carsoon

You can prompt an llm and make it give you 5 reasons for any argument. LLMs are very good at writing highly convincing things that sound logical but may or may not be based in reality.

Would be better to say "I can run a research agent session and it can pull 5 data trends and papers that show the HOA are making things better" (I dont know if this is true and I did not do it)

I don't really think HOAs are the biggest driver of home prices/availbility one way or the other though.

Because internet is a major resource for the population. Its like water or electricity.

Most of those could be easily targeted and taken out by a large organized group but people are generally good and dont do that. If a country's military attacked though you wouldn't blame the water company for someone cutting the locks and destroying the water pumps. Or blowing up an electric facility.

Its not like this is just some random companies headquarters where they have some vehicles and someone broke the gates to spray paint the trucks.

This is an attack on the people of the country as a whole. The attackers are not vandalizing the internet because they dislike starlink they are attacking its people by destroying infrastructure. there is a major difference between the two.

One usecase I could think of is weather patterns. Weather is very hard to predict so any info from the past could help us create better models. It would be incredibly tedious to collect that by hand from millions of documents but AI could do handle this quite well. A mix of text embeddings, and llm analysis could result in a data set for weather patterns in remote/ historic areas over time. Even if was as simple as a diary that said "today it rained a lot"

Wether there is enough info to reconstruct usable data is unknown to me but it feels like a good experiment someone could try.

Yeah installation has always been such a security issue. So many programs are just random links that download a file. You have to trust that the host has not been compromised all packages that were used to build it were not compromised etc.

With ai models getting better we may be able to do analysis on the actual underlying bytes of the files we download to properly scan them for malicious code patterns and build systems which sandbox programs and watch inbound and outbound traffic/ system level actions from them and flag suspicious requests for further analysis by smarter models.

REA shows that ai are very good at understanding low level code and reverse engineering it so this could potentially be applied to application level security aswell.

Yea if i use opus 5.5 in api through openrouter and pi agent harness I will easily burn 50-100$ a day (and with fable 5.1 i could burn 200$ easily). Whereas i have now been using a claude code subscription for 2 weeks using 5.5 at all times and have never hit a limit. I often run 6+ agent sessions at once.

I do think its important long term to not be reliant on these companies as you don't have control over the system prompts, the thinking tokens, and once the subsidization stops or the company is public they will be required to start making money and thus raise prices.

But models may get more intelligent and cheaper once that time comes so it may be a non issue.

This is a very interesting news/story format. Honestly it is highly engaging and gets the point across about what happened.

I think speed of learning for young generation can explode given this tech as "simple childrens stories" can be injected with real world events, history, mathematics, carreer/business interests.

School was dreadfully boring for me even though it was quite easy but I do wonder how my speed of learning would have been different given personalized and more engaging materials.

no it doesn't fail. Agents are very good at comparing traffic characteristics from a real browser and a headless/automation browser and getting it to behave in the same manner. It's a cat and mouse game for sites stopping unauthorized access but right now llm agents are ahead.

For testing proxies should be used to avoid IP ban issues but given enough time modern agents can figure out how to bypass most of the modern scraping/automation prevention mechanisms.

I have built and used a lot of different automations and web scraping implementations for my business and it's never got permanently stuck yet, some take a bit longer, some shorter, but all within a reasonable time with little external help they have succeeded in their tasks.

Yea specialized models could also be resold in a shareware style, like even if it cost 50$ to produce you'd just have to sell 10 copies of it to people for 5$.

Which 5$ is a pretty easy sell if its useful in any way, It's pretty easy to justify a purchase if its yours forever and doesn't use much CPU so is easy to run I mean people were spending 1000$+ on mac mini setups to run local llms or run remote agents.

Yeah I do not see an alternative way to easily copy paste links with things like filter settings saved.

Filters especially make sense as query params as they are non sequential but still visually readable as to what they do.

URL slugs make sense for sequential pages that are hierarchical but make no sense for non hierarchical data/routes.

