It’s Time to Completely Abandon Old-School Programming
Six months of hands-on experience with AI programming
Leo · 2026
Current slideKeys: ← → Space PageUp PageDown F
layout: center class: text-center
Six Months, Zero Handwritten Code
0
Lines of handwritten code
1/10
Time required
100%
AI-generated
Voice rooms · Prediction backend · Lambda → Beanstalk migration · CI · Documentation · Internal tools
layout: section transition: fade
Redefining the Engineer’s Role
From "Writing Code" to "Providing Context"
-
When progress stalls, the solution is never "just try harder"
-
Instead, take a step back and ask: what is the Agent still missing?
-
Context · Tools · Constraints
The quality of context engineering = the quality of AI output
The more precise you write, the less AI goes off track; the vaguer you write, the more rework you get
The Engineer’s Role Is Changing
Before
- Write code line by line
- Debug manually
- Review manually
- Rely on memory for documentation
Now
- Design tasks and define constraints
- Agent writes code + self-tests
- Agent cross-reviews
- Humans intervene only at merge time
From code author → AI context engineer
layout: section transition: fade
Using Agents to Review Agents
Codex Automated Review
The volume of AI-generated code is too large, and purely manual review inevitably misses things
Scheduled daily scans of all frequently developed team repositories have indeed uncovered quite a few hidden bugs
layout: section transition: fade
Make Applications Readable to Agents
Agent Autonomous Verification Workflow
The bottleneck has shifted from "writing code" to manual QA—let Agents directly read the UI, logs, and monitoring
sequenceDiagram
participant C as Agent
participant A as Application
participant D as Chrome DevTools
C->>D: Select target page + clear console
C->>D: Capture pre-action snapshot
C->>A: Trigger UI interaction path
A-->>D: Runtime events
C->>D: Capture post-action snapshot
C->>A: Apply fix + restart
loop Repeat until passing
C->>D: Re-run verification
end
layout: section transition: fade
Turn the Repository into the Single Source of Knowledge
Give the Agent a Map, Not a Manual
-
Context is a scarce resource — huge instruction files crowd out what actually matters
-
If everything is "important," then nothing is important — with too much information, the Agent only does nearest-pattern matching
-
Large files rot extremely fast — a graveyard of outdated rules that becomes a breeding ground for bugs once people stop maintaining them
-
Impossible to validate automatically — coverage, freshness, and ownership all spiral out of control
AGENTS.md ≈ a 100-line table of contents → points to the real documentation under docs/
layout: section transition: fade
From MCP to Skill
Method Matters More Than Connection
layout: two-cols layoutClass: gap-8
MCP vs Skill
MCP solves "connection"
- The Agent can call YApi APIs ✅
- The Agent can check ZenTao bugs ✅
- But it doesn’t know that after changing a field it should sync the docs ❌
- It doesn’t know it should add Chinese comments and enum descriptions ❌
MCP gives the Agent plumbing
Skill teaches the Agent how to use water
Skill teaches the Agent how to use water
::right::
Skill solves "method"
- After changing code → sync docs automatically
- Every field → Chinese comments + enum descriptions
- Fill in request/response examples
- Validate consistency between docs and code
Advantages of Skill:
- Carries methodology, not just connections
- Version-managed and reviewable
- Loaded on demand, without consuming context
ZenTao Skill: Fix a Bug by Pasting a Link
No need to think about how to write the prompt—just paste the bug ticket URL to the Agent
Help me install this skill: https://github.com/leeguooooo/zentao-mcp
YApi Skill: Automating Upstream/Downstream Integration
sequenceDiagram
participant PM as Upstream party
participant Dev as Developer
participant Agent as Agent
participant YApi as YApi
PM->>Dev: Send integration request + YApi endpoint URL
Dev->>Agent: Paste the endpoint URL
Agent->>YApi: Read interface definition via Skill
Agent->>YApi: Automatically navigate to related interfaces
Agent->>Agent: Understand full context and generate integration code
Agent->>Dev: Output implementation + automatically update local docs
Dev->>YApi: `yapi docs-sync` syncs the docs
After Bian Sang got the API URL, he completed the entire broadcast system integration in less than a day
Project-Level Skill Composition: Fully Automated Bug Investigation
flowchart LR
A[ZenTao Bug] -->|ZenTao Skill| B[Read bug details]
B --> C[Locate relevant code]
C -->|Log Skill| D[Query runtime logs]
C -->|DB Skill| E[Query database]
C -->|KV Skill| F[Query cache]
C -->|Token Skill| G[Verify authentication]
D & E & F & G --> H[Analyze root cause]
H --> I[Fix code and submit]
The Agent can not only understand bug descriptions and locate code
but also connect directly to the test environment and pull real data to analyze the issue
