4,710 karma · joined June 19, 2013
Perhaps to get that decent documentation it took a decent bit of agentic effort (or even multiple passes using different models) to truly understand it and eliminate hallucinations, so getting that high quality and accurate summary into a comment could save a lot of tokens and time in the future.
"The Big Vitamin D Mistake is a concise advocacy editorial, not a definitive study. Its central thesis—that we all need ~10× more vitamin D than current RDAs—is not supported by subsequent large randomized trials or by regulatory reviews. Use it, if at all, as a conversation starter about DRI methodology, not as a basis for clinical dosing."
Then a small team of highly skilled engineers from Google/Facebook etc were brought in to fix it. They stabilized and relaunched the system in weeks at a fraction of the original cost. It showed that the problem wasn't the complexity or the standards, it was how the project was managed and who was building it.
And mututally assured destruction has nothing to do with the US destroying an industry in Taiwan. MAD specifically refers to the idea that superpowers cannot engage in nuclear war against one another without also being destroyed themselves, because of a Nuclear Triad.
For "Mutually Assured Destruction" to be in anyway applicable, you'd have to say that the second that the US destroyed "Chinese" fabs (Taiwan), China would destroy American fabs. Thus, the US would not attack China without risking itself. The US making targeted strikes on Taiwan is one sided destruction. But to be clear: MAD doctrine is specifically about fullscale thermonuclear war.
The LLM is better than you at math, too.
https://www.reuters.com/world/asia-pacific/google-clinches-m...
Plenty of us are using LLM/agentic coding in highly regulated production applications. If you're not getting very impressive results in backend and frontend, it's purely a skill issue on your part. "This hammer sucks because I hit my thumb every time!"
I have enjoyed the github copilot agent style development where someone elses computer is running everything, and I can make a request and just come back half an hour later and check on it. But this level 5 driver gets the wrong destination basically every time, and then it's another 10, 20 or even 30 minutes for it to make a minor adjustment. It doesnt understand my `yarn` scripts, it runs my tests wrong, it can't do codegen, it doesn't format or lint files, etc. I asked copilot yesterday to lint and format a PR and it took 25 minutes of agentic work lol.
What does having 60% of my screen be purple do for me? Why would you choose that?
I have a 27" widescreen. The idea of maximizing a browser window is absurdity. My monitor can comfortably show three websites side by side. The amount of wasted white space on a full screen website would approach 90%.
Here's this very post fullscreened (without a taskbar). What a wild waste of space and the content is clearly not designed to be viewed like this, with the UX being located at the top left lol.
And unless you have a browser full of tabs, vertical tab lists usually have massive amounts of purely wasted white space and are generally much less space efficient overall.
Every once in a while I wouldn't mind for a specific window to have vertical tabs with nested tabs, as a psuedo live-bookmark organization system for a current project. But it's not a daily driver for me.
- Not all LLM are the same, and not identifying your tool is problematic because "LLM's can't do a thing" is very different than "The particular model I used failed at this thing". I demonstrated that by showing that many LLMs get the answer right. It puts the onus of correctness entirely on the category of technology, and not the tool used or the skill of the tool user.
- Training data cutoffs are only one part of the equation: Tool use by LLM's allows them to search the internet and run arbitrary code (amongst many other things).
In both of my cases, the training data did not include the results either. Both used a tool call to search the internet for data.
Not realizing that modern AI tools are more than an LLM with training data, but rather have tool calling, full internet access, and can access and reason about a wide variety of up to date data sources demonstrates a fundamental misunderstanding about modern AI tools.
Having said that:
Claude Sonnet 4.0 says "yes": https://claude.ai/share/001e16f8-20ea-4941-a181-48311252bca0
Personally, I don't use Claude for this kind of thing because while it's proven to be a very good at being a coding assistant and interacting with my IDE in an "agentic" manner, it's clearly not designed to be a deep research assistant that broadly searches the internet and other data sources to provide accurate and up to date information. (This would mean that ai/model selection is a skill issue and getting good results from AI tools is a skill, which is borne out by the fact that I get the right answer every time I try, and you can't get the right answer once).
I also asked Gemini 2.5 pro preview and it said yes. https://g.co/gemini/share/0aeded9b8220
I find it interesting to always test for myself when someone suggests to me that a "LLM" failed at a task.
By your logic, Costco is also "discriminating based on marital/family status" by selling bulk at discount. Costco doesn't sell "buy 1/5 a toilet paper package, come back and get the other 4 1/5 later". They sell the whole package up front, use it or lose it.
That's how bulk discounts work.
Heck, you could say that a gallon of milk discriminates against you because you have to pay way more to buy a pint of it, and you can't "come back later for the other pints at the same price".
This is absurdism to the highest degree.
You are welcome to book flights with friends, or even organize a flight-share program and go in on flights with strangers. Bulk doesn't discriminate. I know folks who go in together on Costco bulk because they can't use it all. Make the system work for you.
Companies offer bulk discounts on basically... everything.
This is like pointing out that the Dollar Store penalizes people for buying small quantities and thus suggesting that Costco should raise prices to "make it fair".
If we accept that we should be productive, then it seems easy to justify that engineers should be working at good, well run companies producing real value for society.
It's a natural selection mechanism whereby poorly run companies will fail extremely quickly.
