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FIKA AI Research — Engineering-Productivity Intelligence MVP Challenge

Learn more at powersmy.biz

🚀 Hiring Opportunity

We're hiring! This challenge is part of our recruitment process for engineering positions. We offer both remote and on-site work options to accommodate your preferences and lifestyle.

1 ✦ Context

We need a chat-first, AI-powered view of how every engineer and squad are performing—both technically and in terms of business value shipped. Build a minimum-viable product (MVP) in fewer than seven days that delivers these insights inside Slack or Discord.

2 ✦ Core MVP Requirements (non-negotiables)

Area Requirement
Agent-centric design Use LangChain + LangGraph agents written in Python 3.10+. Provide at least two clear roles—Data Harvester and Diff Analyst—handing off to an Insight Narrator agent via LangGraph edges.
Data ingestion Pull GitHub events via REST or webhooks. The commits API exposes additions, deletions, changed_files per commit (docs.github.com); the List PR files endpoint gives the same per-file counts (docs.github.com).
Metrics Track commits, PR throughput, review latency, cycle time, CI failures plus per-author diff stats (lines ±, files touched). Optionally fall back to git log --numstat for local analysis (stackoverflow.com).
Diff analytics layer Your Diff Analyst agent aggregates churn, flags spikes, and links code-churn outliers to defect risk (research shows churn correlates with bugs) (stackoverflow.com).
AI insight layer Agents transform data into daily, weekly, monthly narratives that map to DORA’s four keys (lead-time, deploy frequency, change-failure, MTTR) (dora.dev). Log every prompt/response for auditability.
Chat-first output A Slack bot (Bolt Python SDK) (api.slack.com) or Discord bot (discord.js slash-command with embeds) (discordjs.guide) must, on /dev-report weekly, post a chart/table + the agent summary. JSON API is optional but the bot is mandatory.
MVP polish One-command bootstrap (docker compose up or make run). Include a seed script with fake GitHub events so reviewers see data instantly.
Docs README.md with bot install guide and an architecture diagram showing LangGraph nodes/edges, storage and chat layer.

3 ✦ Tech Stack (required)

  • Language: Python 3.10+
  • Agent Frameworks: LangChain ≥ 0.1.0 (python.langchain.com) and LangGraph service or OSS package (langchain.com)
  • Chat SDK: Slack Bolt-Python or discord.js (node sidecar acceptable) (api.slack.com, discordjs.guide)
  • Storage: any Python-friendly store (Postgres, SQLite, DuckDB, TinyDB).
  • Viz: matplotlib, Plotly, or quick-chart PNGs.

4 ✦ Stretch Goals (optional)

  • Forecast next week’s cycle time or churn.
  • Code-review “influence map” graph.
  • Pluggable LLM driver (OpenAI ↔ local Llama) in < 15 min.
  • Scheduled digests (bot auto-drops Monday summary).

5 ✦ Deliverables

  1. Pull Request to the challenge repo containing code + docs.
  2. ≤ 3-minute Loom/GIF demo (encouraged).

6 ✦ Timeline

Fork today → PR in 72 hours (extensions on request). We’ll smoke-test your bot in our workspace, then book your interview.

7 ✦ Evaluation Rubric (100 pts)

Category Pts What we look for
LangGraph agent architecture 25 Clear roles, deterministic edges, minimal hallucination.
MVP completeness & correctness 25 Metrics and diff stats accurate; bot responds; seed data works.
Code quality & tests 20 Idiomatic Python, CI green.
Insight value & business mapping 15 Narratives help leadership act.
Dev X & docs 10 Fast start, clear setup, diagrams.
Stretch innovation 5 Any wow factor.

8 ✦ Interview Flow

  1. Code/architecture dive (45 min)
  2. Edge-case & scaling Q&A (30 min)
  3. Product thinking & culture fit (15 min)

9 ✦ Ground Rules

Original work only; public libs are fine. Don’t commit real secrets. We may open-source the winning MVP with credit.

Ready? Fork ✦ Build ✦ PR ✦ Impress us. Questions → [email protected]


Quick Reference Links

These resources should cover everything you need—happy hacking!

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