but also connect directly to the test environment and pull real data to analyze the issue
layout: section transition: fade
Open Source Recommendation: Superpowers
An Agent Skill Framework with 100k+ Stars
Superpowers: A Complete Development Workflow
1
Brainstorming
Think it through before acting, with Socratic questioning
Think it through before acting, with Socratic questioning
2
Writing Plans
Break work into 2–5 minute tasks
Break work into 2–5 minute tasks
3
Git Worktrees
Isolated development environments, zero risk
Isolated development environments, zero risk
4
Test-Driven Dev
Enforced TDD: write tests before code
Enforced TDD: write tests before code
5
Subagent Dev
Multiple Agents in parallel + two review rounds
Multiple Agents in parallel + two review rounds
6
Parallel Agents
Automatically parallelize independent tasks
Automatically parallelize independent tasks
7
Systematic Debug
Four-stage troubleshooting, no speculative fixes
Four-stage troubleshooting, no speculative fixes
8
Verification
Verification required before completion; evidence before conclusions
Verification required before completion; evidence before conclusions
9
Code Review
Bidirectional review, no blind agreement
Bidirectional review, no blind agreement
10
Finishing Branch
End-of-work decision: merge / PR / keep / abandon
End-of-work decision: merge / PR / keep / abandon
Key Feature: Everything Triggers Automatically
🚫
No need to manually remind the Agent to write tests
🚫
No need to manually remind the Agent to think first before acting
🚫
No need to manually remind the Agent to verify results
🚫
No need to manually remind the Agent to do Code Review
Turn good engineering practices from "relying on self-discipline" into "relying on the system"
Subagent Parallel Development Workflow
flowchart TD
P[Implementation plan] --> S1[Subagent 1: Task A]
P --> S2[Subagent 2: Task B]
P --> S3[Subagent 3: Task C]
S1 --> R1[Review: spec compliance]
S2 --> R2[Review: spec compliance]
S3 --> R3[Review: spec compliance]
R1 --> Q1[Review: code quality]
R2 --> Q2[Review: code quality]
R3 --> Q3[Review: code quality]
Q1 & Q2 & Q3 --> M[Merge integration]
Each sub-Agent has isolated context · Two review rounds · Can work autonomously for hours at a time
layout: section transition: fade
Pitfall Guide
The Easiest Mistakes to Make with AI Programming
Seven Major Pitfalls
Pitfall 1
If you don’t hold the line at the start, everything later becomes debt — AI amplifies direction; it does not correct it
Pitfall 2
Context drift — after 20–30 rounds, the Agent starts contradicting earlier decisions
Pitfall 3
Blind trust in "done" — able to run ≠ runs well; happy path ≠ production-ready
Pitfall 4
Not checking the git diff — while fixing one bug, AI may quietly change another field
Pitfall 5
Overcomplicated Agent architectures — flashy multi-role orchestration that becomes a mess in practice
Pitfall 6
Team alignment is hard — one person can make it work, but collaborative workflows must be versioned
Pitfall 7
Abusing LLMs — use deterministic code for deterministic operations; don’t call a model for everything
layout: center class: text-center
The Two Most Important Habits
📋
Check the diff before submitting
More important than any Skill
🔄
Start a new conversation for major features
Avoid context drift
layout: section transition: fade
Summary
From "Writing Code" to "Designing Systems"
AI programming is not about helping you write code faster, but about making it unnecessary for you to write code at all
🗺️
Design context
Use `AGENTS.md` and Skills to tell the Agent what it should know and what it should do
🛡️
Design constraints
Use standards, tests, and review processes to bound the Agent’s behavioral limits
✅
Design verification
Make the Agent’s output reviewable and verifiable by both humans and other Agents
layout: center class: text-center
Do it well →
An engineering team operating efficiently 24/7
Do it poorly →
An exquisitely engineered garbage machine
Fast output, even faster rework
layout: center class: text-center
Technology is accelerating, but the essence of engineering has not changed
Clear requirements · Clean architecture · Rigorous verification
These mattered in the era of handwritten code, and they matter even more in the era of AI-written code
layout: center class: text-center
Thank You
ZenTao Skill → github.com/leeguooooo/zentao-mcp
Superpowers → github.com/obra/superpowers
Q & A
Next slideAudience
It’s Time to Completely Abandon Old-School Programming
Six months of hands-on experience with AI programming
Leo · 2026
layout: center class: text-center
Six Months, Zero Handwritten Code
0
Lines of handwritten code
1/10
Time required
100%
AI-generated
Voice rooms · Prediction backend · Lambda → Beanstalk migration · CI · Documentation · Internal tools
layout: section transition: fade
Redefining the Engineer’s Role
From "Writing Code" to "Providing Context"
-
When progress stalls, the solution is never "just try harder"
-
Instead, take a step back and ask: what is the Agent still missing?
