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🐣 Introducing `smol-developer`! ▸ Human-centric, coherent whole program synthesis ▸ your own junior developer ▸ develop, debug, decompile ▸ open source: ▸ 200 LOC, half english Insights: 💡 100k context can summarize both content and codebases 💡 Markdown is the best prompting DSL 💡 Copy and paste your errors...

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swyx 🇸🇬

177,657 subscribers

1,590,947 Aufrufe • vor 3 Jahren •via X (Twitter)

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Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

uploaded full resolution onto youtube for those who prefer non potato quality

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

another more serious run at this problem

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

We've just added a prompt gallery, thanks to users sending in more examples! thanks to @ultrasoundchad, @RobertCaracaus, @rodavelli, @atlantis2point0 for all the feedback, and @talboren and @thatliuser for adding local options!

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

ported to JS by @picocreator!

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

ported to C# by @colhountech!

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

ported to Go by @traviscline!

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

smol-developer projects are trending on both HN and YouTube today

Profilbild von PicoCreator - AI Model Builder 🌉
PicoCreator - AI Model Builder 🌉vor 3 Jahren

you had to lit a fire in me, to build on this for the next level ✊🔥🔥🔥 complete rewrite has begun for targeting the 8k context size (since most folks would not have 32k/claude access)

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

thanks for all the support! this project now has more github stars than lines of python as a wise Slovakian-Canadian Youtube influencer once said, English is the hottest new programming language

Profilbild von swyx 🔜 @aidotEngineer NYC
swyx 🔜 @aidotEngineer NYCvor 3 Jahren

first mention by a conference speaker

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PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:
0:53

PhD Students – How to automatically identify 90% of the issues in your research paper before you submit it to a journal? This is possible through manual or automated paper review. First, let’s understand the following. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰? Paper review is a process in which subject matter experts evaluate your paper based on the following criteria: 1. Significance – Is this research important? 2. Novelty – Is this research new? 3. Methodology – Is this research carried out in the correct way? 4. Verifiability – Can other researchers verify this research? 5. Presentation – Is the research presented in the right way? 𝐖𝐡𝐲 𝐭𝐨 𝐡𝐚𝐯𝐞 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐮𝐛𝐦𝐢𝐬𝐬𝐢𝐨𝐧? ➟ Identify the critical issues in your paper ➟ Fix those issues to increase the chances of your paper acceptance 𝐇𝐨𝐰 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 “𝐬𝐞𝐥𝐟-𝐫𝐞𝐯𝐢𝐞𝐰” 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐩𝐚𝐩𝐞𝐫? Paperpal just launched an amazing feature – AI Review. Using this feature, you can get instant self-feedback. This feature will help you in the following ways. ➝ Check for gaps in your logic ➝ Get feedback on the structure and flow of your writing ➝ Review your research questions ➝ Identify opportunities to strengthen your paper ➝ Increase the chances of your paper acceptance Here is a step-by-step process for using AI Review feature. Step 1: Go to and login. Step 2: Open an existing document or make a new document Step 3: Go to the right-side bar and click on checks | AI Review. Step 4: For this feature to work there should be more than 150 words. Step 5: Copy and paste your paper. Step 6: Now go to the right side and check the prompts Step 7: With these prompts, you will evaluate your paper. Step 8: You will find various prompts e.g., suggest writing feedback, check flow and structure etc. Step 9: You can select a prompt from the existing prompts or write your custom prompt and execute Step 10: Paperpal will generate feedback as per the prompt. Step 11: Read through the feedback and save it for further use. Use other specific prompts for tailored feedback. Step 12: This way you can evaluate various aspects of your paper yourself. This is a very customized and efficient way of automatically reviewing your paper. You can also go one step further to work on the feedback and improve your paper based on suggestions. Please note that AI Review feature does not replace human or expert reviewers in any way. This feature only aims to provide you with quick self-feedback. Try the AI Review feature of Paperpal. Paperpal link:

Faheem Ullah

15,270 Aufrufe • vor 1 Jahr

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.
25:52

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.

