I've watched devs in 2026 drop $150–200 a month on Claude Code or Antigravity, type "build me an app," and end up with 800 lines of hallucinated garbage plus a shiny "context limit reached" error. I did the same - burned through tokens like they were free beer at a conference happy hour, got nothing usable, and felt like an idiot.
Of course the hype is real: Claude Code (now the most popular coding agent, writing ~90% of Anthropic's own code with Opus 4.5), Antigravity (Google's agent-first IDE that plans autonomously across editor/terminal/browser), Codex (OpenAI's lightweight CLI agent that drafts PRs and runs tests). But "vibe coding" - casually chatting with the agent - fails hard because you're basically asking a very expensive intern to write production code without a spec.
The fix is discipline: a controlled loop instead of chaos. This pipeline is inspired by GitHub's Spec Kit but with more control - multi-model checks, MCP for fresh docs, rules to avoid repetition. Follow these 6 steps, and agents accelerate 3–5x instead of torching your budget.
Step 1: Spec First - Write spec.md Before Any Code
Don't feed the agent any code requests until you have a solid spec.md. Describe the goal, inputs/outputs, constraints, edge cases, and minimum tests. Use your favorite LLM to ask clarifying questions until everything's nailed down. Save it in the repo as spec.md.
Trust me, it'll save your ass - edge cases kill agents otherwise. Without them, Claude Code might choke on CSV quoting hell, Antigravity plans only happy paths, Codex hallucinates invalid inputs.
Example (Word Frequency CLI Analyzer):
# spec.md - Word Frequency CLI Analyzer (MVP)
## Goal
CLI tool that reads a text file (any encoding) and outputs:
- Total word count
- Number of unique words
- Top N most frequent words (default N=10)
- Optional: save results to JSON
Useful for quick text analysis (logs, notes, scraped data, chat exports).
## Inputs/Outputs
- Input: path to text file (required arg)
- Flags/Options:
- --top N (int, default 10): show top N words
- --json (flag): output to results.json instead of stdout
- --min-length M (int, default 3): ignore words shorter than M chars
- Output:
- stdout: human-readable summary + top list
- or JSON file if --json
## Constraints
- Python 3.11+
- No external libraries (only stdlib: argparse, collections, json, pathlib, re)
- Handle files up to 100MB (stream reading, no full load into memory)
- Support UTF-8 + common encodings (utf-8, latin-1, cp1252 fallback)
- Case-insensitive counting (normalize to lower)
## Edge Cases
- Empty file → 0 words, empty top list
- File with only punctuation/spaces → 0 words
- Very large file (>50MB) → must stream, no OOM
- Mixed encodings → try utf-8 first, fallback to latin-1 or ignore errors
- Words with hyphens/apostrophes (e.g. "don't", "well-known") → count as one word
- Non-English text (accents, Cyrillic, emojis) → count words properly (split on whitespace + punctuation)
- Invalid path → clear error message + exit 1
- No args → show usage/help
## Tests (minimum set)
- Happy path: small English text file → correct counts & top words
- Empty file → "0 words found"
- File with only punctuation → "0 words found"
- Large-ish file (simulate 10MB lorem ipsum) → no crash, correct stats
- Non-UTF8 file (latin-1) → reads without crash
- JSON output → valid JSON with structure { "total_words": int, "unique_words": int, "top_words": list of [word, count] }
## Non-Goals (out of scope for MVP)
- GUI
- Real-time streaming from stdin
- Stemming/lemmatization
- Stop words filtering
- Multi-file support
Writing the spec sometimes feels like it takes as long as coding the thing yourself. True sometimes. But this is the investment: the skill of forcing AI to write good code is what pays in 2026. Writing everything by hand? That's dinosaur territory now.
Step 2: Plan & Tickets - Break It Down
Feed spec.md to a strong thinking model. Ask it to create a plan: 5–15 small stages, each deliverable in 1–3 senior dev days, each bringing real value (MVP-0 => killer feature => polish).
Then turn the plan into tiny tickets - one focused change per ticket.
Why it works: Big requests overload context and cause drift. Tiny ones keep models sharp and let you catch errors early. Update the plan every 3 stages when reality bites (Pro Tip: use Obsidian for epics and iterative refactoring).
Step 3: Rules & Context - Set Boundaries
Create rules.md (CLAUDE.md, GEMINI.md etc.) with lint rules, forbidden patterns, test commands, style guides. Agents reference it automatically - saves tokens and sanity (no more repeating "use black" 50 times).
Give context: which files to change, repo structure (connect GitHub in Codex/Claude Code), versions/arch bans, known pitfalls, team conventions.
Bonus: Use MCP (Model Context Protocol) - the 2026 standard (thousands of servers now) - to connect fresh library docs. Agents pull actual APIs instead of hallucinating outdated ones. Huge win.
Step 4: Agent Activation - Let Them Loose (Only Now)
Only now connect the repo and activate agents
Step 5: Tests & Multi-Model Check - Every Single Step
For each ticket: define tests in spec/plan, make the agent run them, fix failures.
Don't trust one agent: one codes, another reviews/refactors (Claude for code, Codex for PR suggestions, Antigravity for verification). Cross-check catches blind spots.
Treat agents like interns: clear plan + reviews = better output. Bonus: You'll level up your PM skills massively.
Step 6: Commit & Iterate - Save Points & Refactor
Commit after every successful ticket/test pass. Use branches for experiments - Git is your undo when an agent make sh * t.
Pro Tip: Refactor every 3–4 stages: ask an agent "evaluate current solution for architecture/best practices" while context is fresh.
Conclusion
In 2026, vibe coding isn’t about how fast you can type code anymore.
It’s about how well you can make AI write the code for you.
You’re no longer the senior dev hammering away at the keyboard for eight hours straight. You’re now part product manager, part architect, part tech lead - except your “team” costs $150–200 a month, never takes vacation, but will happily hallucinate garbage if you don’t give them crystal-clear tasks.
Quick Cheatsheet / Memo Spec => plan => rules & context => tiny tickets => agent execution => tests + multi-model cross-check => commit => refactor every few stages.
Thanks for reading!
More no-BS takes on AI agents, tools, and surviving the token apocalypse in my Telegram channel and on X (Twitter).
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