Rex Averton

@F1_Couch_Pilot @effonecasualfan Just like your driver puts car in toilet.

@PrabhakarAlpha @effonecasualfan Wheel IQ in the toilet 🤡

He's right btw

🚨LEWIS HAMILTON | PODIUM H2H 🟠Hamilton 12 - 12 Alonso 🟢Hamilton 15 - 3 Kovalainen 🔴Hamilton 22 - 25 Button 🟢Hamilton 55 - 50 Rosberg 🟢Hamilton 78 - 58 Bottas 🟢Hamilton 20 - 14 Russell 🔴Hamilton 1 - 9 Leclerc

@brakeboosted Will they now bring in 2026,given new engine for everyone in 2027. I don't think they will use ADUO for 2026. Like they shouldnt waste this year also. Bt logical thinking is to scarifice this year bt they already scarificed last year. It hurts ferrari more .

The FIA has announced today that for 2027 we'll see a nominal increase in ICE power by ~50 kW with an increase in fuel flow. Alongside and a nominal reduction of the MGU-K power by ~50 kW. 450 kW ICE + 300 kW MGU-K. That puts us at a 60/40 power split. A step in the correct direction. These are important changes. But not wholesale ones. I say this because it’s relevant to Ferrari’s plans to introduce an engine upgrade in 2026. I don’t think the changes are drastic enough to warrant a complete scrap of 2026. So an upgrade in the engine this season should still be more than feasible. fia.com/news/f1-propos…

@brakeboosted Well that’s the story of Ferrari. Each year you have fundamental problems and cannot fix in season. Next season what good engine and messed car. A lot of jobs are stake this year , they simply won’t.

@neembu_paani31 lol no news cover it and pretend everything going fine in.

came to take my gas cylinder, realised some people are in line from 3-4am and still waiting for their turn

@Notsu311406 @stogolp world's some percentage atoms are in his body.

@stogolp I partially own this universe .

I partially own 500 multibillion dollar companies (I invested $200 in the S&P 500)

@python_spaces AI

AI Engineering Book resources for FREE To get your FREE copy: - Like - Repost - Comment "AI" - Follow me @python_spaces so I can DM you

@pulkit_mittal_ Same way aptitude is still asked in many exams to filter students. DSA coverage might reduce bt will still be there -easy way to filter mass of applications.

LeetCode rounds evaluate how you solve problems with optimized time and space complexity. Though in real jobs, we never do this from scratch. Libraries already exist for most of it. So LeetCode should’ve become irrelevant once we started using advanced frameworks for development. But it didn’t. Because this round was never about testing how well you use tools on the job. Actually DSA rounds checks your IQ, how you think under constraints and pressure, break down problems, and arrive at efficient solutions from first principles. And Big techs don’t even evaluate you on frameworks or languages, because it takes just 1-2 months for candidate to learn this on job. Same goes for AI orchestration, prompting and all. Truth is we don’t like DSA rounds. But they are the most effective ways for the companies to filter candidates at scale. With a single OA, companies can cut the applicant pool down to a fraction. That’s possible with DSA only at present. AI interviewer looks like an alternate to this. If they become reliable, humans move out of the loop and screening becomes even more scalable.

@FDataAnalysis Enzo seeing his legacy crumble by a drink company. Horibble for ferrari fumbling on their main motto.

I've estimated the ICE (Internal Combustion Engine) power for each manufacturer: big trouble for Red Bull, while Ferrari can be optimistic! 👀 Mercedes: ~576 hp (best) Teams with ≤564 hp (>2% deficit) earn one extra upgrade token per year. RBPT sits at ~565 hp: they may miss the cut! Their PU isn't the problem; the chassis is, which is why Racing Bulls has matched them! Teams with ≤552 hp (>4% deficit) earn TWO extra upgrades per year. Ferrari, Audi, and Honda should all qualify! Ferrari's gap to Mercedes (~29hp) is huge, yet their best-in-class chassis allows them to fight Which team will have the best in-season development? 🤔

@adahstwt Thanks for sharing it.

Everyone wants to become an AI Engineer. 👾 But the roadmap is actually this: math basics (linear algebra, probability) ↓ python ↓ data handling (numpy, pandas) ↓ data visualization ↓ machine learning fundamentals ↓ deep learning (neural networks, backprop) ↓ frameworks (pytorch / tensorflow) ↓ nlp basics ↓ computer vision basics ↓ llms (how they actually work) ↓ prompt engineering ↓ fine-tuning models ↓ vector databases (pinecone / faiss) ↓ embeddings & retrieval ↓ rag systems ↓ agents & tool usage ↓ model evaluation ↓ deployment (apis, fastapi) ↓ docker ↓ cloud (aws / gcp) ↓ scaling inference ↓ monitoring models Now you’re “ready”🎉 but here’s the catch: most people quit before even reaching halfway. Be honest… how far are you in this roadmap?

@prathoshap Hi ,do you have flowchart what sucjects to cover step by step to udnerstand ai models and satrt work on it.

