AIandBlockchain

Urgent!! Claude 4.5: The Truth About 30 Hours and Code

30 hours of nonstop work without losing focus. Leading OSWorld with 61.4%. SWE-bench Verified — up to 82% in advanced setups. And all that — at the exact same price as Sonnet 4. Bold claims? In this episode, we cut through the hype and break down what’s really revolutionary about agent AI. 🧠You’ll learn why long-form coherence changes the game: projects that once took weeks can now shrink into days. We explain how Claude 4.5 maintains state over 30+ hours of multi-step tasks — and what that means for developers, research teams, and production pipelines.We’re speaking in metrics. SWE-bench Verified: 77.2% with a simple scaffold (bash + editor), up to 82.0% with parallel runs and ranking. OSWorld: a leap from ~42% to 61.4% in just 4 months — a real ability to use a computer, not just chat. This isn’t “hello world,” it’s fixing bugs in live repositories and navigating complex interfaces.Real-world data too. One early customer reported that switching from Sonnet 4 to 4.5 in an internal coding benchmark reduced error rates from 9% all the way to 0%. Yes, it was tailored to their workflow, but the signal of a qualitative leap in reliability is hard to ignore.Agents are growing up. Example: Devon AI saw +18% improvement in planning and +12% in end-to-end performance with Sonnet 4.5. Better planning, stronger strategy adherence, less drift — exactly what you need for autonomous pipelines, CI/CD, and RPA. 🎯The tooling is ready: checkpoints in Claude Code, context editing in the API, a dedicated memory tool to move state outside the context window. Plus an Agent SDK — the very same infrastructure powering their frontier products. For web and mobile users: built-in code execution and file creation — spreadsheets, slide decks, docs — right from chat, no manual copy-paste.Domain expertise is leveling up too:Law: handling briefing cycles, drafting judicial opinions, summary judgment analysis.Finance: investment-grade insights, risk modeling, structured product evaluation, portfolio screening — all with less human review.Security: −44% in vulnerability report processing time, +25% accuracy.Safety wasn’t skipped. Released under ASL3, with improvements against prompt injection, reduced sycophancy, reduced “confident hallucinations.” Sensitive classifiers (e.g., CBRN) now generate 10x fewer false positives than before, and 2x fewer since Opus 4 — safer and more usable.And the price? Still $3 input, $15 output per 1M tokens. Same cost, much more power. For teams in the US, Europe, India — the ROI shift is big.Looking ahead: the Imagine with Claude experiment — real-time functional software generation on the fly. No pre-written logic, no predetermined functions. Just describe what you need, and the model builds it instantly. 🛠️If you’re building agent workflows, DevOps bots, auto-code-review, or legal/fintech pipelines — this episode gives you the map, the benchmarks, and the practical context.Want your use case covered in the next episode? Drop a comment. Don’t forget to subscribe, leave a ★ rating, and share this episode with a colleague — that’s how you help us bring you more applied deep dives.Next episode teaser: real case study — “Building a 30-hour Agent: Memory, Checkpoints, OSWorld Tools, and Token Budgeting.”Key Takeaways:30+ hours of coherence: weeks-long projects compressed into days.SWE-bench Verified: 77.2% (baseline) → 82.0% (parallel + ranking).OSWorld 61.4%: leadership in “computer-using ability.”Developer infrastructure: checkpoints, memory tool, API context editing, Agent SDK.Safety: ASL3, fewer false positives, stronger resilience against prompt injection.SEO Tags:Niche: #SWEbenchVerified, #OSWorld, #AgentSDK, #ImagineWithClaudePopular: #artificialintelligence, #machinelearning, #programming, #AILong-tail: #autonomous_agents_for_development, #best_AI_for_coding, #30_hour_long_context, #ASL3_safetyTrending: #Claude45, #DevonAIRead more: https://www.anthropic.com/news/claude-sonnet-4-5

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Openai. AI vs Experts: The Truth Behind the GDP Benchmark

