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atomic.chat ✓

@atomic_chat_hq · California, USA · joined 26 Mar 2026

Local AI chat and Inference Engine. Enhanced by TurboQuant. Team: @gladkos @skinbagwbones @AlexFromAtomic @danyurkin @quantizedden

15 996Followers
65Following
672Posts total
1.3MViews on collected posts

Últimas publicaciones

@atomic_chat_hq @typesafeai All i see is new drone warfare capabilities 474 views · 7 likes · 0 reposts · 1 replies Open on X →
@atomic_chat_hq @typesafeai Jev just solved the nuclear threat 561 views · 6 likes · 0 reposts · 1 replies Open on X →
@atomic_chat_hq @typesafeai I wish it was actually running local. Seems like other labs should be able to replicate this pretty quickly. 746 views · 11 likes · 0 reposts · 1 replies Open on X →
Jev v1.13 dodges rockets with probability calculation 🚀 @typesafeai's new non-LLM model returns decisions instead of text so we had it calculate a safe tile every 330 ms while rockets fell and it survived 25 of 26 for under a cent Run Jev via API -> https://t.co/RbcCOIgVkj http
0:39
54.1K views · 521 likes · 35 reposts · 11 replies Open on X →
Run Ling 3.0 flash VL Atomic Quants via Atomic Chat 🌀 @AntLingAGI gave ling 3.0 flash a vision upgrade, now it can view and analyze images, so we passed it a flappy bird picture to re-create the game GGUF: https://t.co/1GHnMTkiSX Run AI models locally: https://t.co/RbcCOIgVkj h
0:28
1K views · 8 likes · 0 reposts · 2 replies Open on X →
Today, we're open-sourcing Ling-3.0-flash-VL in BF16 and FP8. FP4 and INT4 are coming soon. Beyond visual recognition, it follows visual cues to: - Understand images, video, docs & UIs - Reason, search & verify - Use tools, check results & deliver https://t.co/kImBAjbdV9
274.1K views · 267 likes · 35 reposts · 16 replies Open on X →
Running MiniCPM5-2B locally on a 16GB MacBook 💻 It beats Qwen3.5-4B on benchmarks, so we asked it to find recent AI news, it called web search, pulled back 10 results and summarized the key developments inside Atomic Chat Run local models via https://t.co/RbcCOIgVkj https:
0:19
54.6K views · 410 likes · 28 reposts · 16 replies Open on X →
GPT-6 Astra smokes Claude Fable 5.1 in Blender 💨 We compared GPT-6 Astra and Fable 5.1 on a Porsche 911 GT3 RS build and the difference is visible from the very first shot Output: Astra: $132.03 • 2.58 hours Fable: $114.41 • 2.56 hours Astra's model is not only more https://
0:32
124.6K views · 257 likes · 13 reposts · 17 replies Open on X →
Running Ling 3.0 flash Fin AD quants via Atomic Chat 🌀 It analyzed an .xlsx expenses report and built a markdown table which contains a short per-month summary of all spendings, everything locally GGUF quants: https://t.co/V0WqR3Ql5N https://t.co/SQI3aGxMX0
0:16
8.4K views · 23 likes · 2 reposts · 5 replies Open on X →
We’re open-sourcing Ling-3.0-flash-Fin, a finance-enhanced model for real-world workflows, and FinFIRST, an expert-built benchmark for financial search agents. Two open releases, one goal: making financial AI more accessible and verifiable. https://t.co/VL1FqGuhiq
632.8K views · 357 likes · 74 reposts · 57 replies Open on X →
Running 1-bit Qwen 3.8 Flash Next (79GB) on a MacBook Pro M5 Max 64GB at 30 tok/s 🤯 It ran a 8-minute agent loop with 6 web searches, 3 Python runs and 5 sourced tables. Run local models via Atomic Chat! GGUF: https://t.co/IrKMSfMJDt https://t.co/gT2ys7Z8iJ
1:14
101.8K views · 204 likes · 22 reposts · 26 replies Open on X →

Frente a cuentas del mismo tamaño

11 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. llega a mucha gente, pero pocos de esos espectadores reaccionan.

Visualizaciones medianas54 052esta cuenta924mediana de 10K–100K
Alcance, %337.91%esta cuenta3.62%mediana de 10K–100K
Interacción, %0.84%esta cuenta1.52%mediana de 10K–100K
MétricaEsta cuentaMediana de 10K–100KProporción
Visualizaciones medianas por publicación54 05292458.5×
Alcance (visualizaciones ÷ seguidores)3.4× audience3.62%93.3×
Tasa de interacción0.84%1.52%0.56×

Otras cuentas de este rango →   Comparar con otra cuenta →   Cómo se construyen estas referencias →

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

101.8K26 Aug
632.8K3 Sep
8.4K
124.6K7 Sep
54.6K
274.1K8 Sep
1K
54.1K17 Sep
746
561
474

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

0.26%26 Aug
0.08%3 Sep
0.40%
0.24%7 Sep
0.84%
0.12%8 Sep
1.00%
1.07%17 Sep
1.61%
1.25%
1.69%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes59.9%2 071 in total
Reposts6.0%209 in total
Replies4.4%153 in total
Quotes2.7%95 in total
Bookmarks26.9%931 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.

Cuentas similares

Frequently asked questions about @atomic_chat_hq

How many followers does @atomic_chat_hq have?

15 996 followers.

How many posts has @atomic_chat_hq published?

672 posts.

How do posts by @atomic_chat_hq perform?

Collected posts: 11. Views: 1 252 997. Likes: 2 071.