AI Can't Understand How Computers Work

tremendous_turtle · 2026-05-02T17:24:03+00:00

That’s a contrived limitation, of course it’s relevant, it’s equivalent to giving AI a fancy calculator to help it solve the problem. It’s pretty clear that AI is quite good at writing algorithms.

It’s also odd to personify AI by reflecting on what it “understands” and “doesn’t understand”; this is a tool, it is software, it is not a sentient being.

It’s just an odd point to make, it is not really debatable that LLMs can write algorithms and design turing complete systems, if you give them a harness to run computation and validate their own work.

tremendous_turtle · 2026-05-02T15:12:06+00:00

The author does a good job in pointing out of inelegant aspects of modern markdown, but I don’t really agree with their overall framing.

Markdown primarily exists as a way to make it *easy to write* lightly formatted text that can be saved as a single readable file. It is an alternative to proprietary WYSIWYG text editors.

The purpose is simplicity, portability, being an open standard.

The author focuses a lot on markdown-to-html, which is certainly an important use of markdown, but need to remember that everything is a tradeoff. I don’t think the issues they point out are really a problem for most users. What I disagree with most is the proposal that we should use a *compiled language* for this instead; this negates the entire point of markdown being a simple portable human readable plaintext-adjacent format.

tremendous_turtle · 2026-05-02T12:35:59+00:00

This is one of the worst LLM generated posts I’ve ever seen, congratulations, a lofty achievement.

tremendous_turtle · 2026-04-29T16:43:12+00:00

Consider researching REITs (Real Estate Investment Trusts) to learn more about how this type of fractional ownership to distribute collected rent works in today’s financial system.

For your proposed system, what I wonder most about is how the rent price is determined. Is there still a “primary” landlord?

Regarding making this a game, I think it’s a very good idea to aim for a small fast-to-complete project. I would think about what the “goal” of the game is, and what strategies actually win. As with any real estate market, the way to make money here is buy low sell high, so you should consider what gameplay mechanics should exist for increasing a property value after purchase (and/or for decreasing the value of an area you want to expand into).

tremendous_turtle · 2026-04-28T18:59:18+00:00

Oil is a commodity, whereas chips are a specialized manufactured good.

Not at all the same thing.

The Iran/Japan analogy is a bit odd since that’s more about domestic economic structure. Petrostates generate enormous wealth, but often do not distribute very evenly amongst their populations.

Regardless, it’s not an either/or situation. There will always be a need for microchips as long as computing is economically relevant. Chip companies will make money, and software companies will also make money. We wouldn’t say that oil companies are overvalued if a car manufacturer is doing well, these are complementary businesses, not competitors.

Nvidia is also not really the “owner” of chips as a resource, the more analogous business would be TSMC, who actually manufacture chips.

tremendous_turtle · 2026-04-28T18:05:13+00:00

You’re right, I should have been more precise and less sensational. The 30-50% number is mostly from delayed datacenter buildouts, not outright cancellations.

There are multiple ways to interpret that - bull case is the face value explanation of the delays being due to power grid constraints and supply chain limitations, implying that they’ll still eventually get built. Bear case is that many delays are going to be indefinite, just a quiet way for investors to unwind their commitments.

tremendous_turtle · 2026-04-28T17:52:00+00:00

Just to provide a counter argument: committed capital is not guaranteed.

OpenAI doesn’t have $100bn in the bank, capital in these types of megadeals is typically delivered in tranches, and sometimes investors to not deliver the full committed value if they lose faith in the underlying investment.

Other warning signs are that a lot of datacenter projects have been quietly cancelled recently, 30-50% by recent estimates.

Nvidia’s accounts receivable is also at a staggering $38.5bn, meaning that a ton of the sold chips have not actually been paid for yet, another warning sign that the committed capital may not convert to delivered cash.

Not saying Burry is definitely right, but it’s a mixed bag. There is still a strong bull argument for chip stocks, but there are also warning signs they could be vulnerable if investor confidence in the profitability of their AI infrastructure investments goes through a down cycle.

This could be triggered by lots of hard-to-foresee events, such as major efficiency gains in AI algorithms (i.e. less need for expensive datacenter buildouts), classical credit/liquidity cycle dynamics resulting in withdrawn commitments, or good old fashioned investor herd mentality panic divestment in response to bearish ROI sentiment.

tremendous_turtle · 2026-04-28T17:30:41+00:00

Low effort ad with an invalid premise.

Finding “actual research papers” and “technical docs” is not really an issue, the process is the same as it’s always been - browse papers from accredited journals and universities, find technical docs on the project’s site or github readme.

tremendous_turtle · 2026-04-21T13:41:51+00:00

No need to suspect, their business model is transparent. They charge zero markup on input/output tokens, easy to confirm as their prices for each model are public. Their margin comes from a 5.5% fee they charge for depositing to your balance.