Sep 2600:14:08Tap to summarize

🤖📉 We all feel it: AI is transforming office work. But the usual indicators — hiring stats, GDP growth, tech adoption — always lag behind. They tell us what already happened, not what’s happening right now. So how do we predict how deeply AI will reshape the job market before it happens?In this episode, we break down one of the most ambitious and under-the-radar studies of the year — the GDP Benchmark: a new way to measure how ready AI is to perform real professional work. And no — this isn’t just another model benchmark.🔍 The researchers created actual job tasks, not abstract multiple-choice quizzes — 44 tasks across 9 core sectors that together represent most of the U.S. economy. Financial reports, C-suite presentations, CAD designs — all completed by top AI models and then blind-reviewed by real industry professionals, each with an average of 14 years of experience.Here’s what you’ll learn in this episode:What "long-horizon tasks" are and why they matter more than simple knowledge tests.How AI handles complex, multi-step jobs that demand attention to detail.Why success isn’t just about accuracy, but also about polish, structure, and aesthetics.Which model leads the race — GPT-5 or Claude Opus?What’s still holding AI back (spoiler: 3% of failures are catastrophic).Why human oversight remains absolutely non-negotiable.How better instructions and prompt scaffolding can dramatically boost AI performance — no hardware upgrades needed.💡 Most importantly: the GDP Benchmark is the first serious attempt to build a leading economic indicator of AI's ability to do valuable, real-world work. It offers business leaders, developers, and policymakers a new way to look forward — not just in the rearview mirror.🎯 This episode is for:Executives wondering where and when to deploy AI in workflows.Knowledge workers questioning whether AI will replace or assist them.Researchers and HR leaders looking to measure AI’s real impact on productivity.🤔 And here’s the question to leave you with: if AI can create the report, can it also handle the meeting about that report? GPT may generate slides, but can it lead a strategy session, build trust, or read a room? That’s the next frontier in measuring and developing AI — the messy, human side of work.🔗 Share this episode, drop your thoughts in the comments, and don’t forget to subscribe — next time, we’ll explore real-world tactics to make AI more reliable in business-critical tasks.Key Takeaways:The GDP Benchmark measures AI’s ability to perform real, complex digital work — not just quiz answers.Top models already match or exceed expert-level output in nearly 50% of cases.Most failures come from missed details or incomplete execution — not lack of intelligence.Better prompting and internal review workflows can significantly boost quality.Human-in-the-loop remains essential for trust, safety, and performance.SEO Tags:Niche: #AIinBusiness, #GDPBenchmark, #FutureOfWork, #AIvsHumanPopular: #artificialintelligence, #technology, #automation, #business, #productivityLong-tail: #evaluatingAIwork, #AIimpactoneconomy, #benchmarkingAImodelsTrending: #GPT5, #ClaudeOpus, #AIonTheEdge, #ExpertvsAI

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Google. The Future of Robots: Thinking, Learning, and Reasoning

Sep 2500:12:28Tap to summarize

Imagine a robot that doesn’t just follow your commands but actually thinks, analyzes the situation, and corrects its own mistakes. Sounds like science fiction? In this episode, we break down the revolution in general-purpose robotics powered by Gemini Robotics 1.5 — GR 1.5 and GRE 1.5.🔹 What does this mean in practice?Robots now think in human language — running an inner dialogue, writing down steps, and checking progress. This makes their actions transparent and predictable for people.They can learn skills across different robot bodies — and then perform tasks on new machines without retraining. One robot learns, and all of them get smarter.With the GRE 1.5 “brain”, they can plan complex, real-world processes — from cooking risotto by recipe to sorting trash according to local rules — with far fewer mistakes.But that’s just the beginning. We also explore how this new architecture:solves the data bottleneck with motion transfer,introduces multi-layered safety (risk recognition and automated stress tests),opens the door to using human and synthetic video for scalable training,and why trust and interpretability are becoming critical in AI robotics.This episode shows why GR 1.5 and GRE 1.5 aren’t just an evolution but a foundational shift. Robots are moving from being mere “tools” to becoming partners that can understand, reason, and adapt.❓Now, here’s a question for you: what boring, repetitive, or overly complex task would you be most excited to hand off to a robot like this? Think about it — and share your thoughts in the comments!👉 Don’t forget to subscribe so you won’t miss future episodes. We’ve got even more insights on how cutting-edge technology is reshaping our lives.Key Takeaways:GR 1.5 thinks in human language and self-corrects.Skills transfer seamlessly across different robots via motion transfer.GRE 1.5 reduces planning errors by nearly threefold.SEO Tags:Niche: #robotics, #artificialintelligence, #GeminiRobotics, #generalpurpose_robotsPopular: #AI, #robots, #futuretech, #neuralnetworks, #automationLong-tail: #robots_for_home, #future_of_artificial_intelligence, #AI_robot_learningTrending: #GenerativeAI, #EmbodiedAI, #AIrobotsRead more: https://deepmind.google/discover/blog/gemini-robotics-15-brings-ai-agents-into-the-physical-world/

