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“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like...

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Forward Future

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23,748 görüntüleme • 4 ay önce •via X (Twitter)

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I added the most requested feature to AI Code Translator… Natural language to/from code! You can now: - Use natural language to generate code. - Get natural language explanations from code. Try it: GitHub:
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I added the most requested feature to AI Code Translator… Natural language to/from code! You can now: - Use natural language to generate code. - Get natural language explanations from code. Try it: GitHub:

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Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”
0:56

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

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AI Is Replacing Programmers and Mathematicians—Fast Eric Schmidt predicts that within one year, the majority of programmers will be replaced by AI agents, and graduate-level mathematicians will be rivaled by models trained on mathematical reasoning. Why? Because both code and math follow structured languages, which AI can learn to manipulate with extreme precision using techniques like word prediction and proof systems like Lean.
2:27

AI Is Replacing Programmers and Mathematicians—Fast Eric Schmidt predicts that within one year, the majority of programmers will be replaced by AI agents, and graduate-level mathematicians will be rivaled by models trained on mathematical reasoning. Why? Because both code and math follow structured languages, which AI can learn to manipulate with extreme precision using techniques like word prediction and proof systems like Lean.

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👋 Meet Duet AI for Developers! Start writing code at the speed of thought with #AI code completion, code generation, and a chatbot where you can ask any code-related questions in natural language. ✨ Try it today at no cost →
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👋 Meet Duet AI for Developers! Start writing code at the speed of thought with #AI code completion, code generation, and a chatbot where you can ask any code-related questions in natural language. ✨ Try it today at no cost →

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In Cursor you can *easily* turn comments into code. Just write in natural language what you want in your file. Then highlight it, use CMD+K, and ask the AI to translate it into code - and it will! Turn your thoughts right into apps.
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In Cursor you can *easily* turn comments into code. Just write in natural language what you want in your file. Then highlight it, use CMD+K, and ask the AI to translate it into code - and it will! Turn your thoughts right into apps.

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Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"
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Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Haider.

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i really enjoy 3Blue1Brown's math animations. so i built a tool that converts natural language into math animations using the manim engine and gpt-4o. now you can create your own math animations! give it a try using the link below.
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i really enjoy 3Blue1Brown's math animations. so i built a tool that converts natural language into math animations using the manim engine and gpt-4o. now you can create your own math animations! give it a try using the link below.

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Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.
3:25

Reviewing AI-generated code is the new bottleneck. Writing code was slow before. Now, in a few minutes, you can generate virtually unlimited lines of code using AI. But developers are now spending 70% of their time checking that code. That's the new bottleneck. I've been using Cline, and they have a feature that actually helps with this: You can click a button and have Cline generate an inline explanation for every single line of AI-generated code. Take a look at the attached video. What it does: 1. You can read the code and the explanation together 2. You don't lose context while reviewing code 3. You understand the "why" behind each change And here is the best part: It works on any git diff. You can use it to review PRs, understand commits from other people, or catch up on a codebase you haven't touched in a while.

Santiago

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Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:
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Nobody is writing 90% of their code using AI. Here's the uncomfortable truth: The real productivity gain from using AI to write code is closer to 10%, nowhere near the 90% people claim. Sundar Pichai said in 2024 that 30% of the new code at Google was AI-generated. However, he went on to admit, during Lex Friedman's podcast, that engineering velocity had only increased by about 10%. AI-generated code isn't free code. It still has to be reviewed, tested, and made production-ready. Optimizing a single step (code generation) doesn't boost output if bottlenecks shift elsewhere (code reviews). It doesn't matter how much code you generate if you can't keep up the review process. The solution: Automate as much as you can the review and verification of your code. I'm working with Sonar, who is sponsoring this post, and they will take care of the code quality and security analysis of your code: • They review over 300B lines of code every single day • They cover reliability, security, and maintainability for your code • You can integrate them into your CI/CD pipeline • You can install them in your IDE (I use their VSCode extension) • Support for more than 30 languages Here is a link so you can check them out:

