Nutt D

#healthtech enthusiast

[Apple Design @TheAppleDesign]

iPhone Air - Entire computer in it’s forehead
iMac - Entire computer in it’s chin

This is what Innovation looks like.

[Bayer 04 Leverkusen @bayer04_en]

Admin is exhausted.

Transfer window: Done.

[Fabrizio Romano @FabrizioRomano]

🚨💣 BREAKING: Alexander Isak to Liverpool, here we go! Deal agreed now for £130m transfer fee. Record move for Premier League.

Isak, on his way today for medical tests as new Liverpool player after long term deal agreed months ago.

It was always ONLY Liverpool for Isak.

[Fabrizio Romano @FabrizioRomano]

🚨🔴 BREAKING: Giovanni Leoni to Liverpool, here we go! Deal agreed with Parma for Italian 18yo centre back.

No loan, never discussed… Leoni joins #LFC now as part of Arne Slot plans.

Fee around €35m with sell-on clause.

No convincing ever needed as Leoni wanted Liverpool.

[The Wall Street Journal @WSJ]

Exclusive: AI startup Perplexity makes an unsolicited longshot offer to buy Google’s Chrome browser for $34.5 billion https://t.co/S530iiJW8U

[Nutt D @NutKhamrin]

เราออกแบบธุรกิจโดยใช้ Customer centric เป็นหลัก
แต่ “เมื่อต่อไป customer ของเราจะไม่ใช่แค่มนุษย์ แต่เป็นหุ่นยน��์ล่ะ เราจะทำอย่างไร?"

Customer Journey ของคนใช้ Ai จะเปลี่ยนไป
เจ้าของธุรกิจก็ต้องปรับตัวแน่นอน

[Andrew Ng @AndrewYNg]

In the age of AI, large corporations — not just startups — can move fast. I often speak with large companies’ C-suite and Boards about AI strategy and implementation, and would like to share some ideas that are applicable to big companies. One key is to create an environment where small, scrappy teams don’t need permission to innovate. Let me explain.

Large companies are slower than startups for many reasons. But why are even 3-person, scrappy teams within large companies slower than startups of a similar size? One major reason is that large companies have more to lose, and cannot afford for a small team to build and ship a feature that leaks sensitive information, damages the company brand, hurts revenue, invites regulatory scrutiny, or otherwise damages an important part of the business. To prevent these outcomes, I have seen companies require privacy review, marketing review, financial review, legal review, and so on before a team can ship anything. But if engineers need sign-off from 5 vice presidents before they’re even allowed to launch an MVP (minimum viable product) to run an experiment, how can they ever discover what customers want, iterate quickly, or invent any meaningful new product?

Thanks to AI-assisted coding, the world now has a capability to build software prototypes really fast. But many large companies’ processes – designed to protect against legitimate downside risks – make them unable to take advantage of this capability. In contrast, in small startups with no revenue, no customers, and no brand reputation the downside is limited. In fact, going out of business is a very real possibility anyway, so moving fast makes a superior tradeoff to moving slowly to protect against downside risk. In the worst case, it might invent a new way to go out of business, but in a good case, it might become very valuable.

Fortunately, large companies have a way out of this conundrum. They can create a sandbox environment for teams to experiment in a way that strictly limits the downside risk. Then those teams can go much faster and not have to slow down to get anyone’s permission.

The sandbox environment can be a set of written policies, not necessarily a software implementation of a sandbox. For example, it may permit a team to test the nascent product only on employees of the company and perhaps alpha testers who have signed an NDA, and give no access to sensitive information. It may be allowed to launch product experiments only under newly created brands not tied directly to the company. Perhaps it must operate within a pre-allocated budget for compute.

Within this sandbox, there can be broad scope for experimentation, and — importantly — a team is free to experiment without frequently needing to ask for permission, because the downside they can create is limited. Further, when a prototype shows sufficient promise to bring it to scale, the company can then invest in making sure the software is reliable, secure, treats sensitive information appropriately, is consistent with the company’s brand, and so on.

Under this framework, it is easier to build a company culture that encourages learning, building, and experimentation and celebrates even the inevitable failures that now come with modest cost. Dozens or hundreds of prototypes can be built and quickly discarded as part of the price of finding one or two ideas that turn out to be home runs. This also lets teams move quickly as they churn through those dozens of prototypes needed to get to the valuable ones.

