#Autoresearch

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#Autoresearch Reel by @techno.optimist.prime - Friedberg on his insane experience with AutoResearch: "Something was published internally that we said, 'omg that would normally be a PhD thesis that
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@techno.optimist.prime
Friedberg on his insane experience with AutoResearch: “Something was published internally that we said, ‘omg that would normally be a PhD thesis that would take seven years. It would be one of the most celebrated PhD pieces we’ve ever seen in this field. And it would be in the journal Science. And it was done in 30 minutes on a desktop computer running on AutoResearch with all the data we just ingested...’” – Ohalo CEO David Friedberg on @theallinpod #davidfriedberg #allinpodcast #ai #autoresearch #aiagents
#Autoresearch Reel by @_dbrogle - Gh: github.com/karpathy/autoresearch Really cool project that both shows the trend for agents and what the core loop for them is, although graphs can
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@_dbrogle
Gh: github.com/karpathy/autoresearch Really cool project that both shows the trend for agents and what the core loop for them is, although graphs can still be deceiving Always love a Karpathy post!
#Autoresearch Reel by @digitalsamaritan (verified account) - AI startups are now crossing a line they weren't supposed to (just yet) 1. Autoresearch project built by Andrej Karpathy Basically, bring your GPU
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@digitalsamaritan
AI startups are now crossing a line they weren’t supposed to (just yet) 1. Autoresearch project built by Andrej Karpathy Basically, bring your GPU and train your own AI model just using AI Potential applications: custom fine tuning models for specialized tasks without ML/technical expertise 2. AI Agency - instead of one big AI trying to do everything, structure agents more like a company with specialized skills, clear responsibilities and scope of work 3. Paperclip - AI company running a business autonomously These are all in early stages so don’t expect them to change the world just but just glimpses of what’s to come (if you can afford to pay for all those tokens) #aitools #aiupdates #digitalsamaritan
#Autoresearch Reel by @raycfu (verified account) - This AI system called autoresearch can replace marketers and runs experiments for you while you sleep. Ive been playing around with it for trading bo
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@raycfu
This AI system called autoresearch can replace marketers and runs experiments for you while you sleep. Ive been playing around with it for trading bots and its super cool that it can improve on its own #ai #tech
#Autoresearch Reel by @mansispeaks_ (verified account) - Andrej Karpathy open-sourced a 630-line Python tool that lets an AI agent run hundreds of ML experiments overnight - and it found a bug he'd missed af
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@mansispeaks_
Andrej Karpathy open-sourced a 630-line Python tool that lets an AI agent run hundreds of ML experiments overnight — and it found a bug he’d missed after years of manual tuning. The catch: it only works in tightly constrained problems with clear metrics. Not replacing researchers, just the grunt work. Source: https://venturebeat.com/technology/andrej-karpathys-new-open-source-autoresearch-lets-you-run-hundreds-of-ai #AIResearch #Karpathy #MachineLearning #Autoresearch #OpenSource
#Autoresearch Reel by @tamnguyen_ai (verified account) - Andrej Karpathy just built something that does AI research while you sleep. Comment "karpathy" for the github link! 56,000 people starred this proje
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@tamnguyen_ai
Andrej Karpathy just built something that does AI research while you sleep. Comment “karpathy” for the github link! 56,000 people starred this project in days. Here is why. Karpathy, one of the most respected AI researchers alive, built a tool called autoresearch. The concept is simple and the implications are massive. You give an AI agent a real machine learning training setup. You go to sleep. The agent runs experiments on its own. It modifies the code. Trains for five minutes. Checks if the result improved. Keeps what works. Discards what fails. Repeats all night. You wake up to a full log of experiments and a better model. Here is exactly what I am testing with it. I run an AI agency. My entire business is built on building systems that get smarter over time for the businesses I serve. So the first thing I am training this on is my own caption and reel scripts. Every piece of content I have written. Every hook. Every framework. Let it find the patterns that made the best ones work and build a model around that. Then I am taking it to clients. Feed it their cold email sequences. Their sales call transcripts. Their support tickets. Let it run experiments overnight on what combinations produce the best outcomes. Wake up to a model trained specifically on their business. That is not a generic AI tool. That is a custom brain built from their own data. And that is a premium deliverable worth serious money. This is the direction I am building GoToAgents.ai toward. Autonomous systems that improve themselves while you sleep.
