I was tired of paying to different services just to be able to search and anlalyze data from different sources. I wanted to analyze appstore and playstore reviews for a category of apps, and saw that multiple startups charge subscription fees for what essentially, should be a pay as you go MCP, to be used with your AI applications.

So, made knowledgeforai.com, with which your AI will be able to access reddit, hackernews, play store and app store reviews, twitter and be able to analyze them

Please do check it out and any thoughts will be appreciated.

Comments URL: https://news.ycombinator.com/item?id=46813339

Points: 1

# Comments: 0

especially, because once x0K words crossed, the output becomes worser.

https://github.com/quilrai/LLMWatcher

made this mac app for the same purpose. any thoughts would be appreciated

https://github.com/quilrai/LLMWatcher

here is my take on the same thing, but as a mac app and using BASE_URL for intercepting codex, claude code and hooks for cursor.

Apart from that, may be somebody might find it useful for observing their usage patterns etc.

No grouping or correlation right now. Wanted to get some feedback/ opinions first

I use coding agents (claude code, codex, cursor etc) a lot and wanted a lightweight app which

1 - have searchable logs of all conversations 2 - rate limit or get notified if using too much context length (-especially useful for reminding myself to use a new session)

3 - block api keys etc from going to LLM providers

4 - get overview of tokens, tools used, and other interesting information

So, I made LLMWatcher

Using base url feature supported by most of the coding agents, LLMWatcher sits in the middle of coding sessions and can do all the above + some extra goodies

Also has a LLM Gateway feature, which can be used to get monitored/ proxied URLs with controls for any LLMs

Would love some feedback on this and to know if anyone finds it useful

Note: Always wanted to make desktop apps, and Tauri made it very easy to do so. Attempted with python first but got dragged into signing and packaging hell

Comments URL: https://news.ycombinator.com/item?id=46720191

Points: 3

# Comments: 2

I just scraped data from reddit and other sources so i could build a nsfw classifier and chose to open source the data and the model for general good.

Note that i was a 1 year experienced engineer working solely on this project in my free time, so it was basically impossible for me to review or clear out the few csam images in the 100,000+ images in the dataset.

Although, now i wonder if i should never have open sourced the data. Would have avoided lot of these issues.

I recently released https://trackernews.app/browse which surfaces out and groups content from hn, reddit .. for user defined topics and extracts user defined structured data along with summaries.

a tangential take on similar problem with focus on grouping posts by topics.

would love to get any feedback on the UX/ understandability of the interface

The core idea is that, by defining keywords, and a description, trackernews identifies actually revelvant posts and extracts user-defined data points from these posts.

Along with defining keywords, trackernews also allows the user to define various types of fields which trackernews extracts from the posts.

This allows the users to get a glancable/ searchable dashboards along with important insights surfaced.

I pre-created some topics which people may find interesting for publis browsing

Opinions on LLMs, agents, agentic tools - https://trackernews.app/browse/Opinions%20on%20LLMs

Discussions about Databases - https://trackernews.app/browse/Database%20Debates

Opinions and releases of Mac, IOS apps - https://trackernews.app/browse/Mac%20and%20IOS%20apps

Would love any feedback on the approach, UX and the idea.

Thanks, Praneeth

Comments URL: https://news.ycombinator.com/item?id=46139695

Points: 5

# Comments: 0

Except me and the team at my company nobody uses it.

After I made it lot of solutions came in this space, but IMO this is the easiest one to use still.

There are other ways to train, but this is the basic example.

Comments URL: https://news.ycombinator.com/item?id=37544415

Points: 1

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=29576619

Points: 4

# Comments: 0

It's documented very well, extremely easy to use and the pro version is priced very well.

Comments URL: https://news.ycombinator.com/item?id=27127703

Points: 136

# Comments: 41

Second this. I got the cheapest M1 macbook air in december and it really is streets ahead of competetion in terms of performace/ battery life.

On the intel macbook airs (and on most pros without gpus) playing CS:GO at full resolution was a bad experience (overheating and <60fps).

I got the cheapest M1 macbook air and it plays CS:GO with no issues and without heating up (even when the ambient temperature in my city is ~38 Celsius)