I often speak with large companies about AI strategy and implementation. My quick checklist of things to consider is people, process, and platform. This letter has addressed only part of processes, with an emphasis on moving fast. I’m bullish about what both startups and large companies can do with AI, and I will write about the roles of people and platforms in future letters.

[Original text: https://t.co/Jn1QLnrRlI ]

[Tesla Optimus @Tesla_Optimus]

Was just getting warmed up

[Bayer 04 Leverkusen @bayer04_en]

After almost two-and-a-half years, and during the most successful period in the club's history, our head coach, @XabiAlonso, will leave #Bayer04 at the end of the season.

[Nutt D @NutKhamrin]

ผมได้ยินประโยคนี้จาก podcast ของคุณรวิศ ใน mission to the moon ที่สัมภาษณ์คุณตัน ภาสกรนที ฟังเพลินๆขณะขับรถไปทำงาน อย��่ๆก็เหมือนโดนประโยคนี้ฉีดเข้าเส้น ตาลุกวาวเลยทีเดียว 📷 พอรถติดปุ๊บก็รีบเอาปากกามาจดลงสมุดโน๊ต

[Nutt D @NutKhamrin]

"ไม่มีอะไรเป็นไปไม่ได้ แต่ไม่มีอะไรเป็นอย่างที่เราคิด"
ผมรู้สึกโดนกับประโยคนี้มาก เป็นประโยคที่เหมือนจะย้อนแย้ง
แต่ถ้าคิดตามลึกๆ มันกลับสมบูรณ์แบบในตัว
,
#thread

[Figma @figma]

Your design is the prompt

Bring your existing ideas to life with Figma Make

#Config2025

[R here @UvgtdERt4PWmcjF]

ศัลยแพทย์ใช้เทคโนโลยีเสมือนจริงและการตรวจเส้นเลือด เพื่อสร้างภาพสมองแบบเรียลไทม์ระหว่างขั้นตอนการผ่าตัด เพิ่มความแม่นยำ

[khajochi @khajochi]

50 เขตที่น่าอยู่ที่สุดในกรุงเทพ (เรียงจากน่าอยู่มาก ไปน่าอยู่น้อย)

1. พระนคร
2. ป้อมปราบศัตรูพ่าย
3. ปทุมวัน
4. สัมพันธวงศ��
5. ราชเทวี
6. สาทร
7. บางรัก
8. ดุสิต
9. ห้วยขวาง
10. พญาไท
11. หลักสี่
12. บางกอกใหญ่
13. บางกอกน้อย
14. วัฒนา
15. คลองเตย
16. มีนบุรี
17. ทวีวัฒนา
18. บางพลัด
19. ตลิ่งชัน
20. บางคอแหลม
21. คลองสาน
22. สวนหลวง
23. สะพานสูง
24. ราษฎร์บูรณะ
25. ยานนาวา
26. พระโขนง
27. คันนายาว
28. จตุจักร
29. บางกะปิ
30. ธนบุรี
31. ดอนเมือง
32. ดินแดง
33. ลาดพร้าว
34. บางนา
35. จอมทอง
36. บางบอน
37. บึงกุ่ม
38. บางซื่อ
39. ลาดกระบั���
40. คลองสามวา
41. ภาษีเจริญ
42. หนองจอก
43. ประเวศ
44. วังทองหลาง
45. บางเขน
46. ทุ่งครุ
47. บางขุนเทียน
48. สายไหม
49. บางแค
50. หนองแขม

วัดความน่าอยู่จากคะแนนด้านบริการสาธารณะ , เศรษฐกิจ, สวัสดิภาพ , สิ่งแวดล้อม

ข้อมูลจาก Bangkok Index : RocketMedialab

[Mario Nawfal @MarioNawfal]

🚨 ROBOTS CAN NOW HELP WITH SURGERY — AND THEY’RE ACTUALLY GOOD AT IT

Medtronic tested its Hugo robot in 137 real surgeries — fixing prostates, kidneys, and bladders — and the results were better than doctors expected.

Complication rates were super low: just 3.7% for prostate surgeries, 1.9% for kidney surgeries, and 17.9% for bladder surgeries, all beating safety goals from years of research.

The robot got a 98.5% success rate, way above the 85% goal — meaning it didn’t just pass the test, it basically set the curve.

