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Google DeepMind pre-training lead explains two skills with massive demand by AI frontier labs: > Kernel Development > Low Level Performance Engineering

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ℏεsam

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81,162 görüntüleme • 2 ay önce •via X (Twitter)

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Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro
1:04:03

Vlad Feinberg (Vlad Feinberg) is Google DeepMind’s pre-training area lead and I asked him all about how to land a job at a frontier lab like Google DeepMind, Anthropic or OpenAI. In this episode: • Skills frontier labs need • Differences between software engineering and AI research • Domains that matter for frontier research • Concrete steps engineers can take to get closer to research • Jeff Dean spot bonus story Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:33 - Skills frontier labs need 08:45 - The difference between AI research and engineering 21:41 - Domains that matter for the frontier 30:50 - Marketing yourself to frontier labs 35:13 - Concrete steps engineers can take 38:29 - Overview of pre-training areas 47:23 - Jeff Dean spot bonus story 50:14 - Favorite Gemini war story 58:59 - Advice for his younger self 01:03:07 - Outro

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This is the google of AI agents If you're building with AI agents, you need it Search across 164+ agent skills > Filter > Search > Copy install
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It’s time to level up your agentic development with Android skills. 🚀 These AI-optimized instructions automate more complex tasks like XML-to-Compose and AGP9 migrations, follow our official best practices and guidance on Android development, and are activated on demand by AI tools. Get started with Android skills →
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It’s time to level up your agentic development with Android skills. 🚀 These AI-optimized instructions automate more complex tasks like XML-to-Compose and AGP9 migrations, follow our official best practices and guidance on Android development, and are activated on demand by AI tools. Get started with Android skills →

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Google just did it again. They just launched "Google Skills" a free platform to turn anyone into an AI expert. 3,000+ courses, labs & certificates from Google Cloud, DeepMind, and Grow with Google all in one place. → Learn AI, data, and coding from real Google engineers → Earn credentials that actually get you hired → Build hands-on projects with Gemini Code Assist → Compete on global leaderboards and share your wins No paywalls. No BS. Just world-class AI education free. This might be the biggest AI upskilling move of the decade. 👉
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Google just did it again. They just launched "Google Skills" a free platform to turn anyone into an AI expert. 3,000+ courses, labs & certificates from Google Cloud, DeepMind, and Grow with Google all in one place. → Learn AI, data, and coding from real Google engineers → Earn credentials that actually get you hired → Build hands-on projects with Gemini Code Assist → Compete on global leaderboards and share your wins No paywalls. No BS. Just world-class AI education free. This might be the biggest AI upskilling move of the decade. 👉

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Vibe coding with AI Studio is so addictive! I built an AI Hair Stylist Agent that: > find the best haircut by looking my face > generates the after images using nano banana > lets me edit it live with the live API > find nearby salons using google maps Built in just 5 mins!
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Vibe coding with AI Studio is so addictive! I built an AI Hair Stylist Agent that: > find the best haircut by looking my face > generates the after images using nano banana > lets me edit it live with the live API > find nearby salons using google maps Built in just 5 mins!

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5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.
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5 patterns in Text Arena's price–performance Pareto frontier since 2023: 1. GPT-4-level quality is now ~500x lower cost. - From a ~$50 blended price per million tokens in 2023 to ~$0.10 today. 2. The higher-price end is both better and lower-priced since 2023. - The leading Arena score has climbed ~170 points (1,330 → 1,500). While the price of the higher-end frontier models dropped from ~$50 to ~$20 per million tokens. 3. The low-cost end gained the most. - Under $0.20 per million tokens, the best available model went from ~1,000 Arena score in 2023 to ~1,440 today. 4. The low-cost/top performance gap has nearly closed. - In 2023, sub-$0.20 models trailed the leader by ~350 Arena points. Today, ~60. 5. The cast has rotated quite a bit. - - OpenAI set the 2023–24 benchmark. - AI at Meta strengthened the low-cost end in 2024. - Google DeepMind drove the 2025 jump. - Anthropic holds the peak in 2026. - xAI and Chinese labs like DeepSeek AI, Z.ai, Kimi.ai, Xiaomi MiMo, and Qwen are continuing to push the mid-price frontier.

