Lanjutan
Vibe Coding: Advanced Engineering
AI-assisted engineering for people who already ship. Written for senior and staff engineers who know how to code and now want to run models and agents at professional standard. Context engineering, spec-driven development, your own harness, verification loops, review of generated code, legacy archaeology, large migrations, debugging, security, and the judgment to know when not to use AI at all.
12 pelajaran7.3h praktik11 bahasa
Mulai belajar gratisVibe Coding: Advanced Engineering kursus12 pelajaran, berurutan
- 135 mnt
Context Is The Whole Game
Context window as a budget you spendWhat the model actually sees versus what you think it seesCurating a repo map instead of dumping filesContext rot and why longer is not better - 225 mnt
Pick The Right Brain, Pay The Right Price
Capability tiers and when a bigger model is not the answerReasoning models versus fast modelsThe latency, cost, and quality trianglePrompt caching and why request shape drives the bill - 335 mnt
Spec-Driven Development
Writing the acceptance criteria before the promptExecutable specs as the contract with the modelSpecifying error behavior and boundaries, not just the happy pathPlan before code, and reviewing the plan instead of the diff - 435 mnt
Build Your Harness
The harness as the thing that makes good output the defaultProject rules files and what belongs in themGiving the model tools instead of instructionsSubagents and scoped delegation - 540 mnt
Verification Loops: Make It Prove Itself
Properties and invariants instead of example testsGenerated inputs find what handwritten examples missShrinking a counterexample before handing it overClosing the loop so the model checks itself - 640 mnt
Reading Generated Code Like A Senior
The failure taxonomy of plausible-but-wrong codeReading the docstring and the code as two competing claimsReviewing intent and seams instead of tokensDuplication the model cannot see - 740 mnt
Legacy Archaeology
Facts before narrative when mapping unfamiliar codeCharacterization tests that pin current behaviorDistinguishing intent from accident in old codeUsing a model to generate hypotheses, not conclusions - 840 mnt
Migrations At Scale
Mechanical rewrites versus judgment callsKeeping a large migration reviewableThe escape hatch list of cases the tool refuses to touchStaging, verifying, and rolling back a wide change - 940 mnt
Debugging Code You Did Not Write
Hypothesis-driven debugging instead of guess-and-patchDeterministic reproduction before diagnosisShrinking to the smallest failing inputBisecting a change set and reading the fix for scope - 1040 mnt
The Security Model Of An Agent
Prompt injection as a structural property, not a model bugThe lethal trifecta of private data, untrusted content, and exfiltrationBlast radius, allowlists, and approval gatesGenerated code risks: secrets, dependencies, and licence provenance - 1130 mnt
AI On A Team
Review capacity as the real bottleneckShared conventions and rules files as team artifactsWhat review bots and CI agents are good and bad atMeasuring impact without gaming the metric - 1235 mnt
Judgment: Knowing When Not To
The cases where reaching for a model is the wrong callUnderstanding as the thing you are actually paying forDeliberate practice and skill maintenancePutting the whole method together on one change
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