Update dataset - 1761527572.6297252

annotations/11.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f1c4f177ccfe50a3696c0c942f45438981346ec81c99464b80ba194b5338f3b
+size 603

annotations/12.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23b04809b340ed4351ff5b213e95b16a96224053a74674a3a1ac372e4e562d25
+size 603

annotations/13.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4dd1d1128982fb4615867f14d828dd1e09b550fb6c220cddf57d0e8e66fc4588
+size 604

annotations/14.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f33eb4953b7b482e32c591b6f10a065ca520b77ee8cd4d36b77af25f0f910d1e
+size 599

annotations/15.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfbe3c38c4fe4fbe7cd336c5e3c86bb3f53fff082b0bb0fdab7a9dda85ffa1a2
+size 602

annotations/16.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f615f11f470c0e2538230b7ca93a9809a575b51d40ad9d1e8c3a13b46285c650
+size 599

annotations/17.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3751bb4cd150a363d1a335dfaa4f84500cd63a108b180343313b3679c015396e
+size 599

annotations/18.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd3ae4cdd7180b1689f90371faf19e9e70db9f7c0c34ebdc3c852803cfc2bf9d
+size 604

annotations/19.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bfef98966c657fe946f612142a5987f16ce3139213305b19f03bd5fcc0e607fc
+size 598

annotations/20.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f21aa8fea1718711c927d7a6f3e3bf2b1a176b997c4e09d77e8983d1501239c
+size 599

annotations/21.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55463bdd516496a7c512b8452cba5f761ed505abeef49ee3b4b3a9fa9df98f18
+size 603

annotations/22.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45147b91b931b686dc1e6d395483a6ddb74482dd045b0f88fe8363bfd1527b53
+size 604

annotations/23.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e74adfbe606649bcbbec8b7436a2cd5a7eba62609c84900f4b83b8dc8e83ed77
+size 596

annotations/24.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c0f6dc940546068525917dc4821dda6535bd186e7df6d0c9d2498cd1dc48a70
+size 598

annotations/25.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:adfb4bd4dc58ab10a139a92bab2550d6d29063260ecd5f906b078b7c60af0ae8
+size 598

annotations/26.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4336e3a5f450ab3c528e606b5a02e39bcb1c4e7488e0acbbee7784cc444c93fe
+size 603

annotations/27.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa16f92d2cfa0e80af3f2915abdb4d4e31c96d15c74576e3904fe1de72dee0e9
+size 602

annotations/28.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fe952254097a84d1720617b0d8b593872a667fa09901a847b9368aaf9b3f7f6
+size 600

annotations/29.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:837d7dea72fa1401b3b861d1282f52727c7955a98eb76db0b01eda5a54f08c25
+size 601

annotations/30.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5da54d91c446e68faa415b7012b9381cb9963aa2c53b83d82c977f3862b9a7e
+size 598

annotations/31.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c7432d6afb78d676eb0647f804f486661251f8d45f0c0dd862adc717704e3e7
+size 602

audio/11.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e946b6ca2eb04be83bb174d3e358090f03e670c0a9133da4a06406a24d96da31
+size 2668076

audio/12.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fcc72030e7b00e1fe1cc439adc827178a773a020a0a87e41077bc42ccf42126f
+size 2587436

audio/13.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ea80e81bd6d3a5fd0addc1a52eca7496af6a382ff83a711675817d274b92300
+size 3605804

audio/14.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a851078868d37b85ca594db6811fa7a9410764bf5e55f8de9ff57d0724843d98
+size 5389962

audio/15.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c914d6e441095bd6a40aa5a558418ae5a321ccf83238b073b8f4f16c6154f39
+size 1672842

audio/16.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:736e83bcc12261e6d83c46b915a26d9c4fc4fbffdf441a4b6a1bc896300acf83
+size 649051

audio/17.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5df94fb343c121e973b335952996dfee728aa804f5adfae688651654e30a1c1b
+size 2566124

audio/18.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4249ca4f032cf3a438c3f004a48ed2da00c563e603ef284c892302689999bb96
+size 2980844

audio/19.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc2e20543f4f7eada7275e0c7b9fc256023320eb7f067edc41e84ff81c5f633c
+size 3235436

audio/20.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a692a5efca0e78afd528ef4edcff7d69e64cbb5989ae20f31ad01cf2faeb271
+size 2577644

audio/21.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55a4246d9bf9bfdfc1b28b4add8b4a8c746b7473f10e28ff3f909709017b04eb
+size 2070764

audio/22.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4abd166ccade3f644d84f0b394d7c64f90c5b8adee074b6b0bcfb53b95b8e07d
+size 2814956

audio/23.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:706082dc880f42aa397f9aee429f2f8a4d62fa19417e106e646d6031f91e4f11
+size 7845164

audio/24.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de35c8502abe369ee5876eea1d354a292d135675764ea7606e835a146c7b191c
+size 8816183

audio/25.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e831b32171884e6ac24dd23e3e54973da861f48f44a47a5e8fbecf2bc6720438
+size 4359902

audio/26.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e47a89f43f0de0b54b995ac790cf0041677f8944e208b009dc1caa6029fcf414
+size 888236

