HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
gitsense
chat
JavaScript2025–2026
gitsense / chat

chat

Discover this repository through its Hacker News conversations.

Communication & Social
Explore on GitHubRead Hacker News discussions
2Distinct people
18Mentions
0Explicit recommenders
7GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A public description has not been verified.

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, 6:01:32 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 month18 dated mentions

Observed mentions: 2025-08-12 16:46:52 UTC to 2026-09-17 18:31:30 UTC.

02462025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 7 mentions; 0 submissions, 7 commentsAug 20252025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsFeb 20262026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2026

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
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC707
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC606
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC000
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC000
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC000
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC000
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC000
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC000
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC101
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC202
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC000
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC101

What Hacker News says

7 sources
sdesol·Aug 29, 2025
I don't think you need to be big data to benefit. A major issue we have right now is, we want the coding process to be more "Agentic", but we don't have an easy way for LLMs to determine what to pull into context to solve a problem. This is a problem that I am working on with my personal AI search assistant, which I talk about below: https://github.com/gitsense/chat/blob/main/packages/chat/wid... Analyzers are the "Brains" for my search, but generating the analysis is both tedious and can be costly. I'm working on the tedious part and with batch processing, you can probably process thousands of files for under 5 dollars with Gemini 2.5 Flash. With batch processing and the ability to continuously analyze 10s of thousands of files, I can see companies wanting to make "Agentic" coding smarter, which should help with GPU utilization and drive
Read the rest
down the cost of software development.
In Deploying DeepSeek on 96 H100 GPUs
sdesol·Aug 29, 2025
> An interesting alternative I've been meaning to try out is inverting this flow. This is what I am doing with my AI Search Assistant feature, which I discuss in more detail via the link below: https://github.com/gitsense/chat/blob/main/packages/chat/wid... By default, I provide what I call a "Tiny Overview Analyzer". You can read the prompt for the Analyzer with the link below: https://github.com/gitsense/chat/blob/main/packages/chat/wid... In a nutshell, it generates a very short summary of every document along with keywords. The basic idea is to use BM25 ranking to identify the most relevant documents for the AI to review. For example, my use case is to understand how Aider, Claude Code, etc., store their conversations so that I can make them readable in my chat app. To answer this, I would ask 'How does Aider store conversations?' and
Read the rest
the LLM would construct a deterministic keyword search using terms that would most likely identify how conversations are stored. Once I have the list of files, the LLM is asked again to review the summaries of all matches and suggest which documents should be loaded in full for further review. I've found this approach to be inconsistent, however. What I've found to work much better is just loading the "Tiny Overview" summaries into context and chatting with the LLM. For example, I would ask the same question: "Which files do you think can tell me how Aider stores conversations? Identify up to 20 files and create a context bundle for them so I can load them into context." For a thousand files, you can easily fit three-sentence summaries for each of them without overwhelming the LLM. Once I have my answer, I just need a few clicks to load the files into context, and then the LLM will have full access to the file content and can better answer my question.
In Show HN: PageIndex – Vectorless RAG
sdesol·Aug 26, 2025
> Do people really try to one-shot their AI tasks? Yes. I almost always end with "Do not generate any code unless it can help in our discussions as this is the design stage" I would say, 95% of my code for https://github.com/gitsense/chat in the last 6 months were AI generated, and I would say 80% were one shots. It is important to note that I can easily get into the 30+ messages of back and forth before any code is generated. For complex tasks, I will literally spend an hour or two (that can span days) chatting and thinking about a problem with the LLM and I do expect the LLM to one shot them.
In The Leverage Paradox in AI
sdesol·Aug 14, 2025
> I wish people could be a bit more open about what they build. I would say for the last 6 months, 95% of the code for my chat app (https://github.com/gitsense/chat) was AI generated (98% human architected). I believe what I created in the last 6 months was far from trivial. One of the features that AI helped a lot with, was the AI Search Assistant feature. You can learn more about it here https://github.com/gitsense/chat/blob/main/packages/chat/wid... As a debugging partner, LLMs are invaluable. I could easily load all the backend search code into context and have it trace a query and create a context bundle with just the affected files. Once I had that, I would use my tool to filter the context to just those files and then chat with the LLM to figure out what went wrong or why the search was slow. I very much agree with the author of t
Read the rest
he blog post about why LLMs can't really build software. AI is an industry game changer as it can truly 3x to 4x senior developers in my opinion. I should also note that I spend about $2 a day on LLM API calls (99% to Gemini 2.5 Flash) and I probably have to read 200+ LLM generated messages a day and reply back in great detail about 5 times a day (think of an email instead of chat message). Note: The demo on that I have in the README hasn't been setup, as I am still in the process of finalizing things for release but the NPM install instructions should work.
In Why LLMs can't really build software
sdesol·Aug 12, 2025
I've been able to create a very advanced search engine for my chat app that is more than enterprise ready. I've spent a decade thinking about search, but in a different language. Like you, I needed to explain what I knew about writing a search engine in Java for the LLM, to write it in JavaScript using libraries I did not know and it got me 95% of the way there. It is also incredibly important to note that the 5% that I needed to figure out was the difference between throw away code and something useful. You absolutely need domain knowledge but LLMs are more than enterprise ready in my opinion. Here is some documentation on how my search solution is used in my app to show that it is not a hobby feature. https://github.com/gitsense/chat/blob/main/packages/chat/wid...
In Claude Sonnet 4 now supports 1M tokens of context
sdesol·Aug 12, 2025
> But they have to get better at understanding the repo by asking the right questions. How I am tackling this problem is making it dead simple for users to create analyzers that are designed to enriched text data. You can read more about how it would be used in a search at https://github.com/gitsense/chat/blob/main/packages/chat/wid... The basic idea is, users would construct analyzers with the help of LLMs to extract the proper metadata that can be semantically searched. So when the user does an AI Assisted search with my tool, I would load all the analyzers (description and schema) into the system prompt and the LLM can determine which analyzers can be used to answer the question. A very simplistic analyzer would be to make it easy to identify backend and frontend code so you can just use the command `!ask find all frontend files` and t
Read the rest
he LLM will construct a deterministic search that knows to match for frontend files.
In Claude Sonnet 4 now supports 1M tokens of context
sdesol·Aug 12, 2025
> I really desperately need LLMs to maintain extremely effective context I actually built this. I'm still not ready to say "use the tool yet" but you can learn more about it at https://github.com/gitsense/chat. The demo link is not up yet as I need to finalize an admin tool but you should be able to follow the npm instructions to play around with. The basic idea is, you should be able to load your entire repo or repos and use the context builder to help you refine it. Or you can can create custom analyzers that you can do 'AI Assisted' searches with like execute `!ask find all frontend code that does [this]` and the because the analyzer knows how to extract the correct metadata to support that query, you'll be able to easily build the context using it.
In Claude Sonnet 4 now supports 1M tokens of context

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.