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Arxiv. When Data Becomes Pricier Than Compute: The New AI Era

Sep 2500:12:47Tap to summarize

Imagine this paradox: compute power for training AI models is growing 4× every year, yet the pool of high-quality data barely grows by 3%. The result? For the first time, it’s not hardware but data that has become the biggest bottleneck for large language models.In this episode, we explore what this shift means for the future of AI. Why do standard scaling approaches—like just making models bigger or endlessly reusing limited datasets—actually backfire? And more importantly, what algorithmic tricks let us squeeze every drop of performance from scarce data?We dive into:Why classic scaling laws (like Chinchilla) break down under fixed datasets.How cranking up regularization (30× higher than standard!) prevents overfitting.Why ensembles of models outperform even an “infinitely large” single model—and how just three models together can beat the theoretical maximum of one giant.How knowledge distillation turns unwieldy ensembles into compact, efficient models ready for deployment.The stunning numbers: from a 5× boost in data efficiency to an eye-popping 17.5× reduction in dataset size for domain adaptation.Who should listen? Engineers, researchers, and curious minds who want to understand how LLM training is shifting in a world where compute is becoming “free,” but high-quality data is the new luxury.And here’s the question for you: if compute is no longer a constraint, which forgotten algorithms and older AI ideas should we bring back to life? Could they hold the key to the next big breakthrough?Subscribe now so you don’t miss new insights—and share your thoughts in the comments. Sometimes the discussion is just as valuable as the episode itself.Key Takeaways:Compute is no longer the bottleneck—data is the real scarce resource.Strong regularization and ensembling massively boost data efficiency.Distillation makes ensemble power practical for deployment.Algorithmic techniques can deliver up to 17.5× data savings in real tasks.SEO Tags:Niche: #LLM, #DataEfficiency, #Regularization, #EnsemblingPopular: #ArtificialIntelligence, #MachineLearning, #DeepLearning, #AITrends, #TechPodcastLong-tail: #OptimizingModelTraining, #DataEfficiencyInAI, #FutureOfLLMsTrending: #AI2025, #GenerativeAI, #LLMResearchRead more: https://arxiv.org/abs/2509.14786

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Arxiv. Small Batches, Big Shift in LLM Training

Sep 500:16:52Tap to summarize

What if everything you thought you knew about training large language models turned out to be… not quite right? 🤯In this episode, we dive deep into a topic that could completely change the way we think about LLM training. We’re talking about batch size — yes, it sounds dry and technical, but new research shows that tiny batches, even as small as one, don’t just work — they can actually bring major advantages.🔍 In this episode you’ll learn:Why the dogma of “huge batches for stability” came about in the first place.How LLM training is fundamentally different from classical optimization — and why “smaller” can actually beat “bigger.”The secret setting researchers had overlooked for years: scaling Adam’s β2 with a constant “token half-life.”Why plain old SGD is suddenly back in the game — and how it can make large-scale training more accessible.Why gradient accumulation may actually hurt memory efficiency instead of helping, and what to do instead.💡 Why it matters for you:If you’re working with LLMs — whether it’s research, fine-tuning, or just making the most out of limited GPUs — this episode can save you weeks of trial and error, countless headaches, and lots of resources. Small batches are not a compromise; they’re a path to robustness, efficiency, and democratized access to cutting-edge AI.❓Question for you: which other “sacred cows” of machine learning deserve a second look?Share your thoughts — your insight might spark the next breakthrough.👉 Subscribe now so you don’t miss future episodes. Next time, we’ll explore how different optimization strategies impact scaling and inference speed.Key Takeaways:Small batches (even size 1) can be stable and efficient.The secret is scaling Adam’s β2 correctly using token half-life.SGD and Adafactor with small batches unlock new memory and efficiency gains.Gradient accumulation often backfires in this setup.This shift makes LLM training more accessible beyond supercomputers.SEO Tags:Niche: #LLMtraining, #batchsize, #AdamOptimization, #SGDPopular: #ArtificialIntelligence, #MachineLearning, #NeuralNetworks, #GPT, #DeepLearningLong-tail: #SmallBatchLLMTraining, #EfficientLanguageModelTraining, #OptimizerScalingTrending: #AIresearch, #GenerativeAI, #openAIRead more: https://arxiv.org/abs/2507.07101

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DeepSeek. Secrets of Smart LLMs: How Small Models Beat Giants

