Though I've made a few attempts to use it, generally I already know the answers to most trivial questions. And by going into more complex questions and scenarios, it becomes apparent that ChatGPT lacks a deep understanding of the suggestions it provides. It turns out to be a pretty frustrating experience.
Also I've noticed that by the time I've crafted intricate prompts, I could have easily skimmed through a few pages of official docs and found a solution.
That said, considering the widespread buzz surrounding ChatGPT, it's entirely possible that I may simply be using it incorrectly.
However, when I asked that the model in question needs to be unmanaged and backed by a SQL view (which I pasted), which had previously been applied from a migration script, it became pretty obvious that ChatGPT is just putting together sentences that are highly likely to be true, but not really understanding the architecture of Django.
So that's one example - and it's not even a particularly complex scenario.
And wow, is it convenient for writing tests. I just copy the entire views.py in and list all of the tests I want. The key is to be explicit with what you want.
- Salesforce API
- AWS
- React Native
The react native docs are good enough that I really empathize with what you're saying - wouldn't I just be faster reading the docs? However, for salesforce I feel exactly the opposite - the docs are all over the place and message boards tend to be of low quality for the subject.
For what it's worth, AWS felt somewhere in the middle depending on the service.
I've used it to write unit test methods for Salesforce apex classes & triggers. For the most part, the unit test code it spits out is actually decent. Occasionally I'll have to clean it up a bit or make minor corrections. But it has greatly helped free up some time doing mundane stuff.
I know a lot of people leverage gpt for basic writing tasks. I'm confident in my writing ability and enjoy writing so I don't use it for that either.
I might try using image models at some point to generate some pixel art, but on the whole I've found these tools pretty useless and am left wondering what I'm missing. To me it seems like they only work for a very specific case: "I have no background knowledge in domain X and am ok with a quick solution that is adequate but likely not optimal, and I don't need to worry about about correctness." Unfortunately, that's completely out of the realm of the sort of work I like to do.
Another option is you want to learn about X, you can ask ChatGPT to give you a list of authors or subjects that discuss that topic deeper, which give you a head start, as you can then ask follow up questions such as where their central argument fit in the bigger scheme, which were more influential etc.
Things it proved useful for:
- learning new programming languages and solving hackerrank or codewars katas in languages I didn't know. It sped up and lowered my learning curve.
- writing recommendation letters based on my inputs ("write a recommendation letter for X, knowing those are his strengths and what he's good at).
Things it proved terrible for:
- personal assistant. I was hoping to use ChatGPT as a tool that would help me reason. I did try to use it as a companion learning the new Epic Games programming language "Verse". ChatGPT proved absolutely terrible for this task. I would feed him paragraphs about verse calculus, from the pdf and then question him about what I fed him, but have few exchanges more and he would start allucinating.
- text editing. I tried to feed him paragraphs and then tell him, modify this or that. But it's capped in the number of tokens and it would make mistakes. Say I told him "modify this line", he would reprint it, but if I told him "hey, so what's paragraph 2", again he would get stale versions
- calendar assistant. I would tell him what my priorities and appointments where but it was very unhelpful to provide help and required much too much interaction.
In the end I just stopped using it, besides few niche use cases (which haven't happened recently) it just does not provide value where I want to find this value: as an assistant.
I'm still moderately optimistic about such use cases being a reality in the future, because the idea of having a companion I can converse with about my daily tasks and work tasks (sort of a jarvis) is really where I expect to find value.
Where I don't expect it to provide value is overhyped things like google or bing search, there I want to find websites.
As a side note, although I can’t confirm this it seems like the general quality of answers for both 3.5 and 4 decreased over time. I suspect they are doing further RLHF which has shown to make the model stupider for lack of a better word.
GPT-4 can actually do stuff: it hallucinates an order of magnitude less often, generates working code, and can explain complex subjects with nuance. It's still a bit of an idiot: it's output is intern level at best, but having a lightning fast intern at my disposal 27/4 has already revolutionized my workflow.
