All data is saved under backlog folder as human‑readable Markdown with the following format task-<task-id> - <task-title>.md (e.g. task-12 - Fix typo.md).
Iteration timeline
==================
• 50 % task success - added README.md + CLAUDE.md so the model knew the project.
• 75 % - wrote one markdown file per task; Codex plans, Claude codes.
• 95 %+ - built Backlog.md, a CLI that turns a high-level spec into those task files automatically (yes, using Claude/Codex to build the tool).
Three step loop that works for me 1. Generate tasks - Codex / Claude Opus → self-review.
2. Generate plan - same agent, “plan” mode → tweak if needed.
3. Implement - Claude Sonnet / Codex → review & merge.
For simple features I can even run this from my phone: ChatGPT app (Codex) → GitHub app → ChatGPT app → GitHub merge.
Repo: https://github.com/MrLesk/Backlog.md
Would love feedback and happy to answer questions!
Would love to see an actual end to end example video of you creating, planning, and implementing a task using your preferred models and apps.
- SWE-bench leaderboard: https://www.swebench.com/
- Which metrics for e.g. "SWE-Lancer: a benchmark of freelance software engineering tasks from Upwork"? https://news.ycombinator.com/item?id=43101314
- MetaGPT, MGX: https://github.com/FoundationAgents/MetaGPT :
> Software Company as Multi-Agent System
> MetaGPT takes a one line requirement as input and outputs user stories / competitive analysis / requirements / data structures / APIs / documents, etc. Internally, MetaGPT includes product managers / architects / project managers / engineers. It provides the entire process of a software company along with carefully orchestrated SOPs.
- Mutation-Guided LLM-based Test Generation: https://news.ycombinator.com/item?id=42953885
- https://news.ycombinator.com/item?id=41333249 :
- codefuse-ai/Awesome-Code-LLM > Analysis of AI-Generated Code, Benchmarks: https://github.com/codefuse-ai/Awesome-Code-LLM :
> 8.2 Benchmarks: Integrated Benchmarks, Evaluation Metrics, Program Synthesis, Visually Grounded Program, Synthesis, Code Reasoning and QA, Text-to-SQL, Code Translation, Program Repair, Code Summarization, Defect/Vulnerability Detection, Code Retrieval, Type Inference, Commit Message Generation, Repo-Level Coding
- underlines/awesome-ml/tools.md > Benchmarking: https://github.com/underlines/awesome-ml/blob/master/llm-too...
- formal methods workflows, coverage-guided fuzzing: https://news.ycombinator.com/item?id=40884466
- "Large Language Models Based Fuzzing Techniques: A Survey" (2024) https://arxiv.org/abs/2402.00350
Though i've not had much luck in getting Claude to natively use MCPs, so maybe that's off base heh.
Many of my tasks already exists in forms of a Jira ticket, would be interesting to prompt it to take over a specific ticket & update its ticket progress as well.
Backlog is more for smaller projects where you wouldn’t normally have a project management tool
This looks much more thought out, thanks for sharing!
Had similar success with making some more markdown files to help guide the agent but never would have thought of something this useful.
Will try your workflow and backlog on a build this week.
I see its a TS app so I am sure the bun bundle is the install, but always good to include in your 5 min intro.
Joking aside there is a npm/bun install -g backlog.md at the top but I can add an extra one in the 5 min intro.
I am using Bun’s new fullstack single file builds. I’m really impressed by how easy it was to set up everything.