The discussion centers on a project using AI and networked microphones to locate a mysterious beep, with users debating the practical efficiency of this high-tech approach versus simple manual methods like physical binary search or unpacking boxes. Participants also argue over whether the project represents genuine learning and utility or merely an unnecessary expenditure of computational resources for a trivial problem.
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superpowers
Shell2025–2026
obra / superpowers
superpowers
An agentic skills framework & software development methodology that works.
64Distinct people
77Mentions
3Explicit recommenders
316.7KGitHub stars
What to know before exploring
What it is
An agentic skills framework & software development methodology that works.
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
https://github.com/obra/superpowers I use Opus/Sol with for /brainstorming, deepseek (on Pi) for /subagent-driven-development I love it, the docs are easily editable and when…
cthulberg used a recommendation phrase. 3 indexed comments match. This is a phrase match, not a verdict.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- gastownhall/beads · linked in 2 of the same discussions
- github/spec-kit · linked in 2 of the same discussions
- gsd-build/get-shit-done · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 12:03:30 PM UTC.
Mentions over time
HN mentions by month77 dated mentions
Observed mentions: 2025-10-11 14:41:58 UTC to 2026-09-26 02:10:10 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Oct 2025 | 2025-10-01 00:00:00 UTC | 2025-11-01 00:00:00 UTC | 6 | 1 | 5 |
| Nov 2025 | 2025-11-01 00:00:00 UTC | 2025-12-01 00:00:00 UTC | 3 | 0 | 3 |
| Dec 2025 | 2025-12-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 5 | 1 | 4 |
| Jan 2026 | 2026-01-01 00:00:00 UTC | 2026-02-01 00:00:00 UTC | 7 | 2 | 5 |
| Feb 2026 | 2026-02-01 00:00:00 UTC | 2026-03-01 00:00:00 UTC | 10 | 2 | 8 |
| Mar 2026 | 2026-03-01 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 9 | 3 | 6 |
| Apr 2026 | 2026-04-01 00:00:00 UTC | 2026-05-01 00:00:00 UTC | 12 | 3 | 9 |
| May 2026 | 2026-05-01 00:00:00 UTC | 2026-06-01 00:00:00 UTC | 5 | 2 | 3 |
| Jun 2026 | 2026-06-01 00:00:00 UTC | 2026-07-01 00:00:00 UTC | 6 | 2 | 4 |
| Jul 2026 | 2026-07-01 00:00:00 UTC | 2026-08-01 00:00:00 UTC | 10 | 0 | 10 |
| Aug 2026 | 2026-08-01 00:00:00 UTC | 2026-09-01 00:00:00 UTC | 2 | 0 | 2 |
| Sep 2026 | 2026-09-01 00:00:00 UTC | 2026-10-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
10 sourcesBIbizzletk·Feb 27, 2026
How do you think your framework compares to the very popular Claude Superpowers framework? It looks like it has a lot of overlap.
https://github.com/obra/superpowers
In Show HN: CodeLeash: framework for quality agent development, NOT an orchestratorGood point. It's flaky, because the orchestration is fully Opus driven. It has tools, skills, multiple turns. I was inspired inspired by the OpenClaw system. The UX should improve the most by tuning the internal `.md`s, but I'm also thinking about adding stricter workflow for each type of physical product like https://github.com/obra/superpowers does. Toys, enclosures, statues, servo robots, all need different amount of user feedback and artifact refinement.
Yeah, ordering actually works, we just had the first order :)
In Show HN: Conjure – 3D printed objects from text description onlyThere are a few prompt frameworks that essentially codify these types of workflows by adding skills and prompts
https://github.com/obra/superpowers
https://github.com/jlevy/tbd
In How I use Claude Code: Separation of planning and executionThis is what I do with the obra/superpowers[0] set of skills.
1. Use brainstorming to come up with the plan using the Socratic method
2. Write a high level design plan to file
3. I review the design plan
4. Write an implementation plan to file. We've already discussed this in detail, so usually it just needs skimming.
5. Use the worktree skill with subagent driven development skill
6. Agent does the work using subagents that for each task:
a. Implements the task
b. Spec reviews the completed task
c. Code reviews the completed task
7. When all tasks complete: create a PR for me to review
8. Go back to the agent with any comments
9. If finished, delete the plan files and merge the PR
[0]: https://github.com/obra/superpowers
In How I use Claude Code: Separation of planning and executionI didn’t create this, but on the surface it looks like a reasonable median between “Superpowers”[1] and “Gas Town”[2]. Posting as I don’t see any discussion on it here yet.
