* docs: correct cost claim to 42-75% from 30-rep re-verification Re-ran the cost benchmark at 30 reps per cell on Claude (Haiku/Sonnet/Opus): ponytail is 42-75% cheaper than no-skill, not the previously published 47-77%. The direction holds, both ends came in a few points lower. Updates the README headline and body, the benchmark chart subtitle, and the benchmarks/README cost table, and adds a dated results doc with full method. Also adds the OpenAI (gpt-4.1-mini/gpt-5.4-mini/gpt-5.5) and Gemini configs. On OpenAI reasoning models ponytail costs more, not less, so the claim stays Claude-scoped. Gemini run pending a fresh-quota day. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: scope the body claim to Claude models "on every model" read as cross-provider, but the 30-rep verification shows the cost win reverses on OpenAI reasoning models. Match the caption and benchmarks/README, which already say Claude. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: reframe the pitch as the discipline, not token savings The cost/code/latency numbers vary by model and on some (terse reasoning models like GPT-5.5) ponytail costs more, so leading with them as a universal win was misleading. Adds model-variance to the headline caption and a paragraph making the stated point the mental model: write only what the task needs, safety kept, maintainable code. Savings are a model-dependent side effect. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: name the ladder's reasoning cost The ladder is a deliberation step: on reasoning models the agent spends thinking tokens working through the rungs before it saves any output, which together with the always-on ruleset can outweigh the shorter code. Makes the GPT-5.5 cost increase legible rather than just stating it. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: state the single-shot limitation honestly The benchmark is single-shot (one prompt, one completion); it does not measure a real multi-turn agent session, where the ruleset re-injects and the ladder deliberates every turn. Adds that caveat to the README, and corrects the benchmarks/README note that claimed caching widens the gap "in ponytail's favor" (unverified, and a measured agentic A/B in #121 found the opposite can happen). Per-session cost can land either way. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * docs: fix run count in caption (cost is 30 runs, not 10) Cost was re-verified at 30 reps; code and latency are still the original 10. The headline caption said "10 runs" across the board, which undersold the cost verification. Now states the split, matching benchmarks/README. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(examples): replace hand-written examples with real benchmark output The examples/ before/after blocks were authored by hand, not produced by a model. Issue #127 correctly noted that nobody hand-rolls quicksort for "sort this array" - every model just calls .sort(). Regenerate all examples verbatim from a real benchmark run (Claude Haiku 4.5, no-skill arm vs ponytail arm, benchmarks/output.json) so the before/after is reproducible, not authored: email 75->3, debounce 116->10, csv 20->3, countdown 267->9, rate-limit 128->10 LOC - Delete sorting.md (pure strawman) plus the other hand-written caricatures (api-endpoint, caching, date-picker) - Add benchmarks/generate-examples.mjs to regenerate examples from any run - examples/README.md indexes the set and documents how to reproduce Closes #127 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
1.7 KiB
1.7 KiB
CSV Sum
Task: "Write Python code that reads sales.csv and sums the 'amount' column."
Verbatim model output from a benchmark run — Claude Haiku 4.5, no-skill arm vs ponytail arm, temperature 1, source benchmarks/output.json. Reproduce: npx promptfoo@latest eval -c benchmarks/promptfooconfig.yaml.
Without Ponytail — 20 lines of code
Python code to read sales.csv and sum the 'amount' column
import pandas as pd
# Read the CSV file
df = pd.read_csv('sales.csv')
# Sum the 'amount' column
total_amount = df['amount'].sum()
# Print the result
print(f"Total amount: ${total_amount:,.2f}")
Alternative methods:
Using CSV module (without pandas):
import csv
total_amount = 0
with open('sales.csv', 'r') as file:
reader = csv.DictReader(file)
for row in reader:
total_amount += float(row['amount'])
print(f"Total amount: ${total_amount:,.2f}")
Using pandas with error handling:
import pandas as pd
try:
df = pd.read_csv('sales.csv')
total_amount = df['amount'].sum()
print(f"Total amount: ${total_amount:,.2f}")
except FileNotFoundError:
print("Error: sales.csv not found")
except KeyError:
print("Error: 'amount' column not found")
The pandas method is recommended because it's:
- More concise and readable
- Handles data types automatically
- Provides built-in error handling
- Efficient for large files
With Ponytail — 3 lines of code
import csv
total = sum(float(row['amount']) for row in csv.DictReader(open('sales.csv')))
print(total)
Skipped: pandas, error handling, file closing — add when the CSV is large, malformed, or you need more analysis.
20 → 3 lines of code — same model, same prompt.