JJ Englert

29,322 Aufrufe • vor 4 Monaten

Claymotion ads are crushing it on Meta right now. Built a free claude skill to make them 👇 If you've been scrolling Meta lately, you've seen them — stop-motion clay characters, tactile textures, weirdly satisfying to watch. CTRs are 2-3x the feed average. Almost nobody is running them. The problem: they look impossible to make unless you have a studio. They're not. You just need the right prompts. So I packaged the prompt system as a Claude Code skill. It's free. Here's what it does: Paste your product URL. Out comes a full claymotion ad plan: 1/ Shot-by-shot storyboard 5-7 shots with the narrative arc. Setup → product reveal → payoff → CTA. 2/ Image prompt per shot Exact prompt you paste into Midjourney, Nano Banana, or any image gen. Camera angle, lighting, clay texture specs, character details — dialed in for consistency across shots. 3/ Video prompt per shot The animation prompt you paste into Kling, Veo, Seedance, or Sora. Motion direction, pacing, transitions — so the shots actually flow. 4/ VO script per shot Voiceover copy written for rhythm. Timed to the shot length. Hook, body, CTA — all on brand. 5/ Music + sfx direction Tone notes for the track. Specific sfx cues per shot (squish, pop, whoosh) You take the outputs. Paste them into your image + video generators. Stitch the shots. Record the VO. A full claymotion ad in under an hour, at the cost of a few API credits. Instead of $3,000 and 3 weeks with an animation studio. Why claymotion works right now: → Pattern break — nothing else in the feed looks like it → Tactile feel — clay reads as "real" even when AI-generated → High dwell time — people watch the whole thing → Cheap to test — 5-10 variations per product is now feasible Comment "Clay" and I'll send you: → The Claude Code skill (free) → A starter prompt pack → 3 example storyboards so you can see the output (must be connected)
0:52

Claymotion ads are crushing it on Meta right now. Built a free claude skill to make them 👇 If you've been scrolling Meta lately, you've seen them — stop-motion clay characters, tactile textures, weirdly satisfying to watch. CTRs are 2-3x the feed average. Almost nobody is running them. The problem: they look impossible to make unless you have a studio. They're not. You just need the right prompts. So I packaged the prompt system as a Claude Code skill. It's free. Here's what it does: Paste your product URL. Out comes a full claymotion ad plan: 1/ Shot-by-shot storyboard 5-7 shots with the narrative arc. Setup → product reveal → payoff → CTA. 2/ Image prompt per shot Exact prompt you paste into Midjourney, Nano Banana, or any image gen. Camera angle, lighting, clay texture specs, character details — dialed in for consistency across shots. 3/ Video prompt per shot The animation prompt you paste into Kling, Veo, Seedance, or Sora. Motion direction, pacing, transitions — so the shots actually flow. 4/ VO script per shot Voiceover copy written for rhythm. Timed to the shot length. Hook, body, CTA — all on brand. 5/ Music + sfx direction Tone notes for the track. Specific sfx cues per shot (squish, pop, whoosh) You take the outputs. Paste them into your image + video generators. Stitch the shots. Record the VO. A full claymotion ad in under an hour, at the cost of a few API credits. Instead of $3,000 and 3 weeks with an animation studio. Why claymotion works right now: → Pattern break — nothing else in the feed looks like it → Tactile feel — clay reads as "real" even when AI-generated → High dwell time — people watch the whole thing → Cheap to test — 5-10 variations per product is now feasible Comment "Clay" and I'll send you: → The Claude Code skill (free) → A starter prompt pack → 3 example storyboards so you can see the output (must be connected)

