Ask HN: What hacks/tips do you use to make AI work better for you?

Im currently using the ChatBox desktop app which I quite like. You can setup as many chats as you want with different providers (openai, claude, google etc) and different custom instructions. I can move between different providers for a 2nd opinion and also easily setup different chats for specific tasks like content writer, developer, product manager and so on.

https://chatboxai.app/en

> These services retain historical records of interactions.

Thats not universally true, for example AWS hosts their own version of Claude specifically for non-retention and guarantee that your data and requests are not used for training. This is legally backed up and governments and banks use this version to guarantee that submitted queries are not retained.

I’m a developer with about the same amount of experience as you (22 years) and LLMs are incredibly useful to me, but only really as an advanced tab completion (I use paid version of cursor with the latest Claude model) and it easily 5x’s my productivity. The most benefit comes from refactoring code where I change one line, the llm detects what I’m doing and then updates all the other lines in the file. Could I do this manually? Yes absolutely, but it just turned a 2 minute activity into (literally) a 2 second activity.

These micro speed ups have a benefit of time for sure, but there’s a WAY, WAAAY larger benefit: my momentum stays up because I’m not getting cognitively fatigued doing trivialities.

Do I read and check what the llm writes? Of course.

Does it make mistakes? Sometimes, but until I have access to the all-knowing perfect god machine I’m doing cost benefit on the imperfect one, and it’s still worth it A LOT.

And no, I don’t write SPA TODO apps, I am the founder of a quantum chemistry startup, LLMs write a lot of our helpers, deployment code, review our scientific experiments and help us brainstorm, write our documentation, tests and much more. The whole company uses them and they are all more productive by doing so.

How do we know it works? We just hit experimental parity and labs have verified that our simulations match predictions with a negligible margin of error. Could we have built this without LLMs? Yes sure, but we did it in 4.5 months, I estimate it would have taken at least 12 without them -

Again - do they make mistakes? Yes, but who doesn’t? The benefits FAR outweigh the negatives.

I started with BASICA and GWBASIC in the 80s, and though I've had some diversions, I would say there haven't been many days since where I haven't thought about solving problems with code. I still don't feel particularly qualified to answer, but I guess I probably am.

> Are all of these posts just astroturfed?! I ask that sincerely.

This is amusing to me - since GPT 4 or so, I've been wondering if the real fake grass is actually folks saying this shit is useless.

I think I'd need a bit more insight into how you are trying to use it to really help, but one thing you wrote did stand out to me:

> the llm is suggesting an API access paradigm that became deprecated

Don't trust its specific knowledge any more than you absolutely have to. For example, I was recently working with the Harvest API, and even though it can easily recite a fair bit about such a well-known API on its own, I would never trust it to.

Go find the relevant bits from API/library docs and share in your prompt. Enclose each bit of text using unambiguous delimiters (triple-backtick works well), and let it know what's up at the start. Here's a slightly contrived example prompt I might use:

---

Harvest / ERP integration - currently we have code to create time entries with a duration, but I'd like to be able to also create entries without a duration (effectively starting a timer). Please update providers/harvest.py, services/time_entries.py, endpoints/time_entries.api accordingly. I've included project structure and relevant API docs after my code. Please output all changed files complete with no omissions.

providers/harvest.py: " contents "

services/time_entries.py: " contents "

endpoints/time_entries.api: " contents "

project structure: " /app /endpoints time_entries.py project_reports.py user_analytics.py /services time_entries.py project_reports.py user_analytics.py /providers harvest.py "

harvest docs: " relevant object definitions, endpoint request/response details, pagination parameters, etc "

---

I have a couple simple scripts that assist with sanitization / reversal (using keyword db), concatenation of files with titles and backticks > clipboard buffer, and updating files from clipboard either with a single diff confirmation or hunk-based (like `git add -p`). It isn't perfect, but I am absolutely certain it saves me so much time.

Also, I never let it choose libraries for me. If I am starting something from scratch, I generally always tell it exactly what I want to use, and as above, any time I think it might get confused, I provide reference material. If I'm not sure what I want to use, I will first ask it about options and research on my own.

LLMs have dramatically different results depending on the domain. Getting LLMs to help me learn typescript is a joy, getting them to help me fix distributed consensus problems in my fully bespoke codebase make them look worse than useless.

Some people will find them amazing, some will find them a net negative.

Although finding truly zero use for them makes it hard for me to believe that this person really tried with creativity and an open mind