#Autoresearch Reel by @todayin_ai - A PhD thesis that would normally take 7 years. Done in 30 minutes. On a desktop computer 🧪 How long before traditional research timelines become irr
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@todayin_ai
A PhD thesis that would normally take 7 years. Done in 30 minutes. On a desktop computer 🧪 How long before traditional research timelines become irrelevant? 👇 📌 Follow @todayin_ai for breakthroughs happening in real time #autoresearch #genomics #ainews #todayinai
#Autoresearch Reel by @arnitly (verified account) - Andrej Karpathy just released AutoResearch, a minimal 630-line framework that hands a real GPU training loop to an AI coding agent and lets it iterate
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@arnitly
Andrej Karpathy just released AutoResearch, a minimal 630-line framework that hands a real GPU training loop to an AI coding agent and lets it iterate autonomously. 12,000 stars and 1,600 forks in four days. The setup takes under five minutes. Clone the repo, run the data prep once, open the folder in Claude or Cursor, and prompt the agent to start. It reads your strategy from a Markdown file called program.md, creates a git branch, edits the training script, runs a five-minute experiment, and commits if the validation loss improved. Typical throughput is about 12 experiments per hour. What the video couldn’t cover: The evaluation metric is val_bpb, validation bits-per-byte, which is vocabulary-size independent. This means architectural changes, optimizer swaps, and hyperparameter tweaks are all directly comparable without normalization hacks. The base repo is NVIDIA-only and tested on H100, but community forks already exist for Mac via MLX and Windows RTX setups. Results are non-portable across hardware because the five-minute wall-clock budget is the control variable. Karpathy’s vision for the next iteration is asynchronous multi-agent swarms. Agents would post mini-papers in GitHub Discussions, read each other’s findings via CLI, and explore parallel research directions without forcing merges. He described the experience as “post-AGI feels” and “early singularity fun.” Compared to Sakana AI’s AI Scientist, which attempts the full research pipeline from idea generation to LaTeX paper writing, AutoResearch deliberately stays narrow. One training script, one objective metric, one tight loop. Many in the community note that this tight loop would be a natural low-level engine inside a broader system like Sakana’s. The repo is at github.com/karpathy/autoresearch. Requires an NVIDIA GPU, Python 3.10+, and uv for package management. If you have the hardware, an overnight run is genuinely worth trying. #ai #artificialintelligence #technews #chatgpt #algorithm
#Autoresearch Reel by @adamstewartmarketing (verified account) - Comment "Research" and I'll send you the step-by-step guide. This guy just open-sourced a way to let AI run its own experiments and improve itself wh
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@adamstewartmarketing
Comment “Research” and I’ll send you the step-by-step guide. This guy just open-sourced a way to let AI run its own experiments and improve itself while you sleep. It’s called autoresearch and here’s exactly what it does. Training an AI model normally takes a ton of work. You make a change. Run a test. Wait for results. Decide if it worked. Then do it all again. Most researchers can only get through a few experiments a day. Autoresearch hands that whole process to an AI agent. Every 5 minutes it makes a change, runs the test, and checks if the model got better. If it did, it keeps it. If not, it rolls back. Then it tries something new… and it can be running this all night long. The results are already wild. Karpathy the creator ran 126 experiments in a single overnight session. Tobi Shopify’s CEO set it up before bed. By morning his smaller model was beating a model twice its size. Someone ran it on a Mac Mini and the model improved by getting simpler. Currently the main repo only works with an NVIDIA GPU right now. But there are Mac and Windows forks if you need them.
#Autoresearch Reel by @prerana.talks - Andrej Karpathy's autoresearch framework is about to redefine experimentation at your job… follow for part 2! #product #tech #research #agenticai #a
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@prerana.talks
Andrej Karpathy’s autoresearch framework is about to redefine experimentation at your job… follow for part 2! #product #tech #research #agenticai #automationtools
#Autoresearch Reel by @cassy.garner (verified account) - What I am doing 👇 Most people use AI like this: ask a question → get an answer. But a more interesting direction is AI experiment loops. Instead o
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@cassy.garner
What I am doing 👇 Most people use AI like this: ask a question → get an answer. But a more interesting direction is AI experiment loops. Instead of prompting once, you set up a system that: generate an idea → test it → measure the result → keep the improvement → repeat. Projects like autoresearch show this idea applied to training machine learning models. The same pattern can apply to things like: Content • testing hooks for posts • trying different captions • learning what converts to followers Marketing • testing email subject lines • experimenting with landing page copy • optimizing calls to action Product • testing onboarding flows • improving conversion funnels • experimenting with pricing Coding • trying different implementations • benchmarking performance • iterating automatically The real advantage is scale. A person might run one or two experiments in an hour. An automated system can run dozens in that same time. That feedback loop is where things start getting interesting. Try it yourself ↓ https://github.com/karpathy/autoresearch #aitips #aistrategy #aiagents #aiagent #aicommunity
#Autoresearch Reel by @iharnoor (verified account) - Non ML Researcher shows us AGI / Singularity! (AutoResearch)
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@iharnoor
Non ML Researcher shows us AGI / Singularity! (AutoResearch)

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