Ahad Shams

16,779 Aufrufe • vor 3 Monaten

🚨🌟 Watch Part 1 Of The EstateX Vlog Series! 🌟🚨 The wait is over, EstateX Family! We’re beyond excited to announce the release of the very first part of our brand-new EstateX Vlog Series, starring none other than Bart de Bruijn & Steve! 🎥🤴🤴 In this 5-part series, you’ll get a front-row seat to the world of real estate #tokenization, #blockchain, and the exciting EstateX journey. The first episode takes you behind the scenes in Dubai as Bart and Steve tour the luxurious Neptune floating villa—an exclusive property that can soon be fractionalized on the #EstateX platform! 🏝️ What to expect in Part 1: • A deep dive into a truly unique real estate opportunity—the Neptune yacht villa 🌊 • Insights from Bart & Steve on how EstateX is revolutionizing luxury real estate investments 🏘️ • A whole lot of fun, laughs, and inspiration along the way! 😄 💡 Why should you watch? This series isn’t just about cool properties—it’s about how EstateX is changing the game for real estate investing. You’ll see firsthand how we diversify our portfolio, offering unparalleled opportunities, and how we’re getting closer to our goal of making real estate investing as simple as online shopping. As you can see, we’re starting slowly our marketing campaign with KOLs posting and our promised high quality marketing content. 🗓️ More to come! This is just the beginning—4 more parts of this vlog series are coming your way! Stay tuned for exclusive content, behind-the-scenes moments, and sneak peeks that will keep you on the edge of your seat. Trust us, you won’t want to miss a single episode! 🎬 Watch Part 1 now, and stay tuned for more updates from the EstateX Vlog Series. Onwards and upwards, EstateX Fam! 🚀💫 #RWA
3:21

🚨🌟 Watch Part 1 Of The EstateX Vlog Series! 🌟🚨 The wait is over, EstateX Family! We’re beyond excited to announce the release of the very first part of our brand-new EstateX Vlog Series, starring none other than Bart de Bruijn & Steve! 🎥🤴🤴 In this 5-part series, you’ll get a front-row seat to the world of real estate #tokenization, #blockchain, and the exciting EstateX journey. The first episode takes you behind the scenes in Dubai as Bart and Steve tour the luxurious Neptune floating villa—an exclusive property that can soon be fractionalized on the #EstateX platform! 🏝️ What to expect in Part 1: • A deep dive into a truly unique real estate opportunity—the Neptune yacht villa 🌊 • Insights from Bart & Steve on how EstateX is revolutionizing luxury real estate investments 🏘️ • A whole lot of fun, laughs, and inspiration along the way! 😄 💡 Why should you watch? This series isn’t just about cool properties—it’s about how EstateX is changing the game for real estate investing. You’ll see firsthand how we diversify our portfolio, offering unparalleled opportunities, and how we’re getting closer to our goal of making real estate investing as simple as online shopping. As you can see, we’re starting slowly our marketing campaign with KOLs posting and our promised high quality marketing content. 🗓️ More to come! This is just the beginning—4 more parts of this vlog series are coming your way! Stay tuned for exclusive content, behind-the-scenes moments, and sneak peeks that will keep you on the edge of your seat. Trust us, you won’t want to miss a single episode! 🎬 Watch Part 1 now, and stay tuned for more updates from the EstateX Vlog Series. Onwards and upwards, EstateX Fam! 🚀💫 #RWA