Sep 100:18:33Tap to summarize

Imagine this: a 27B language model outperforming giants with 340B and even 671B parameters. Sounds impossible? But that’s exactly what happened thanks to breakthrough research in generative reward modeling. In this episode, we unpack one of the most exciting advances in recent years — Self-Principled Critique Tuning (SPCT) and the new DeepSeek GRM architecture that’s changing how we think about training and using LLMs.We start with the core challenge: how do you get models not just to output text, but to truly understand what’s useful for humans? Why is generating honest, high-quality reward signals the bottleneck for all of Reinforcement Learning? You’ll learn why traditional approaches — scalar and pairwise reward models — fail in the messy real world, and what makes SPCT different.Here’s the twist: DeepSeek GRM doesn’t rely on fixed rules. It generates evaluation principles on the fly, writes detailed critiques, and… learns to be flexible. But the real magic comes next: instead of just making the model bigger, researchers introduced inference-time scaling. The model generates multiple sets of critiques, votes for the best, and then a “Meta RM” filters out the noise, keeping only the most reliable judgments.The result? A system that’s not only more accurate and fair but can outperform much larger models. And the best part — it does so efficiently. This isn’t just about numbers on a benchmark chart. It’s a glimpse of a future where powerful AI isn’t locked away in corporate data centers but becomes accessible to researchers, startups, and maybe even all of us.In this episode, we answer:How does SPCT work and why are “principles” the key to smart self-critique?What is inference-time scaling, and how does it turn medium-sized models into champions?Can a smaller but “smarter” AI really rival the giants with hundreds of billions of parameters?Most importantly: what does this mean for the future of AI, democratization of technology, and ethical model use?We leave you with this thought: if AI can not only think but also judge itself using principles, maybe we’re standing at the edge of a new era of self-learning and fairer systems.👉 Follow the show so you don’t miss new episodes, and share your thoughts in the comments: do you believe “smart scaling” will beat the race for sheer size?Key Takeaways:SPCT teaches models to generate their own evaluation principles and adaptive critiques.Inference-time scaling makes smaller models competitive with massive ones.Meta RM filters weak judgments, boosting the quality of final reward signals.SEO Tags:Niche: #ReinforcementLearning, #RewardModeling, #LLMResearch, #DeepSeekGRMPopular: #AI, #MachineLearning, #ArtificialIntelligence, #ChatGPT, #NeuralNetworksLong-tail: #inference_time_scaling, #self_principled_critique_tuning, #generative_reward_modelsTrending: #AIethics, #AIfuture, #DemocratizingAIRead more: https://arxiv.org/pdf/2504.02495

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Arxiv. The Grain of Truth: How Reflective Oracles Change the Game

Aug 3100:18:46Tap to summarize

What if there were a way to cut through the endless loop of mutual reasoning — “I think that he thinks that I think”? In this episode, we explore one of the most elegant and surprising breakthroughs in game theory and AI. Our guide is a recent paper by Cole Wyth, Marcus Hutter, Jan Leike, and Jessica Taylor, which shows how to use reflective oracles to finally crack a decades-old puzzle — the grain of truth problem.🔍 In this deep dive, you’ll discover:Why classical approaches to rationality in infinite games kept hitting dead ends.How reflective oracles let an agent predict its own behavior without logical paradoxes.What the Zeta strategy is, and why it guarantees a “grain of truth” even in unknown games.How rational players, equipped with this framework, naturally converge to Nash equilibria — even if the game is infinite and its rules aren’t known in advance.Why this opens the door to AI that can learn, adapt, and coordinate in truly novel environments.💡 Why it matters for you:This episode isn’t just about math and abstractions. It’s about a fundamental shift in how we understand rationality and learning. If you’re curious about AI, strategic thinking, or how humans manage to cooperate in complex systems, you’ll gain a new perspective on why Nash equilibria appear not as artificial assumptions, but as natural results of rational behavior.We also touch on human cognition: could our social norms and cultural “unwritten rules” function like implicit oracles, helping us avoid infinite regress and coordinate effectively?🎧 At the end, we leave you with a provocative question: could your own mind be running on implicit “oracles,” allowing you to act rationally even when information is overwhelming or contradictory?👉 If this topic excites you, hit subscribe to the podcast so you don’t miss upcoming deep dives. And in the comments, share: where in your own life have you felt stuck in that “infinite regress” of overthinking?Key Takeaways:Reflective oracles resolve the paradox of infinite reasoning.The Zeta strategy ensures a grain of truth across all strategies.Players converge to ε-Nash equilibria even in unknown games.The framework applies to building self-learning AI agents.Possible parallels with human cognition and culture.SEO Tags:Niche: #GameTheory, #ArtificialIntelligence, #GrainOfTruth, #ReflectiveOraclesPopular: #AI, #MachineLearning, #NeuralNetworks, #NashEquilibrium, #DecisionMakingLong-tail: #GrainOfTruthProblem, #ReflectiveOracleAI, #BayesianPlayers, #UnknownGamesAITrending: #AGI, #AIethics, #SelfPredictiveAIRead more: https://arxiv.org/pdf/2508.16245

