saran945

CLI-Anything generates a complete command-line interface for existing software (if codebase available). they've already generated CLIs for 11 complex applications, I guess this is cool, can be used to make classic software to Agentic enabled. .

currencies are hard code as of now, Will connect with some api's.

"100 watt hours to joules", Thanks for let me know - its a parsing error.

“The AMT/AIT was also weaponized by certain political elements in India to proliferate harassment against the Brahmins of Tamil Nadu”

I was born and raised in Tamil Nadu, having lived there for over two decades. In my experience, I have not witnessed any widespread harassment specifically targeting Brahmins. While isolated incidents may exist—just as they do for various communities across all states—there is no substantial evidence to suggest a systemic issue. Could you provide concrete examples, statistics, or credible sources to substantiate this claim ?

Extracting a list page using an LLM costs $1, so I reused the selectors when extracting data from the same page in subsequent operations to optimize costs. the extraction works well for search result pages as well. As far I know none of the websites provide as an RSS feed for search results.

I have downloaded exe for windows, Chrome saying "Failed virus detected"

"We evaluated REAP using a dataset designed to highlight the limitations of large language models (LLMs), comparing zero-shot prompting with REAP-enhanced prompts across six state-of-the-art models: OpenAI’s o1-preview, o1-mini, GPT-4o, GPT-4o-mini, Google’s Gemini 1.5 Pro, and Claude 3.5 Sonnet. The results show significant performance improvements, with o1-mini increasing by 40.97%, GPT-4o by 66.26%, and GPT-4o-mini by 112.93%. While OpenAI's o1-preview already demonstrated strong baseline performance, it still showed modest gains. In addition to the performance improvements, REAP provides a cost-effective solution. For instance, GPT-4o-mini, which is about 100 times cheaper than o1-preview, delivered competitive results."

Cool.

1. Currently, it's a proof of concept only. In the next release, I will make sure to include some use cases/examples.

2. I haven’t finalized the pricing yet. For now, it's free. When I release, the starting price will be around $7-10 per ~200 runs/questions. I’m also considering offering a free tier; I’ll work that out later. Thank you.

I made using https://www.screen.studio/

Extended version Autogen. Added 8 new tools and modified some prompts. I never expected this much quality. linking my tiny benchmarking test in a tweet. https://x.com/saran945/status/1815441284812738734

I don't understand your question. . .

Do you need more details about How it finds the answer? I already shared most of the part in this thread.

The home page was done in 60 minutes, the whole product was done in 3 days. I was testing and releasing without any intention.

I slept, sorry for the delay reply.

The quality of LLM does not improve my product much, I am facing extraction issues, will have to improve continually.

"You are going to see a lot of competition in this space." I welcome them all :)

Huginn is a great product, though I never used, I am aware of the value it provides. Alertfor is not similar to Huginn. my product is part of something else, I extracted and unleased as separate product. Will go in different direction.

The rules suggesting that I would get more feedback if there is no signups. Anyways, luckily I have got enough feedback :) next release will have some public access without signups.

sure. I do not stop.

I have designed the product for consumers and professionals. Since it's a new kind of product (?), I'm unsure what to call it. The agents continuously search the web on behalf of the user and create a feed for each query. Let me think more . . .

RAG is to enhance the generation capabilities of a language model by integrating external knowledge sources (DB, KB etc). This is done by retrieving relevant information from a knowledge base or document store and using it to inform or augment the model's responses.

Langchain is a library specifically designed to facilitate the development of applications using LLMs. It provides tools and utilities to build complex NLP pipelines with ease.

Alertfor is a SaaS product that combines LLMs with agents to provide automated search.

Does this answer your question ?

Awesome!!

Kindly try now with same question. I made some changes. I use Bing API as a initial resource selector. there were some issues. fixed it.

I use LLM+Agentic framework, it runs every 6 hours. In the next release I will provide UI to customize it (daily, weekly, 6 hours).

This is a best advice, repeatedly hearing it.

focus on a promising niche that has a problem and is willing to spend money on it is often regarded as best practice. many successful companies have been built on this principle. Unfortunately, this approach has never worked for me. I have worked on many products and followed these best practices, but I have yet to see success. Over the past 12+ years, I have experimented these best practices but nothing has worked. It would work for founders who has good networking /connections, living in CA, best in marketing, have good followers in Social media etc. Founder like me have no such assets. this is a big chicken or egg problem.

Instead, I decided to take a different approach and I create imaginative products based on ideas I'm passionate about and publish them continuously. If there is an interest, I then optimize the product for the interested customers, taking their feedback into account and continuing development accordingly.

Please bear with me, Its not showing on mobile.

https://vimeo.com/991338343

It's a surprise to see weekly queries. If you don't mind, could you please share one or two examples of these queries?

I've added all suggestions from entire thread to my log and will prioritize implementing them.

Regarding pricing, I don't know offering a free tier is right approach or not .. I believe that the queries are complex and long. not sure they could be shared among users. Anyways, for next 2 or 3 months its going to be beta. my current focus is adding value, will ask the users about pricing later. Note that, for each question, it crawls 10-15 pages, or even more if you ask to enrich a table. there is LLM cost, now a days webpages are really huge.

Thank you again for your input and support. Please let me know if you have any other thoughts or questions.

You may be using Mobile, Its showing me in my Laptop. I will work on style issues on mobile. The twitter link is my blunder mistake. .

Made a quick video/demo and added in the home page. Hope this helps to understand the product.

It is an expected behavior. context is missing. if you know any websites that has your request then you may mention it in your query with context. so that you can force the agent to look the info you need.

for e.g any available 3 bedroom units currently for rent in Seattle use these resources: Zillow, redfin, realtors.

its free during beta (may be 2 or 3 months). pricing will be around $10 for basic version (200 job runs or questions per month), my current focus is collecting all the feedbacks in my log to fix it. . .

this is strange. I saw emails signups and sending emails recently. kindly try with other email. if the problem persists please communicate me saran@tealme.com

my bad, Sure, will make one and upload soon. I did not expect this much interest.

Currently, I compare the previous & current answers (with clue) and send both to the LLM to determine if an alert is required for the given alert-task (question + Clue). I should admit that in some corner cases, such as when comparing list page answers, it is not working as expected. I suppose to parse those list as pre-processing step. The alert is triggered on the clue that you give while creating alert.

Not suitable for real-time data changes. Although I can schedule the scraper to run every minute, this is not an ideal use case for such a setup. This approach is more suitable for web page data that changes every few hours.

Instead of asking simple questions like "Who is the current NBA champion?", I would like to approach this project as a complex data collector.

For example:

I live in Seattle and want to buy a MacBook Pro with the following specifications: - 8-Core CPU - 10-Core GPU - 16GB Unified Memory - 1TB SSD Storage

Can you compare online prices and provide me with a report?

Alert Setup: Please alert me if there are any price drops in any store.

The answers provided by the app are sourced directly from the web, and the LLM is used as 1. tool learner/selector 2. query interpreter 3. page understanding and response generation. The answer includes citations from the sources used.

For example. Query: What is the temperature in Seattle? Answer: 17°C Alert Setup: Alert me if it reaches 23°C.

The app continuously monitors the source for updates on the queried information. In this example, the temperature data is regularly checked against the alert condition set by the user.

The real-time aspect depends on how frequently we scrape data using scheduled jobs. Currently, the app checks the data at set intervals (6 hours) to ensure timely notifications.

"Love seeing these creative ideas using LLMs to solve existing problems" - yeah, since web wasn't improve as semantic web, I have to use LLM to traverse, understand and collect the web data.