[1] https://github.com/obra/superpowers
[2] https://github.com/steveyegge/gastown
In Claude Code is powerful. Pilot makes it reliableSuperpowers is from Obra (Jesse Vincent), quintessential hacker and was a leader in the Perl community back in the day (still?).
https://github.com/obra/superpowers
In Ask HN: Why is my Claude experience so bad? What am I doing wrong?Add https://github.com/obra/superpowers
and then try again.
In Ask HN: Why is my Claude experience so bad? What am I doing wrong?Show HN: Metaswarm: Production-ready agent swarms, MIT license
A few weeks ago I posted about GoodToGo https://news.ycombinator.com/item?id=46656759 - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system.
I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs — research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks.
So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BE
Read the rest
ADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot.
The thing that surprised me most was the design review gate. Five agents — PM, Architect, Designer, Security, CTO — review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps.
This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics.
A few learnings:
Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates — BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates.
The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own.
It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes.
metaswarm stands on two projects: https://github.com/steveyegge/beads by Steve Yegge (git-native task tracking and knowledge priming) and https://github.com/obra/superpowers by Jesse Vincent (disciplined agentic workflows — TDD, brainstorming, systematic debugging). Both were essential.
Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to.
$ cd my-project-name
$ npx metaswarm init
MIT licensed. IANAL. YMMV. Issues/PRs welcome!
Show HN: 127 PRs to Prod this wknd with 18 AI agents: metaswarm. MIT licensed
A few weeks ago I posted about GoodToGo https://news.ycombinator.com/item?id=46656759 - a tool that gives AI agents a deterministic answer to "is this PR ready to merge?" Several people asked about the larger orchestration system I mentioned. This is that system.
I got tired of being a project manager for Claude Code. It writes code fine, but shipping production code is seven or eight jobs — research, planning, design review, implementation, code review, security audit, PR creation, CI babysitting. I was doing all the coordination myself. The agent typed fast. I was still the bottleneck. What I really needed was an orchestrator of orchestrators - swarms of swarms of agents with deterministic quality checks.
So I built metaswarm. It breaks work into phases and assigns each to a specialist swarm orchestrator. It manages handoffs and uses BE
Read the rest
ADS for deterministic gates that persist across /compact, /clear, and even across sessions. Point it at a GitHub issue or brainstorm with it (it uses Superpowers to ask clarifying questions) and it creates epics, tasks, and dependencies, then runs the full pipeline to a merged PR - including outside code review like CodeRabbit, Greptile, and Bugbot.
The thing that surprised me most was the design review gate. Five agents — PM, Architect, Designer, Security, CTO — review every plan in parallel before a line of code gets written. All five must approve. Three rounds max, then it escalates to a human. I expected a rubber stamp. It catches real design problems, dependency issues, security gaps.
This weekend I pointed it at my backlog. 127 PRs merged. Every one hit 100% test coverage. No human wrote code, reviewed code, or clicked merge. OK, I guided it a bit, mostly helping with plans for some of the epics.
A few learnings:
Agent checklists are theater. Agents skipped coverage checks, misread thresholds, or decided they didn't apply. Prompts alone weren't enough. The fix was deterministic gates — BEADS, pre-push hooks, CI jobs all on top of the agent completion check. The gates block bad code whether or not the agent cooperates.
The agents are just markdown files. No custom runtime, no server, and while I built it on TypeScript, the agents are language-agnostic. You can read all of them, edit them, add your own.
It self-reflects too. After every merged PR, the system extracts patterns, gotchas, and decisions into a JSONL knowledge base. Agents only load entries relevant to the files they're touching. The more it ships, the fewer mistakes it makes. It learns as it goes.
metaswarm stands on two projects: https://github.com/steveyegge/beads by Steve Yegge (git-native task tracking and knowledge priming) and https://github.com/obra/superpowers by Jesse Vincent (disciplined agentic workflows — TDD, brainstorming, systematic debugging). Both were essential.
Background: I founded Technorati, Linuxcare, and Warmstart; tech exec at Lyft and Reddit. I built metaswarm because I needed autonomous agents that could ship to a production codebase with the same standards I'd hold a human team to.
$ cd my-project-name
$ npx metaswarm init
MIT licensed. IANAL. YMMV. Issues/PRs welcome!