Voy mihail

[Jamieson O'Reilly @theonejvo]

In before the reddit mod types get triggered

1. The Fable 5 prompt is published by Anthropic

2. Spoke to @elder_plinius he said the prompt published by Anthropic is only partial, meaning the prompt he extracted is potentially more contextual/bigger.

3. While using Fable 5’s prompt might make Opus 4.8 seem marginally better at certain tasks (design etc), it is important to understand that (a) this not been properly benchmarked against a vanilla 4.8 and (b) it doesn’t matter how creative a prompt might be, you can’t really compare it to model weights (imo).

[Discover @0x_Discover]

$394,000 in 2 months.

Claude + Polymarket + Bitcoin (up/down)

Performance:

• +$6,935 today
• +$30,548 this week
• +$215,231 this month

56,067 predictions
68% win rate

Recent trades:

→ $2,464 → $7,361 (+198%)
→ $1,551 → $6,165 (+297%)
→ $3,043 → $9,212 (+202%)

Unlike scalpers betting on 5-min moves,

this account trades 24h BTC direction.

But the edge is the same ↓
System:

• Claude processes structured prompts
• Connects via API to Polymarket
• Uses MCP plugins + custom skills
• Runs a full intraday trading terminal
• Applies Kelly Criterion for sizing
• Stays consistently profitable

Profile: https://t.co/F2dg3B8pPW

Copytrade https://t.co/IPY41UFgnZ

Here’s the reality:

In 2025 → you needed a dev team + months to build this

In 2026 → you just need Claude

If you’re not using AI right now

you’re losing money every single hour.

You’re still early.

[Voy mihail @MihailVoy]

@KingTunde_SZN 70

[François Asselineau 🇫🇷 @f_asselineau]

🇨🇳LA CHINE ÉCRASE LA PRODUCTION MONDIALE D'AUTOMOBILES
Déjà 1er marché automobile mondial depuis 2008, la Chine a pulvérisé tous les records en 2025

🥇1er producteur mondial
🇨🇳35,3 M véhicules
🇯🇵 25 M
🇺🇸 9,3M
🇩🇪 4,1 M
🇫🇷 0,9 M

🥇1er exportateur mondial
.
https://t.co/aeQwo53Pas

[François Asselineau 🇫🇷 @f_asselineau]

🇪🇺☠️ L'UE S'ATTELLE À LA DESTRUCTION DE NOTRE INDUSTRIE D'ARMEMENT
après avoir activement saboté l'agriculture familiale, la distribution, les TPE/PME, les industries sidérurgique,automobile,chimique, ciment,verre,aéronautique,spatiale, les transports,etc.
https://t.co/qYcXZ8kFSl

[François Asselineau 🇫🇷 @f_asselineau]

📺 RETROUVEZ LE 𝗗𝗜𝗥𝗘𝗖𝗧 𝗻°𝟵𝟰
du Mercredi 𝟭𝟰 𝗝𝗔𝗡𝗩𝗜𝗘𝗥 𝟮𝟬𝟮𝟲
1-fin du mouvement agricole
2-manifestations en Iran
3-de Villiers et Philippot
4-vote électronique
5-France et contraintes de 🇪🇺
6-grand-père Glucksmann au KGB
7-affaire Boulin
https://t.co/Ldzujbry42

[Charly Wargnier @DataChaz]

MIT and Oxford released their $2,500 agentic AI curriculum on GitHub at no cost.

15,000 people already paid for it.

Now it's on GitHub!

It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.

Repo in 🧵 ↓

[Voy mihail @MihailVoy]

@recap_david Made by grok

[ivan.scales @the_one_ivan_]

@heykahn Try using some new stuff guys.

Here are 123 tools,
feel free to use them and see whst works best for you:

[Artificial Analysis @ArtificialAnlys]

MoonshotAI has released Kimi K2 Thinking, a new reasoning variant of Kimi K2 that achieves #1 in the Tau2 Bench Telecom agentic benchmark and is potentially the new leading open weights model

Kimi K2 Thinking is one of the largest open weights models ever, at 1T total parameters with 32B active. K2 Thinking is the first reasoning model release within @Kimi_Moonshot's Kimi K2 model family, following non-reasoning Kimi K2 Instruct models released previously in July and September 2025.

Key takeaways:
➤ Strong performance on agentic tasks: Kimi K2 Thinking achieves 93% in 𝜏²-Bench Telecom, an agentic tool use benchmark where the model acts as a customer service agent. This is the highest score we have independently measured. Tool use in long horizon agentic contexts was a strength of Kimi K2 Instruct and it appears this new Thinking variant makes substantial gains
➤ Reasoning variant of Kimi K2 Instruct: The model, as per its naming, is a reasoning variant of Kimi K2 Instruct. The model has the same architecture and same number of parameters (though different precision) as Kimi K2 Instruct and like K2 Instruct only supports text as an input (and output) modality
➤ 1T parameters but INT4 instead of FP8: Unlike Moonshot’s prior Kimi K2 Instruct releases that used FP8 precision, this model has been released natively in INT4 precision. Moonshot used quantization aware training in the post-training phase to achieve this. The impact of this is that K2 Thinking is only ~594GB, compared to just over 1TB for K2 Instruct and K2 Instruct 0905 - which translates into efficiency gains for inference and training. A potential reason for INT4 is that pre-Blackwell NVIDIA GPUs do not have support for FP4, making INT4 more suitable for achieving efficiency gains on earlier hardware.

Our full set of Artificial Analysis Intelligence Index benchmarks are in progress and we will provide an update as soon as they are complete.

[Kimi.ai @Kimi_Moonshot]

🚀 Hello, Kimi K2 Thinking!
The Open-Source Thinking Agent Model is here.

🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%)
🔹 Executes up to 200 – 300 sequential tool calls without human interference
🔹 Excels in reasoning, agentic search, and coding
🔹 256K context window

Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns.

K2 Thinking is now live on https://t.co/YutVbwktG0 in chat mode, with full agentic mode coming soon. It is also accessible via API.

🔌 API is live: https://t.co/EOZkbOwCN4
🔗 Tech blog: https://t.co/n7xxaszqzF
🔗 Weights & code: https://t.co/4ukcXB0iP6

[Voy mihail @MihailVoy]

@bunnyfly_off ❤️

[Voy mihail @MihailVoy]

@tomalxbr A

[Voy mihail @MihailVoy]

@IvankaNews_ 8

[Voy mihail @MihailVoy]

@spiritualwibe 111

[Spiritual Energy @spiritualwibe]

Type '111' to claim it.

[Voy mihail @MihailVoy]

@PsyGuy007 A

[Voy mihail @MihailVoy]

@PsyGuy007 Pour bien évidemment