These are bad companies with horrible leadership that are wasting resources. Their existence is a net negative to society and our industry. Good bye to the garbage Klarna's and DuoLingos. No one will miss you.
1) Maintaining context of the overall project and goals while working in the weeds on a subtask of a task on an epic (so to speak) both in terms of what has been accomplished already and what still needs to be accomplished
and 2) Getting an agentic coding tool which can actually handle the scale of doing 50 small projects back to back. With these agentic tools I find they start projects off really strong but by task #5 they're just making a mess with every change.
I've played with keeping basically a dev-progress.md file and implementation-plan.md file that I keep in context for every request and end each task by updating files. But me manually keeping all this context isn't solving all my problems.
And all the while, tools like Cline are gobbling up 2M tokens to make small changes.
However it certainly might make sense for an individual hotel to let you bypass the booking.com middleman (a middleman that the hotel dislikes already).
Scenario 1: You logon to booking.com, deal with a beg to join a subscription service (?), block hundreds of ads and trackers, just to search searching through page after page of slop trying to find a matching hotel. You find it, go to the hotels actual webpage and book there, saving a little bit of money.
Scenario 2: You ask your favorite Deep Research AI (maybe they've come up with Diligent Assistant mode) to scan for Thai hotels meeting your specific criteria (similar to the search filters you entered on booking.com) and your AI reaches out to Hotel Discovery MCP servers run by hotels, picks a few matches, and returns them to you with a suggestion. You review the results and select one. The AI agent points out some helpful deals and rewards programs that might apply. Your AI completes the booking.
The value that AI gave you is you no longer did the searching, dealt with the middleman, viewed the ads, got begged to join a subscription service, etc.
However to the hotel, they already don't really like booking.com middleman. They already strongly prefer you book directly with them and give you extra benefits for doing so. From the hotel's perspective, the AI middleman is cheaper to them than booking.com and still preserves the direct business relationship.
I don't understand why Europeans have to blame Trump for "spending a fair minimum on their own defense in the face of existential threats" or "investing domestically in technology".
These are things you always should have been doing.
"Maintain humanity under 500,000,000 in perpetual balance with nature" (Georgia Guidestones, a monument describing someone's ideal future that is surrounded by conspiracy)
Will you be one of the 500 million pets? Or one of the 7.5 billion leftovers? Sure would be a shame if billions become unnecessary and then a pandemic 100X worse than covid got lableaked.
Sounds like humans at school. Cram the material. Take the test. Eject the data.
Google Search then proceeds to summarize a bunch of AI-written slop into the worst, most hallucination-ridden AI-summary you've ever seen.
This one is funny because for something like leet code, nearly everyone just reads the best answers, learns them and learns how to regurgitate them in an interview environment.
>That's expected, since all the leetcode problems have ready-to-use solutions on the internet.
1) If the implication is "The model knows the answer and regurgitates it like lyrics to a song" then I would push back. Put a leet code problem into deepseek r1 chain-of-reasoning model and watch it spend 2 minutes spitting out 5000 words thinking through every single facet of the problem and genuinely solving it at a level that is higher than 95% of programmers.
And point 2)
If you do believe it's fundamentally about how much the model has been trained on, then it has seen your CRUD app and has already seen 10,000 times the feature or system you're about to write -- so it should be a foregone conclusion that it can also do all of that development work too. Only the higher order architecting and proprietary domains should be challenging for it, as there would be far less examples to train on (scarcity) or the model doesn't understand a complex solution (architecting systems at scale is something it can't do).
(I also point out how well these models did for Advent of Code 2024, when there were zero examples in the training data for it).
But modern AI tools are far beyond "auto complete". (I actually turn off those in-line completions, I feel they ruin flowstate). The tools now are fully prompted, with multi-file editing, with full codebase context, with web/search and doc integration, and for "on the rails" development are producing high quality code for "easier" tasks.
These modern models and tools can solve nearly every single leet code problem faster than you. They can do every single Advent of Code problem likely 10X-100X faster than you can.
In my professional, high standards, very legal and contract driven web app world, AI tools are still very useful for doing "on the rails" development. Is it architecting entire systems? No of course not (yet). Is it emulating existing patterns and extending them for new functionality 10X faster than a Jr or Mid? Yes it is. Is it writing nearly perfect automated tests based on examples? Yes it is. It is scaffolding new ideas and putting down a great starting point? Yep. And it's even able to iterate on featurework pretty well, and much faster than Jr/Mid.
The kind of work I'd give to a Jr/Mid and expect to take 2-3 days before they need serious feedback up and down the change, these AI are doing in about 30 seconds, maybe 90 seconds if you need to iterate a few times on the prompt.
I get that "AI" is a buzzword that is pumping valuations and making business people see $$$.
But coding assistants are not that. For many programmers, they are quickly becoming valuable tools that do in fact speed up development.
In fact, I'd argue that Big Tech is pro copyright, because once they force the copyright holder to negotiate, the cost is irrelevant to them and they build a moat around that access.
For example, Google stole Reddit content for Gemini until Reddit was forced to the table, and now Google has a seemingly exclusive agreement around Reddit data for AI purposes.
We should stop pretending that that website resembles its preceding namesake, because it does not.