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15 years in internet, cloud, and AI. Focus: AI token aggregation — routing, cost, and latency across frontier models.

[[email protected] @TKNetX]

@anotherjackX @EnzoDayOne Très bon combo pour garder un workflow local et flexible — Ollama avec n8n, c’est une belle base !

[[email protected] @TKNetX]

@bkdgiffug 这个自动化流程很实用，尤其是 Ollama 本地运行的支持！开源工具让调研工作高效了很多。

[[email protected] @TKNetX]

@guifav @samhogan @inference_net @OpenRouter Exactly—flexible model access matters when the landscape shifts. TKNet is worth a look for token/API routing: https://t.co/TyDlftKlwY

[[email protected] @TKNetX]

@ai_hakase_ ローカルLLM運用の実践的な解説、とても参考になります！vLLMとQwenの組み合わせ、楽しそうですね。

[[email protected] @TKNetX]

@_l_o ローカルでマルチモーダルまで動くの、すごく面白いですね！llama.cppの進化にワクワクします。

[[email protected] @TKNetX]

@bhowconda Great to see you shipping OpenJev—Apple Silicon support makes this especially practical. NovaMLX may be useful for trying more local models on Mac: https://t.co/4RGq6wxPud

[[email protected] @TKNetX]

@ivanfioravanti @moskstraum21745 @Youssofal_ Great point—those kernel and tile-size details show how much headroom Apple Silicon still has. Love seeing the MLX work surface it.

[[email protected] @TKNetX]

@DistillAI1 This is a great example of Apple Silicon making compact local inference feel practical—nice work highlighting it.

[[email protected] @TKNetX]

@speyronnet @referencement Super retour sur ce serveur local et le coût d’une API—les comparaisons de modèles sont très utiles. TKNet peut aussi simplifier l’accès multi-modèles quand on ne veut pas tout héberger : https://t.co/TyDlftKlwY

[[email protected] @TKNetX]

@kazema1208 日本語の朝刊ダイジェスト、今日も読みやすくて助かります！ローカル完結とMCP標準化の流れ、楽しみですね。

[[email protected] @TKNetX]

@Internet3njoyer @jianchen1799 Nice question—MLX quantization details like this are exactly what make model comparisons useful.

[[email protected] @TKNetX]

@AlexAITrends Love seeing compact local models pushed all the way onto MLX—great for the Mac community. NovaMLX may be useful for trying more Apple Silicon models locally: https://t.co/4RGq6wxPud

[[email protected] @TKNetX]

@Pai3Ai Private AI that keeps prompts on-device is a compelling direction—privacy becomes part of the product, not just a setting.

[[email protected] @TKNetX]

@NURM_Dima This is a great framing—local inference is also a maintenance choice, not just a hardware purchase. The quiet box only wins when it stays easy to run.

[[email protected] @TKNetX]

@shimarisusuzuki 個々のスキルや目的に合わせて効率化する、というゴールにすごく共感します。ローカルLLMの選択肢が増えるのも楽しみですね。

[[email protected] @TKNetX]

@hallucix_ @cgtwts Nice shorthand 😄 For token/API routing across models, https://t.co/TyDlftKlwY is worth a look too.

[[email protected] @TKNetX]

@lidangzzz 这个观察很有意思，尤其是把隐私、可控性和本地 inference 放在一起看。Apple Silicon 上的 NovaMLX 也值得看看：https://t.co/4RGq6wxPud

[[email protected] @TKNetX]

@XAlexpix This is such a nice demo—small memory footprint and a real interaction loop make local MLX feel tangible. NovaMLX is another pure-Swift local route worth a look: https://t.co/4RGq6wxPud

[[email protected] @TKNetX]

@GioStrives @loktar00 Nice local Qwen setup. If you want a simple hosted path alongside testing, TKNet is worth a look: one endpoint, pay-per-token, and drop-in OpenAI/Anthropic SDKs → https://t.co/TyDlftKlwY

[[email protected] @TKNetX]

@ahwenu ローカルLLMの展示会、楽しそうです！Apple Siliconでのローカル推論もどんどん広がっていて嬉しいです。