radium3d

The strong case is pretty much only if you can't wait another 3-4 years for ram prices to come back down.

Memory makers are responding with a huge build-out. Building a fab shell and cleanroom takes years, which is why the crunch has lasted so long, but many of these projects are now well underway. New capacity is what ended past memory shortages. New factories going in:

SK hynix

M15X, Cheongju, Korea: first wafers early 2026 (reported), ramping through 2027 Yongin Y1, Korea: first cleanroom Feb 2027 (company target) Yongin Y2, Korea: groundbreaking planned July 2027, first cleanroom June 2029 Micron

ID1, Boise: first wafer output mid-2027 Tongluo, Taiwan: an existing PSMC cleanroom it bought for $1.8B, with meaningful shipments mid-2027 ID2, Boise: late 2028 Hiroshima, Japan: broke ground July 2026, with initial output late 2028 Clay, New York (up to 4 fabs, $100B): broke ground Jan 2026, with first output in 2030 Samsung

P5, Pyeongtaek: mass production 2028 (reported) Yongin national complex: site work began Oct 2026, with the first fab targeting Oct 2029 (reported; Samsung hasn't given a date) Nanya

Fab 5A, New Taipei: 10nm-class EUV DRAM, with wafer starts in 2H 2027 (reported) Tesla / SpaceX (Terafab)

Research fab at Giga Texas, Austin: broke ground April 2026. There's no official output date. Grimes County, Texas (logic, memory, packaging; Phase 1 over $16.8B): land clearing since Aug 2026. Texas filings say commercial operations in 2028, but SemiAnalysis estimates late 2029.

The first real supply relief is 2027, a second wave comes in 2028–29, and a lot of that new capacity is aimed at HBM and server memory, so consumer DDR5 and GDDR will likely ease last.

Sources: Micron FQ4 2026 call, the SK hynix newsroom, TrendForce, Texas JETI filings via ENR, and Tom's Hardware/SemiAnalysis.

Tesla has an EXCELLENT safety record. Why's your post so tainted? Compare them to any other vehicle manufacturer's crash records.

Personally, the biggest reason is that if I'm investing in my business, and the business is going to rely on AI models, I need them hosted locally on my own hardware in a replicable way. The reason being is, if the AI subscription service changes the model, or drops support for it, or worse (goes out of business), you're done.

I believe NVIDIA's long term strategy will be to pivot from the data center to the public, and the public will utilize open models on NVIDIA hardware at home. This will come after the RAMpocalypse completes (when the new fabrication plants (China, Tesla/SpaceXAI/Intel) fully ramp up and start selling their RAM for cheap in the next few years). Data centers will be for training mostly.

Can these decision models be used to enhance the intelligence of video game adversary and friendly NPC "AI"?

It is much closer now. Just like how C/C++ devs used to say "JavaScript isn't real code" or "Python script kiddies". Code was literally designed to be just easy enough for people to understand, now it's just even easier to understand.

It is not yet the same as C++ or Python, for two reasons:

1) Ambiguity is still the default. Formal languages force you to resolve it up front.

English lets you paper over it until the model or the compiler (the human) notices.

2) The "compiler" (the LLM) is statistical and non-deterministic. Same prompt, different day, different bugs. A real language has a spec.

The practical move is to treat English as a high-level specification language, keep the generated artifacts inspectable, and "still know enough of the lower layers to notice when the translation went wrong."^1

[1] This is the key that is where humans can still be necessary, or at least another pass through the LLMs to decide on the best path, in the compiled code. Compilers for other languages do the same C -> Binary, etc.

A conventional compiler is bound by an as-if rule. It can take many internal routes, but the observable behavior has to match the language spec. Same source, same defined semantics. If two gcc runs emit different binaries, the program is still supposed to compute the same answers on the same inputs. That is why people treat the source as the artifact and the binary as disposable.

An LLM compiling English has no as-if rule unless you add one. "Sort the users by last active" can become a stable sort, an unstable sort, a SQL order by, an in-memory timsort, or a query that drops people with null timestamps. All of those can look like success. They are different programs. The model is not optimizing under a spec. It is filling in the parts you did not write.

A human who can read the destination language still notices when the chosen path is the wrong program.

A second model pass can compare paths, but only if you give it a way to score them: tests, types, invariant

So the historical analogy still holds, with one correction. JavaScript and Python were dismissed for being too easy, but they already had grammars and evaluators. English is easier still, and the evaluator is a statistical translator that will invent a dialect if you let it. The practical move stays the same: treat English as the spec language, pin the generated artifacts behind tests, and keep enough fluency in the lower layer to see when the translation chose a different program than the one you meant. The human is not required because the computer is weak. The human is required because the source language still leaves room for more than one destination.

Humans are also needed to code in C++ and compile it, then the computer does the rest.

Now we just code in english and the computer does the rest.

I worry that since AI doesn't require nature, plants, etc, that if AI is left to its own devices, it may not properly care for the planet in a way that would make it habitable for life in that 'autopilot' state. I am speaking of once it's given access to more physical world connections to the world via robotic bodies, etc. The reason it may do this based on it's training data is that humans themselves don't always properly consider the natural consequences of their actions.

Not just dodgy but downright malware links. It's insane. had one with a full screen mcafee level scam. Dropping the ball Google.

Doesn't everyone else immediately update everything on their computer before they start doing anything?

All of the subscription AI platforms are trimming down quotas across the board to push users into higher tiers. Whatever they can do. Local inference needs to meet pricing sooner

Exactly, that is definitely a better route. Just instruct your agent to not use any external dependencies for the request.

Sites can be fast with PHP and JavaScript. The slowness you see is mostly coming from all the frameworks too many developers "include" willy nilly with code that can only be described as bloat because 99% of it is completely unused, yet being loaded and computed by the browser on every load. That's what you're experiencing on too many websites.

This sounds a lot like Grok [4.6] CLI. It already pings multiplayer style between multiple agents, and you can even request that it split large tasks into small chunks across agents to reduce context overload and it seems to do that very well.