PyTorch Repository Review
Inspect a PyTorch project for correctness, training, tensor-shape, autograd and performance problems before proposing changes.
Turn the ideas on Programmer.ie into practical AI workflows you can run against your own code, models and agent systems.
These prompts are designed to move from reading to doing.
Each prompt is connected to a Programmer.ie learning path and asks an AI system to inspect real evidence before recommending changes. Copy one into ChatGPT, Codex, Claude, or another repository-aware assistant and point it at the system you want to understand or improve.
Debug training systems and apply the machinery-level techniques from the PyTorch curriculum.
View curriculum →Use these when you want to inspect the whole repository through the lens of this learning path.
Inspect a PyTorch project for correctness, training, tensor-shape, autograd and performance problems before proposing changes.
Focused workflows that apply one mechanism or diagnostic method from a specific chapter.
Trace multi-head attention dimensions and masking semantics through a real implementation to find silent QKV, head, transpose and mask errors.
Trace the computation graph in a real PyTorch training path and find detach, requires_grad, backward, in-place and NaN-gradient failures using repository evidence.
Diagnose CUDA OOM, low GPU utilization and slow PyTorch training by measuring memory, synchronization, input wait and compute before applying optimizations.
Measure where a PyTorch training pipeline waits for data and diagnose DataLoader, worker, transfer, preprocessing and prefetch bottlenecks before tuning the model.
Trace tensor dimensions through a real PyTorch codebase and find reshape, view, permute, squeeze, concatenation and silent broadcasting failures before changing model architecture.
Apply the systematic debugging method from PyTorch Zero to Hero Step 08 to a real repository without guessing or rewriting the model prematurely.
Inspect model architecture and decide which learned mechanisms are actually justified by the problem.
View curriculum →Use these when you want to inspect the whole repository through the lens of this learning path.
Map a learned system into its actual model components, decision boundaries and training signals before judging the architecture.
Focused workflows that apply one mechanism or diagnostic method from a specific chapter.
Apply Models From First Principles Step 01 to a real repository by reconstructing what a scalar quality model is actually learning, what evidence reaches it, and whether its score is meaningful.
Apply Models From First Principles Step 04 to test whether hierarchical recurrent state is actually necessary and whether its fast/slow timescales remain semantically coherent.
Apply Models From First Principles Step 03 to determine whether Q, V, policy or other learned submodels should be separate components or simpler shared heads.
Apply Models From First Principles Step 02 to verify whether a shared representation with multiple learned heads has coherent targets, losses and downstream roles.
Apply Models From First Principles Step 05 to determine whether repeated application of a compact learned model creates useful iterative computation or just repeated cost.
Use the final Models From First Principles comparison to determine what learned components the project actually needs and which complexity is unjustified.
Apply planning, tools, memory, search and verification patterns to real agent repositories.
View curriculum →Use these when you want to inspect the whole repository through the lens of this learning path.
Trace an AI agent from intent through state, tools, memory and verification, then identify where complexity is actually helping.
Focused workflows that apply one mechanism or diagnostic method from a specific chapter.
Apply Agents From First Principles Step 06 to a real repository and inspect whether tool descriptions, schemas, routing and result contracts make the action space understandable and safe.
Apply Agents From First Principles Step 05 to a real repository and test whether state, progress signals, budgets and termination reasons prevent unproductive agent loops.
Apply Agents From First Principles Step 07 to a real repository and test whether working, semantic and episodic memory improve decisions without injecting stale or weakly grounded state.
Apply Agents From First Principles Step 08 to a real repository and inspect whether branching, scoring, pruning and search budgets improve outcomes over simpler one-path execution.
Apply Agents From First Principles Step 04 to a real repository and determine whether plans are explicit, executable, state-aware and independently validated before actions run.
Apply Agents From First Principles Step 09 to find places where an agent trusts its own narration instead of authoritative external evidence.
Audit production agent systems for reliability, authority, distributed execution and operational safety.
View curriculum →Use these when you want to inspect the whole repository through the lens of this learning path.
Audit a production agent for verification, authority, reliability, durable state, trust boundaries and unnecessary complexity.
Focused workflows that apply one mechanism or diagnostic method from a specific chapter.
Apply Advanced Agents Step 29 to determine what an agent may do autonomously, which actions require escalation, and whether human approval receives enough evidence to be meaningful.
Apply Advanced Agents Step 25 to determine whether a historical agent run can actually be reconstructed, explained and compared after models, prompts, tools and state have changed.
Apply Advanced Agents Step 22 to determine whether one unhealthy model, browser, API, verifier or datastore can cascade across the whole agent platform.
Apply Advanced Agents Step 38 to determine whether goals, plans, commitments, queued work and executed actions are represented as distinct state with correct cancellation, retry and handoff semantics.
Apply Advanced Agents Step 19 to a real codebase and determine whether parallel exploration is isolated, cancellable and side-effect-safe.
Apply Advanced Agents Step 43 to determine whether reasoning is separated from enforceable control over authority, budgets, placement, releases, reliability and escalation.
Apply Advanced Agents Step 26 to build an evidence-backed incident-forensics timeline, identify the earliest meaningful divergence, and separate root cause from downstream symptoms.