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Péter Szilágyi

@peter_szilagyi · joined 02 Sep 2010

Founder @dark_dot_bio | Former Go Ethereum Lead (2015-2025)

72 402Followers
393Following
12 360Posts total
128.4KViews on collected posts

Son gönderiler

😇 https://t.co/wOvdFxPIPH
721 views · 4 likes · 0 reposts · 1 replies Open on X →
Dark Bio teaser ;) - "Ser, what do I see here?" - "The tool through which AI operates your Ark!" 🤯 https://t.co/JRasd3zwsu
2.5K views · 4 likes · 0 reposts · 1 replies Open on X →
@peter_szilagyi I had opposite on a .net codebase in all honesty 5.1 fixed it in hours, opus and astra spent weeks of tokens (resets on astra) and still did not fix. for me 5.1 is far better so it seems "it depends" 1.4K views · 4 likes · 0 reposts · 1 replies Open on X →
@peter_szilagyi give openserv a spin. its an inference api with a reasoning compiler, makes agents more reliable and you can use smaller models and get the same/better results - https://t.co/Tzcd9EHRjo 398 views · 25 likes · 1 reposts · 1 replies Open on X →
@peter_szilagyi Honestly just use opus instead of fable, the results are so similar between them at a fraction of the cost 4.1K views · 5 likes · 0 reposts · 3 replies Open on X →
After using Astra heavily for a week concurrently with Fable... it's lightyears better :/ - Faster: same bug 45 vs 9 mins to track down. - Cheaper: 1 day is enough for me to max out my $200 Fable sub for the week; takes me 2 days on Astra on $100. - Better: Fable makes bad bugs. 64.5K views · 435 likes · 5 reposts · 51 replies Open on X →
Writing network protocols with Claude and Codex feels like mounting a constant social engineering attack against the LLM. You need to talk to it just right so it doesn't freak out and throw out the thread due to "cyber protections". 3.6K views · 30 likes · 0 reposts · 3 replies Open on X →
This is a first draft. We'll keep iterating on it in the coming months as the system develops and as we hear back from readers. Comments, corrections, and pushback are all welcome. 2.3K views · 16 likes · 0 reposts · 0 replies Open on X →
Dark Bio whitepaper is out! We're building a system in which a person's health data, their genome most of all, no longer has to live with a custodian to be useful. A system, where data does not move, rather stays on a small device the owner holds. https://t.co/zScmBuyRNo
46.1K views · 618 likes · 63 reposts · 40 replies Open on X →
Read it here: https://t.co/NMALFldJhP 2.8K views · 30 likes · 0 reposts · 1 replies Open on X →

Aynı büyüklükteki hesaplara karşı

Son 90 güne ait 7 gönderi, 10K–100K takipçi aralığıyla yan yana. boyutuna göre olağan erişim, çoğundan daha zayıf tepki.

Medyan görüntülenme2 455bu hesap1 00210K–100K için medyan
Erişim, %3.39%bu hesap3.71%10K–100K için medyan
Etkileşim, %0.69%bu hesap1.64%10K–100K için medyan
ÖlçütBu hesap10K–100K için medyanOran
Gönderi başına medyan görüntülenme2 4551 0022.45×
Erişim (görüntülenme ÷ takipçi)3.39%3.71%0.91×
Etkileşim oranı0.69%1.64%0.42×

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

2.8K26 May
46.1K
2.3K
3.6K10 Sep
64.5K11 Sep
4.1K
398
1.4K
2.5K
72113 Sep

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.12%26 May
1.56%
0.68%
0.92%10 Sep
0.76%11 Sep
0.19%
6.78%
0.35%
0.20%
0.69%13 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.64%.

What the audience does

Likes69.2%1 171 in total
Reposts4.1%69 in total
Replies6.0%102 in total
Bookmarks20.6%349 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.

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