder & CEO

NeuralMech

Led a cross-functional team of 11, including Berkeley software engineers and an ex-CERN PhD; secured a $3.3M term sheet.

Directed applied-AI product work spanning robotics visualization and simulation, machine-learning experiments, and a pricing engine.

Specified and acquired AI training hardware to support in-house model training and deployment.