Agro Alternatives

agro reviews and mentions

• Microsoft Amplifier
  2 projects | news.ycombinator.com | 11 Oct 2025

  I've actually written my own a homebrew framework like this which is a.) cli-coder agnostic and b.) leans heavily on git worktrees [0].

  The secret weapon to this approach is asking for 2-4 solutions to your prompt running in parallel. This helps avoid the most time consuming aspect of ai-coding: reviewing a large commit, and ultimately finding the approach to the ai took is hopeless or requires major revision.

  By generating multiple solutions, you can cutdown investing fully into the first solution and use clever ways to select from all the 2-4 candidate solutions and usually apply a small tweak at the end. Anyone else doing something like this?

  [0]: https://github.com/sutt/agro

• My tips for using LLM agents to create software
  1 project | news.ycombinator.com | 23 Aug 2025

  Not OP but I know it can be difficult to really difficult to measure or communicate this to people who aren't familiar with the codebase or the problem being solved.

  Other than just dumping 10M tokens of chats into a gist and say read through everything I said back and forth with claude for a week.

  But, I think I've got the start of a useful summary format: it that takes every prompt and points to the corresponding code commit produced by ai + adds a line diff amount and summary of the task. Check it out below.

  https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

  (In this case it's an python cli ai-coding framework that I'm using to build the package itself)

• The AGENTS.md Standard: A simple, open format for guiding coding agents
  10 projects | news.ycombinator.com | 19 Aug 2025

  └── swap/ # Temporary files (gitignored)

  Every markdown file in guides/ gets copied to any agent (aider, claude, gemini) I kick-off.

  I gitignore this .agdocs directory by default. I find this useful because otherwise you get into _please commit or stash your changes_ when trying to switch branches.

  But I also run an rsync script before each release to copy the .agdocs to a git tracked mirrored directory [1].

  [0]: https://github.com/sutt/agro/blob/master/README.md#layout

  [1]: https://github.com/sutt/vidstr/tree/master/.public-agdocs/gu...

• Ask HN: Have any successful startups been made by 'vibe coding'?
  3 projects | news.ycombinator.com | 19 Aug 2025

  Yeah, I've been able to stack ~200 AI-generated commits on top of each other. Over 95% of the code is now AI generated.

  You can see each prompt and each commit that was generated here: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

  Since AI-Gen code is such a roll of the dice, the key is to roll the dice a lot - usually generate at least 3 potential solutions from at least two separate providers at the outset - and get good at quickly reviewing the offered solutions, or iterating on the prompt and regenerating.

• New AI Coding Teammate: Gemini CLI GitHub Actions
  4 projects | news.ycombinator.com | 7 Aug 2025

  Last year, I was actually working on a bounty platform for Github PR's.

  The low quality human-authored PR's that came in (due to the incentive we offered) combined with the fact that a draft PR could be made for pennies with AI made this concept dead in the water as far as I'm concerned.

  The pain point of getting some attention and action on your opensource codebase is really no longer relevant, in fact the pain point seems to be moving to how to optimize the limited reviewer / maintainer bandwidth under the onslaught of proposed suggestions.

  To this end I've been experimenting with a framework that builds PR's from the major agents and but with a focus on how to structure the tasks and review process that optimize the review => accept/revise cycle. If you're interested I've been writing up some case studies here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

• Jules, our asynchronous coding agent, is now available for everyone
  7 projects | news.ycombinator.com | 6 Aug 2025

  I think the Github-PR model for agent code suggestions is the path of least resistance for getting adoption from today's developers working in an existing codebase. It makes sense: these developers are already used to the idea and the ergonomics of doing code reviews this way.

  But pushing this existing process - which was designed for limited participation of scarce people - onto a use-case of managing a potentially huge reservoir of agent suggestions is going to get brittle quickly. Basically more suggestions require a more streamlined and scriptable review workflow.

  Which is why I think working in the command line with your agents - similar to Claude and Aider - is going to be where human maintainers can most leverage the deep scalability of async and parallel agents.

  > is way better than having to set up git worktrees or any other type of sandbox yourself

  I've built up a helper library that does this for you for either aider or claude here: https://github.com/sutt/agro. And for FOSS purposes, I want to prevent MS, OpenAI, etc from controlling the means of production for software where you need to use their infra for sandboxing your dev environment.

  And I've been writing about how to use CLI tricks to review the outputs on some case studies as well: https://github.com/sutt/agro/blob/master/docs/case-studies/i...

• I launched 17 side projects. Result? I'm rich in expired domains
  4 projects | news.ycombinator.com | 30 Jul 2025

  Currently working on a python package: agro.

  It helps devs build parallel agents from the cli. (think claude code, gemini, aider)

  And simplifies the git workflow for testing and accepting the best solution.

  Check it out: https://github.com/sutt/agro

  I keep a little text file pr.txt and try to link my project to HN / X / other aggregators once a day...

  This way I spend a few hours each day coding, and a few minutes each day promoting and refining me message.

• Claude Code Introduces Specialized Sub-Agents
  6 projects | news.ycombinator.com | 25 Jul 2025

  Great point, I've found Sonnet really can't be beat on many tasks, but increasingly finding Gemini-Pro and o3 handle the tough bugs and refactors best.

  That's why I've been using agro to launch agents from each of the main LLM vendors and checking their results when I'm stuck: https://github.com/sutt/agro/blob/master/docs/index.md

• Anthropic teams use Claude Code
  4 projects | news.ycombinator.com | 24 Jul 2025

  I can share my own ai-generated codebase:

  - there's a devlog showing all the prompts and accepted outputs: https://github.com/sutt/agro/blob/master/docs/dev-summary-v1...

  - and you can look at the ai-generated tests (as is being discussed above) and see they aren't very well thought out for the behavior, but are syntactically impressive: https://github.com/sutt/agro/tree/master/tests

  - check out the case-studies in the docs if you're interested in more ideas.

• Why I'm Betting Against AI Agents in 2025 (Despite Building Them)
  3 projects | news.ycombinator.com | 20 Jul 2025

  Yes I agree: highly-focused-scope + low-stakes + high-chorelike-task is the sweet spot for agents currently.

  I wrote a little about one such task, getting agents to supplement my markdown dev-log here: https://github.com/sutt/agro/blob/master/docs/case-studies/a...

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