-
Context · Tools · Constraints
The quality of context engineering = the quality of AI output
The more precise you write, the less AI goes off track; the vaguer you write, the more rework you get
The Engineer’s Role Is Changing
Before
- Write code line by line
- Debug manually
- Review manually
- Rely on memory for documentation
Now
- Design tasks and define constraints
- Agent writes code + self-tests
- Agent cross-reviews
- Humans intervene only at merge time
From code author → AI context engineer
layout: section transition: fade
Using Agents to Review Agents
Codex Automated Review
The volume of AI-generated code is too large, and purely manual review inevitably misses things
Scheduled daily scans of all frequently developed team repositories have indeed uncovered quite a few hidden bugs
layout: section transition: fade
Make Applications Readable to Agents
Agent Autonomous Verification Workflow
The bottleneck has shifted from "writing code" to manual QA—let Agents directly read the UI, logs, and monitoring
sequenceDiagram
participant C as Agent
participant A as Application
participant D as Chrome DevTools
C->>D: Select target page + clear console
C->>D: Capture pre-action snapshot
C->>A: Trigger UI interaction path
A-->>D: Runtime events
C->>D: Capture post-action snapshot
C->>A: Apply fix + restart
loop Repeat until passing
C->>D: Re-run verification
end
layout: section transition: fade
Turn the Repository into the Single Source of Knowledge
Give the Agent a Map, Not a Manual
-
Context is a scarce resource — huge instruction files crowd out what actually matters
-
If everything is "important," then nothing is important — with too much information, the Agent only does nearest-pattern matching
-
Large files rot extremely fast — a graveyard of outdated rules that becomes a breeding ground for bugs once people stop maintaining them
-
Impossible to validate automatically — coverage, freshness, and ownership all spiral out of control
AGENTS.md ≈ a 100-line table of contents → points to the real documentation under docs/
layout: section transition: fade
From MCP to Skill
Method Matters More Than Connection
layout: two-cols layoutClass: gap-8
MCP vs Skill
MCP solves "connection"
- The Agent can call YApi APIs ✅
- The Agent can check ZenTao bugs ✅
- But it doesn’t know that after changing a field it should sync the docs ❌
- It doesn’t know it should add Chinese comments and enum descriptions ❌
MCP gives the Agent plumbing
Skill teaches the Agent how to use water
Skill teaches the Agent how to use water
::right::
Skill solves "method"
- After changing code → sync docs automatically
- Every field → Chinese comments + enum descriptions
- Fill in request/response examples
- Validate consistency between docs and code
Advantages of Skill:
- Carries methodology, not just connections
- Version-managed and reviewable
- Loaded on demand, without consuming context
ZenTao Skill: Fix a Bug by Pasting a Link
No need to think about how to write the prompt—just paste the bug ticket URL to the Agent
Help me install this skill: https://github.com/leeguooooo/zentao-mcp
YApi Skill: Automating Upstream/Downstream Integration
sequenceDiagram
participant PM as Upstream party
participant Dev as Developer
participant Agent as Agent
participant YApi as YApi
PM->>Dev: Send integration request + YApi endpoint URL
Dev->>Agent: Paste the endpoint URL
Agent->>YApi: Read interface definition via Skill
Agent->>YApi: Automatically navigate to related interfaces
Agent->>Agent: Understand full context and generate integration code
Agent->>Dev: Output implementation + automatically update local docs
Dev->>YApi: `yapi docs-sync` syncs the docs
After Bian Sang got the API URL, he completed the entire broadcast system integration in less than a day
Project-Level Skill Composition: Fully Automated Bug Investigation
flowchart LR
A[ZenTao Bug] -->|ZenTao Skill| B[Read bug details]
B --> C[Locate relevant code]
C -->|Log Skill| D[Query runtime logs]
C -->|DB Skill| E[Query database]
C -->|KV Skill| F[Query cache]
C -->|Token Skill| G[Verify authentication]
D & E & F & G --> H[Analyze root cause]
H --> I[Fix code and submit]
The Agent can not only understand bug descriptions and locate code
but also connect directly to the test environment and pull real data to analyze the issue
but also connect directly to the test environment and pull real data to analyze the issue
layout: section transition: fade
