Alessandro Pogliaghi

Software Engineer | shipping product autonomy @PostHog | Fuck Cancer🖕🏻

[Alessandro Pogliaghi @tatoalo]

@Meta @WhatsApp Your AI flagging mechanism works as reliably as your VR stuff, you’re embarrassing

[Alessandro Pogliaghi @tatoalo]

lol AI psychosis is growing stronger each day

[Jim Cramer @jimcramer]

We must NOT let our companies use these Chinese models to save a few bucks. OpenAI and Anthropic are correct. This is vital national security. Please read Bing West's just released Cat 5. I respect the Chinese people greatly but these companies are run by the PLA for heaven's sakes.

[Alessandro Pogliaghi @tatoalo]

"One probable outcome of an open-weight-model-dominant world is full AI communism"

[Dean W. Ball @deanwball]

Some observations on Kimi:

1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run.

2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China.

3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.

4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business.

5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.

6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

[Tibo @thsottiaux]

GPT-5.6 Sol confirmed to be an extremely good model

[Mario Zechner @badlogicgames]

betting on open weights + open source stacks. anything else feels like feudalism. i like market mechanics. i hate feudalism.

[Jean P.D. Meijer ― 🇪🇺 eu/acc @initjean]

imagine how many product managers this took

[John Carmack @ID_AA_Carmack]

Memory cost and capacity are significant issues for AI accelerators.

Unlike game rendering, model inference can have a deterministic memory access pattern. You don’t need “random access memory” at all for model weights, and you could tolerate cold-start latencies in the multiple milliseconds, as long as continuous reads were delivered at the necessary bandwidth.

NAND flash is over 100 times cheaper per GB than HBM, so there should be opportunity there, even after giving a flash controller a 1024 bit interface with HBM bandwidth.

You could make a specialized pin protocol that just supported pipelined transfer of full 16KB+ pages from the flash to program-managed accelerator scratchpad memory and improve per-pin performance over HBM, but it might be more convenient to make it still look like a true random access memory with very fragile performance characteristics, where anything but sequential reads falls off a 1000x+ performance cliff.

That has the advantage of automatically using existing cache hierarchies, and providing a natural path to update the flash memory with new model weights. With the stream-to-scratch interface, code has to be completely rewritten before it works at all, while the ram-emulation interface will start off just extremely slow, and you can incrementally sort out the changes for full performance.

There may be cases where there isn’t enough scratchpad SRAM to hold the weights for a layer, which might force you to deploy the old optical drive optimization technique of duplicating data in multiple places on a sequential read to avoid seeking, but there would be capacity to burn.

It might be possible to do something like cuda graph capture to record a memory access trace and have everything magically remapped to a linear sequence, but deploying programmer / agent elbow grease to manage transfers and access in a scratch ram ring buffer would be lower risk.

A split memory system consisting of some channels of flash and some channels of HBM will probably be suboptimal compared to a uniform memory, but it could be much cheaper, and allow much larger models to be run.

I think th case is strong for inference, but you have to stretch more for training. You can still linearize all the weight memory accesses, both reads and writes, but flash memory would quickly wear out from the writes, even if they were all perfectly page aligned. Replacing low-latency HBM with massively parallel cheap(er) DRAM at high latency might still be a worthwhile cost savings.

[Alessandro Pogliaghi @tatoalo]

Oh no this means *checks notes* affordable houses? unheard of

[Pubity @pubity]

China's Real Estate market has erased all gains from the last 20 years.

Housing prices are expected to fall even further, in line with the Chinese policy that "houses are for living, not for speculation".

[Kotaku @Kotaku]

Microsoft's CEO strings 17 words together into one of the worst sentences I've ever read in the English language as he launches a new AI company ahead of more mass layoffs:

"The future of the firm is a learning loop in which human capital and token capital compound."

https://t.co/X38OO14ip4

[doomer @uncledoomer]

mcdonalds CEO says making burgers at home is moving down a "very dangerous path"

[ᐱ ᑎ ᑐ ᒋ ᕮ ᒍ @Andr3jH]

It's a brutal world

[Thomas 🇮🇪🇵🇸 @MeathGuerilla]

The great thing about these pan European heatwaves is that Ireland just gets really nice pleasant warm weather out of them whilst everyone else dies

[Alessandro Pogliaghi @tatoalo]

turns out @posthog can speak you like your italian mom, what are you waiting for? 🤌

cc: @pauldambra for the assist

[waiting. @GreenOnionDuck]

As a software engineer I have to unfortunately inform you that if medicine was held to the standard of software engineering there would be mass death.

[Josh Johnson @secondfret]

Meta HQ right now.

[Alessandro Pogliaghi @tatoalo]

Staircases

Co-authored by Claude

[Alessandro Pogliaghi @tatoalo]

🤡

[Anthropic @AnthropicAI]

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees.

The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance.

Access to all other Claude models is not affected.

We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

Read our full statement: https://t.co/bwn0sximKZ

[Daksh Gupta @dakshgup]

boris cherny goes on a podcast every three months and says something like “i’ve stopped breathing now i just wrote a breath.md” and the next day everyone in sf stops breathing

[PostHog @posthog]

https://t.co/3yQg4zapQK

[Alessandro Pogliaghi @tatoalo]

If us Italian would come together as one as we are currently doing against the Ferrari Luce, we would probably become top tier 🫠