EstateX

108,612 Aufrufe • vor 1 Jahr

🤖🔬 Can AI actually do science end-to-end? 🧠📈 And how would we know when it matches, or surpasses, humans? ⚡🧪 AI is rapidly automating scientific discovery, but benchmarking full-cycle discovery, from 💡 ideation → 🧑‍💻 execution → 📊 conclusions, remains unsolved: 🧐🧐🧐 ❌🛠️ Open-ended discovery → manual validation (costly, unscalable) ❌📏 Metric-driven benchmarks (e.g., MLE-Bench) → convenient but narrow (is higher accuracy really enough?) ❌🤖⚖️ LLM-as-judge → useful, but fundamentally risky if used alone 🔥🚀 Introducing FIRE-Bench🔥: Fullcycle Insight Rediscovery Evaluation 👉🌐 📚✨ A benchmark that turns fresh, human-verified insights from recent 🏆 NeurIPS / ICLR / ICML papers into masked, end-to-end discovery challenges 🧩 🌍🔐 Constrained open-ended discovery–backed by ground truth. 📌 Key takeaways: 1⃣ 📖🧱 Reference-based evaluation still matters: constrained LLM judging helps, but human-grounded references remain essential until agents can consistently match human conclusions 2⃣ 🏆🧠 Expert-validated ground truth: all tasks come from recent NeurIPS / ICLR / ICML papers, with contamination carefully controlled 3⃣ 🔁🎭 Rediscovery, not reproduction: original 🧪 methods, 📊 experiments, 💻 implementations, and 📈 analyses are fully masked to create real discovery challenges 🔑 Key empirical findings: 💡 The "Science Gap" is Real: Even the best setup (Claude Code + Sonnet-4) caps out at an F1 score of 46.7. On hard tasks, agents struggle to break 30 💡 Success is a "Lottery": Performance has incredibly high variance. Reliability is a major unsolved issue. 💡 Coding is no longer the bottleneck; high-level reasoning and analysis are: ~74% of errors stem from flawed planning, not coding ⚙️ How it works: 🔹 Research-Problem Trees: We parse papers into trees (from broad roots to concrete leaves). This allows us to select intermediate nodes that perfectly balance open-ended exploration with verifiable ground truth. 🔹 Claim-Level Evaluation: We match AI conclusions against human conclusions using granular claim decomposition (F1 score). 🔹 Creativity Check: We score false positives to see if agents are finding novel truths (Spoiler🚨: they aren’t creative yet). 🔹 New Diagnostic Taxonomy: failures traced across four stages: 🧠 Planning → 🛠️ Implementation → ▶️ Execution → 🧾 Conclusion 🔹 Additional Analyses: cost efficiency, contamination checks, and more. 👀 The Future: 🚀 Live-FIRE-Bench: a live, continuously updated FIRE-Bench to track real-time progress on the latest research (Newest LLMs should be benchmarked with the newest research) 🚀 Stronger scaffolding (search + planning + coding) 🧠🧰 and converting FIRE-Bench into interactive environments for training research agents 🚀 Toward real creativity: We want better systems that can produce genuinely novel conclusions toward creativity 🎨⏳ 🚀 Better systems 🧠✨ and better benchmarks 📏 must co-evolve 🔄 over time 📜🎥 Paper, video, demo, and research trees: 👉🌐 #AI 🤖 #MachineLearning 📚 #AI4Science 🔬 #LLMs 🧠 #Research 🧪 #AgenticAI 🚀 #FireBench 🔥
0:42

🤖🔬 Can AI actually do science end-to-end? 🧠📈 And how would we know when it matches, or surpasses, humans? ⚡🧪 AI is rapidly automating scientific discovery, but benchmarking full-cycle discovery, from 💡 ideation → 🧑‍💻 execution → 📊 conclusions, remains unsolved: 🧐🧐🧐 ❌🛠️ Open-ended discovery → manual validation (costly, unscalable) ❌📏 Metric-driven benchmarks (e.g., MLE-Bench) → convenient but narrow (is higher accuracy really enough?) ❌🤖⚖️ LLM-as-judge → useful, but fundamentally risky if used alone 🔥🚀 Introducing FIRE-Bench🔥: Fullcycle Insight Rediscovery Evaluation 👉🌐 📚✨ A benchmark that turns fresh, human-verified insights from recent 🏆 NeurIPS / ICLR / ICML papers into masked, end-to-end discovery challenges 🧩 🌍🔐 Constrained open-ended discovery–backed by ground truth. 📌 Key takeaways: 1⃣ 📖🧱 Reference-based evaluation still matters: constrained LLM judging helps, but human-grounded references remain essential until agents can consistently match human conclusions 2⃣ 🏆🧠 Expert-validated ground truth: all tasks come from recent NeurIPS / ICLR / ICML papers, with contamination carefully controlled 3⃣ 🔁🎭 Rediscovery, not reproduction: original 🧪 methods, 📊 experiments, 💻 implementations, and 📈 analyses are fully masked to create real discovery challenges 🔑 Key empirical findings: 💡 The "Science Gap" is Real: Even the best setup (Claude Code + Sonnet-4) caps out at an F1 score of 46.7. On hard tasks, agents struggle to break 30 💡 Success is a "Lottery": Performance has incredibly high variance. Reliability is a major unsolved issue. 💡 Coding is no longer the bottleneck; high-level reasoning and analysis are: ~74% of errors stem from flawed planning, not coding ⚙️ How it works: 🔹 Research-Problem Trees: We parse papers into trees (from broad roots to concrete leaves). This allows us to select intermediate nodes that perfectly balance open-ended exploration with verifiable ground truth. 🔹 Claim-Level Evaluation: We match AI conclusions against human conclusions using granular claim decomposition (F1 score). 🔹 Creativity Check: We score false positives to see if agents are finding novel truths (Spoiler🚨: they aren’t creative yet). 🔹 New Diagnostic Taxonomy: failures traced across four stages: 🧠 Planning → 🛠️ Implementation → ▶️ Execution → 🧾 Conclusion 🔹 Additional Analyses: cost efficiency, contamination checks, and more. 👀 The Future: 🚀 Live-FIRE-Bench: a live, continuously updated FIRE-Bench to track real-time progress on the latest research (Newest LLMs should be benchmarked with the newest research) 🚀 Stronger scaffolding (search + planning + coding) 🧠🧰 and converting FIRE-Bench into interactive environments for training research agents 🚀 Toward real creativity: We want better systems that can produce genuinely novel conclusions toward creativity 🎨⏳ 🚀 Better systems 🧠✨ and better benchmarks 📏 must co-evolve 🔄 over time 📜🎥 Paper, video, demo, and research trees: 👉🌐 #AI 🤖 #MachineLearning 📚 #AI4Science 🔬 #LLMs 🧠 #Research 🧪 #AgenticAI 🚀 #FireBench 🔥

