The Rise of Deep Model Customization
🔎 The most interesting shift in these projects: teams treat the base model as replaceable. Thomson Reuters expects to start from newer open models as they appear. C3 AI has repeated its process across five generations. Cursor reused its training machinery for Composer 2.5. The durable asset is the model factory, not any single model.
https://t.co/p8dW6wWZNO
Enterprise AI and Deep Model Customization.
🔎 Companies including Thomson Reuters, Cursor, C3 AI, Cisco, and Reddit are developing AI models that understand their specific data, workflows, and products.
👉 https://t.co/2SujMiZpMh
Deep model customization is emerging as a real pattern. Companies like Thomson Reuters, Cursor, C3 AI, ByteDance, Cisco, and Reddit are all going well beyond basic fine-tuning.
👉 https://t.co/2SujMiZpMh
Amir Bar-Lev delivers another standout doc with The Last First. This one is on complex human dynamics, hubris, and commercial exploitation on K2. I wasn't aware of how a Nepalese team outmaneuvered ("deceived") & struck out on their own, to summit first
https://t.co/QLCu2397cT
👍👍 Tenzing a new 🎬 by Jennifer Peedom and Luke Davies
🎥 this is a moving portrait of the man who reached Everest’s summit alongside Edmund Hillary, and follows the true story of their historic 1953 climb
https://t.co/jjoZRBCfDk
Running AI on your own devices or internal servers is becoming more realistic
🎯 Local Models Are Improving. That Doesn't Mean They Go Everywhere.
When you run a model locally, proprietary code and internal business data don't leave your infrastructure. That's a meaningful shift for companies that handle sensitive information and want to know exactly where it's being processed.
→ https://t.co/r7w0DeGkBm
Will AI wipe out jobs in a sudden wave, or is the reality far more nuanced? @MIT FutureTech director @ProfNeilT shares ground-level empirical data on how AI adoption actually unfolds across occupations.
https://t.co/M4xk2AW8Hl
Been testing Gemini 4 Argon over the last few days 🔎
Verdict: It’s surprisingly good and definitely worth including in your mix of proprietary frontier models.
That said, long-term? Still firmly Team Open-Weights 🙌
Before You Move AI to the Edge, Read This
🔎 Where should each AI task run: on a laptop, an internal server, or with an outside provider? Narrow, easy-to-check work such as transcription and document classification may fit employee devices.
→ https://t.co/IRNeKULQHB
Running AI Closer to Home
🎯 Local AI isn't about replacing the cloud. It's about matching each task to the right place: laptop, internal server, or outside provider. Getting that routing right matters more than the hardware specs.
→ https://t.co/IRNeKULQHB
🆕 Running AI on your own devices or internal servers is becoming more realistic
🎯 Local Models Are Improving. That Doesn't Mean They Go Everywhere.
When you run a model locally, proprietary code and internal business data don't leave your infrastructure. That's a meaningful shift for companies that handle sensitive information and want to know exactly where it's being processed.
→ https://t.co/JNZK7sJxNM