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Arxiv. Seed 1.5 Thinking: The AI That Learns to Reason

Aug 2500:18:03Tap to summarize

What if artificial intelligence stopped just guessing answers — and started to actually think? 🚀 In this episode, we dive into one of the most talked-about breakthroughs in AI — Seed 1.5 Thinking from ByteDance. This model, as its creators claim, makes a real leap toward genuine reasoning — the ability to deliberate, verify its own logic, and plan before responding.Here’s what we cover:How the “think before respond” principle works — and why it changes everything.Why the “mixture of experts” architecture makes the model both powerful and efficient (activating just 20B of 200B parameters).Record-breaking performance on the toughest benchmarks — from math olympiads to competitive coding.The new training methods: chain-of-thought data, reasoning verifiers, RL algorithms like VAPO and DPO, and an infrastructure that speeds up training by 3×.And most surprisingly — how rigorous math training helps Seed 1.5 Thinking write more creative texts and generate nuanced dialogues.Why does this matter for you?This episode isn’t just about AI solving equations. It’s about how AI is learning to reason, to check its own steps, and even to create. That changes how we think of AI — from a simple tool into a true partner for tackling complex problems and generating fresh ideas.Now imagine: an AI that can spot flaws in its own reasoning, propose alternative solutions, and still write a compelling story. What does that mean for science, engineering, business, and creativity? Where do we now draw the line between human and machine intelligence?👉 Tune in, share your thoughts in the comments, and don’t forget to subscribe — in the next episode we’ll explore how new models are beginning to collaborate with humans in real time.Key Takeaways:Seed 1.5 Thinking uses internal reasoning to improve responses.On math and coding benchmarks, it scores at the level of top students and programmers.A new training approach with chain-of-thought data and verifiers teaches the model “how to think.”Its creative tasks prove that structured planning = more convincing writing.The big shift: AI as a partner in reasoning, not just an answer generator.SEO Tags:Niche: #ArtificialIntelligence, #ReasoningAI, #Seed15Thinking, #ByteDanceAIPopular: #AI, #MachineLearning, #FutureOfAI, #NeuralNetworks, #GPTLong-tail: #AIforMath, #AIforCoding, #HowAIThinks, #AIinCreativityTrending: #AIReasoning, #NextGenAI, #AIvsHumanRead more: https://arxiv.org/abs/2504.13914

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Why Even the Best AIs Still Fail at Math

Aug 2100:19:03Tap to summarize

What do you do when AI stops making mistakes?..Today's episode takes you to the cutting edge of artificial intelligence — where success itself has become a problem. Imagine a model that solves almost every math competition problem. It doesn’t stumble. It doesn’t fail. It just wins. Again and again.But if AI is now the perfect student... what’s left for the teacher to teach? That’s the crisis researchers are facing: most existing math benchmarks no longer pose a real challenge to today’s top LLMs — models like GPT-5, Grok, and Gemini Pro.The solution? Math Arena Apex — a brand-new, ultra-difficult benchmark designed to finally test the limits of AI in mathematical reasoning.In this episode, you'll learn:Why being "too good" is actually a research problemHow Apex was built: 12 of the hardest problems, curated from hundreds of elite competitionsTwo radically different ways to define what it means for an AI to "solve" a math problemWhat repeated failure patterns reveal about the weaknesses of even the most advanced modelsHow LLMs like GPT-5 and Grok often give confident but wrong answers — complete with convincing pseudo-proofsWhy visualization, doubt, and stepping back — key traits of human intuition — remain out of reach for current AIThis episode is packed with real examples, like:The problem that every model failed — but any human could solve in seconds with a quick sketchThe trap that fooled all LLMs into giving the exact same wrong answerHow a small nudge like “this problem isn’t as easy as it looks” sometimes unlocks better answers from models🔍 We’re not just asking what these models can’t do — we’re asking why. You'll get a front-row seat to the current frontier of AI limitations, where language models fall short not due to lack of power, but due to the absence of something deeper: real mathematical intuition.🎓 If you're into AI, math, competitions, or the future of technology — this episode is full of insights you won’t want to miss.👇 A question for you:Do you think AI will ever develop that uniquely human intuition — the ability to feel when an answer is too simple, or spot a trap in the obvious approach? Or will we always need to design new traps to expose its limits?🎧 Stick around to the end — we’re not just exploring failure, but also asking: What comes after Apex?Key Takeaways:Even frontier AIs have hit a ceiling on traditional math tasks, prompting the need for a new level of difficultyApex reveals fundamental weaknesses in current LLMs: lack of visual reasoning, inability to self-correct, and misplaced confidenceModel mistakes are often systematic — a red flag pointing toward deeper limitations in architecture and training methodsSEO Tags:Niche: #AIinMath, #MathArenaApex, #LLMlimitations, #mathreasoningPopular: #ArtificialIntelligence, #GPT5, #MachineLearning, #TechTrends, #FutureOfAILong-tail: #AIerrorsinmathematics, #LimitsofLLMs, #mathintuitioninAITrending: #AI2025, #GPTvsMath, #ApexBenchmarkRead more: https://matharena.ai/apex/