The tech industry convinced an entire generation of developers that they can skip the math. They are wrong. You cannot build foundational architecture with a "GenAI in 5 days" bootcamp. Real engineering requires staring at the equations until they make sense. If you actually want to build state-of-the-art models, you cannot skip the math. No fluff, no quick-bytes. Just the mathematical foundations of Deep Generative Modeling. Here is a course I designed to put everything you need to learn.

@Akintola_steve lol it’s like why study maths when everyone thing is done by computer. Basics and fundamentals are needed when nothing works and manual intervention is required.

Even if it sounds harsh to say, one candid piece of advice I’ll give you is this: you’re wasting your time.

@NinaDSchick This isnt agi. This is sector specific powerful AI. Making agi is still difficult,memory context ,token etc are issue. Anthropic knew thats they have task specific AI. It cannot do law,medicine ,science at par with experts through one model .

Claude Mythos. Ten trillion parameters: the first model in this weight class. Estimated training cost: ten billion dollars. On the hardest coding test in the industry (SWE bench) it scores 94%. It found a security flaw in a system that had been running for 27 years, one that every human engineer and every automated check had missed. It found another bug that had survived five million test runs over 16 years. (It did so overnight.) It is so capable in cybersecurity that Anthropic will not release it to the public, instead it is launching Project Glasswing along with 100m in compute credits to help secure software. Only twelve partners currently have access: Amazon, Cisco, Apple, Google, Microsoft, NVIDIA, JPMorgan Chase, Crowdstrike, Palo Alto, AWS, The Linux Foundation, Broadcom. (I'm sure the Pentagon is on the line?) This is not a product launch: it is a controlled deployment of a system too powerful to distribute freely. Tell me this isn't (very expensive) AGI?

@IntuitMachine They need governing agency like IAEA for AI. They need to withold powerful AI models to make smooth transition except in healthcare sector. They need to slowly integrate models in sectors so there is less panic.

On Claude Mythos 0:00 - We haven't trained it specifically to be good at cyber, we trained it to be good at code. But as a side effect of being good at code, it's also good at cyber. 0:20 - The model that we're experimenting with is by and large as good as a professional human at identifying bugs. 0:34 - this model is able to create exploits out of three, four, sometimes five vulnerabilities that in sequence give you some kind of very sophisticated end outcome. 0:51 - Obviously, capabilities in a model like this could do harm if in the wrong hands, and so we won't be releasing this model widely. 2:02 - I've found more bugs in the last couple of weeks than I've found in the rest of my life combined. 3:07 - We've spoken to officials across the US government and we've offered to work with them and collaborate to assess the risks of these models and to help defend against the risks of these models. 3:37 - It is essential that we come together and work together across industry to help build better defensive capabilities. No single organization sees the whole picture and can tackle this on their own.

@RichaaaaSingh And you realise by crying no one is going to help you. All trust and special feeling towards god that made us feel from child goes away. Stand there powerless and accept fate. More power to you🙏

29 Dec’25 💔

@yaarsariiii Om shanti 🙏.

A friend of mine lost both her parents in span of a year , she is the only child and without any support from her relatives. The last rites of her father was done today. Kindly take a minute and pray for her parents and for her specially to fight this war🙏

@basicprompts AI

@DhravyaShah @supermemory Congrats Dhravya

Excited to announce that I've raised $3 Million to build @supermemory, the best memory for LLMs and agents. I turned 20 last month Memory is one of the hardest challenges in AI right now. I realized this when building the first version of supermemory, which was merely a bookmarking and notetaking tool I was building as a side-project in dorm two years ago when I was 18. There weren't many good solutions, so I built my own vector DB, content parsers and an engine that works like the human brain. This is my life's work - I dropped out of college, moved to SF, and continued to build out the product as a solo founder. Today, I am delighted that we have one of the best and fastest memory products in the world, with many hundreds of enterprises and builders building apps on top of supermemory. And this is just the start. grateful to my investors @SusaVentures (@chadbyers), Browder Capital (@joshuabrowder), SF1 (@ItzSuds), @julianweisser (@solofounders), and angels like @dok2001 (Cloudflare), Jeff Dean and @OfficialLoganK (Google), @zeeg (Sentry), @Theo, and many others who have supported me in this journey. We are hiring across engineering, research and product roles. Join us in the journey of creating the best memory engine on 🌎

@ayushunleashed Nice thinking for clean and bug free code.

I think a Good Engineer is the one who solves core issues from first principles instead of just solving side effects. Don't focus on finishing tickets, focus on solving the core issue. A lot of times the bugs are side effects, product manager will report a bug, you'll fix that particular issue and then produce a new one as side effect. If you keep doing this the product never improves. Instead if you can find the core issue and solve it then no. of bugs reported will decrease. Example - a bug reported because a variation of key was missing from key detection system and detection failed. Dumb solution - add the missing key variant manually, bug is solved. This solved the specific case but you are likely to encounter it again for other keys. Good solution - build a key variant generator that could generate all possible keys for any given key without compromising much on speed. Now you solved the core issue, and increased maintainability Great solution: Question the premise of having a key detection system. Can we use a new technique like llm, just define the general rules so we don't have to add these keys in the first place.