Anything that's not straightforward? Even if not flat-out wrong, it lacks any real nuance.
I also played with it for some trivial coding and, again, could maybe have saved me a few minutes but nothing earth shattering.
So, yeah, maybe I'm doing it wrong but it's not remotely a go-to tool.
GPT is super prone to error on any niche thing, be it subtle nuance or concrete details, but i don't actually find it to be any more or less reliable to the state of the web. Ie most content i consume from the web is full of errors, incorrect facts, etc. I don't find my suspicion of this to be any different than that of GPT. However GPT has a great way of having the relationships to error-ridden results in the web already established. I can ask it about this web of things to get a better understanding when i drill into concrete facts via standard search.
GPT is far from a gimmick in my usage, but i also don't think it's revolutionary yet. I use it because i'm exploring and finding it useful, but also because each new leap OpenAI makes is pretty damn impressive. This week they're rolling out Plugins i believe. Every step they take is fun to be with, fun to use, fun to experience. That alone is worth the $20/m for me.
If I forget some api or technique in some language, I ask it. Depending on its results, I may or may not start browsing for more details or to verify.
Try asking it things you would google for. I'm finding it to be a pretty good "I'd like an answer to this question, I don't want to wade through a bunch of google results trying to find the one that answers this question" engine. Sometimes it fails, but often it's really good.
This is probably why google has an "all hands on deck about AI" going on right now, I've been using google a lot less lately between bing and chatgpt. However, last week I noticed that Google Bard provides an answer PLUS gives reference links, which ChatGPT can't do.
[1] https://web.archive.org/web/20190808123852/http://larvatus.c...
And LLMs are a new thing too, it takes some getting used to. I admit I almost got burned a couple times when, for a few moments, I bought the bullshit GPT tried to sell me. Much like with Googling experience of old (sadly made near-useless over the last decade, with Google changing the fundamental ideas behind what search queries are) - as you keep using LLMs, you develop an intuition about prompting them and interpreting results. You get a feel for what is probably bullshit, what needs to be double-checked, and what can be safely taken at face value.
Questions I ask on topics I am not familiar with are much further from the limits of its knowledge. I find it to be an amazing tool for quickly getting a structured overview of a new subject, including pros and cons of different alternatives and risks I should be aware of.
The examples I'm thinking of where people completely dismissed ChatGPT were asking things like "tell me about <MY NAME>", "Explain this thing I wrote my thesis on".
In other words, throwing a toy problem at it, getting a bad answer, then making up their mind that it's not useful.
I'm not advocating blind trust in it, I'm just saying don't try a couple things and decide it's garbage. You're doing yourself a disservice.
I love asking it for unit tests and docstrings for my code. Is it perfect? No. But does it give me a starting point for something that I otherwise might not do? Absolutely.
I've asked it a lot of things that I'm familiar with the topic and can immediately eyeball it's answer, either because I'm having a brainfart or because it's a little fiddly to work out. It can be good at those.
I've asked it a lot of questions with things that I'm not at all familiar with, probably the best example is Powershell, and it's provided good answers, or answers that were at least able to lead me in the right direction so I can iterate towards an answer. But then I also tried to use it to write an AWK script and it failed miserably, but got 80% of the way there. Which, honestly, is about the best I've ever been able to do with any complex AWK script. :-)
Yes it can, you just have to specifiy that it do so.
I find that it tends to limit the hallucination, or hilights when it's hallucinating.
I asked Bing's Search AI the same question and it claims that it updates itself every day to stay current on the latest news and happenings.
2 years is a LONG time in our current society, so keep that in mind when it provides answers.