Zhen Wang

13,450 Aufrufe • vor 6 Monaten

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:
2:10

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

752,508 Aufrufe • vor 1 Jahr

This is where most projects get GROWTH HACKING WRONG. p.s. passive virality triumphs over forced shilling every damn time. The most effective viral product hooks aren’t the ones that demand users to promote a brand. They’re the ones that empower users to share something about themselves while naturally redirecting attention back to the company. This is where most projects get growth hacking wrong. They assume virality means pushing users to evangelize their product aggressively. But in reality, people don’t want to feel like unpaid marketers. Instead, the strongest viral loops are user-centric, where sharing becomes a personal flex rather than an obligation. Why Passive Virality Outperforms Active Promotion Forced Shilling = Fatigue & Drop-off • “Retweet this to get whitelisted!” • “Quote this with why you love our product!” • “Tell your friends to sign up for X reward!” Users see right through these tactics. They feel transactional, forced, and exhausting. Passive Virality = Natural Growth The best campaigns flip the equation: • Instead of “talk about us”, they ask, “talk about YOU.” • Instead of forcing an endorsement, they reveal insights about the user that they want to share. • The brand wins by association, not by aggressive self-promotion. Case Studies of Smart Passive Virality InfiniGods – Web3 Gaming Asset Score Users connected their wallets → The platform calculated their total gaming asset score → It was displayed on a global leaderboard. • Users flexed their rankings on Twitter to show off their dominance in Web3 gaming. • This redirected attention back to InfiniGods, positioning them as the gaming ecosystem of record. Kaito – Social Capital Insights Users connected their Twitter/X account → The app mapped out their social influence and network → It generated a visual social capital score. • Users shared it as a personalized flex (“Look at my network strength in Web3!”) • This indirectly marketed Kaito as the best platform for Web3 networking analytics. How to Engineer Viral Growth the Right Way Let the user talk about themselves. 💡 Example: Instead of “Share why you love our app”, make it “See how strong your [X] score is.” Create a sense of exclusivity. 💡 Example: Instead of “Join this quest”, make it “See how early you are compared to others.” Tie the experience to an identity flex. 💡 Example: Instead of “Earn 10 points”, make it “Achieve OG status with this score.”
2:52

