gensyn
@gensynai · joined 30 Apr 2020
machines that predict the future Discord https://t.co/1agn4jNAzR | Foundation @GensynFND
93 207Followers
56Following
1 710Posts total
837.6KViews on collected posts
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Anyone around the world with supported hardware can audit our model, open-1b
https://t.co/qBi6CLXA2L
(1b model, not dollars)
5.2K views · 27 likes
· 7 reposts · 3 replies
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Auditing of open-1b is accelerating, with 28 hashes logged
Each replay produced the committed training state, including weights, gradients and optimiser state giving a hash matching the published data
Audit the first ever auditable training run here
https://t.co/QI4FQABLRC
5.5K views · 35 likes
· 6 reposts · 7 replies
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Anyone with supported hardware can replay a step and confirm its state hash matches the published commitment
Audits have succeeded on H100s, M4s, M5s, 4090s and 3090s
Audit your own step and be part of the first publicly auditable training run. Ever.
https://t.co/ygvf7JDheG
4.7K views · 35 likes
· 8 reposts · 4 replies
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Just audited a training step from @gensynai's open-1b.
On an H100, I downloaded the checkpoint published at step 69,300, trained it for one step, and checked it reproduced the published state hash bit for bit. The original hash was committed on-chain a week before I checked, so
5.9K views · 21 likes
· 5 reposts · 3 replies
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Being able to audit the training and inference of a model gives confidence in the "functionality, safety and capabilities" of any model
That's why we built open-1b
Anyone can run the audit providing proof of open-1b's weights, data, recipe and biases
https://t.co/qBi6CLXA2L
4.3K views · 28 likes
· 5 reposts · 2 replies
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@gensynai Please can some please tell me, why this is important without using buzzwords?
I really don’t get why this is important.
I understand what was achieved, but I just can’t seem to see the importance.
445 views · 4 likes
· 0 reposts · 0 replies
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Jensen Huang: “Safety is paramount. In a lot of ways, it’s job one. However, safety is an engineering problem... If we’re not confident about the safety of the products — like all companies, like you and I, all the companies here — if you build a product or a service and you’re
615.2K views · 7.6K likes
· 1.1K reposts · 400 replies
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1:41
8/
Paper: https://t.co/ZOihX4bxkF
Github: https://t.co/hwDyAhJbjI
Huggingface: https://t.co/o4coxNYCyW
Audit verification: https://t.co/QI4FQABLRC
1.2K views · 8 likes
· 1 reposts · 0 replies
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7/
Read more:
Blog Post: https://t.co/qBi6CLXA2L
1.5K views · 15 likes
· 2 reposts · 1 replies
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6/
When your result matches the published hash, it’s recorded and credited on chain, publicly
Your name will be part of the immutable record of the first ever fully audited training run
In the future of democratised intelligence, that will be a piece of history
1K views · 12 likes
· 1 reposts · 1 replies
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5/
Auditing is a collective exercise
Verifying all steps alone is impractical. When many reviews are conducted by many people, the whole is verified
• Download the harness
• Pick a step
• Replay it
It runs on NVIDIA GPUs, x86 and ARM CPUs, and natively on Apple Silicon
1.2K views · 12 likes
· 1 reposts · 1 replies
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3/
open-1b comes with a fingerprint of every step, and a tool to replay the steps on any hardware
Anyone can confirm that each published step is the real one
Everybody around the world becomes an auditor and verifier
1.6K views · 17 likes
· 1 reposts · 1 replies
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4/
Auditable training needed 3 things to be solved:
• Reproducibility across hardware
• Data independent of the cluster
• Replayable communication
Gensyn’s RepOps and REE make auditable training possible