audio/27.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fe09dc5bf67af9a6f92fcf38b02508567fb8ce34984e744908386add67de18f
+size 3113324

audio/28.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a5c658f0ee134e0c31a9ca939ad7805a9a76a321f5e3728dd575ce734c250ae
+size 1484396

audio/29.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73519e4c374f1b7aa73fafe009ab248ad470a0a17e9b522d265af6293a246021
+size 1006406

audio/30.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7ed9e2ba97d3231457b3e699f67130488af59df2827599cecbaa4f054e1ccf1
+size 1524716

audio/31.mp3

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff2b40d06add3f07ca26e609ca0fef0270b9f4e72bbfe33a31bf193bcee7e96b
+size 4384556

transcripts/uncorrected/11.txt

@@ -0,0 +1 @@
+Okay, I'd like to create an app which does the following. The purpose of the app is to visualize how different countries, ideologies, systems approach common policy challenges. An example of a policy challenge that I'm just providing for explaining how I could see this working is second-hand smoke control. Some countries have very strict regulations, some countries have very lax enforcement. And probably there is not really much distinction by system of government but the user prompts it called policy visualizer and the user enters a policy challenge. So another example might be minimum alcohol purchasing laws.<br><br>Once Gemini receives this prompt, its task will be to research how different countries in the first instance approach this topic. And from that analysis, it can identify commonalities or clusters. The research process happens in the back end. And the user is shown some kind of progress indicators like researching what it's doing basically. Not a huge amount of verbosity but just a few cues so the user knows that it's not stuck or it's actually doing something.<br><br>Once Gemini concludes its first pass it will have grouped not necessarily every country in the world but based on the clusters it identifies it found groups. Each group is given a label. The label might be laissez-faire, permissive. These may be either recognized labels or what Gemini feels it's best to describe them as. And the countries are displayed with their national flags in alphabetical order.<br><br>The next functionality is that the user can click on the cluster and Gemini will describe what it is about this law that it considered them to be a cluster. In other words, the way in which they approach the challenge. That's a modal. Then the user can click on any country and it can see how that country approaches it. So I might click on the flag of Germany and either an accordion or a modal it show how Germany approaches in this case gun control and its cluster.<br><br>Country level is always a tab and only if there's other taxonomies. By taxonomy I mean that we think there's a very, Gemini says there's a very big difference and how different right-wing versus left-wing approaches we're going to do. We're going to create one more tab with that. But that should be kind of only if there's very compelling reason to do so. Or if it has significant data to share. So if it feels like there's enough data about how US states approach an issue at the state level, it might create a tab called US States and then follow the same pattern in which it groups them into clusters.<br><br>The objective is to, rather than searching through Google to see how different countries do different things, to start with your question and then get this visualisation. And I think the icing on the cake would be an analysis. So this is a visual presentation and then there may be analysis showing significant differences, some similarities. So there's like a report, a textual report, but the main tab, because I think it's the most interesting one, is the visualization, the policy visualizer.

transcripts/uncorrected/12.txt

@@ -0,0 +1 @@
+Alright, so the plan is for this repository, I want to create an audio media streaming interface for my home network. And there's a few things I want to roll into this one too.<br><br>Number 1 is media playback. So I have a volume on the NAS called AudioShare. The NAS is 10.0.0.50. So connect to the NAS, you'll find the AudioShare volume and let's mount that as the media library. It'll have a lot of tracks already populated.<br><br>Second thing is a soundboard. So I'll create a folder within that audio share volume called soundboard. And in the soundboard I just upload some stupid sound effects I do one to start it off Like laughing sound.<br><br>And then I also want to create a intercom system. and the functionality for the intercom is that from this computer, sorry from the interface which will be audio.residence.jlm.com I'd like to have the push to talk and the start and stop. PUSH TO TALK<br><br>So for the speaker networking this is where I would like you to give me your thoughts on what makes the most sense So I've used before MPD. I've installed MPD clients on... So the devices are, there is a device called Nursery Pi in SSH. Bedroom Pi, R-Pi and Smart TV. Each one is connected to a speaker. That's the network.<br><br>I tried MPD, putting an MPD client on each device. MPD has been the most reliable But it seems kind of a pity to use this when there are protocols like SnapServer that are designed specifically for this use case. However, using Home Assistant, I found SnapServer to be very buggy. I could never really get it to work and many more and the system that's reliable.<br><br>I find with MPD, because you need to select the speaker on the client devices, those bindings frequently broke. So I'd like to have something that kind of, the speakers are really never going to change. In the sense that I'm going to, I have a sound card for the Raspberry Pi. That's the speaker. and for as long as I use this system that's gonna be the configuration. So I want to set up something that once it's in place it's pretty much just gonna work.<br><br>So I leave that call up to you and please create a... Create a folder in the repository providing your recommendations just before you begin and what you suggest as the best implementation for the multi-speaker network whether it is broadcasting to a bunch of MCD clients from the Web UI or whether it's creating a single Snap server or something else that manages the networking I don't envision much of a need to select individual speakers by which I mean, I think that for the most part the occasions I'm using this I'll just play media to the pool but of course it would be nice to be able to select that !