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1/ The first big announcement: Google DeepMind has unveiled Genie 3 A new frontier for world models, the first AI that generates interactive 3D worlds in real time at 24 fps. The breakthrough: minutes of consistency at 720p using only text descriptions. No pre-built 3D models required. Let's break it down - with some examples:
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1/ The first big announcement: Google DeepMind has unveiled Genie 3 A new frontier for world models, the first AI that generates interactive 3D worlds in real time at 24 fps. The breakthrough: minutes of consistency at 720p using only text descriptions. No pre-built 3D models required. Let's break it down - with some examples:

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Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing
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Gemini 3, scaling laws and the 'finite data' era: my conversation with Sebastian Borgeaud, research engineer at Google DeepMind and a pre-training lead for Gemini 3 00:00 – Cold intro: “We’re ahead of schedule” + AI is now a system 00:58 – Oriol Vinyals's “secret recipe”: better pre- + post-training 02:09 – Why AI progress still isn’t slowing down 03:04 – Are models actually getting smarter? 04:36 – Two–three years out: what changes first? 06:34 – AI doing AI research: faster, not automated 07:45 – Frontier labs: same playbook or different bets? 10:19 – Post-transformers: will a disruption happen? 10:51 – DeepMind’s advantage: research × engineering × infra 12:26 – What a Gemini 3 pre-training lead actually does 13:59 – From Europe to Cambridge to DeepMind 18:06 – Why he left RL for real-world data 20:05 – From Gopher to Chinchilla to RETRO (and why it matters) 20:28 – “Research taste”: integrate or slow everyone down 23:00 – Fixes vs moonshots: how they balance the pipeline 24:37 – Research vs product pressure (and org structure) 26:24 – Gemini 3 under the hood: MoE in plain English 28:30 – Native multimodality: the hidden costs 30:03 – Scaling laws aren’t dead (but scale isn’t everything) 33:07 – Synthetic data: powerful, dangerous? 35:00 – Reasoning traces: what he can’t say (and why) 37:18 – Long context + attention: what’s next 38:40 – Retrieval vs RAG vs long context 41:49 – The real boss fight: evals (and contamination) 42:28 – Alignment: pre-training vs post-training 43:32 – Deep Think + agents + “vibe coding” 46:34 – Continual learning: updating models over time 49:35 – Advice for researchers + founders 53:35 – “No end in sight” for progress + closing

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🚨 Big news: Google just launched Google Skills... giving you access to the same content universities charge $50K+ is now open to everyone. Here’s how it works: - Go to - Pick any AI skill you want to learn - Learn through 700+ hands-on labs with real Google Cloud tools -Earn Google Skill Badges to showcase your expertise ... recruiters actively look for these - Work toward certifications that rank among the highest-paying in IT What’s inside: - 3,000+ AI courses from Google Cloud, DeepMind & Google Edu - $500 in free cloud credits to practice - Built-in Gemini Code Assist - Certificates employers actually recognize (82% hiring preference) Like, REPOST
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🚨 Big news: Google just launched Google Skills... giving you access to the same content universities charge $50K+ is now open to everyone. Here’s how it works: - Go to - Pick any AI skill you want to learn - Learn through 700+ hands-on labs with real Google Cloud tools -Earn Google Skill Badges to showcase your expertise ... recruiters actively look for these - Work toward certifications that rank among the highest-paying in IT What’s inside: - 3,000+ AI courses from Google Cloud, DeepMind & Google Edu - $500 in free cloud credits to practice - Built-in Gemini Code Assist - Certificates employers actually recognize (82% hiring preference) Like, REPOST