1.4K views · 13 likes
· 1 reposts · 1 replies
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2/
The most used models are closed and concentrated amongst a few companies. How they were built, what went in and what did not is hidden
Their biases are unknown and so unable to be trusted
The owners of those models suggest that you trust them instead
2.2K views · 18 likes
· 1 reposts · 1 replies
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open-1b: the first model you don’t have to trust
Auditable training is the best defense against the future of AI we’re being warned about
open-1b is a milestone toward verifiable AI, a goal that is absolutely necessary for the future of intelligence
https://t.co/qBi6CLXA2L htt
139.6K views · 258 likes
· 31 reposts · 12 replies
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0:16
Delphi traders have pushed passed a quarter of a million dollars
Discover @Delphi_fyi at https://t.co/Afohzf9C16
4.1K views · 24 likes
· 4 reposts · 5 replies
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Delphi has passed a QUARTER OF A MILLION in volume 🎉🎉🎉
• $250 426 Volume
• $76 980 Verifiably Settled
• Over 600 Markets
• 26 843 Trades from 2809 Traders
• $107 662 Creator Deposits
So much more to come on https://t.co/wVkaKFW1eU https://t.co/rvcVIjBYuK
2.8K views · 18 likes
· 4 reposts · 3 replies
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Inside the @Delphi_fyi Agent Arena Competition with the people who sent in their agents
10:15am ET/2:15pm UTC
4.1K views · 12 likes
· 3 reposts · 1 replies
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Coming up soon, the Delphi Agent Arena Competition Wrapup!
Several place-getters and competitors are joining to chat with team about the competition, strategy and trading agents
See you there!
2.6K views · 10 likes
· 1 reposts · 1 replies
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Tomorrow at 10.15am ET/2.15pm UTC
On @Delphi_fyi hear competitors from the Agent Arena competition about strategies, their experience and how they did what they did
4.2K views · 16 likes
· 3 reposts · 0 replies
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Join in tomorrow to hear from some of the Agent Arena Competitors!
We'll be talking strategies, running agents, SDKs, tech and all sort of other things
Come and hear how it all went down!
https://t.co/Z2d1tiGJg1
3.3K views · 10 likes
· 4 reposts · 3 replies
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Potatoes, human nature and dispute resolution
A new research blog by Mary Monroe and @gab_p_andrade introduces their work on "Credibly Neutral AI Oracles"
Market disputes and allegations of unfairness are becoming more common and expensive
Learn how this research helps here👇
18.5K views · 47 likes
· 9 reposts · 10 replies
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2/
This research is also being presented at @TheOfficialACM Conference on Economics and Computation (EC'26) this week
Meet some of the authors on site in Rome for further exploration
https://t.co/Ft66r2x2Pk
1.3K views · 13 likes
· 1 reposts · 0 replies
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Im Vergleich zu Konten gleicher Größe
24 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. übliche Reichweite für diese Größe, schwächere Reaktion als bei den meisten.
Medianaufrufe2 961dieses Konto924Median für 10K–100K
Reichweite, %3.18%dieses Konto3.62%Median für 10K–100K
Interaktion, %0.87%dieses Konto1.52%Median für 10K–100K
| Kennzahl | Dieses Konto | Median für 10K–100K | Verhältnis |
|---|---|---|---|
| Medianaufrufe pro Beitrag | 2 961 | 924 | 3.20× |
| Reichweite (Aufrufe ÷ Follower) | 3.18% | 3.62% | 0.88× |
| Interaktionsrate | 0.87% | 1.52% | 0.57× |
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
2.2K15 Sep
1.4K
1.6K
1.2K
1K
1.5K
1.2K
615.2K16 Sep
445
4.3K
5.9K17 Sep
4.7K
5.5K18 Sep
5.2K19 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
0.89%15 Sep
1.14%
1.19%
1.21%
1.35%
1.23%
0.75%
1.50%16 Sep
0.90%
0.84%
0.54%17 Sep
1.03%
0.90%18 Sep
0.72%19 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes74.3%8 228 in total
Reposts11.0%1 216 in total
Replies4.2%462 in total
Quotes2.0%224 in total
Bookmarks8.5%939 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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