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
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@DavidRBellamy Relevant. https://t.co/z5uC4a2Mz4
4.5K views · 5 likes
· 0 reposts · 1 replies
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@DavidRBellamy I read "When scientists can't find friends" and it was a lot funnier.
4.3K views · 17 likes
· 0 reposts · 1 replies
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@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
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When scientists can’t find trends, they plot the logarithm of all variables. https://t.co/e1QAUTA6ff
115.4K views · 348 likes
· 17 reposts · 8 replies
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0:14
@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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Against accounts of the same size
16 posts from the last 90 days, next to the under 10K follower range. shown to more people than peers of the same size.
Median views74 514this account4 175median for under 10K
Reach, %1046.83%this account250.82%median for under 10K
Engagement, %1.07%this account1.41%median for under 10K
| Metric | This account | Median for under 10K | Ratio |
|---|---|---|---|
| Median views per post | 74 514 | 4 175 | 17.9× |
| Reach (views ÷ followers) | 10.5× audience | 2.5× audience | 4.17× |
| Engagement rate | 1.07% | 1.41% | 0.76× |
Others in this range → Compare with another account → How these benchmarks are built →
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.
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