transcripts/uncorrected/13.txt

@@ -0,0 +1 @@
+Building a Reporting Disclosure. I have a few thoughts. One, I can create a model. A model is actually quite feasible. It would be, but it's a data annotation project. It's saying, here's a PDF, here are the actual variables. In other words, here's the scope 3, scope 2, scope 1, here are the units, train it like that.<br><br>Second thought is if I did want to put together a dataset of sustainability disclosure reports, I think you could argue a public fair use clause for the PDFs being there.<br><br>And then the one I did with Gemini the other day which was basically a parsing AI tool seemed to work and could probably be used in production and which works even maybe as a way of trying to get in touch with Google is they have They have definitely an AI for good division who may let's say provide Gemini credits for the actual deployment of it on Cloud Run. Because from my first run of it, it was very, very promising for the task of parsing the reports.<br><br>And that would greatly the feature would be when it extracts the data human human in the loop is done by seeing what it is matching it to a company in the database or to a known company Let's take Google itself as an example. Detects its stock ticker, detects its stock exchange. And then you click like add to database meaning that you're adding the validated data and it could even pull out the metadata from the document pull out the source and that would be a great way of building up a human validated database in other words you take the reports you say either everything everything looks good to me or this is wrong either way you add it then of course you've got the missing financials and the rest of the world.<br><br>But that would probably be because there is thousands of sustainability disclosures, especially when you consider I think beyond the US globally, and it's beyond. So certainly it's a task for a model, but it's also human in the loop. The ultimate question is if Gemini stock performs 99% sufficiently well in the task of extracting this data from the sustainability reports. A model might actually not even be necessary because out of the box it's almost perfect. That is, I suspect, what the case would be.

transcripts/uncorrected/14.txt

@@ -0,0 +1 @@
+The purpose of the repository basically is to model or suggest the idea of using AI agents to scope out gap filling and extending multi-agent networks based on their inferred understanding of the purpose of a multi-agent network.<br><br>I think iterative workflow is the best. It suggests to the user what about this agent the user says yes or no, rather than the batch system. Although it could do both, but let's make the defaults the kind of individual review system.

transcripts/uncorrected/15.txt

@@ -0,0 +1 @@
+Okay, I'd like to create an app with Gemini. It's going to do the following. It will be called MyEQCreator. Here's how it works.<br><br>The user will, there will be a microphone recording interface, or the user can upload a file. Either way, the user should aim to upload a three minute audio sample. Audio Sample goes to Gemini and Gemini will parse the submitted audio to determine speaker characteristics, namely their vocal range, frequency distribution. And when it does this its goal way to provide an EQ preset for the user.<br><br>I use Audacity for lightweight audio editing and if I had a Daniel voice preset that had these EQ settings built in or that could even use via a CLI I would use it but that would require maybe a second pass Gemini would generate it according to that file spec.<br><br>What would be very useful and impressive in addition would be after the analysis a five second audio sample might be visualized and the frequencies highlighted to illustrate to the user where the frequency distribution falls for their particular voice.

transcripts/uncorrected/16.txt

@@ -0,0 +1 @@
+It would be great to run the demo. I'm opening, creating a .env. And it would be useful so people can see straight up how it works to have a page that just says demo.<br><br>And it'll have so we'll need to run the audio data through the pipeline just as if we were using it capture the results into the repo here and just display that on the front end I've just provided the Gemini API key so let's try to do that I I also deleted, I think we just need one readme and the instructions for the app can be attached.

transcripts/uncorrected/17.txt

@@ -0,0 +1 @@
+Hello, yeah, I'm looking for, okay, I'm trying to find a phone case for the Nord 3 5G from OnePlus. I want something which has MagSafe, a magnet built into the case itself, and something good quality and that's just a good protective case for the phone.<br><br>Do you know of any recommendations? Any ones on AliExpress or if Otterbox makes a case for this phone or anyone else? It's a slightly older OnePlus, so it's tricky to find a compatible case for it.<br><br>So if you happen to know, you should know of any products on AliExpress and product numbers, list them please.

transcripts/uncorrected/18.txt

@@ -0,0 +1 @@
+I'd like to create a content recommendation app. This will be using... I'd like to get recommendations for movies to watch, things on Netflix, YouTube that are up to date. I'm based in Israel. I like watching things that are based on a true story or true stories. I prefer to watch things that are recent so it has to be up to date and the pitfall with these apps is that they'll recommend stuff that you've already seen or you don't want to watch so it would have to have some memory that it makes recommendations preferably one at a time and I can say like add to watch list or add to recommendation list or not interested or I've seen and the app would need to remember these responses so that it doesn't. It's just the same thing over and over again.<br><br>I know there's TMDB API which is great for getting movies. I have an API key I can provide. And I'd like to maybe say recommend across all categories just recommend movies. The Netflix thing it's very hard to get recommendations that are geo-sensitive for Netflix but that would probably be the ideal meaning that I'm based in Israel and if stuff isn't available here that should be considered as recommendations.