Show HN: Boring Report, a news app that uses AI to desensationalize the news (boringreport.org)
In today's world, catchy headlines and articles often distract readers from the facts and relevant information. By utilizing OpenAI's language models, Boring Report processes sensationalist news articles, transforms them into the content you see, and helps readers focus on the essential details. We recently updated our iOS app experience, so any and all feedback would be appreciated.
App Link: https://apps.apple.com/us/app/boring-report-news-by-ai/id644...
345 comments
[ 3.1 ms ] story [ 312 ms ] threadI think what you're really saying is: "I'm less interested in the work this person did to make something cool; selfishly, I'd like to know if it follows my biases so I can judge it based on politics."
Or you're just curious how it works and haven't spent the effort trying it out, which is valid.
Could it not be that as a person with an interest in AI and general technology I am wondering if there is any detail on what does an "AI" look for to determine a "sensationalist" title and how does it "desensationalize" it?
I have scanned the twitter feed and the app and just wondering if there is anything on how it works. The answer can be no.
But let's be frank... It's clear through your verbiage that you were either thinking about this politically or were actually just curious as I stated in my previous message.
I never said you were lazy, there are many reasons why you might not be able to try out the product right now and it's totally normal to ask questions when you assume others might have answers. It's also possible to do selfish things, or think selfishly at times and not be a selfish person, I was not trying to antagonize.
Sorry for misunderstanding your intentions, for some reason when you asked "What does desensationalize exactly mean?" I read it particularly negatively.
To me, it's just a comment asking how it may work (technically). Pretty normal to ask that on HN. The persons feelings or intentions aren't reflected in the comment and I think aren't relevant to the question either way.
Wouldn't someone who was intellectually curious just ask: "That's interesting, I wonder how it works?" and not: "What does desensationalized exactly mean?" which is overly negative. Incidentally, I'm being accused of being negative for pointing that out.
My intentions were not negative, if it was interpreted that way I apologize, same as you I type the way I type.
> What exactly does "desensationalize the news" mean?
as being snarky rather than curious.
* rapturous applause * if this works.
Media literacy is about way more than about the wording of headlines. It's also about understanding why a headline was selected, who benefits from a story, whether the story is internally logically consistent, and why were the people quoted selected, context of the story that you wouldn't know just from reading it, etc.
I say this as someone that wrote a browser plugin to do something similar in like 2011 by screening words that indicated the headline was pointless.
A reasonable premise! But easier said than done. I wonder how this app counteracts the hallucination and lying behavior of LLMs.* Would be pretty bad to trade off easier-to-decipher human bias and sensationalism for distorted truths and lies from an obfuscated sequence of dot products!
* I assume they are using LLMs because they state:
> By utilizing the power of advanced AI language models capable of generating human-like text,
rewriting facts is like 90% of all writing jobs
I don't think there's a generalized solution to this problem for all information domains, so when search engine companies implement it it'll be low quality. What remains to be seen is if the money is in being a curator/aggregator within a niche, as Boring Report aims to be; or if it will be in selling the specialized summarization tech to the content creators directly - for use by them when they publish. I think the latter leans B2B and will have higher quality since the content creator signs off on it. But we'll see. Either way, the right mental model for LLMs may be to treat them as memetic compression algorithms.
Previously translation has focused on going between languages.
I think there's just as much benefit to translating within a given language.
* Translate corporate speak to plain English.
* Translate passive aggressive to calm and peaceful.
* Translate sensationalist to neutral (like the OP).
* Translate implicit and heavy with subtext to direct and assertive.
One time, local-dialect subtitles completely changed a movie experience for me
It was an American movie. Where I was, they usually watch movies with english audio and neutral Spanish subtitles
This time, instead of neutral Spanish, they used local Spanish with slang… wow, what a difference in the way the movie felt
Having local language and slang can convey meaning and emotions so much more effectively
Otherwise, it's someone typing as fast as the movie goes on the first viewing.
https://tosdr.org/
https://www.chatpdf.com/
Of course the utility will depend on the training data you collect. For example to go from corporate speak -> english you would need thousands of pairs of "translated" sentences. Or you could use an LLM to translate for you, paying as you go
this probably functions decently well as a "europe vs america" discriminator.
https://chrome.google.com/webstore/detail/clickbait-remover-...
Further discussion on HN:
https://www.google.com/search?q=site%3Anews.ycombinator.com+...