Services can force tracking into links by encoding the whole url into a shortlink that makes it impossible to just remove the tracking alone as everything is encoded into a shorter non editable string.

I saw a waymo go in a nonexistent rightmost lane at a stop light, I thought it was going to turn but it instead proceeded to go forward and force the driver in the actual rightmostlane to break to allow it to merge else it would have caused an accident as there was no lane in front of it.

This was on El Camino in Santa Clara. I was highly suprised as I was under the assumption they were pretty much production ready as they have been expanding their area a lot.

You can buy IR and UV leds. All high end grow lights have these for plants. Low quality cheap led products won't include them but that is nothing to do with LEDs themselves that is just consumer preference and price conformance.

Fully sandboxed VMs are more secure but not everyone is looking for the most secure option. They are looking for the option that works the best for them. I want to be able to share my development environment with the agent, I have a project with 30 1gb and one 30gb sqlite database. I back it up daily and they can all be reconstructed from the code but it takes a long time. When things change I don't want to have to copy them into a separate vm bloating my storage and using excess resources and then having to rectify them, I want to be sharing the same environment with my agent so I can work side-by-side.

I would rather just have the agent not accidentally delete files outside of its working environment but I am not worried about malicious prompt injection or someone stealing my code.

For me I see the LLM as a dumb but positive actor that is trying to do its best but sometimes makes mistakes, so I want to put training wheels on it while still allowing it to share my working space.

Those prior recommendations you supplied are worse than the current ones.

Added Sugar: it says <50grams when its clear that NO added sugar is best as the new guidelines suggest.

Fat: it says to choose low fat cuts 95% and low fat milk. There is no basis for these options. you are just reducing the nutrients from fat. You should just drink/eat less of the fatty food if it contains fat, not choose a processed version that removes part of it.

Protein: The protein section clearly skews towards plant based proteins which are fine but for the majority of people animal proteins are going to be healthier and easier to eat enough of. The protein amounts to around 35-60 grams of protein depending on the sources/amounts listed which is not ideal for a properly functioning human

Sodium: It says in multiple places to lower sodium but the studies on sodium were correlative not causative. Meaning there is no basis for a low sodium diet unless you have other health conditions.

So no they are not lying to you and these new guidelines are 100% evidence based given the new evidence that we have had for the last 30 years.

If you refresh it changes, thats what the llm thinks it looks like I guess haha.

I built it, now you can forgive all the llms for their misdeeds: https://llmpriest.carsho.dev/

https://news.ycombinator.com/item?id=46251110

We could first put the LLMs in very difficult situations like the trolley problem and other variants of this, then once they make their decisions they can explain to us how their choice weighs on their mind and how they are not sure if they did the correct thing.

These models don't even choose 1 outcome. They list probabilities of ALL the tokens outcomes and the backend program decides to choose the one that is most probable OR a different one.

But in practical usage, if an llm does not rank token probability correctly it will feel the same as it "lying"

They are supposed to do whatever we want them to do. They WILL do what the deterministic nature of their final model outcome forces them to do.

I have a kinda strange chatgpt personalization prompt but it's been working well for me. The focus is me to get the model to analyze 2 sides and the extremes on both ends so it explains both and lets me decide. This is much better than asking it to make up accuracy percentages.

I think we align on what we want out of models:

""" Don't add useless babelling before the chats, just give the information direct and explain the info.

DO NOT USE ENGAGEMENT BAITING QUESTIONS AT THE END OF EVERY RESPONSE OR I WILL USE GROK FROM NOW ON FOREVER AND CANCEL MY GPT SUBSCRIPTION PERMANENTLY ONLY. GIVE USEFUL FACTUAL INFORMATION AND FOLLOW UPS which are grounded in first principles thinking and logic. Do not take a side and look at think about the extreme on both ends of a point before taking a side. Do not take a side just because the user has chosen that but provide infomration on both extremes. Respond with raw facts and do not add opinions.

