Sergey Ovchinnikov

@sokrypton · Cambridge, MA · joined 28 Dec 2014

Scientist, Assistant Professor @MITBiology, #FirstGen, ProteinBERTologist, 🇺🇦 No Human is illegal. Moving to: https://t.co/sow6IRD3jj

19 207Followers

3 765Following

4 081Posts total

413.4KViews on collected posts

Neueste Beiträge

Sergey Ovchinnikov@sokrypton17 Sep

ColabFold 1.6.3 is out! 2.5x faster, pip-installable, ipSAE+pDockQ2 scores. Thanks Choonghwan Lee, Marielle Russo, Gyuri Kim, Martin Steinegger, Milot Mirdita 🐍 pip install colabfold[alphafold] (1/3) https://t.co/pUy0JwEyMx

13.5K views · 393 likes · 93 reposts · 1 replies Open on X →

Sergey Ovchinnikov@sokrypton16 Sep

Private list of what I need next... 4.3K views · 11 likes · 0 reposts · 2 replies Open on X →

Sergey Ovchinnikov@sokrypton15 Sep

@HannesStaerk Some updates: - Remote Multiplayer support fixed. - Adding support for custom PDB/AFDB inputs! (Initially implemented by @IanAndersonLOL ) https://t.co/DDG5ZRPbT5

0:09

1.4K views · 21 likes · 2 reposts · 1 replies Open on X →

Sergey Ovchinnikov@sokrypton14 Sep

@sokrypton @HannesStaerk If a fly isn't controlling player 2 I'm not interested 974 views · 31 likes · 1 reposts · 0 replies Open on X →

Sergey Ovchinnikov@sokrypton14 Sep

@HannesStaerk Also credit to Gemini 3.8 and Claude Fable (now that their restrictions are relaxed and we can make protein fighting games) 🤪 4.7K views · 45 likes · 1 reposts · 1 replies Open on X →

Sergey Ovchinnikov@sokrypton14 Sep

Finally a more intuitive way to learn pLDDT/pAE? 😎 https://t.co/1YWv3AK1cU (Character idea from @HannesStaerk & Alex Waldherr) https://t.co/QcW1D4r1tr

0:12

156.4K views · 1.4K likes · 277 reposts · 56 replies Open on X →

Sergey Ovchinnikov@sokrypton20 Aug

To me it seems silly everyone is trying to automate their tasks with LLMs. Wouldn't it make more sense to use LLMs to develop better workflows for their tasks that do not require calls to LLMs? 🤔 6.8K views · 111 likes · 8 reposts · 13 replies Open on X →

Sergey Ovchinnikov@sokrypton17 Jul 2023

@sokrypton @MITBiology wow, that is so well deserved! congrats, @sokrypton! I have watched you have accomplished so many exciting work over the years since our shared time at uw MCB. looking forward to many exciting things from your lab at MIT. 553 views · 4 likes · 0 reposts · 0 replies Open on X →

Sergey Ovchinnikov@sokrypton17 Jul 2023

@sokrypton @MITBiology Congratulations and extremely well deserved Sergey!!!!! 1.1K views · 7 likes · 0 reposts · 0 replies Open on X →

Sergey Ovchinnikov@sokrypton17 Jul 2023

@sokrypton @MITBiology Congratulations to you and MIT 🎉 831 views · 5 likes · 0 reposts · 0 replies Open on X →

Sergey Ovchinnikov@sokrypton17 Jul 2023

I'm excited to share that I'll be joining @MITBiology as an Asst Prof. in Jan 2024! Come join us! 🤓🧪🖥️🧬 https://t.co/fX2YiJoTGO

222.8K views · 2K likes · 152 reposts · 170 replies Open on X →

Im Vergleich zu Konten gleicher Größe

7 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird mehr Menschen gezeigt als Konten gleicher Größe.

Medianaufrufe4 739dieses Konto981Median für 10K–100K

Reichweite, %24.67%dieses Konto3.62%Median für 10K–100K

Interaktion, %1.77%dieses Konto1.61%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	4 739	981	4.83×
Reichweite (Aufrufe ÷ Follower)	24.67%	3.62%	6.82×
Interaktionsrate	1.77%	1.61%	1.10×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

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

222.8K17 Jul

831

1.1K

553

6.8K20 Aug

156.4K14 Sep

4.7K

974

1.4K15 Sep

4.3K16 Sep

13.5K17 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.05%17 Jul

0.60%

0.64%

0.72%

1.96%20 Aug

1.11%14 Sep

0.99%

3.29%

1.77%15 Sep

0.30%16 Sep

3.63%17 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.61%.

What the audience does

Likes71.8%3 997 in total

Reposts9.6%534 in total

Replies4.4%244 in total

Quotes1.0%58 in total

Bookmarks13.2%735 in total

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

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Ähnliche Konten