But also yes, it is not the greatest thing ever. It is mostly mediocre for me as well. And lots of the most exciting things, like DMing a dungeons and dragons campaign, is actually mostly terrible because it recreates the same scenarios over and over and it never remembers any of the information that a DM should know like anything on your character sheet, when to ask you to roll the dice, or even which version of the rules its supposed to be using even if you ask it directly to use that particular version of the rules. But in fact, it is wild that it has read all of the rules. I was using it to run a star wars saga edition game for myself (I'm not that lonely I promise). And at some point it occured to me this is all copywrited information, is it allowed to read this and then regurgitate it to me? How much of this is fair use? If I wrote a campaign for the system could I regurgitate rules like this in teh campaign book? Who owns all this material that is created not from imagination but from highly complex combinatorics?
It's far from perfect but we're continually working on improving it and you can check it out here if you're interested: https://www.fables.gg/
- Suggesting and writing YouTube shorts scripts about facts. However these turned out very sensationalised and less factual.
- I have a website with categories based on real world things. In the past I paid hundreds of dollars for mediocre short descriptions. With ChatGPT I paid $0.04 for good and long descriptions.
- I experimented with automated blog articles. It doesn't work for me because of their 'morals' but if you write boring articles about non controversial things without any opinions it works better than any (cheap) paid writer I had so far.
So far I haven't had a single coding issue I thought GPT could help me. I don't understand the buzz either.
However it was close, close enough that I could find the right method trough GitHub that I didn't find before.
Not bad, not amazing, but I see the potential
Early on, ChatGPT knocked a bunch of highly technical questions I sent it out of the park. It trivially reproduced insight from hours of diving through old forums and gave me further info that eventually proved out. More recently, it has completely hallucinated responses to the last 3 technical questions I gave it in its familiar "invent an API that would be convenient" style. It's the same ChatGPT, but two very different experiences as a function of subject.
I hear this all the time but never with a transcript. I wonder how much experts “read into” responses to make them seem more impressive. (Accidentally you understand, no malice). Or if in the moment it feels impressive but on review it’s mostly banal / platitudes / vague.
The few times I’ve used it for precise answers they were wrong in subtle but significant ways.
My apologies if you are an expert toilet cleaner, the point is it's more useful than how-to-wiki or YouTube for getting you up and running or refreshing info you may have forgotten.
Avoid asking it for VERY obscure things you know nothing about, because it probably doesn't either, but won't say, and the hallucinations start.
The falsified stuff can be pretty awful, and it has a tendency to double down.
I put the section of code into chat gpt and the error and it said that the function I was using returned a collection of the object and I was using it as if it was the actual object. Obviously I had not noticed the punctuation that the debugger used to indicate a collection.
I recently was looking for the melting points and hardness of various materials. Obvious ones were easily available, but what is the melting point of brick or granite? I put in a big list of materials and chat gpt got them all.
Finally today I had a shopping list and I asked chat GPT to organize by aisle for the store I was going to. It did a bad job, but it was mostly correct. I could easily fix the mistakes myself.
I think for many things chat gpt gets 90% of the way there and the 10% you have to fix is no big deal.
I really like it for generating sql queries and regular expressions. Two things that take me a lot of time that I do infrequently enough that I can never remember how it works.
A friend of mine said he feels like chat GPT will enable a resurgence in the generalist programmer. Im a solid developer but I dont do enough to be completely immersed. Chat GPT is amazingly productive for new areas or areas I dont do that often. Recently Ive used it for linux networking, oauth, some reasonably complex SQL queries, ruby threading, and working with the Ruby 2D API.
This is pretty unintuitive, especially given the hype around it.
Once you DO learn how to use it the productivity boost it can provide for all sorts of different things is substantial. I use it a dozen or so times a day at this point.
I've written a bit about how I use it here: https://simonwillison.net/series/using-chatgpt/
At the moment, I can't find a serious use case for me. I find it really hard to guide the AI in the direction I want or requires extra work I'm not ready to put in [1].
[1] https://martinfowler.com/articles/2023-chatgpt-xu-hao.html
I'd forgotten about this weirdness, but ChatGPT explained it.