He said that today, but in an interview with Bloomberg. The source article[2] just illustrates it with an archive photo from 2022, when he testified in a Senate hearing. Similarly, the Disney article[3] starts non-sensically The Disney+ logo was displayed on a TV screen in Paris on December 26, 2019. Disney shares decreased by 9%[...] (I don't think displaying the logo 4 years ago is to blame).
I suppose you should just stop parsing image subtitles. The two articles I checked were otherwise accurate.
[1] https://www.boringreport.org/app/all/645cfc85bab323b21e6195e... I had to use the developer tools to copy paste the text, obnoxious. You also can't right- or middle-click the source link (to copy it or open it in a background tab). Don't hijack basic browser functionality.
[2] https://www.cnbc.com/2023/05/11/jpms-jamie-dimon-warns-of-ma...
[3] https://www.boringreport.org/app/all/645d0cebbaef7c040f89ca4...
Asimov, The Last Question https://youtu.be/ojEq-tTjcc0
At the moment GPTs are trained on so much data across so many domains that you have to treat it like a person who has similar knowledge.
If I just walk up to you and start sputtering jargon about a very specific complex topic, when you were just chatting to another friend about all sorts of everyday topics, you're not going to be able to reply to me immediately.
With these GPTs it helps to get it "in the mood" for your topic by preloading keywords and shifting the topic over and deeper so your desired topic is clearer to the attention mechanism.
Given that the choice of which articles to write is incredibly biased to begin with this approach does not seem effective.
What could theoretically work is an “AI news agency” that “summarizes” many different sources to generate unbiased articles.
Selection bias is a given. You always have to keep that in mind. But when you actually want to read a specific article, summarizers are useful. For news and general population content, debullshitifiers could come in handy too.
Point being, the texts are not random. There's some nugget of valuable content in it, but it's usually wrapped by enormous layer of SEO, ad hooks, word count padding, and/or general nonsense. Reducing signal-to-noise ratio here - stripping all those layers of bullshit - is strictly useful.
“Debullshitification” reads as de-biasing which is not what you just itemized.
My point is rather that Fox News+LLM (as an example) is still biased but would appear/may be incorrectly presented as unbiased to a reader not acutely aware of selection bias which is probably not something an average reader is well informed about.
And honestly, I immediately knew what that meant when I read it. My preferred news source, which isn’t horrendously partisan, still has…exactly what I’d call bullshit. If that’s removed, I’ll get more bang for my buck in reading it, and that both provides immense value, and something that I’d call “debullshitification”, whilst working purely from the articles provided.
> verb: bullshit; 3rd person present: bullshits; past tense: bullshitted; past participle: bullshitted; gerund or present participle: bullshitting
> talk nonsense to (someone), typically to be misleading or deceptive.
It’s reasonable to interpret debullshitification as removing bias (i.e. what is misleading or deceptive in the news article) in this context rather than the “fluff” listed.
As I stated in the comment you replied to, GP has a different definition and I agreed removing fluff definitely has value.
Speaking nonsense, misleading, and deceiving aren't the same as "adding bias." They're just techniques that can be used to do so.
NewsMinimalist does this, it’s quite interesting. I’ve been using it since its introduction, and its been a fun way to get lots of summarized, de-sensationalized headlines. Specifically I enjoy setting it to 6.0 and reading the headlines that have impact that didn’t quite reach the 6.5+ threshold.
https://news.ycombinator.com/item?id=35795388
https://www.theflipside.io
In addition I think it'd help if papers hammered the party line of Dems and Republicans far harder. My running joke / dare is: send sportscasters to DC for a year. At some point they'll call BS on everything and everyone, and start questioning with both barrels. BS is less tolerated in sports.
Take taxes. Trickle down is BS. But it's also true the top 5% or so pay 40%-60% of taxes while the US Congress continues to spend in debt. How we'd get here? Who's primarily to blame (Congress). And what is Congress gonna do to fix it?
Show votes by Congress members year by year against deficit, debt, and ratio paid by corps, rich, middle, and poorer Americans. I wanna see both aisles running for cover.
Biden's budget envoy was in Congress about 6-8 weeks ago. She mentioned biden's plan was raising taxes on corporations and individuals with $400k or more in earnings. But when the republicans pointed out the fact above (5% paying more than half) she had nothing.
What's the republican code here to de-construct? Well lack of fairness, and an implied destruction of jobs and income for workers if taxes are higher. Ok, how do you defeat that? Dems are empty. And tax payers will ultimately have to bear up under both parties stupidity if this continues.
