Yes, some companies avoid hiring programmers who prefer indie work. They worry these programmers might not fully commit to their job or could develop competing products. Others fear they might prioritize their indie projects over company work.
A few weeks ago, I created Xplained, an AI-based answering engine that uses web data and LLM to provide responses (like perplexity). I am planning to sell my hobby project on Acquire.com and could use your advice.
What are your experiences with selling projects? How did you determine the valuation, pitch to buyers, and ensure a smooth sale process?
I'm looking for practical tips and insights to navigate this transition effectively.
Looks like a Novel.sh wrapper. It would be useful if it could have templates by use cases like product document or customer support or FAQ or product listing. Also, maybe some AI assistant to help with content writing.
Hey. Its a good practical application, especially to reduce cost. But at about 0.6 similarity, I get some cache hit. Maybe with more examples and for a high use app, the cache hit would increase based on a higher similarity scores.
Still early days on this I guess, but any observations that can help improve the hit rates?
I liked the idea. A good start to a promising app. It does need some features to make it useful and some other improvement ts on UX. People have listed others, but I would like a filter or search at least for starters. Also, progressive disclosure on the news details.
On the cost side, you could try Gemini Pro, currently free with usage limitations. But since the content is saved, it should be fine.
How are you sourcing news and deciding what news to keep? Search engine or some kind of feedback? Would like more details here.
Its just a quick POC made in a day. I am still working on integrating a backend and optimizing the queries. Also, it currently uses free APIs with small usage limits. Plan to upgrade soon.
Iterations and assigning roles to the GPT help. Give a small block of code, ask GPT to review this with a panel of developers (frontend, backend, ...), users and UI/UX experts for 5 iterations, and then provide the final code. GPT 4 works better. However, a lot of times the code is outdated and creates new issues. Manual review and fixes are needed for most custom code blocks. It works well for standard code modules.
Hey. I had the same idea as you a while back. Did some samples as well. Used a pretty similar approach till the "Generating Other Scales" section. But was not happy with the output. However, I found them as a good seed to generate music using AI systems from that time. The current systems should provide better results.
I have been using elastic index for a while now. The best way I have found is to use a hybrid search - match all with embedding + exact+fuzzy match combination as a way to boost results.
Reranking also provide a significant improvement to the response quality.
Another way to improve results for domain specific RAG systems is to use some heuristics to boost results. E.g., penalize results that contain certain negative keywords or boost results with certain patterns.
For RAG, given the limited context size and potential hallucinations, best prompt + best data will provide you with best response.
Prompts can be improved greatly to get the LLM to throw a good response with reduced hallucinations. A lot of techniques are seen on Twitter and can be explored to find a good fit.