I also managed to get ChatGPT to write two pieces of fairly complex C++ boilerplate - one was a std:vector that used mmap() and mremap() to grow linearly rather than by a fixed factor (also avoiding memory copy on resize)
Then I made it write a vector whose iterator was an integer index rather than a pointer.
I made it write all the unit tests and benchmarks for these and it did everything correctly except not knowing that munmap() needs the size parameter rounded to the nearest page.
Obviously I hardly managed to get everything correct on a single prompt. It took an iterative conversation and successive refinement
To ask GPT about something and be able to determine whether its answer is false or not, you already need to be competent and knowledgeable in that topic. If you're already competent and knowledgeable in a topic, you don't need to ask GPT about it because you already know it.
ChatGPT is not an expert, it's not even written to try to be one, it's fancy phone keyboard prediction with a random element added.
It's great for creative stuff like "write a cut poem about a puppy" but so far literally every technical question I've asked it has been answered incorrectly.
[1] assuming you write raw SQL rarely if ever by hand
For programming, I often ask it for some modules in language X that can do Y.
Sometimes it surprises me and lists something I have not heard of.
Other times it makes stuff up.
I think to use it well, you have to be some what of a subject matter expert in the topic you are asking about.
Generally, I know the concepts he's working on but I've run into a couple problems. First, they don't use textbooks at his school. It's just poorly made slideshows from the teacher that don't clearly explain any topics. So, to get to parity with his understanding, I'd have to hear the teacher explain it.
Second, his work will generally reference names of laws, theorems, etc. that I'm not familiar with. Usually, I'd search for a document, Khan academy video, or some other YouTube video but this has been time-consuming.
I started asking ChatGPT: "Take on the role of a high school teacher in an advanced physics course. Give me a basic explanation of [insert law name here]. Please provide at least two examples and a reference URL."
This has been incredibly useful.
Strangely i use ChatGPT4 every day but am using Phind less and less. When i have something to search the web for, Kagi is faster for me. When i want to search GPT4 (thoughts/whatever) ChatGPT is faster for me. Phind is cool, but kinda feeling like the worse version of each.. if that makes sense.
It has replaced 95% of my previous DuckDuckGo searches for development info.
I even used it with another developer to solve a mission critical bug based on some very vague symptoms. It's saved so much time, I'll never go back.
It also seems much more useful than ChatGPT or StackOverflow by themselves
Frankly a cli version that could pipe into my code editor (Helix) would be amazing.
This feels remarkably like when we were arguing over scrypt vs bcrypt and used to benchmark our GPUs by seeing how many dozens of bit/litecoins we could generate in a night.
In four or five years, we won't recognize the world we're living in today.
For example, I tried Otter's meeting summarizer, and it was incomprehensible, but when I implemented it myself with GPT-4 I got stellar results.
Separately I like combining serper.dev and Scrapingbee with GPT-4 / langchain to summarizing scientific articles and news for me on top of my (ugly non-sharable) AI scripts, but they're basic
The context is that I am someone who codes in intense bursts every few months, so a lot of the details never really transition from short-term to long-term memory. ChatGPT is perfect for this.
I’m skeptical of most prompt-based tools. I’d rather just get to the source and tweak ChatGPT to talk about exactly what I want.
As such they provide a very specific abstraction for broad range of tasks. The moat for most prompt based tools is very small. These tools are similar to the moat of a spreadsheet template.
At the end of the day whether it’s a spreadsheet to track my mortgage or a tailored prompt, I typically want to fine tune it for just me.
GPT-4 has been superior to Phind in my experience, especially on technical questions. And the web browsing beta closes all the holes remaining. Downside is speed, but it's a fire and forget thing.
I tried AutoGPT and the like, but found the self-iteration not very helpful when tackling real world problems. GPT-4 gives enough step-by-step instructions to do anything I need it to do, without it being wired into my file system.
Prompt engineering seems to be a thing of the past with GPT-4 too, though you still need to give context, which "prompt engineering tools" don't help with.
