David Bellamy ✓

@DavidRBellamy

Training infrastructure @ifm_ai building open source AI prev: first RS @LilaSciences, @Harvard PhD

7 118Followers

624Following

1 004Posts total

2.9MViews on collected posts

Son gönderiler

David Bellamy ✓@DavidRBellamy23 Sep

@DavidRBellamy Relevant. https://t.co/z5uC4a2Mz4 4.5K views · 5 likes · 0 reposts · 1 replies Open on X →

David Bellamy ✓@DavidRBellamy22 Sep

@DavidRBellamy I read "When scientists can't find friends" and it was a lot funnier. 4.3K views · 17 likes · 0 reposts · 1 replies Open on X →

David Bellamy ✓@DavidRBellamy22 Sep

@DavidRBellamy If data is across several orders of magnitude and you want to investigate scaling laws, loglog is useful. 4.6K views · 49 likes · 0 reposts · 2 replies Open on X →

David Bellamy ✓@DavidRBellamy22 Sep

When scientists can’t find trends, they plot the logarithm of all variables. https://t.co/e1QAUTA6ff

0:14

115.4K views · 348 likes · 17 reposts · 8 replies Open on X →

David Bellamy ✓@DavidRBellamy14 Sep

@DavidRBellamy Us veterans in the cybersecurity industry are listening, and many of us have similar takes 21.3K views · 239 likes · 0 reposts · 8 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

In sum, when you actually know something about building a laboratory, lab automation, and what goes into synthesizing a virus and testing its properties, it becomes clear that AI does not impact this very much. At best, it provides bad actors with a quicker way than the 59.2K views · 1K likes · 39 reposts · 67 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

And at the end of the day, even if a fully automated, API-driven lethal viral synthesis lab existed and an AI wields it month over month, year over year, to perform cell/mouse experiments to create a lethal virus, that lethality is being measured in model organisms not in humans. 54.2K views · 609 likes · 11 reposts · 6 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

So the idea that 'RSI' - ie, the accelerating hillclimbing on fully verifiable, digital-only benchmarks (predominantly programming and math) - can somehow transform the entire wet lab virology field and its industry is utterly delusional. Simply procuring the machines needed to 73.1K views · 753 likes · 28 reposts · 11 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

Second of all, the science of creating an infectious virus is not airtight the way this fearmongering assumes. It is largely unsolved and advancing this requires real-world iterations that are bound by the laws of physics. An experiment in this hypothetical lab cannot experiment 97.4K views · 734 likes · 18 reposts · 16 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

The thing is, already this lab cannot exist today. This would be the single most advanced lab facility in the world from an automation and API integration standpoint. I know because I literally worked on building a fully automated, API-driven lab previously. 66.6K views · 896 likes · 12 reposts · 9 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

And yet, *even in this case*, every lab requires physical supplies. Would this lab order pre-assembled DNA sequences (i.e. viral genomes)? Well, DNA synthesis companies have safeguards on the sequences they build. So I guess this superintelligence is able to design a novel enough 76K views · 741 likes · 7 reposts · 5 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

Now let's imagine that this lab is fully API-driven. That this >>$100M lab built specifically to synthesize a dangerous family of viruses is able to be operated completely autonomously. Everyone should be asking themselves at this point: why the hell would this ever exist in 88.1K views · 938 likes · 17 reposts · 24 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

Let me steelman the fearmongering as much as I can. Imagine a 'fully automated' viral synthesis laboratory. I'm talking automated freezers, automated cell culture room, the whole nine yards. This would be an extremely expensive lab - >>$100M. And there is no such thing as 1 lab 101.5K views · 956 likes · 18 reposts · 16 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

Designing a virus that can evade all forms of pandemic counterdefense is not something that a 'genius in a datacenter' can do. This is something that requires contact with the physical world and iteration. 115.9K views · 1.2K likes · 31 reposts · 19 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

Could an LLM propose a viral genome to synthesize? Sure. Could it be synthesize-able? Sure. Could it be infectious? Sure, it could just be a replica or a slight modification of a viral genome we already know. This really isn't the bottleneck to creating dangerous viruses. The 135.9K views · 1.6K likes · 60 reposts · 26 replies Open on X →

David Bellamy ✓@DavidRBellamy13 Sep

I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands. And I think that the takes on AI killing us all by creating dangerous viruses is total bogus. 1.9M views · 14.4K likes · 1.9K reposts · 571 replies Open on X →

Aynı büyüklükteki hesaplara karşı

Son 90 güne ait 16 gönderi, under 10K takipçi aralığıyla yan yana. aynı büyüklükteki hesaplardan daha çok kişiye gösteriliyor.

Medyan görüntülenme74 514bu hesap4 175under 10K için medyan

Erişim, %1046.83%bu hesap250.82%under 10K için medyan

Etkileşim, %1.07%bu hesap1.41%under 10K için medyan

Ölçüt	Bu hesap	under 10K için medyan	Oran
Gönderi başına medyan görüntülenme	74 514	4 175	17.9×
Erişim (görüntülenme ÷ takipçi)	10.5× audience	2.5× audience	4.17×
Etkileşim oranı	1.07%	1.41%	0.76×

Bu aralıktaki diğer hesaplar → Başka bir hesapla karşılaştır → Bu kıyas değerleri nasıl kuruluyor →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

115.9K13 Sep

101.5K

88.1K

76K

66.6K

97.4K

73.1K

54.2K

59.2K

21.3K14 Sep

115.4K22 Sep

4.6K

4.3K

4.5K23 Sep

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.06%13 Sep

0.98%

1.11%

0.99%

1.38%

0.79%

1.09%

1.16%

1.96%

1.16%14 Sep

0.32%22 Sep

1.10%

0.42%

0.13%23 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.

What the audience does

Likes74.2%24 438 in total

Reposts6.5%2 152 in total

Replies2.4%790 in total

Quotes1.5%485 in total

Bookmarks15.4%5 056 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

Followers by day

19 Sep

Daily snapshots since 19 Sep 2026; the dashed line is the starting count.

Son gönderiler

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Growth & engagement

Views per post

Engagement rate per post

What the audience does

Followers by day

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