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Can AI Beat NumPy? Algotune Reveals the Truth

Aug 1400:15:35Tap to summarize

🎯 What if a language model could not only write working code, but also make already optimized code even faster? That’s exactly what the new research paper Algotune explores. In this episode, we take a deep dive into the world of AI code optimization — where the goal isn’t just to “get it right,” but to beat the best.🧠 Imagine taking highly tuned libraries like NumPy, SciPy, NetworkX — and asking an AI to make them run faster. No changing the task. No cutting corners. Just better code. Sounds wild? It is. But the researchers made it real.In this episode, you'll learn:What Algotune is and how it redefines what success means for language modelsHow LMs are compared against best-in-class open-source librariesThe 3 main optimization strategies most LMs used — and what that reveals about AI's current capabilitiesWhy most improvements were surface-level, not algorithmic breakthroughsWhere even the best models failed, and why that mattersHow the AI agent Algotuner learns by trying, testing, and iterating — all under a strict LM query budget💥 One of the most mind-blowing parts? In some cases, the speedups reached 142x — simply by switching to a better library function or rewriting the code at a lower level. And all of this happened without any human help.But here’s the tough truth: even the most advanced LLMs still aren’t inventing new algorithms. They’re highly skilled craftsmen — not creative inventors. Yet.❓So here’s a question for you: If AI eventually learns to invent entirely new algorithms, ones that outperform human-designed solutions — how would that reshape programming, science, and technology itself?🔥 Plug into this episode and find out how close we might already be. If you work with AI, code, or just want to understand where things are headed, this one’s a must-listen.📌 Don’t forget to subscribe, leave a review, and share the episode with your team. And stay tuned — in our next deep dive, we’ll explore an even bigger question: can LLMs optimize science itself?Key Takeaways:Algotune is the first benchmark where LMs must speed up already optimized code, not just solve basic tasksSome LMs achieved up to 600x speedups using smart substitutions and advanced toolsThe main insight: AI isn’t inventing new algorithms — it’s just applying known techniques betterThe AI agent Algotuner uses a feedback loop: propose, test, improve — all within a limited query budgetSEO Tags:Niche: #codeoptimization, #languagemodels, #AIprogramming, #benchmarkingAIPopular: #artificialintelligence, #Python, #NumPy, #SciPy, #machinelearningLong-tail: #Pythoncodeacceleration, #AIoptimizedlibraries, #LLMcodeperformanceTrending: #LLMoptimization, #AIinDev, #futureofcodingRead more: https://arxiv.org/abs/2507.15887

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All episodes

Urgent!! Claude 4.5: The Truth About 30 Hours and Code

Openai. AI vs Experts: The Truth Behind the GDP Benchmark

Google. The Future of Robots: Thinking, Learning, and Reasoning

Arxiv. When Data Becomes Pricier Than Compute: The New AI Era

Arxiv. Small Batches, Big Shift in LLM Training

DeepSeek. Secrets of Smart LLMs: How Small Models Beat Giants

Arxiv. The Grain of Truth: How Reflective Oracles Change the Game

Arxiv. Seed 1.5 Thinking: The AI That Learns to Reason

Why Even the Best AIs Still Fail at Math

Can AI Beat NumPy? Algotune Reveals the Truth