Plugins are also pretty powerful. It seems to hallucinate a lot with Zapier, but it's still the best tool by far.
https://www.brusselstimes.com/430098/belgian-man-commits-sui...
Some example AI commands that are built in (you can of course create your own commands): - Improve Writing - Change Tone to Friendly / Confident / Professional / Casual - Fix Spelling and Grammar - Find Bugs in Code - Explain Code Step by Step - Explain This in Simple Terms
When you do more than 5-10 sales calls a week, writing up notes and emails can take hours of your time.
It's tedious work but also must be done (otherwise you might forget what's going on in a deal when it's time to do another call down the line!).
Also, the quality of the summaries and emails must be good (clear, readable) but not necessarily great (we're not looking to win a Pulitzer here).
It's the perfect kind of task for GPT.
love it!
Rather ironically I'm using ChatGPT to teach me about AI, explaining concepts like tensors and attention layers. It's a great way to make sure I'm in good with Roko's Basilisk since my AI-generated immortal soul will be able to cite my ChatGPT log as proof that I helped bring the Basilisk into existence.
https://github.com/underlines/awesome-marketing-datascience/...
https://kagi.com/summarizer/index.html
Also +1 for ChatPDF - it's great!
Also a plug for a weekly AI-related digest: https://perprompt.com/
> Alice give Bob 5 dollars. Bob give Fred 10 dollar. Fred buy a house. Alice rent Fred's house. Alice pay Fred 500 dollar....
[0]: https://gist.github.com/alfanick/3ecac79f9590bae6819e410c338...
But I like to ask Bing questions, if i have a simple thought of 'what does this mean' or 'how does this work' to get a high level overview of something that is not vital that I know everything, it's just something I heard and realised I had no idea about what it was/how it worked.
And i've started using heypi.com as a personal coach, just talking through anything I am feeling/struggling with and i'm really liking that at the moment. I've felt for the longest time I could do with someone to bounce ideas around or talk to about life and struggled to find someone that understood my nonsense and overthinking, AI seems to be a good job with it and i don't have to worry about being insecure with what I am talking about as it's just an AI.
I’d be uncomfortable if there were a digital transcript of my conversations with my therapist stored in some company’s servers, but I get that talking to a machine would be easier than a person for some people/topics.
The result is aider, which is a command-line tool that allows you to code with GPT-4 in the terminal. Ask GPT for features, improvements, or bug fixes and aider will directly apply the suggested changes to your source files. Each change is automatically committed to git with a descriptive commit message.
https://paul-gauthier.github.io/aider/
It helps to look at some chat transcripts, to get a sense of what it's like to actually code with GPT:
Then I switched to having it use sed which was better but still not totally consistent.
Can you add a license?
I tried `sed` as well, every variant of `diff` output, various json structured formats, etc. The edit block syntax I chose is modeled after the conflict resolution markup you get from a failed `git merge`. I find it's very helpful to ask GPT to output in a format that is already in popular use. It has probably seen plenty of examples in its training data.
Asking GPT to output edits really only works ~reliably in GPT-4. Even then, my parser has to be permissive and go with the flow. GPT-3.5 was a lost cause for outputting any editing syntax -- it only worked reliably by outputting the entire modified source file. Which was slow and wasted the small context window.
Both 3.5 and 4 seem to be able to think more clearly about the actual coding task when you allow them to use an output format they're comfortable with. If you require them to output a bespoke format, or try too hard to ask for "minimal" edits... the quality of the actual code changes suffers. The models are used to seeing uninterrupted blocks of code in plain text, and so they seem to do best if you choose an output format that embraces that.