Out of 137 surgeries, only 2 needed to switch back to regular surgery — 1 because of a robot glitch, and 1 because of a tricky patient case.

This doesn’t mean robots are replacing surgeons tomorrow, but it does mean your next doctor might have a very expensive metal sidekick.

Source: RTTNews

[Oz Levin @Fpl_OzL]

OR 11 🌍
4 FT's
0.7m
Planning on rolling a transfer and BB in gw33
What's your plan?

#FPL

[Tim Cook @tim_cook]

Steve saw the world not just as it was, but as it could be. His vision continues to inspire us to push boundaries and create the future. Today, on his 70th birthday, we honor his legacy and his enduring impact.

[Andrew Ng @AndrewYNg]

The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path to AI progress. Despite the massive focus on and hype around processing power, algorithmic innovations are rapidly pushing down training costs.

About a week ago, DeepSeek, a company based in China, released DeepSeek-R1, a remarkable model whose performance on benchmarks is comparable to OpenAI’s o1. Further, it was released as an open weight model with a permissive MIT license. At Davos last week, I got a lot of questions about it from non-technical business leaders. And on Monday, the stock market saw a “DeepSeek selloff”: The share prices of Nvidia and a number of other U.S. tech companies plunged. (As of the time of writing, some have recovered somewhat.)

Here’s what I think DeepSeek has caused many people to realize:

China is catching up to the U.S. in generative AI. When ChatGPT was launched in November 2022, the U.S. was significantly ahead of China in generative AI. Impressions change slowly, and so even recently I heard friends in both the U.S. and China say they thought China was behind. But in reality, this gap has rapidly eroded over the past two years. With models from China such as Qwen (which my teams have used for months), Kimi, InternVL, and DeepSeek, China had clearly been closing the gap, and in areas such as video generation there were already moments where China seemed to be in the lead.

I’m thrilled that DeepSeek-R1 was released as an open weight model, with a technical report that shares many details. In contrast, a number of U.S. companies have pushed for regulation to stifle open source by hyping up hypothetical AI dangers such as human extinction. It is now clear that open source/open weight models are a key part of the AI supply chain: Many companies will use them. If the U.S. continues to stymie open source, China will come to dominate this part of the supply chain and many businesses will end up using models that reflect China’s values much more than America’s.

Open weight models are commoditizing the foundation-model layer. As I wrote previously, LLM token prices have been falling rapidly, and open weights have contributed to this trend and given developers more choice. OpenAI’s o1 costs $60 per million output tokens; DeepSeek R1 costs $2.19. This nearly 30x difference brought the trend of falling prices to the attention of many people.

The business of training foundation models and selling API access is tough. Many companies in this area are still looking for a path to recouping the massive cost of model training. Sequoia’s article “AI’s $600B Question” lays out the challenge well (but, to be clear, I think the foundation model companies are doing great work, and I hope they succeed). In contrast, building applications on top of foundation models presents many great business opportunities. Now that others have spent billions training such models, you can access these models for mere dollars to build customer service chatbots, email summarizers, AI doctors, legal document assistants, and much more.

Scaling up isn’t the only path to AI progress. There’s been a lot of hype around scaling up models as a way to drive progress. To be fair, I was an early proponent of scaling up models. A number of companies raised billions of dollars by generating buzz around the narrative that, with more capital, they could (i) scale up and (ii) predictably drive improvements. Consequently, there has been a huge focus on scaling up, as opposed to a more nuanced view that gives due attention to the many different ways we can make progress. Driven in part by the U.S. AI chip embargo, the DeepSeek team had to innovate on many optimizations to run on less-capable H800 GPUs rather than H100s, leading ultimately to a model trained (omitting research costs) for under $6M of compute.

It remains to be seen if this will actually reduce demand for compute. Sometimes making each unit of a good cheaper can result in more dollars in total going to buy that good. I think the demand for intelligence and compute has practically no ceiling over the long term, so I remain bullish that humanity will use more intelligence even as it gets cheaper.

I saw many different interpretations of DeepSeek’s progress here in X, as if it was a Rorschach test that allowed many people to project their own meaning onto it. I think DeepSeek-R1 has geopolitical implications that are yet to be worked out. And it���s also great for AI application builders. My team has already been brainstorming ideas that are newly possible only because we have easy access to an open advanced reasoning model. This continues to be a great time to build!

[Original text: https://t.co/yiOHeGJgLZ ]