Santiago

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Good news for devs working in Visual Studio Code! The MongoDB MCP Server connects your MongoDB data to AI clients, such as GitHub Copilot in VS Code. Now you can ask for collection schemas and data samples, manage users and access, and generate queries all using natural language, right from your editor. Give it a try 👉
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Good news for devs working in Visual Studio Code! The MongoDB MCP Server connects your MongoDB data to AI clients, such as GitHub Copilot in VS Code. Now you can ask for collection schemas and data samples, manage users and access, and generate queries all using natural language, right from your editor. Give it a try 👉

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🚨 UNCANNY VALLEY: LANGUAGE MODEL TECH IS NOW DRIVING AI THAT CAN SEE AND REASON TOO Swupnil Sahai 🎾🧠: “The new architecture that’s come out for these language models works really well for vision now too. So the kind of math you can use to go from that input to be output is becoming the same now. We’re actually using much lighter weight models. So it’s a slightly different architecture, but it’s the same concept.”
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🚨 UNCANNY VALLEY: LANGUAGE MODEL TECH IS NOW DRIVING AI THAT CAN SEE AND REASON TOO Swupnil Sahai 🎾🧠: “The new architecture that’s come out for these language models works really well for vision now too. So the kind of math you can use to go from that input to be output is becoming the same now. We’re actually using much lighter weight models. So it’s a slightly different architecture, but it’s the same concept.”

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Natural language instruction PLUS deterministic execution is the real unlock for AI agents that work consistently. With Headless 360, you can build these agents with the tools you already use: Claude Code, Cursor, Codex, Windsurf, Agentforce Vibes, Agentforce builder, and more.

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Cursor CEO Michael Truell on the future of writing code: “ Our goal with Cursor is to invent a new type of programming.” “It looks like a world where you have a representation of the logic of your software that does look more like English.” “You can imagine kind of an evolution of programming language towards pseudocode. You have written down the logic of the software, and you can edit that at a high level.” “It won't be the impenetrable millions of lines of code, it'll instead be something that's much terser and easier to understand and easier to navigate.” Source: Michael Truell (CEO Cursor) with Lenny Rachitsky on Lenny's Podcast
2:50

Cursor CEO Michael Truell on the future of writing code: “ Our goal with Cursor is to invent a new type of programming.” “It looks like a world where you have a representation of the logic of your software that does look more like English.” “You can imagine kind of an evolution of programming language towards pseudocode. You have written down the logic of the software, and you can edit that at a high level.” “It won't be the impenetrable millions of lines of code, it'll instead be something that's much terser and easier to understand and easier to navigate.” Source: Michael Truell (CEO Cursor) with Lenny Rachitsky on Lenny's Podcast

a16z

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prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding
0:57

prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

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How do you actually formally verify the code underpinning Ethereum's future? In this episode (the finale of the lean Ethereum miniseries), Nico sits down with Alex Hicks (Alexander Hicks), lead of Protocol Snarkification at the Ethereum Foundation, to break down formal verification from first principles. They cover: – What formal verification actually is and the trust boundaries between proof assistants, SMT solvers, and kernels – The full verification stack for RISC-V ZKVMs: from SAIL specs to constraint extraction to soundness proofs – Why writing constraints directly in Lean makes proofs 10–100x more ergonomic – How AI is now proving hard theorems in hours for $200 — and what that unlocks for the whole pipeline They also explore the boundaries problem, why specs can have bugs too, and the end goal of a full Lean stack that bypasses Rust and LLVM entirely. Listen to the full episode ------------------------------------------------------------ TIMECODES: 09:16 – What is formal verification? Proof assistants vs SMT solvers 18:33 – Formal verification of code: specs, semantics, and trust boundaries 29:30 – Formally verifying the Lean Ethereum stack: RISC-V ZKVMs in focus 33:02 – Extracting ZKVM constraints into Lean and proving soundness 36:35 – Writing constraints directly in Lean: 10–100x better proof ergonomics 44:02 – Proving Polishchuk–Spielman in 8 hours for $200 with AI 51:01 – The end goal: a full Lean stack bypassing Rust and LLVM
56:07