Open Source Recommendation: Superpowers
An Agent Skill Framework with 100k+ Stars
Superpowers: A Complete Development Workflow
1
Brainstorming
Think it through before acting, with Socratic questioning
Think it through before acting, with Socratic questioning
2
Writing Plans
Break work into 2–5 minute tasks
Break work into 2–5 minute tasks
3
Git Worktrees
Isolated development environments, zero risk
Isolated development environments, zero risk
4
Test-Driven Dev
Enforced TDD: write tests before code
Enforced TDD: write tests before code
5
Subagent Dev
Multiple Agents in parallel + two review rounds
Multiple Agents in parallel + two review rounds
6
Parallel Agents
Automatically parallelize independent tasks
Automatically parallelize independent tasks
7
Systematic Debug
Four-stage troubleshooting, no speculative fixes
Four-stage troubleshooting, no speculative fixes
8
Verification
Verification required before completion; evidence before conclusions
Verification required before completion; evidence before conclusions
9
Code Review
Bidirectional review, no blind agreement
Bidirectional review, no blind agreement
10
Finishing Branch
End-of-work decision: merge / PR / keep / abandon
End-of-work decision: merge / PR / keep / abandon
Key Feature: Everything Triggers Automatically
🚫
No need to manually remind the Agent to write tests
🚫
No need to manually remind the Agent to think first before acting
🚫
No need to manually remind the Agent to verify results
🚫
No need to manually remind the Agent to do Code Review
Turn good engineering practices from "relying on self-discipline" into "relying on the system"
Subagent Parallel Development Workflow
flowchart TD
P[Implementation plan] --> S1[Subagent 1: Task A]
P --> S2[Subagent 2: Task B]
P --> S3[Subagent 3: Task C]
S1 --> R1[Review: spec compliance]
S2 --> R2[Review: spec compliance]
S3 --> R3[Review: spec compliance]
R1 --> Q1[Review: code quality]
R2 --> Q2[Review: code quality]
R3 --> Q3[Review: code quality]
Q1 & Q2 & Q3 --> M[Merge integration]
Each sub-Agent has isolated context · Two review rounds · Can work autonomously for hours at a time
layout: section transition: fade
Pitfall Guide
The Easiest Mistakes to Make with AI Programming
Seven Major Pitfalls
Pitfall 1
If you don’t hold the line at the start, everything later becomes debt — AI amplifies direction; it does not correct it
Pitfall 2
Context drift — after 20–30 rounds, the Agent starts contradicting earlier decisions
Pitfall 3
Blind trust in "done" — able to run ≠ runs well; happy path ≠ production-ready
Pitfall 4
Not checking the git diff — while fixing one bug, AI may quietly change another field
Pitfall 5
Overcomplicated Agent architectures — flashy multi-role orchestration that becomes a mess in practice
Pitfall 6
Team alignment is hard — one person can make it work, but collaborative workflows must be versioned
Pitfall 7
Abusing LLMs — use deterministic code for deterministic operations; don’t call a model for everything
layout: center class: text-center
The Two Most Important Habits
📋
Check the diff before submitting
More important than any Skill
🔄
Start a new conversation for major features
Avoid context drift
layout: section transition: fade
Summary
From "Writing Code" to "Designing Systems"
AI programming is not about helping you write code faster, but about making it unnecessary for you to write code at all
🗺️
Design context
Use `AGENTS.md` and Skills to tell the Agent what it should know and what it should do
🛡️
Design constraints
Use standards, tests, and review processes to bound the Agent’s behavioral limits
✅
Design verification
Make the Agent’s output reviewable and verifiable by both humans and other Agents
layout: center class: text-center
Do it well →
An engineering team operating efficiently 24/7
Do it poorly →
An exquisitely engineered garbage machine
Fast output, even faster rework
layout: center class: text-center
Technology is accelerating, but the essence of engineering has not changed
Clear requirements · Clean architecture · Rigorous verification
These mattered in the era of handwritten code, and they matter even more in the era of AI-written code
layout: center class: text-center
Thank You
ZenTao Skill → github.com/leeguooooo/zentao-mcp
Superpowers → github.com/obra/superpowers
Q & A
Last slide
Timer
Keys: ← → Space PageUp PageDown F
Use an extended display: put the audience view on the projector and keep this presenter view on your laptop.