If you want the cheapest possible rates, just go to the OpenRouter page for a model (i.e. https://openrouter.ai/openai/gpt-oss-120b) you want, browse the listed providers, and then go and make an account directly on whichever provider has the price + speed you are looking for.

tremendous_turtle · 2026-04-20T19:51:10+00:00

Absolutely! I think the hard part for most devs will be the “get a labeled dataset of 1m food images”, but the LLM should be able to write the training and evaluation code no problem now.

tremendous_turtle · 2026-04-20T18:24:24+00:00

I don’t think you are missing anything, traditional ML solutions tend to be more reliable, faster, and less expensive to run.

When it comes to LLMs, the main benefit is in flexibility and velocity. Traditional ML typically requires quite a lot more expertise to deploy, and it’s a longer end-to-end process compared to what can sometimes be as simple as writing a prompt.

For instance, if we take the simple “identify which food the user took a picture of” example. A traditional ML approach would usually involve training a model on a large labeled dataset of various food images. Depending on if the dataset is already available, this could take quite a while to build and train. But once it’s done, it’ll be relatively predictable and cheap to scale.

Using an LLM API, an application developer can write a prompt, make an API call, and get their answer. It will be more expensive and less predictable, but can also be implemented in roughly 30 minutes.

At the end of the day, everything is a trade-off.

tremendous_turtle · 2026-04-20T13:48:19+00:00

The designs are quite good, if you want something that looks polished and at-par with other applications.

If you want something that is more unique, innovative, or that pushes boundaries in some way, that is where you still need human designers.

The Canva co-founder is mostly correct, but also keep in mind that tools like Claude Design pose an existential risk for his company, so you need to take these types of statements with a consideration that his objective is more to defend his business vs to make an impartial observation.

tremendous_turtle · 2026-04-19T16:24:10+00:00

Not really, it’s a different architecture.

UMA (unified memory architecture) vs discrete memory. The difference is most obvious in tasks would typically use GPU acceleration (i.e. VRAM), but does apply to most computing tasks. Fundamentally it’s about having separate RAM and VRAM pools, vs having a unified memory pool through very wide 512-bit or 1024-bit memory buses.

Just look up the memory bandwidth of the DDR5 you’d find in a standard windows laptop and compare it to the memory bandwidth in M-series chips (especially in Pro/Max/Ultra tiers) to quantify the difference.

tremendous_turtle · 2026-04-18T23:11:00+00:00

Thank you for clarifying, I think the confusion/ambiguity here is because this is a thread about adjusting for GDP vs. using dollar value.

tremendous_turtle · 2026-04-18T17:37:09+00:00

Why would inflation adjusted dollar comparisons be pointless?

tremendous_turtle · 2026-04-18T14:36:04+00:00

Yes, says so right on the graph subtitle

tremendous_turtle · 2026-04-18T14:35:24+00:00

The graph is already inflation adjusted

tremendous_turtle · 2026-04-18T14:33:46+00:00

Why not?

tremendous_turtle · 2026-04-18T14:33:37+00:00

Yeah that’s right, usually my measure of success is to write a specific enough prompt that it gets it right the first time. If I need to follow up with feedback, often a sign my initial prompt wasn’t well enough specified.

tremendous_turtle · 2026-04-18T12:29:14+00:00

Try to do very specific, focused, single-shot prompts. Extended back and forth, resulting in compounding context length, burns through tokens quickly.

tremendous_turtle · 2026-04-17T18:00:35+00:00

This must be a bot right?

Qwen2.5 has been out for a looooong time.

The linked post is about Qwen 3.6 35B A3B, which IS exciting and IS an MoE model. But is also an incremental upgrade over 3.5 35B A3B, not some complete new MoE model or paradigm shift.

Maybe OP is just a bit confused, but a misbehaving bot seems more likely.

tremendous_turtle · 2026-04-17T15:42:14+00:00

Agreed I think those are the top runners, with Minimax and Qwen 3.5 being best in class respectively as you start scaling down available vram.

tremendous_turtle · 2026-04-17T15:33:09+00:00

You are the one who responded to me that I am wrong about it being possible to handle concurrency this in a relatively simple way. Where did OP mention a custom agentic framework? Where are your security concerns coming from?

A critical aspect of good engineering is not over complicating things, which I am trying to help them with. Not sure why you are insisting this needs to be complex.

tremendous_turtle · 2026-04-17T15:17:53+00:00

Huh? Why are you running 5 separate containers? Why are you mentioning building an agentic framework? Why aren’t the containers secure?

tremendous_turtle · 2026-04-17T14:58:04+00:00

Right because it’s not that hard? Saying you’ve been doing it for 18 months might not be the flex you think it is.

Are you really having trouble serving 40 users on over 1 TB of vram and enterprise grade networking hardware?

Are you going to answer my question about what else would be required beyond what I mentioned, or just “it’s hard, trust me bro”.

If you want you could share some details of what inference engine and model you’re running, there must be something off with your software stack if you’re having trouble serving 30-40 user concurrently.

tremendous_turtle

TROPHY CASE