PYTHON + CLAUDE PIPELINE THAT DRAFTS, SCORES, AND TELEGRAMS UPWORK PROPOSALS HIT 31% RESPONSE
An engineer built a Python system that drafts and scores Upwork proposals with Claude, then sends top picks to Telegram, landing a 31% response rate. In [this ...
An engineer built a Python system that drafts and scores Upwork proposals with Claude, then sends top picks to Telegram, landing a 31% response rate.
In this walkthrough, the author shows a weekend-built pipeline: Claude drafts the proposal, a simple scoring algorithm filters for fit, and Telegram delivers the shortlist fast. He sent 47 proposals in a month, wrote only 3 by hand, and the AI-assisted ones hit 31% response.
This is a clean pattern for LLM + rule-based scoring + push notifications. Swap the domain and you have a template for RFP triage, inbound leads, or support replies that need speed and relevance.
Shows a tight LLM workflow that outperforms a manual baseline with a measurable 31% response rate.
Demonstrates a simple, reproducible pattern: generate, score, then notify for human-in-the-loop review.
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terminal
Replicate the generate-score-notify loop on a small internal queue (e.g., RFPs or support tickets) and track latency and acceptance rate.
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terminal
A/B test scored vs. unscored LLM drafts to quantify lift in quality and time-to-first-response.
Legacy codebase integration strategies...
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Integrate the notification step with existing channels (e.g., swap Telegram for your team’s chat) and add audit logging of prompts and outputs.
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Gate rollout behind rate limits and human approval to avoid policy violations or low-quality sends.
Fresh architecture paradigms...
- 01.
Design the pipeline as pluggable stages: generator (LLM), scorer (rules or model), and notifier, with metrics at each hop.
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Start with a simple heuristic scorer and evolve to a learned model once you have labeled outcomes.