I get around this by making a deliberate attempt to organize my notes after I take them. I will usually try to turn them into a word doc with a lot of word art, but if they need to be in a repo or something, I will go with markdown.

While Tesla has (in some years) avoided traditional marketing, the ceo is known for spending ridiculous amounts of money on publicity stunts like having a submarine shipped to a cave and buying Twitter to boost public perception of his companies.

I think this is the exception that proves the rule.

The marketing industry in the US is built not only to get the word out about your product, but also to gatekeep who can compete in our free market.

With marketing being so pervasive as to monetize the entire internet it effectively levies a tax on every business that wants to compete.

If you dont have the marketing budget to outspend your competition then they have no competition.

In a literal, ancient Greek sense for sure, but in modern English Anthropomorphic would describe the act of attributing human characteristics to non-human entities.

Seems pretty apt for a company that produces one of the more anthropomorphized technologies.

One big pain point that has existed forever and has never really been addresses adequately is the ability to come up with requirements.

Sure, it sounds easy, I need the app to do x, y and z. But requirements change in real time because of lack of foresight, change of business needs, an unexpected roadblock and more contribute to changing requirements.

So, the advice to come up with the requirements by yourself or with the LLM miss the biggest pain point.

I'd like to see a resurgence of flow charts, IPO (Input, Processing and Output) charts and other tools to organize requirements spring up to help with really nailing down requirements.

I will say, though, some of the pain is relieved because the agent can perform a huge refactor in a couple of minutes, but that opens a whole new can of worms.

Soon, I predict we will see a pretty significant jump in price that will make a 10% productivity gain seem tiny compared to the associated bills.

For now, these companies are trying to reach critical mass so their users are so dependant on their tech that they have to keep paying at least in the short term.

Comments URL: https://news.ycombinator.com/item?id=47051108

Points: 2

# Comments: 0

If the proverbial marketer that you were referring to had some experience with coding, I dont see why they wouldnt be able to review the output and see any obvious flaws.

My whole point is that LLMs are of limited use when you are already an expert or when you know nothing about the subject. However, they really seem to help elevate beginner/intermediate level tangential skillsets.

Obviously everything is still evolving and your results may vary.

For instance, as a SWE, I get just a little help with boilerplate from the AI. I could usually have done it better, but sometimes the ask is both simple enough and boring enough that the code from the LLM actually produces something very close to what I would produce.

On the other side of the coin, a non-technical person using AI would be unable to properly understand and review the output.

Where it shines is on things that I am OK at. Like writing marketing copy. I can get by myself, but its slightly outside of my wheelhouse, but as long as I have a solid understanding of the product I can use AI to compliment my beginner/intermediate skills and produce something better than I would produce on my own.

A similar thing is writing tutorials. I write some code and documentation, but the tutorials are enough of a slog that I get distracted by my distaste for it. This is a good fit for AI.

I think this is where we will see AI help the most. Where someone's skillset includes the task at hand but at a secondary level where the user might doubt themselves or get distracted with the misery the task brings them.

Same thing, but from the trades instead of the military.

Sure, we do not pay $800 at the pharmacy when we go to pick up the prescription, but every cent the insurance company pays, we are paying by proxie with added admin costs.

Comments URL: https://news.ycombinator.com/item?id=45964654

Points: 2

# Comments: 0

It turns out the answer is Yes.

Drag and drop a .log or .txt file into the page, and it parses, filters, and visualizes entirely in your browser — no server, no upload, no dependencies. It supports text and regex filters, named-group field extraction, sorting, summary stats, and JSON/CSV export.

You can open it directly on GitHub Pages or just clone the repo and double-click it. Source: https://github.com/notesofcliff/logsieve

Built as a "one-problem" weekend project — it’s basically "just" 900 lines of HTML, CSS, and JS.

Comments URL: https://news.ycombinator.com/item?id=45822312

Points: 1

# Comments: 0

I like the hybrid approach of being able to be ad-supported or paid with no ads. I would like to see more of it.

What I don't like is a paid service like Amazon Prime that also includes ads. They include ads in their search results and they include tons of ads in their video library.

FWIW: Hulu offers paid access to content with ads but offers an upgrade to get rid of most of the ads, so there seems to be a whole lot of testing what works in this area going on right now, which I see as a good thing, I just hope that once everything settles the predominant model will be fair and respect user privacy.

> The first 128 characters of Unicode, which are the same as the ASCII character set (characters 0-127), are encoded in UTF-8 using a single byte with the exact same binary value as their ASCII representation. This means that any file containing only ASCII characters is also a valid UTF-8 file

Also, even though use in log messages is discouraged, I go ahead and use them. It will let me know if there is some code path where the proper variable is never set. This usually comes out through testing, especially during fuzzing so I guess it really only works because of my testing, otherwise it would come up during runtime...

Those platforms seem to be web analytics platforms, purpose built to track and analyze data from visitors to your web site.

Delve, on the other hand, concentrates on ingesting large quantities of structured, semi-structured and unstructured text based data. Think log files, csv, json, xml, item descriptions, etc.

The closest product to Delve would be something like Splunk or ELK.

I would say Delve is easier to get started with than Splunk or ELK, but actually offers more control since you can easily write your own search commands and field extractions. Also, role based access control is implemented to give fine-grained access to data.

Also configuration is all in one place, backend databases are configurable (sqlite, postgres, MySQL and any other database that works with the django ORM) and more.

Please let me know if you'd like any more details.