1. The main prompt is pretty long (https://github.com/paul-gauthier/aider/blob/main/aider/promp...), but I suspect that it could be abbreviated. I'm think the same criteria/restrictions could be enforced with fewer characters, enabling (slightly) larger files to be supported
E.g. "I want you to act as an expert software engineer and pair programmer." (69 chars) -> "Act as a software dev + pair programmer" (39 chars) - I suspect the LLM would behave similarly with these two phrases
2. It would be cool to be able to open "file sections" rather than entire files (still trying to figure out how to do this for my own tool). But the idea I had would be to support opening files like `path/to/main.go[123:200]` or `/path/to/main.go[func:FuncName]`
^the way I've gotten around this limitation with your tool is by copying functions/structures relevant to my request into a temporary file, then opening aider on the temporary file and making my request. Then I copy/paste the code generated to the actual file I want it in. It did a really good job, I'm just trying to figure out ways to reduce the number of manual steps for that
Anyway, great job, and I'm really excited to keep using this tool.
By the way, could you by any chance update the README with instructions for running directly from source rather than via pip install?
I have explored the "line ranges" concept you describe a fair amount. I took another run at it again last week. I still haven't unlocked a formulation that GPT-4 can reliably work with.
It's really hard to get GPT-4 to stop trying to edit parts of the files that we haven't shown it yet. It frequently hallucinates what's in the missing sections and then happily starts editing it.
It's like ChatGPT but gives you the option to change / edit and rerun prompts more effectively.
Describing the environment relies heavily on a CMDB, so this is not a one-size-fits-all approach and this is functioning entirely in my personal lab of ~100 servers. That said, ChatGPT has given me the best results compared to locally run LLMs
Getting answers has replaced using search in about 80% of cases.
- how do i run a function when a value changes in svelte?
- how do i get the current tab id from inside a content script?
- what is the origin of the term 'use your illusion'?
- what is the average salary of a developer advocate in new york city?
It is also GPT-4 for free (for now).
Edit: and I strongly suggest you create a "Delete your account" page or at least a way to contact the person responsible to do it :).
Yes interestingly GPT-4 identifies as GPT-3, and you need to be quite explicit about asking it to search for the answer in papers (see collapsed prompt).
But it is excellent at using the content of papers to ground itself, it is really hard to make it hallucinate or provide evidence for scientifically incorrect claims. And it is so much faster than searching for whole papers and reading through them.
We are focusing on the core value of giving it access to scientific knowledge right now, but we are working hard to mature everything surrounding it too.
It's so calm and to the point, I'm never going back to anything else.
Yeah, the initial idea was to uncover globally significant news, but a lot of people are asking for ways to find "locally important" or "industry important" news. So I'm shifting my direction a little bit.
It's currently possible to filter news by broad category in a paid version (health, science, tech). I plan to expand the list of categories, so it's possible to go deeper. I also plan to add news in other languages (translated) and add country filters, so it's possible to see only news related to individual country/region.
The top 6 stories at the moment are duplicates of a single story:
7.7 - Russia launches intense air attack on Kyiv, Ukraine claims to have shot down all 18 missiles.
7.1 - Series of explosions heard in Kyiv as Russia attacks.
7.1 - Massive Russian missile strike hits Kyiv in attempt to destroy Ukraine's new air defence systems.
6.9 - Kyiv targeted by dense Russian missile and drone attack.
6.9 - Russian drones and ballistic missiles attack Ukraine's capital after President Zelensky secures new arms pledges.
6.9 - Russia launches intense air attack on Kyiv with drones, cruise missiles, and possible ballistic missiles.
https://www.spronket.com/sharedConfig?shared-config=23924690
https://github.com/ferrislucas/promptr
From the README: Promptr is a CLI tool that makes it easy to apply GPT's code change recommendations with a single command. With Promptr, you can quickly refactor code, implement classes to pass tests, and experiment with LLMs. No more copying code from the ChatGPT window into your editor.
plz-cli, a terminal copilot (not just an autocomplete - you can ask it to explain, refactor, or well - do anything), https://github.com/m1guelpf/plz-cli
Code GPT, a Visual Studio Code copilot, https://marketplace.visualstudio.com/items?itemName=DanielSa...