This is where most projects get GROWTH HACKING WRONG. p.s. passive virality triumphs over forced shilling every damn time. The most effective viral product hooks aren’t the ones that demand users to promote a brand. They’re the ones that empower users to share something about themselves while naturally redirecting attention back to the company. This is where most projects get growth hacking wrong. They assume virality means pushing users to evangelize their product aggressively. But in reality, people don’t want to feel like unpaid marketers. Instead, the strongest viral loops are user-centric, where sharing becomes a personal flex rather than an obligation. Why Passive Virality Outperforms Active Promotion Forced Shilling = Fatigue & Drop-off • “Retweet this to get whitelisted!” • “Quote this with why you love our product!” • “Tell your friends to sign up for X reward!” Users see right through these tactics. They feel transactional, forced, and exhausting. Passive Virality = Natural Growth The best campaigns flip the equation: • Instead of “talk about us”, they ask, “talk about YOU.” • Instead of forcing an endorsement, they reveal insights about the user that they want to share. • The brand wins by association, not by aggressive self-promotion. Case Studies of Smart Passive Virality InfiniGods – Web3 Gaming Asset Score Users connected their wallets → The platform calculated their total gaming asset score → It was displayed on a global leaderboard. • Users flexed their rankings on Twitter to show off their dominance in Web3 gaming. • This redirected attention back to InfiniGods, positioning them as the gaming ecosystem of record. Kaito – Social Capital Insights Users connected their Twitter/X account → The app mapped out their social influence and network → It generated a visual social capital score. • Users shared it as a personalized flex (“Look at my network strength in Web3!”) • This indirectly marketed Kaito as the best platform for Web3 networking analytics. How to Engineer Viral Growth the Right Way Let the user talk about themselves. 💡 Example: Instead of “Share why you love our app”, make it “See how strong your [X] score is.” Create a sense of exclusivity. 💡 Example: Instead of “Join this quest”, make it “See how early you are compared to others.” Tie the experience to an identity flex. 💡 Example: Instead of “Earn 10 points”, make it “Achieve OG status with this score.”

Ruthy | Surgence Labs

15,001 Aufrufe • vor 1 Jahr

Mean Green Nation, I caught up with Mean Green Sports & the best Athletic Director in the land, Jared Mosley last week & when I asked him the million dollar💵 question of what we, as fans, can do to help, he mentioned that one of their big goals is reaching 1️⃣0️⃣0️⃣ MGAF donors at the $10,000 level or above before the end of the year. We currently sit at 6️⃣5️⃣, but I’m jumping in and pledging $10,000 to be number 6️⃣6️⃣. I’m also challenging everyone that’s been wondering how they can help our program, to do the same thing, by giving what you can💵, to what you’re passionate💚 about.. Your 9️⃣-1️⃣ Mean Green need our support & there’s never been a better time to help. AND, for for those that join me in my pledge, the Athletic Department is throwing in a pair of club level tickets🎟️ to the final home game of the season vs Temple on 11/28, on top of all the other awesome benefits! Remember, your season tickets, your merchandise purchases, and your financial support of the Mean Green Athletic Fund & the Green Light Fund what allows Athletic Director Jared Mosley , General Manager Raj Murti & Mean Green Sports to compete off the field, for the talent that helps us continue to soar to new heights on the field.🦅 I’ll see you around at DATCU and the Super Pit, and as always! 💚🖤Go Mean Green!🖤💚🦅 Make your pledge to the Mean Green Athletic Fund - Buy 🎟️ for Temple on 11/28 - 🟢💡Donate to the Green Light Fund:
1:06

Mean Green Nation, I caught up with Mean Green Sports & the best Athletic Director in the land, Jared Mosley last week & when I asked him the million dollar💵 question of what we, as fans, can do to help, he mentioned that one of their big goals is reaching 1️⃣0️⃣0️⃣ MGAF donors at the $10,000 level or above before the end of the year. We currently sit at 6️⃣5️⃣, but I’m jumping in and pledging $10,000 to be number 6️⃣6️⃣. I’m also challenging everyone that’s been wondering how they can help our program, to do the same thing, by giving what you can💵, to what you’re passionate💚 about.. Your 9️⃣-1️⃣ Mean Green need our support & there’s never been a better time to help. AND, for for those that join me in my pledge, the Athletic Department is throwing in a pair of club level tickets🎟️ to the final home game of the season vs Temple on 11/28, on top of all the other awesome benefits! Remember, your season tickets, your merchandise purchases, and your financial support of the Mean Green Athletic Fund & the Green Light Fund what allows Athletic Director Jared Mosley , General Manager Raj Murti & Mean Green Sports to compete off the field, for the talent that helps us continue to soar to new heights on the field.🦅 I’ll see you around at DATCU and the Super Pit, and as always! 💚🖤Go Mean Green!🖤💚🦅 Make your pledge to the Mean Green Athletic Fund - Buy 🎟️ for Temple on 11/28 - 🟢💡Donate to the Green Light Fund:

#24🦅 Damon Gochneaur

14,739 Aufrufe • vor 8 Monaten

I built a Claude skill that turns Claude Code into your personal coding tutor. The core insight: Claude Opus 4.5 is already the best tutor in the world. Anthropic cooked with this model! It has incredible emotional intelligence and deep coding knowledge. What this skill does is just provide a harness—a way for Claude to agentically build the right context about YOU so it can personalize the tutoring experience in exactly the right way. Here's what makes it work: Learner profile from day one. The first time you use it, Claude interviews you. It asks about your programming background, your goal (where do you want this to take you?), and who you are as a person. This gets saved and informs every single tutorial it ever writes for you. From the very first interaction, everything is 100% personalized. Tutorials that use YOUR code. When you ask to learn something, Claude doesn't give you generic examples from some blog post. It finds examples in the actual codebase you're working in. This makes concepts stick in a way abstract examples never do. Quiz mode with spaced repetition. You can run "/quiz-me" and Claude will test you on concepts you've learned. It tracks your understanding score for each tutorial. Then it uses spaced repetition to prioritize the next quiz—concepts you're shaky on come back in 2 days, concepts you've mastered fade to 55+ day intervals. It literally builds retention into the learning process. One central knowledge base across all your projects. Whether you're joining a new company and want to understand their codebase, learning from an open source project, or leveling up on your own vibe-coded project—all your tutorials live in one place (~/coding-tutor-tutorials/). So your personal coding-tutor accompanies you across all your coding adventures. The whole thing is a feedback loop: learn → quiz → retain → learn more → quiz → retain. Your tutorials evolve, your knowledge compounds, and Claude gets better at teaching YOU specifically over time. To install it in Claude Code: • Run /plugin to open the plugin manager • Add marketplace nityeshaga/claude-code-essentials • Enable coding-tutor plugin Here's the Github: And here's 20-mins of me walking you through how to use this plugin and how it works 👇🏽 Let me know if you use it to teach yourself something cool!
21:41

I built a Claude skill that turns Claude Code into your personal coding tutor. The core insight: Claude Opus 4.5 is already the best tutor in the world. Anthropic cooked with this model! It has incredible emotional intelligence and deep coding knowledge. What this skill does is just provide a harness—a way for Claude to agentically build the right context about YOU so it can personalize the tutoring experience in exactly the right way. Here's what makes it work: Learner profile from day one. The first time you use it, Claude interviews you. It asks about your programming background, your goal (where do you want this to take you?), and who you are as a person. This gets saved and informs every single tutorial it ever writes for you. From the very first interaction, everything is 100% personalized. Tutorials that use YOUR code. When you ask to learn something, Claude doesn't give you generic examples from some blog post. It finds examples in the actual codebase you're working in. This makes concepts stick in a way abstract examples never do. Quiz mode with spaced repetition. You can run "/quiz-me" and Claude will test you on concepts you've learned. It tracks your understanding score for each tutorial. Then it uses spaced repetition to prioritize the next quiz—concepts you're shaky on come back in 2 days, concepts you've mastered fade to 55+ day intervals. It literally builds retention into the learning process. One central knowledge base across all your projects. Whether you're joining a new company and want to understand their codebase, learning from an open source project, or leveling up on your own vibe-coded project—all your tutorials live in one place (~/coding-tutor-tutorials/). So your personal coding-tutor accompanies you across all your coding adventures. The whole thing is a feedback loop: learn → quiz → retain → learn more → quiz → retain. Your tutorials evolve, your knowledge compounds, and Claude gets better at teaching YOU specifically over time. To install it in Claude Code: • Run /plugin to open the plugin manager • Add marketplace nityeshaga/claude-code-essentials • Enable coding-tutor plugin Here's the Github: And here's 20-mins of me walking you through how to use this plugin and how it works 👇🏽 Let me know if you use it to teach yourself something cool!

