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jacob-ai-bot
jacob
HISTORICAL RECORD2024–2024
jacob-ai-bot / jacob · Not publicly available

jacob

Discover this repository through its Hacker News conversations.

2Distinct people
4Mentions
0Explicit recommenders
REPOSITORY AT A GLANCE

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What it is

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Where it is used

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What people are saying

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Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 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 week4 dated mentions

Observed mentions: 2024-05-23 16:05:47 UTC to 2024-07-19 10:19:23 UTC. Weeks start Monday, UTC.

0122024-05-20 00:00:00 UTC to 2024-05-27 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsMay 202024-05-27 00:00:00 UTC to 2024-06-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-03 00:00:00 UTC to 2024-06-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-10 00:00:00 UTC to 2024-06-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-06-17 00:00:00 UTC to 2024-06-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJun 172024-06-24 00:00:00 UTC to 2024-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-07-01 00:00:00 UTC to 2024-07-08 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-08 00:00:00 UTC to 2024-07-15 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-07-15 00:00:00 UTC to 2024-07-22 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 15

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 202024-05-20 00:00:00 UTC2024-05-27 00:00:00 UTC211
May 272024-05-27 00:00:00 UTC2024-06-03 00:00:00 UTC000
Jun 32024-06-03 00:00:00 UTC2024-06-10 00:00:00 UTC000
Jun 102024-06-10 00:00:00 UTC2024-06-17 00:00:00 UTC000
Jun 172024-06-17 00:00:00 UTC2024-06-24 00:00:00 UTC000
Jun 242024-06-24 00:00:00 UTC2024-07-01 00:00:00 UTC101
Jul 12024-07-01 00:00:00 UTC2024-07-08 00:00:00 UTC000
Jul 82024-07-08 00:00:00 UTC2024-07-15 00:00:00 UTC000
Jul 152024-07-15 00:00:00 UTC2024-07-22 00:00:00 UTC101

What Hacker News says

4 sources
Nice job on this, it’s a really interesting approach. I’ve been developing an open-source coding agent over the past year, and RAG just wasn’t working at all. I switched to a repo map approach (which sounds similar to what aider is doing) and that helped a bit but still wasn’t great. However, a few weeks ago I built an agent that takes in a new GitHub issue and is given a variety of tools to do research on the background information to complete the issue. The tools include internet searches or clarifying questions to ask the person who wrote the ticket. But the most useful tool is the ability to look at the codebase and create a detailed markdown file of various files, explanations of what each file does, relevant code samples or snippets from the files, etc.. It’s still early, but anecdotally I’ve seen a huge increase in the quality of t
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he code that uses this research as part of the context (along with the repo map and other details). It’s also able to tackle much more complex issues than it could before. I definitely think you’re on to something here with this wiki approach. I’ll be curious to dig in and see the details of how you are creating these. Here is my research code if you’re interested: https://github.com/jacob-ai-bot/jacob/blob/feature/agent/src... And here’s an example of the research output (everything past the exit criteria section): https://github.com/kleneway/jacob/issues/62
In Show HN: How we leapfrogged traditional vector based RAG with a 'language map'
We architected JACoB to make it relatively easy to add new frameworks. There are templates that dynamically build prompts based on the languages and tools defined in a jacob.config file that lives in your repo. Then you can just add new files with custom instructions for the new language. Here's the prompts folder with examples for the technologies we support today: https://github.com/jacob-ai-bot/jacob/tree/main/src/server/p... I'm personally not a Rails dev so I haven't tried it, but my intuition is that Rails would actually be a good candidate to work well with JACoB due to the relatively standard conventions that most projects use.
In Show HN: JACoB – Open-Source AI Coding Agent for Real-World Productivity
bfioca·May 23, 2024Submission

Show HN: JACoB – Open-Source AI Coding Agent for Real-World Productivity

Hi, we're Kevin, Chris, and Brian, the creators of JACoB (Just Another Coding Bot), an open-source AI coding agent designed to streamline the development process. As veteran developers, we know that while AI isn't perfect, it can be a valuable tool for tasks like code reviews, converting designs to React components, and writing high-quality GitHub issues. We’ve been building JACoB over the past year to assist with our real work, and today we’re open sourcing it under an Apache 2.0 license. Our goal is to create the first fully open-source tool that matches the quality and polish of many closed-source alternatives. JACoB focuses on specific, time-consuming tasks that AI handles well, like generating code from Figma designs, fixing simple bugs, and writing boilerplate code like API endpoints. You can even converse with JACoB to refine exist
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ing GitHub issues or help write new ones. It integrates seamlessly into your workflow, from GitHub issues to pull requests, ensuring all code passes tests before finalizing. JACoB is available on GitHub today, where you can customize it to your needs, such as running with local models. We're also developing a hosted version - sign up for the waitlist at https://jacb.ai. Currently, JACoB works best with JavaScript or TypeScript projects, especially with frameworks like Next.js. To evaluate JACoB’s real-world abilities, we created our own benchmark arena since none existed for design-to-code tools. JACoB outperformed eight popular tools and human-written code, scoring an ELO of 1183, over 100 points higher than the nearest competitor. More details on how we achieved this are available in the technical whitepaper found in the repo. AI is transforming software development, and we believe developers should lead this change, not large corporations. JACoB is our contribution to that future, respecting your privacy and security while remaining transparent and open. We'd love your feedback. Visit our GitHub repo to try it out and find more demo videos and a link to our technical whitepaper. https://github.com/jacob-ai-bot/jacob Let's build the future of AI in software development together. We’re excited to hear your thoughts and answer any questions!
40 points · Open discussion

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