🎮 The Next Input — Issue #206

In partnership with

⚡ The Briefing — 60 sec

• Meta launches Muse Code, an AI agent for large codebases In a world of Claude Code and Codex, I’ve got no idea where Muse fits yet. Parallel agents tackling large repositories could be useful, especially if Meta competes aggressively on cost. But we will see. The coding-agent cage match gains another contestant.

• AI could create 44,000 Australian jobs and deliver a $116bn economic boost A much-needed counterweight to the endless “AI takes all the jobs” headlines. The upside is real—but only if Australia actually builds the infrastructure, skills and businesses required to capture it rather than politely watching everybody else do so.

• Anthropic reportedly wants to make its own AI chips for Claude It’ll end up this way for all of them, I reckon. Once compute becomes your largest cost, greatest constraint and strategic dependency, designing your own silicon stops looking ambitious and starts looking inevitable.

🛠️ The Playbook — Coding Agent Benchmark Arena

Mission
Build a repeatable evaluation system that determines which coding agent performs best on your actual repositories, workflows and budget.

Difficulty
Intermediate

Build time
3–5 hours

ROI
Prevents tool-chasing, reduces coding-agent spend and gives developers evidence for choosing the right agent for each task.

0) Why This Matters

The coding-agent market is getting crowded.

Codex. Claude Code. Muse Code. Copilot. Cursor. Whatever launches next Thursday.

Every provider will demonstrate impressive benchmark results and carefully selected examples. None of those examples contain your architecture, your technical debt, your naming conventions or that one service everybody is quietly afraid to touch.

The useful question is not:

Which coding agent is best?

It is:

Which agent performs best on this kind of work, inside this repository, under our constraints?

Build the test harness once. Let the agents fight for the workload.

1) Architecture

2) Workflow

1. Select ten completed engineering tasks representing real work across bug fixes, features, refactors and tests.

2. Convert each task into a clean specification with identical context and measurable acceptance criteria.

3. Run every coding agent inside separate worktrees or containers against the same repository state.

4. Execute automated tests, security checks and build validation without manual intervention.

5. Have a developer review each result blindly for correctness, maintainability and unnecessary complexity.

6. Route future tasks to the highest-performing agent by task type, cost and risk.

3) Example Prompts

Repository Task Specification

Convert the following completed engineering task into a vendor-neutral coding-agent benchmark.

Original task:
[PASTE TASK]

Repository context:
[PASTE CONTEXT]

Produce:
1. objective
2. relevant files or directories
3. constraints
4. acceptance criteria
5. required tests
6. prohibited changes
7. expected output format

Do not include implementation hints derived from the original solution.

Agent Execution Prompt

You are completing a controlled software-engineering benchmark.

Task:
[PASTE SPECIFICATION]

Requirements:
- inspect the repository before modifying files
- explain your proposed approach briefly
- make the smallest maintainable change
- preserve existing behaviour
- add or update tests
- run all available validation
- do not deploy or merge
- report files changed, tests run and unresolved concerns

Work only inside the provided environment.

Blind Review Prompt

You are reviewing an anonymised coding-agent submission.

Evaluate:
- functional correctness
- adherence to acceptance criteria
- maintainability
- architectural consistency
- security
- test quality
- unnecessary complexity
- likelihood of introducing regressions

Return:
1. blocking issues
2. non-blocking issues
3. score from 1–10 for each category
4. estimated human cleanup time
5. accept, revise or reject recommendation

4) Guardrails

• Use identical repository snapshots and task instructions for every agent.

• Remove provider names before human review.

• Include simple and difficult tasks rather than only showcase examples.

• Never allow benchmark agents to access production credentials.

• Track retries and human intervention as part of the total cost.

• Evaluate maintainability, not merely whether the tests pass.

• Keep benchmark tasks private if they contain proprietary code.

• Re-run the suite after significant model or pricing changes.

5) Pilot Rollout — 3 hours

1. Choose three recently completed tasks with known correct solutions.

2. Rewrite them as provider-neutral specifications with acceptance criteria.

3. Configure isolated worktrees and the same automated checks for each agent.

4. Run Codex, Claude Code and one alternative agent against every task.

5. Conduct a blind developer review and record cost, speed and cleanup time.

6. Publish a routing rule stating which agent should handle each task category.

6) Metrics

• Acceptance-criteria pass rate

• Automated test success rate

• Human cleanup time

• Cost per accepted change

• Median task completion time

• Security issues introduced

• Unnecessary code added

• Retry and intervention rate

• Developer acceptance rate

• Performance by task category

Pro Tip: Do not select a coding agent because it won somebody else’s benchmark. Make it earn access to your repository.

🎯 The Arsenal — Tools & Platforms

• OpenAI Codex · handles repository-level coding, testing and implementation workflows · Link

• Claude Code · supports terminal-based analysis and complex codebase work · Link

• GitHub Actions · runs identical automated validation across agent submissions · Link

• Docker · provides isolated and reproducible benchmark environments · Link

• Power BI · compares agent quality, cost, latency and intervention requirements · Link

Copy-paste prompt block:

You are an AI coding-agent evaluation architect.

Design a benchmark system for my engineering environment.

Repositories:
[LIST]

Languages and frameworks:
[LIST]

Common engineering tasks:
[LIST]

Current coding agents:
[LIST]

Required tests and security checks:
[LIST]

Budget constraints:
[LIST]

The system must:
- use representative real-world tasks
- give every agent identical context
- isolate execution environments
- validate outputs automatically
- support blind human review
- measure total cost including retries and cleanup
- compare performance by task category
- produce routing recommendations
- remain reusable as new agents and models launch

Return:
1. benchmark architecture
2. task-selection methodology
3. evaluation rubric
4. execution workflow
5. security controls
6. reporting dashboard
7. agent-routing rules
8. pilot rollout
9. operational metrics

💡 Free Office Hours

The coding-agent market is about to become noisy, crowded and increasingly tied to each lab’s infrastructure strategy. The answer is not loyalty. It is a benchmark grounded in your own work and an architecture that lets the strongest tool win each task.

Book here: https://calendly.com

Own Search With Podcasts

Your competitors are fighting over the same keywords. The smartest brands are building the authority that search engines, AI platforms, and customers trust everywhere.

Every relevant podcast appearance can produce branded mentions, backlinks, transcripts, citations, clips, expert content, and third-party proof that keeps compounding across search and AI discovery.

PodPitch searches millions of podcasts, finds the shows that matter to your market, develops the angle, sends personalized pitches, and follows up automatically until your experts are booked.

Growth teams are already using podcast appearances to build distributed authority that cannot be manufactured by publishing another generic SEO article.

Only 20 SEO, AEO, and GEO demo spots are available this month. Once they’re claimed, the offer disappears.

Start building searchable authority now, before your competitors own the conversations shaping your market.

Appear on 3,853,234 Podcasts

🕹️ Game Over

The agents can fight over the code.

The frontier labs can fight over the chips.

Australia just needs to make sure it captures some of the value before the invoice arrives.

— Aaron Automating the boring. Amplifying the brilliant.

Subscribe: link