I didn't grouse too much about how the government (mis)spends money so long as debt to GDP isn't stupid and there's some attempts to get real. But in the last 10 years, I've changed. Who wants to send cash to DC? DC has got serious trust problems.
As long as this is novel content, it should be parsed, I think.
Start with those and then figure out how to scrape a site as your input and spit out the existing API format and you'll come in through a clever side route, essentially having a two phase assembly line.
Also this will allow users to customize their "feed" as a free side effect of the architecture and furthermore you'll be able to isolate your scraping -> API transform on a per site basis, also as a free consequence and lastly, you can parallelize the work much cleaner and even have the public add their own "transformer" for their favorite news site
Putting aside any actually truthful captions, how do I know that "image of X" is actually an image of X?
Reading some of the Bellingcat investigations, and time spent, doesn't bode well.
I guess you could TinEye and index/hash the entire web's worth of rich media, then spot discrepancies (listed as X here, but Y there), but that seems horrendous in compute/bandwidth/storage terms.
Yes, but the usefulness of being able to automate that identification in near-real-time to debunk the firehose of falsehoods we get from everywhere would be astronomical
Anyone reading would have a huge edge in both being more accurately grounded in reality and being able to identify the biggest/hottest disinformation streams
https://realitydefender.com/
No, it's not. This is done because stories with images perform better, and obtaining images (& licenses) for photos of every event is not always possible.
Indeed, a major incentive towards inaccuracy in journalism is the pursuit of impact.
If I read a story about a riot and the included picture is from a different but similar urban disaster scene that shows buildings on fire and windows broken I come away from the article with an internal expectation of the disaster scene including fire damage and broken glass -- but that isn't necessarily the case.
This happened constantly with the reporting around the BLM social unrest.
Articles sell better with additional sensationalism, but when the narrative being espoused doesn't conform to reality then it is a distortion, regardless of the motivating factor.
With the current advancements, is there finally a browser extension that just hides clickbait titles/thumbnails?
Wonder if you can make Fox News boring too.
0, https://www.forbes.com/sites/markjoyella/2023/02/01/fox-news...
i'd also like to see a service which shows me the news about subjects which have managed to stay in the news for at least a week. just drop all the 24-48 hour rage/hype cycles.
One big complaint I have for reading news through RSS, is that there's no natural hierarchy/priority to the news. There's no front page, no headline, no size in RSS feeds. Given the way news agencies generates those feeds, there are _tons_ of repetition, tiny updates, some insignificant one-liner interview about some significant events. Not to mention the "no update at this point" updates. Entries that are not informative look exactly the same as---but often outnumbers---the entries that are informative.
An ideal news feed processor to me, would be one that reads through last weeks RSS feeds, and merges the all those tiny updates into coherent articles, ranked by the significance of the event. Sort of turning newspaper into a journal.
The merging and reflow should be well-within an LM's capability. However, I'm not sure if OpenAI's API can swallow an entire week's worth of RSS, or produce multiple full-sized articles, but this is something that I'd like to try when I get some free weekends.
The main differences compared to what you are thinking are two things. One for the `Significance of the event` I've used the number of publishers talking about that event. So more publishers == more important. Two, I've done this in a daily fashion instead of a weekly report.
I can also confirm that the LM has the capability to do at least a days worth (2500+) of articles. I would doubt its capability to produce an entire article but it does a great job at a small summary.
Here is the link if you wanted to check it out. https://apps.apple.com/us/app/quill-news-digest/id1669557131
After adding a sufficient amount of sources I've noticed a decent reduction in the echo chamber. Although by ranking importance by the most talked about topic, it is going to have some sort of echo chamber.
Adding left and right leaning publishers for instance has helped. Although one might say something is good and something is bad, the embeddings pick it up as the same topic.
In a way it also cuts through bias.
Will check this out over a few days.
Out of curiosity have you looked at deep you can go while still getting quality results (i.e. beyond top 2-3 in each heading)?
“hey, remember how everyone was panicking about the price of eggs a few months ago? Well, prices are normal now but only one person wrote about it so you probably didn’t hear that”
Only the passing ones are then delivered to me.
I'm not releasing this as a product, it's too simple, but it works surprisingly well, and it's trivial to add criteria for what you deem to be important, or not.
Full thread: https://news.ycombinator.com/item?id=35795388