psadri

We miss you Steve.

The average is $x. But that's global which means in some places like the US it is 10x. And in other less wealthy areas it is 0.1x.

There is also the strange paradox that the people who are willing to pay are actually the most desirable advertising targets (because they clearly have $ to spend). So my guess is that for that segment, the revenue is 100x.

I often wished for a Google Sheets like service that had an easier to use API and a bit more control over the schema. A cross between sheets and a headless CMS.

Nature _is_ technology. It’s far too advanced for us to understand. And perhaps due to not-invented-here syndrome, we tend to try and recreate it (crudely).

The problem is always discovery. There are lots and lots of little sites out there but without a search engine or directory you don’t know about them. Even back in the day Yahoo had a directory. And once you need such an aggregator, it will naturally create a choke point.

I do miss the earlier "heavy" models that had encyclopedic knowledge vs the new "lighter" models that rely on web search. Relying on web search surfaces a shallow layer of knowledge (thanks to SEO and all the other challenges of ranking web results) vs having ingested / memorized basically the entirety of human written knowledge beyond what's typically reachable within the first 10 results of a web search (eg: digitized offline libraries).

Haven’t tried this. Does it mix well with tool calls? Or does it force a response where you might have expected a tool call?

Somewhat related data point - our local elementary school used to have 5 first grade classes a few years ago and now they are down to 3. It's a large % drop over just a few years. This is going to move like a wave through the rest of the years all the way through to university.

I’m curious about this project (I’m working on something similar). Anyway to get in contact with you?

I have learnt to not ask leading questions. Always phrase questions in a neutral way and ask for pro/con analysis of each option.

Just here to say thank you for creating and maintaining MDN. It’s an amazing resource that I’ve used most days of my working life.

Metal detector + gate that denies entry if it detects metals?

At scale you need to use more tricks. For example, only inject examples if the tool is going to be needed. Or amass lessons, then ask the LLM to summarize them to prune redundant information before it is used in the context.

You could instruct the LLM to formulate a “lesson” based on the error and add this to the tool instructions for future runs.

Another approach would be more comfortable travel at today’s speeds (or even slower). Imagine boarding a plane with a luxury hotel like experience. A buffet breakfast, some work/reading in a nice library, followed by some treadmill / stationary bike time, then a shower, lunch, a massage in the spa. Dinner later before a classical concert and finally heading to sleep in a comfy bed. Then wake up and disembark at your destination.

How about tracking deltas between blood draws starting ant youngish age when things are on average presumed to be in a good state? When a new feature turns up in a subsequent blood draw, could it then be something more concerning?

Similar to the source of Mad-cow disease: feeding animal remains to cattle in the UK.

Anthropic recommends not using frameworks and simply writing code against the LLM apis, at least to begin with.

https://www.anthropic.com/engineering/building-effective-age...

I have collected some of the techniques I have developed/used in reducing LLM context size:

https://www.notion.so/LLM-Context-Engineering-21b814d6a64980...

Some of these are in use in an in-house AI chat application that has a heavy emphasis on tool calls.

I agree. I recently started exposing my kids to programming and I chose a C64 emulator. The BASIC REPL is so simple/limited that it doesn’t overwhelm the kids with irrelevant syntax (those can come later, if they are interested). The fact that a 1..100 loop can introduce a noticeable delay. You can literally see the computer working. Primitive graphics and sound provide immediate feedback which makes learning engaging and fun.

I often spot the mosquito but lose track of it after a second (against a patterned background) or if it rapdily changes direction (which they seemed to have evolved to do).

What I really want is an iOS app that helps me locate insects in a room. Start video feed, analyze pixels for a small object flying around and draw a green box around each while flying and a red box once it lands somewhere. I can take care of the rest.

I don't disagree that code is far more efficient than inference. But what are those apps? A lot of apps are fetching some data, massaging it, rendering UI to let the user view / update data. Could the AI do some of that "work" for you, so that you don't need those dashboards / buttons / forms in the first place? Maybe you have an agent that does a db query (instead of a human viewing a dashboard) and takes some action (instead of the human clicking a button).

I love the idea of Phoenix and server side rendering (I happen to work on SkyMass, a related project).

This is a tangential comment and should not detract from what Chris and team have created. I think closing the loop between agent and the running output is a great/critical step forward.

However, I find using AI to build transitional Apps with a UI is a bit like improving the way automobile steering wheels are made. In a world that soon won't need steering wheels at all.

If the AI is so good to write the code for an App, how much longer before you won't need those Apps in the first place? And then the question is, what will fill the role that Apps play today.

Back at Polyvore we used real post-it notes on a board to track progress. We had a weekly cadence but the tasks were granular enough to be completed in a day or two. Each week we’d peel off and crumble the completed tasks into a bin. It was a really satisfying experience in a way that digital facsimiles can’t quite match.

Don’t forgot to realign the dilithium crystals once in a while.

Horizontal scaling. I love this out of the box thinking.

They should add a “cmd-enter” for ask, and “enter” to go.

Separately, if I were at cursor (or any other company for that matter), I’d have the AI scouring HN comments for “I wish x did y” suggestions.

I usually add “discuss first. Don’t modify code yet”. Then we do some back and forth. And finally, “apply”.

What do monkeys think about humans?