Instead I can describe my precise situation to ChatGTP and get something that is almost 95% ready to plug in straight to my code.
I will give an example.
I work on asp.net, heavy sql backend application. Sometimes while I am working on a big task, I skip a few things as I develop and hone in on my ultimate solution. Then I go back and tidy things up. I would sometimes mock data that I would have had to write onto real sql tables into temporary tables and at the end, I would go and turn those temp tables into real tables. ChatGPT has been very good at say turning those temporary (staging) work into real work
e.g. Hey ChatGTP, here's my settings table which I have defined as a temporary table, can you write me a script that turns this into the real table, and another script for the data import script
e.g. Hey chatGTP, I need to output this xml from this sql, can you have a go at turning this into something like this, here's the table schema I'm working with
iOS app "Friday" lets you use to talk to ChatGPT. It seems to be just simple glue (I'm sure there are many similar ones) between speech recognition, GPT, and text-to-speech, but the end result is that when you're bored you can have fun discussions without typing.
It also does a relatively good job of writing unit tests.
It also supports sending it your clipboard contents when you launch it and it parses the words "clipboard". Good for when in a pinch.
I mapped some repetitive prompts to my text replacement on my phone that auto-expand when I type in the input field `*<some shortcut here>`
1: https://www.macstories.net/ios/introducing-s-gpt-a-shortcut-...
Have tried about another 7 ai text generators/editors and so far is the best
https://www.gnod.com/search/ai
So far I know of 3:
- Phind
- Perplexity
- YouChat
If you know more, let me know and I'll add them.
but I know nothing about who made it, can any of you help?
It’s very similar to ChatPDF, but you can include multiple documents and it has much better context selection. This leads to better answers in practice (less “the source does not contain information on…” and hallucinations)
I am mostly a technical person and not the best for selling/marketing. So I built Nureply to help me meet with potential customers and learn from them directly.
Take a look at here -> https://nureply.com
I prefer using an LLM locally if possible since it gives me more control and I don't have to worry about the additional cost or OpenAIs infrastructure being under load.
https://www.perplexity.ai/ is favourite "search" tool (ironically beating GPT enhanced Bing) for outright speed and quality of results.
> It uses AI (ChatGPT-4) to read the top 1000 news every day and rank them by significance on a scale from 0 to 10 based on event magnitude, scale, potential, and source credibility.
3 articles in Top 20 about Israel ?
They should work on how exactly is the significance determined, and significant for whom ?
That was the initial idea. Significant events don't stop being significant once we get tired of hearing about them.
But there's definitely a problem of duplicates. When separate news sites post about a similar event it get rated relatively similar by ChatGPT, which creates clusters like we see today.
I want to solve this soon by combining similar stories into single block with one title.
- scale is the number of people affected by the event described in the news story. - magnitude is the strength of the effect. - potential is the likeliness of the event to lead to other, more significant events. - source credibility considers how trustworthy is the source, and what is its track record.
Then these parameters are combined into a single score.
Also fair criticism re: repeats. I plan to solve this by clustering similar news, so one event is only given one title.
You can pass it a URL and perform actions on the webpage.
Additionally, OpenAI Chat is a useful tool for day-to-day tasks.
What? how?
Works great. I have an openai api key and I've configured the plugin to use gpt-4.
None
Chatgpt
An iPhone app to use gpt-4
An ai newsletter that sends me new tools (I’ve found lots of cool tools from it but none that I use regularly)
I think that’s it? Kinda surprising. There are so many gpt powered products I’ve tried but none I’ve stuck with.
4 is good but you still need to use it properly
It's not perfect but it's at a point now where it is allowing me to make significant progress on personal projects that I would not otherwise have time to do. I already sit in front of a computer at work all day. I want to minimize doing that outside of work, but I still have lots of code projects I want to do on the side.
Main repo: https://github.com/mobyvb/pull-pal
Examples:
- Drafting an action plan and coming up with open questions based on specific requirements (issue: https://github.com/mobyvb/download-simulator-2023/issues/1, PR created by bot: https://github.com/mobyvb/download-simulator-2023/pull/2/fil...)
