271–280 of 349 posts
I built a RAG implementation for 35k books/articles/wiki pages/web pages i collected over the years(it took about 6 weeks on 3070ti 100% constant usage). I query it with various steps of data extraction/narrative building/refining etc, over LLMs. Almost daily i figure out new steps to add to the pipeline and honestly, i could not imagine learning about niche topic x from so many perspectives/periods in such a short t…
Our CEO believes LLMs are a fad, so there's nothing really strategic about it in the company's roadmap, but I was able to assemble a skunkworks team of enthusiasts who integrated ChatGPT into one of our eLearning products. It allows a course author to improve writing, it makes suggestions about content, etc. Technologically, it's nothing special, just a bunch of pre-made prompts. The reception was kind of lukewarm be…
So was your CEO correct? Was this a fad? It seems interest has certainly cooled, at least for your market segment, according to your experiment?
In fact, when we showcased our prototype of the "improve my writing" feature that we quickly put together in one week in early 2023 to a few select clients, their feedback was very enthusiastic. However, it took several months to bring it into production due to several bureaucratic hurdles: clearance from the legal department, the product owner delaying the release because of other priorities, and so on.
Now that the first feature has received a lukewarm reception because of the delayed release, we have neither a dedicated team nor a budget for adding more LLM-based features. Implementing proper and useful RAG is more complex and requires a certain level of expertise (vector DB integration, chunking strategy/indexing, tricks like HyDE, reranking, etc.), compared to just using the command "hey ChatGPT, improve this: %s." It is now unlikely that we'll have anything cool anytime soon without the support of the CEO or product owner (and they probably believe I proved it was indeed a fad). Most teams are currently busy with ordinary features from our backlog that do not require LLMs, and no one cares anymore.
My "stack" is just Apple Shortcuts making HTTP POST API calls to OpenAI, which does stuff in MacOS via BetterTouchTool. I trigger each by hotkey or typing a few letter into Spotlight (with Alfred). One transcribes and summarizes whatever youtube URL is highlighted. One does grammar and style correction of whatever is highlighted (and replaces it). One simply replaces the Dictate key with OpenAI Whisper but otherwise…
I am stunned at how few people are sharing projects using LLMs for real estate. I own the domain homestocompare and I am working on a project that will use AI to help compare homes. Unfortunately I don't have a working demo yet but please reach out to me if you would be interested in finding out more.
I've done a handful of fun hardware + LLM projects... * I built a real life Pokedex to recognize Pokemon [video] https://www.youtube.com/watch?v=wVcerPofkE0 * I used ChatGPT to filter nice comments and print them in my office [video] https://www.youtube.com/watch?v=AonMzGUN9gQ * I built a general purpose chat assistant into an old intercom [video] https://www.youtube.com/watch?v=-zDdpeTdv84 Again, nothing terribly us…
I built an Interactive Resume AI chatbot where anyone can ask questions about my experience and skills: https://www.jon-olson.com/resume_ai/ The backend is a Python FastAPI that uses ChromaDB to store my resume and Q&A pairs, OpenAI, and Airtable to log requests and responses. The UI is Sveltekit. I'm currently building a different tool and will apply some learnings to my Interactive Resume AI. Instead of Airtable, I…
Resume AI is cool, really nicely done, mate !
The actual questions I got did not provide a response that is to my liking. Most of that is due in part because I'm using gpt3.5 since gpt4-turbo is a lot more expensive, and I can learn a lot more by using an inferior LLM.
For example, using an llm router to analyze the query and route to a specific helper function with a specific prompt would be helpful. Sometimes a user starts with a greeting but the response is a pre-written "Sorry an answer cannot be found". Questions are typically grouped into a category such as skills, experience, project, personal (ie: where are you located), preferences (ie: favorite language), and general interview questions (ie: why should I hire you). Questions in categories can be better answered by using a different prompt and/or RAG technique.
I am stunned at how few people are sharing projects using LLMs for real estate. I own the domain homestocompare and I am working on a project that will use AI to help compare homes. Unfortunately I don't have a working demo yet but please reach out to me if you would be interested in finding out more.
hey would love to hear more! I built a bunch of projects in real estate and happy to collaborate
You can reach me via my twitter (in my profile) or reddit:
I built an Interactive Resume AI chatbot where anyone can ask questions about my experience and skills: https://www.jon-olson.com/resume_ai/ The backend is a Python FastAPI that uses ChromaDB to store my resume and Q&A pairs, OpenAI, and Airtable to log requests and responses. The UI is Sveltekit. I'm currently building a different tool and will apply some learnings to my Interactive Resume AI. Instead of Airtable, I…
I’m sorry it’s been tough. The job market for seniors and leads is still quite strong in Australia if you can move here
I was contacted by a company recruiter for a small healthcare SaaS in California and had 3 interviews recently. When I looked up the job, only 7 people had applied in 2 weeks on LinkedIn. They are a very real company with very real people, but their job post is not getting seen (it's not a promoted post).
My next AI project will be to scrape LinkedIn jobs, analyze it for repost/promoted behavior, group it by consulting/headhunters vs company job post, eliminate duplicates, and filter based on my skillset and hard-no qualities (such as can't work if I live in California, must be in EST but I'm in PST timezone, requires Java experience, etc).
"Widjosumarajzer" = video summarizer It's just a hodgepodge of prototype scripts, but one that I actually used on a few occasions already. Most of the work is manual, but does seem easily run as "fire and forget" with maybe some ways to correct afterwards. First, I'm using the pyannote for speech recognition: it converts audio to text, while being able to discern speakers: SPEAKER_01, _02, etc. The diarization provid…
Is pyannote the best diarization library you found? What's SOA? I've been using a saas product (Gladia) and I'm getting close to my 10-hour mark.