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Agentic-Systems-Lab
rigorous
Python2025–2025
Agentic-Systems-Lab / rigorous

rigorous

A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.

3Distinct people
7Mentions
0Explicit recommenders
253GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A comprehensive suite of tools, built to liberate science by making the creation, evaluation, and dissemination of research more transparent, affordable, and efficient.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:59:58 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour7 dated mentions

Observed mentions: 2025-05-31 13:51:16 UTC to 2025-06-01 03:01:37 UTC.

01232025-05-31 13:00:00 UTC to 2025-05-31 14:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsMay 31, 13:002025-05-31 14:00:00 UTC to 2025-05-31 15:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-31 15:00:00 UTC to 2025-05-31 16:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-31 16:00:00 UTC to 2025-05-31 17:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-31 17:00:00 UTC to 2025-05-31 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-31 18:00:00 UTC to 2025-05-31 19:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-31 19:00:00 UTC to 2025-05-31 20:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2025-05-31 20:00:00 UTC to 2025-05-31 21:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 31, 20:002025-05-31 21:00:00 UTC to 2025-05-31 22:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-05-31 22:00:00 UTC to 2025-05-31 23:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-31 23:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-06-01 01:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 01:00:00 UTC to 2025-06-01 02:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 02:00:00 UTC to 2025-06-01 03:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 03:00:00 UTC to 2025-06-01 04:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 1, 03:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 31, 13:002025-05-31 13:00:00 UTC2025-05-31 14:00:00 UTC110
May 31, 14:002025-05-31 14:00:00 UTC2025-05-31 15:00:00 UTC000
May 31, 15:002025-05-31 15:00:00 UTC2025-05-31 16:00:00 UTC000
May 31, 16:002025-05-31 16:00:00 UTC2025-05-31 17:00:00 UTC000
May 31, 17:002025-05-31 17:00:00 UTC2025-05-31 18:00:00 UTC000
May 31, 18:002025-05-31 18:00:00 UTC2025-05-31 19:00:00 UTC101
May 31, 19:002025-05-31 19:00:00 UTC2025-05-31 20:00:00 UTC303
May 31, 20:002025-05-31 20:00:00 UTC2025-05-31 21:00:00 UTC000
May 31, 21:002025-05-31 21:00:00 UTC2025-05-31 22:00:00 UTC000
May 31, 22:002025-05-31 22:00:00 UTC2025-05-31 23:00:00 UTC101
May 31, 23:002025-05-31 23:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 1, 00:002025-06-01 00:00:00 UTC2025-06-01 01:00:00 UTC000
Jun 1, 01:002025-06-01 01:00:00 UTC2025-06-01 02:00:00 UTC000
Jun 1, 02:002025-06-01 02:00:00 UTC2025-06-01 03:00:00 UTC000
Jun 1, 03:002025-06-01 03:00:00 UTC2025-06-01 04:00:00 UTC101

What Hacker News says

1 sources
I had a quick look at the repo as I wondered what you meant by multiple specialized agents. Funsamentally each of those 24 agents seem to be just: "load from pdf > put text into this prompt > Call OpenAI API" So is it actually just posting 24 different prompts to a generalist AI? I'm also wondering about the prompts, one I read said "find 3-4 problems per section....find 10-15 problems per paper". What happens when you put up a good paper, does this force it to find meaningless, nit-picky, problems? Have you tried papers which are acknowledged to be well written on it? From a programming perspective the code has got a lot of room from improvements. The big one is if you'd used the same interface for each "agent" you could have had them all self register and call themselves in a loop rather than having to do what you've done in this file:
Read the rest
https://github.com/robertjakob/rigorous/blob/main/Agent1_Pee... TBH, that's a bit of a WTF file. The `def _determine_research_type` method looks like a placeholder you've forgotten about too, as it use a pretty wonky way to determine the paper type. Also, you really didn't need specialized classes for each prompt, you could have just had the prompts as text files a single class loaded as templates that you just replace text into. That will mean you're going to have a lot of work whenever you need to update the way your prompting works, having to change 24 files each time, probably cut/pasting which is error prone. I've done it before where you have the templates in a folder, and the program just dynamically loads them. So you can add more really easily. Next stage is to add pre-processor directives to your loader that allows you to put some config at the top of each text file. I'm also not looking that hard at the code, but it seems you dump the entire paper into each prompt, rather than just the section it needs to review, which seems like an easy money saver if you asked an AI to chop up the paper, then just inject the section needed to reduce your costs for tokens. Although you then run the risk of it chopping it up badly. Finally, and this is a real nitpick but it's twitch inducing when reading the prompts, comments in javascript are two forward slashes not a hash.
In Show HN: AI Peer Reviewer – Multiagent system for scientific manuscript analysis

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