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Google just made every $50K master's degree look like a scam. They dropped "Google Skills" - 3,000+ AI courses from DeepMind, Cloud, and Google Education in one platform. And it's 100% FREE for Google Cloud users. The same content universities charge $60K for: - DeepMind's actual AI research training - 700+ hands-on labs with real cloud environments - Gemini Code Assist built INTO the learning - Direct hiring paths at 150+ companies While everyone's drowning in student debt, smart people are getting: ✓ Skills that actually get you hired ✓ Certificates employers recognize (82% hiring preference) ✓ Zero cost if you have Google Cloud ✓ Or $29/month vs $1,600/month for Udacity The kicker? 26 million people completed courses BEFORE this consolidation. You're competing against people learning AI from the team that BUILT Gemini. How to actually use this (not just browse): 1. Start with "AI Essentials" - no coding required 2. Use the hands-on labs (this is where 90% quit) 3. Get skill badges - they show up on LinkedIn 4. Target Google Cloud certification - top 2 highest paying IT certs 5. Join the 150-company hiring consortium The education industrial complex is panicking because anyone can now: → Learn from DeepMind researchers directly → Practice with $500 in free Cloud credits → Get hired without a degree One person's $60K tuition = 2,070 months of Google Skills. Let that sink in. Comment "SKILLS" and I'll send you: ✓ The exact learning path that gets you hired fastest ✓ Which certifications actually pay ✓ How to access everything free Your competition is still applying to universities. Time to eat their lunch.
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Google just made every $50K master's degree look like a scam. They dropped "Google Skills" - 3,000+ AI courses from DeepMind, Cloud, and Google Education in one platform. And it's 100% FREE for Google Cloud users. The same content universities charge $60K for: - DeepMind's actual AI research training - 700+ hands-on labs with real cloud environments - Gemini Code Assist built INTO the learning - Direct hiring paths at 150+ companies While everyone's drowning in student debt, smart people are getting: ✓ Skills that actually get you hired ✓ Certificates employers recognize (82% hiring preference) ✓ Zero cost if you have Google Cloud ✓ Or $29/month vs $1,600/month for Udacity The kicker? 26 million people completed courses BEFORE this consolidation. You're competing against people learning AI from the team that BUILT Gemini. How to actually use this (not just browse): 1. Start with "AI Essentials" - no coding required 2. Use the hands-on labs (this is where 90% quit) 3. Get skill badges - they show up on LinkedIn 4. Target Google Cloud certification - top 2 highest paying IT certs 5. Join the 150-company hiring consortium The education industrial complex is panicking because anyone can now: → Learn from DeepMind researchers directly → Practice with $500 in free Cloud credits → Get hired without a degree One person's $60K tuition = 2,070 months of Google Skills. Let that sink in. Comment "SKILLS" and I'll send you: ✓ The exact learning path that gets you hired fastest ✓ Which certifications actually pay ✓ How to access everything free Your competition is still applying to universities. Time to eat their lunch.

Nozz

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I vibe-coded this yesterday with Gemini-3.0-Pro with an extremely low-effort prompt (~200 words, with no punctuation) spread over two turns. The second turn was only because I forgot to add something. Congratulations to Google DeepMind on this incredible model release! To top it off Google AI Studio makes creating and sharing apps as easy as a google doc. I also one-click deployed this app without knowing anything about Google Cloud's CloudRun feature. This should truly democratize software building. Play with 🔈 on.
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I vibe-coded this yesterday with Gemini-3.0-Pro with an extremely low-effort prompt (~200 words, with no punctuation) spread over two turns. The second turn was only because I forgot to add something. Congratulations to Google DeepMind on this incredible model release! To top it off Google AI Studio makes creating and sharing apps as easy as a google doc. I also one-click deployed this app without knowing anything about Google Cloud's CloudRun feature. This should truly democratize software building. Play with 🔈 on.

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