How do you actually formally verify the code underpinning Ethereum's future? In this episode (the finale of the lean Ethereum miniseries), Nico sits down with Alex Hicks (Alexander Hicks), lead of Protocol Snarkification at the Ethereum Foundation, to break down formal verification from first principles. They cover: – What formal verification actually is and the trust boundaries between proof assistants, SMT solvers, and kernels – The full verification stack for RISC-V ZKVMs: from SAIL specs to constraint extraction to soundness proofs – Why writing constraints directly in Lean makes proofs 10–100x more ergonomic – How AI is now proving hard theorems in hours for $200 — and what that unlocks for the whole pipeline They also explore the boundaries problem, why specs can have bugs too, and the end goal of a full Lean stack that bypasses Rust and LLVM entirely. Listen to the full episode ------------------------------------------------------------ TIMECODES: 09:16 – What is formal verification? Proof assistants vs SMT solvers 18:33 – Formal verification of code: specs, semantics, and trust boundaries 29:30 – Formally verifying the Lean Ethereum stack: RISC-V ZKVMs in focus 33:02 – Extracting ZKVM constraints into Lean and proving soundness 36:35 – Writing constraints directly in Lean: 10–100x better proof ergonomics 44:02 – Proving Polishchuk–Spielman in 8 hours for $200 with AI 51:01 – The end goal: a full Lean stack bypassing Rust and LLVM

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This is MicroCAD. The programming language for your 3D projects. You can design your own Lego block in a few lines of code, ready to be exported as an STL file for 3D printing.
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This is MicroCAD. The programming language for your 3D projects. You can design your own Lego block in a few lines of code, ready to be exported as an STL file for 3D printing.

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Cursor CEO Michael Truell on the future of writing code: "Our goal with Cursor is to invent a new type of programming." "It looks like a world where you have a representation of the logic of your software that does look more like English." "You can imagine kind of an evolution of programming language towards pseudocode. You have written down the logic of the software, and you can edit that at a high level." "It won't be the impenetrable millions of lines of code, it'll instead be something that's much terser and easier to understand and easier to navigate." Michael Truell with Lenny Rachitsky on Lenny's Podcast
2:50

Cursor CEO Michael Truell on the future of writing code: "Our goal with Cursor is to invent a new type of programming." "It looks like a world where you have a representation of the logic of your software that does look more like English." "You can imagine kind of an evolution of programming language towards pseudocode. You have written down the logic of the software, and you can edit that at a high level." "It won't be the impenetrable millions of lines of code, it'll instead be something that's much terser and easier to understand and easier to navigate." Michael Truell with Lenny Rachitsky on Lenny's Podcast

a16z

979,430 görüntüleme • 2 gün önce

If AI can code 100x faster, why aren't you shipping 100x faster? Because AI code is not production-ready code, and definitely not code where you understand and can vouch for every line Introducing the Command Center alpha. Support our Product Hunt launch!
0:22

If AI can code 100x faster, why aren't you shipping 100x faster? Because AI code is not production-ready code, and definitely not code where you understand and can vouch for every line Introducing the Command Center alpha. Support our Product Hunt launch!

Jimmy Koppel

72,259 görüntüleme • 7 ay önce

Earlier today we released local development for Kaggle Benchmarks. 🚀 You can now write, validate and run AI evaluation tasks directly from your preferred dev environment — VSCode, Antigravity, Claude Code, and more. Go from idea to working eval using natural language with the write-kaggle-benchmarks skill.
4:39

Earlier today we released local development for Kaggle Benchmarks. 🚀 You can now write, validate and run AI evaluation tasks directly from your preferred dev environment — VSCode, Antigravity, Claude Code, and more. Go from idea to working eval using natural language with the write-kaggle-benchmarks skill.

Kaggle

16,240 görüntüleme • 14 gün önce