- See also asking the bot to write code based on a step in the generated action plan: https://github.com/mobyvb/download-simulator-2023/issues/3 and PR https://github.com/mobyvb/download-simulator-2023/pull/4. In this PR, pay attention to the comments I left; the bot takes feedback from the comments and will update the code accordingly, allowing you to iterate on a single PR before merging code
- Basic updates to existing HTML file (issue: https://github.com/mobyvb/pull-pal/issues/4, PR: https://github.com/mobyvb/pull-pal/pull/5/files)
- Writing an Arduino script from scratch based on specific requirements (issue: https://github.com/mobyvb/midi-looper/issues/1, PR: https://github.com/mobyvb/midi-looper/pull/2/files)
Still lots of improvement to go but I'm having a lot of fun.
Exposition if you want:
My experience using GPT4 for programming has been pretty fun. First I was experimenting with prompts like "Given <this code>, how do I accomplish <this task>?" I have been discovering things about what types of tasks it's particularly good at (generating action plans, breaking tasks down into subtasks, writing simple-ish code). The code is usually stuff I could figure out myself, but it's still (sometimes) more efficient than me trying to write it, then do some google searching, update it, etc... The chat format also makes iterative improvement fairly easy, e.g. "you forgot to use <some variable>" or "please add comments explaining what the code does" or "replace this text in the html with some generated content about a digital assistant tool"
I'm a manager and team lead so I find myself writing a lot of tickets based on high-level product requirements for my team to work on. Because I am very familiar with the code base, I often provide a lot of technical detail, e.g. providing links to specific files, functions, and PRs relevant to the issue. I found that prompting GPT4 with a similar level of detail resulted in success. However, it was still really good at more general tasks.
An example of a task that GPT performs pretty well at: "write an index.html landing page with a content section that is vertically and horizontally centered using flexbox. In the content section, generate a heading and paragraph talking about an AI-powered digital assistant for programmers. Add some basic styling to the page, with soft colors. The font of the heading and paragraph should be different. Serve index.html from a main.go file on port 8080. Also add an endpoint to the server at POST /api/number which returns a random integer between 14 and 37. In index.html, add a button that calls this endpoint and displays the number on the page"
(GPT4 can handle this prompt easily with no errors in the code; GPT3 will struggle, so it needs to be broken down more)
I could do all of the stuff in that example myself. But so can AI. I prefer to write out what I want and get some code that's usually 90%-100% perfect, make some slight modifications, maybe ask for some different color options, etc.. Point is, that's probably 30 mins saved for the same end result (honestly, better, considering my design skills are nonexistent).
I work a lot with Github repos already, so it was straightforward to me to replace the ChatGPT interface with a "Github interface" which I'm already familiar with and like (open issue, reference issue from PR, merge PR, issue auto closed). I also like being able to iterate on a "pending" change in a PR by leaving comments before merge. Also, I can do it from my phone!
To see the specific prompts this tool is using as the foundation (at the moment), see
* https://github.com/mobyvb/pull-pal/blob/main/llm/prompts/cod...
* https://github.com/mobyvb/pull-pal/blob/main/llm/prompts/com...
If you have read this far, I hope it sounds interesting to you. The tool is GPL licensed, and I would love if other people tried it out so that I can get feedback on the best improvements to make/bugs to fix.
Side note: having gotten access to Copilot Chat, it’s disturbing how quickly the ChatGPT UI has become established in my mind as the standard. Copilot Chat, despite being integrated into VS Code, feels clunky and alien compared to ChatGPT in a separate window. Funny how fast new things become the standard by which others are measured.
https://en.wikipedia.org/wiki/GPT-3 https://en.wikipedia.org/wiki/GPT-4
I had genuinely never seen GPT used for General Purpose Technology until I followed your link to Wikipedia.