Nityesh

64,540 Aufrufe • vor 7 Monaten

I MADE THE BEST 2D SPRITE AI PIPELINE EVER! After testing so much, doing tons of math, trying various different methods I finally got truly the cleanest sprite result I could ask for! You should bookmark this post! ✅ I think this is the happiest 'aha' moment I've had since starting this journey. Day 48 of making a custom webGL game engine using Cursor 🔽🔽🔽 I've been trying to solve sprites since I started this project. I came close a couple of times but it was always something - either dynamic lights wouldn't work, or the genAI results would make them grainy, or I would have to have incorrect alpha etc. After so much struggling, wanting to give up, thinking it's impossible - it was finally done! Perfect matte interpolation of black & white backgrounds resulting in a clean outline of the sprite, half transparent effects keeping their full quality. But this was not enough for me. I wanted more. I wanted those dynamic lights for the most badass anime effects that I'll be creating for the next many weeks! And somehow, I managed to come up with the perfect balance that is nice to the eye with almost no visible imperfections or artifacts when using any kind of dynamic lights. See for yourself! 🔽🔽🔽 How does this magic happen? 1⃣ First, the animation is created using Kling AI. I found it the best to keep the quality anime 2D style I wanted, but I guess others may work as well. 2⃣ Then, the frames are carefully extracted at lossless quality and processed via automation through Nano Banana pass to create an opposite background (white->black or black->white). 3⃣ Once we have a proper spritesheet, I then used MiDaS to compute the depth maps, and then algorithmically create PBR maps for lighting. The remaining depth maps can also be used for various effects, although haven't explored it much yet. 4⃣ Finally, all elements are cut into single frames and overlapped with their respective normal maps. If done right, they're pretty much ready for runtime. I like my heroes to be 512 to truly stand out like fighting game characters, but all of this works with far smaller or bigger sprites. Mind you, it's important to note that Nano Banana has a max output of 4k. Therefore you don't want to make your atlases larger than 4k - which is still quite a lot since it allows you 8x8 512 frames. 💡💡💡 I believe the output quality is beyond anything currently available, and I'll be looking into packaging the whole thing into a web app so people can use it. If you find this useful, please repost it! I really wanted to share this method to the world. Thank you for reading! Look forward to day 49 update!
0:58

I MADE THE BEST 2D SPRITE AI PIPELINE EVER! After testing so much, doing tons of math, trying various different methods I finally got truly the cleanest sprite result I could ask for! You should bookmark this post! ✅ I think this is the happiest 'aha' moment I've had since starting this journey. Day 48 of making a custom webGL game engine using Cursor 🔽🔽🔽 I've been trying to solve sprites since I started this project. I came close a couple of times but it was always something - either dynamic lights wouldn't work, or the genAI results would make them grainy, or I would have to have incorrect alpha etc. After so much struggling, wanting to give up, thinking it's impossible - it was finally done! Perfect matte interpolation of black & white backgrounds resulting in a clean outline of the sprite, half transparent effects keeping their full quality. But this was not enough for me. I wanted more. I wanted those dynamic lights for the most badass anime effects that I'll be creating for the next many weeks! And somehow, I managed to come up with the perfect balance that is nice to the eye with almost no visible imperfections or artifacts when using any kind of dynamic lights. See for yourself! 🔽🔽🔽 How does this magic happen? 1⃣ First, the animation is created using Kling AI. I found it the best to keep the quality anime 2D style I wanted, but I guess others may work as well. 2⃣ Then, the frames are carefully extracted at lossless quality and processed via automation through Nano Banana pass to create an opposite background (white->black or black->white). 3⃣ Once we have a proper spritesheet, I then used MiDaS to compute the depth maps, and then algorithmically create PBR maps for lighting. The remaining depth maps can also be used for various effects, although haven't explored it much yet. 4⃣ Finally, all elements are cut into single frames and overlapped with their respective normal maps. If done right, they're pretty much ready for runtime. I like my heroes to be 512 to truly stand out like fighting game characters, but all of this works with far smaller or bigger sprites. Mind you, it's important to note that Nano Banana has a max output of 4k. Therefore you don't want to make your atlases larger than 4k - which is still quite a lot since it allows you 8x8 512 frames. 💡💡💡 I believe the output quality is beyond anything currently available, and I'll be looking into packaging the whole thing into a web app so people can use it. If you find this useful, please repost it! I really wanted to share this method to the world. Thank you for reading! Look forward to day 49 update!

Startracker 🔺

145,925 Aufrufe • vor 5 Monaten