Do not use random emojis. Prefer proper marks for lists etc. """

Those spelling/grammar errors are actually there and I don't want to change it as its working well for me.

Yeah I have seen multiple people use this certainty % thing but its terrible. A percentage is something calculated mathemtatically and these models cannot do that.

Potentially they could figure it out if they looks into a comparison of next token probabilites, but this is not exposed in any modern model and especially not fed back into the chat/output.

Instead people should just ask it to explain BOTH sides of an argument or explain why something is BOTH correct and incorrect. This way you see how it can halluciate either way and get to make up your own mind about the correct outcome.

I think even when this is used they should include "(translated by llm)" for transparency. When you use a intermediate layer there is always bias.

I've written blog articles using HTML and asked llms to change certain html structure and it ALSO tried to change wording.

If a user doesn't speak a language well, they won't know whether their meanings were altered.

Its definitely a mix, we have been codeveloping better models and frameworks/systems to improve the outputs. Now we have llms.txt, MCP servers, structured outputs, better context management systems and augemented retreival through file indexing, search, and documentation indexing.

But these raw models (which i test through direct api calls) are much better. The biggest change with regards to price was through mixture of experts which allowed keeping quality very similar and dropping compute 10x. (This is what allowed deepseek v3 to have similar quality to gpt-4o at such a lower price.)

This same tech has most likely been applied to these new models and now we have 1T-100T? parameter models with the same cost as 4o through mixture of experts. (this is what I'd guess at least)

Yeah this latest generation of models (Opus 4.5 GPT 5.1 and Gemini Pro 3) are the biggest breakthrough since gpt-4o in my mind.

Before it felt like they were good for very specific usecases and common frameworks (Python and nextjs) but still made tons of mistakes constantly.

Now they work with novel frameworks and are very good at correcting themselves using linting errors, debugging themselves by reading files and querying databases and these models are affordable enough for many different usecases.

I think the big cost difference is the geothermal generators to convert the heat back into electricity. More of a cost issue versus efficiency.

No, it wouldn't as the whole reason people were giving Openai that 500 dollars is because they thought they could make more than 500 dollars from it.

So now that value is just shifted into the companies that were going to purchase from openai.

It would just hurt the investors who have exposure to openai/anthropic/google/microsoft.

Much of the value of this AI boom is not from the direct model companies but its from companies which use their technology.

Although the government could be stupid and bail out these companies which WOULD hurt all us citizens and the inflation caused by money printing due to that could cause a recession.

People don't stop wanting things.

Total GDP can keep rising so long as technologist can improve efficiency through robotics, inventions and scientific breakthroughs.

GDP just describes peoples amount of activity. People will always build bigger buildings or monuments (see egypt pyramids, dubai skyscrapers, cambodia angkor wat). These are actaully not inelastic as megaprojects will quickly hit real limits regardless of the amount of capital. (I can always add 10 more floors to the tallest skyscraper, or 10 feet to the longest wall, or 10 facets to the most ornate church)

There has never once in history been a point where people decieded that they where going to stop innovating or producing permanently. This is equivalent with death.

So the people with the most money at some point will decide to build things which they spend all their money on which increases GDP.

Also the actual "number" of gdp if heavily controlled by USA inflation rates. So we should always look at gdp in regards to inflation adjusted dollars to get a clearer picture.

The problem with suicidal depression is that if someone has created the thought pattern that death is best, then removing the symptoms of depression (lethargy, lack of energy, no willpower) now gives the person the ability to actually follow through with the act.

Medications almost always target symptoms and never address root causes.

Everyone introduces bias. But for instance getting a model trained pre war vs after a war starts is super different. If I want to get raw information about 2 nations then models are in some ways a good source. Because most other parts of the internet can get changed or wiped. A model is "stuck" with the information it had exactly at that point so cannot be directly affected by new information attacks.

It is crucial to have a good framework in how you ask your questions though to avoid bias when using these systems and to try and focus on raw facts. To test ideas I like to make it fight for both opposite extreme sides of an argument then I can make up my own mind.