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Hmm just noticed that there’s a new 100B stealth model called "elephant-alpha" sitting at No.1 on OpenRouter trending list . Seems really good for agentic tasks and coding in my tests. The "no-yap" energy is the real standout. So far it works very well, even for browser automation. It... show more

Paul Couvert

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33,544 Aufrufe • vor 4 Monaten •via X (Twitter)

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Most AI agents force you into a new browser. Meet Composite, it isn't just a hosted browser session in the cloud, or a completely separate browser. Composite actually does the clicking, typing, and navigation inside your real tabs, in your existing browser. - Uses your existing browser, so there’s no migration, you keep your bookmarks, extensions, workflows - No lock-in. It works with any site, any AI model, you’re in control. - Composite does the clicks, types, and navigation for you. In your tabs. No migration. No lock-in. No learning curve. Download and start now:

Most AI agents force you into a new browser. Meet Composite, it isn't just a hosted browser session in the cloud, or a completely separate browser. Composite actually does the clicking, typing, and navigation inside your real tabs, in your existing browser. - Uses your existing browser, so there’s no migration, you keep your bookmarks, extensions, workflows - No lock-in. It works with any site, any AI model, you’re in control. - Composite does the clicks, types, and navigation for you. In your tabs. No migration. No lock-in. No learning curve. Download and start now:

The AI Colony

48,585 Aufrufe • vor 1 Jahr

🚨BREAKING: Automation just changed forever. Octoparse AI Copilot just launched on Product Hunt …and I don’t think people realize how big this is for no-code automation. If you’ve ever wanted to automate browser tasks, scrape websites, clean data, or build workflows without coding, this might be the easiest on-ramp you’ll ever get. Here’s how it works (and why it’s different): 👇

🚨BREAKING: Automation just changed forever. Octoparse AI Copilot just launched on Product Hunt …and I don’t think people realize how big this is for no-code automation. If you’ve ever wanted to automate browser tasks, scrape websites, clean data, or build workflows without coding, this might be the easiest on-ramp you’ll ever get. Here’s how it works (and why it’s different): 👇

Chidanand Tripathi

75,077 Aufrufe • vor 8 Monaten

How to get OpenClaw🦞 to code for you at almost $0 costs. 1. Power your openclaw with Ollama (if you're on a mac f.e.), or with haiku or kimi k2.5 (if you're on VPS). Or pony-alpha on openrouter which I just discovered today (completely free) 2. Use the Kilo CLI for coding, since it lets you use MiniMax M2.1 for free, a very good coding model Works surprisingly well and doesn't bankrupt you

How to get OpenClaw🦞 to code for you at almost $0 costs. 1. Power your openclaw with Ollama (if you're on a mac f.e.), or with haiku or kimi k2.5 (if you're on VPS). Or pony-alpha on openrouter which I just discovered today (completely free) 2. Use the Kilo CLI for coding, since it lets you use MiniMax M2.1 for free, a very good coding model Works surprisingly well and doesn't bankrupt you

Moritz Kremb

56,200 Aufrufe • vor 6 Monaten

MolmoAct2 kinda just works out of the box in an unseen environment and with unseen objects. I find it really crazy that I can just download a model from the internet and plug it into my robot and it can do some tasks at a limited capacity right away. this video is at 2x speed

MolmoAct2 kinda just works out of the box in an unseen environment and with unseen objects. I find it really crazy that I can just download a model from the internet and plug it into my robot and it can do some tasks at a limited capacity right away. this video is at 2x speed

atharva ☆

32,346 Aufrufe • vor 3 Monaten

AOC just dragged CVS into the sunlight: She asked if our healthcare system is “market concentration.” CVS’s chair said no, it “works really well for the consumer.” AOC: It works really well for CVS. Insurance takes a cut. The PBM takes a cut. The drug company takes a cut. And the patient gets screwed.

AOC just dragged CVS into the sunlight: She asked if our healthcare system is “market concentration.” CVS’s chair said no, it “works really well for the consumer.” AOC: It works really well for CVS. Insurance takes a cut. The PBM takes a cut. The drug company takes a cut. And the patient gets screwed.

Brian Allen

113,016 Aufrufe • vor 6 Monaten

This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:

This is real time. You can see how stable the motion is. A system placing droplets between 1 nanoliter and 1 microliter inside a 96 well plate with almost perfect repeatability. No shake. No drift. No overshoot. The robot keeps the tip on track even at this scale where tiny vibrations normally ruin the result. Why this matters: •In wet lab automation, volume accuracy is everything •Nano scale tasks fail fast if the robot is even slightly off •Reliable deposition unlocks fast screening and repeatable experiments •You can run full workflows without manual checking or correction This is the kind of precision that turns lab robots from “helpful tool” into a full automation pipeline. Credit: M2-Automation GmbH —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

287,056 Aufrufe • vor 9 Monaten

Saw Elephant-alpha sitting at #1 on the trending leaderboard—even beating out Claude Opus 4.7—so I was dying to see what the hype was about. After throwing some messy code refactoring and JSON tasks at it, my takeaway is simple: this thing is incredibly tight. It’s a pure work-oriented model. It’s built for the grind: cleaning up massive datasets, pumping out structured docs, and hitting precise code fixes without making you wait. I specifically went to AI Benchy to verify my hunches, and the numbers are pretty wild: it’s essentially doing the same heavy lifting as the big names but with about 75% less chatter. If you're tired of paying for filler text, give it a spin on OpenRouter.

Saw Elephant-alpha sitting at #1 on the trending leaderboard—even beating out Claude Opus 4.7—so I was dying to see what the hype was about. After throwing some messy code refactoring and JSON tasks at it, my takeaway is simple: this thing is incredibly tight. It’s a pure work-oriented model. It’s built for the grind: cleaning up massive datasets, pumping out structured docs, and hitting precise code fixes without making you wait. I specifically went to AI Benchy to verify my hunches, and the numbers are pretty wild: it’s essentially doing the same heavy lifting as the big names but with about 75% less chatter. If you're tired of paying for filler text, give it a spin on OpenRouter.

Rohan Paul

50,987 Aufrufe • vor 4 Monaten

Anthropic's new model is extraordinary and it just revealed a problem that most enterprise AI buyers have not fully reckoned with yet (Save this), The model is genuinely impressive, and Chamath Palihapitiya assessment is that Anthropic continues to push the frontier harder than almost anyone. But that same update also showed their hand on something that changes the risk calculus for every business using Claude. Anthropic's new architecture stores every prompt you send for 30 days, no exceptions, not even for enterprise customers with zero-data retention agreements. The mechanism works like this, Anthropic now evaluates your prompt before generating output, deciding what it will and will not respond to, which means your query gets filtered before you even see a response. For individual users, that introduces a meaningful risk of censorship. For companies, Chamath says it is almost a non starter, and the reason is not just the data retention itself, it is the exposure that comes from operating at scale inside a large organization. A downstream scientist using the Claude APIs could accidentally trip a filter without knowing it, a business executive inside your company could trip it, and a molecular biology researcher could trip it and all of a sudden the company gets silently cut off from a tool it has embedded into critical workflows, with no warning and no recourse. Chamath gives Anthropic credit for being honest about how the system works, saying they tell the truth but notes that in this case the truth is not good. What this moment actually signals is a structural shift in how serious companies need to think about AI governance, because the question is no longer just which model performs best on benchmarks. It is who controls the model, who is learning from your data, and whether you are comfortable with a single point of failure sitting at the center of your competitive advantage. The answer for most enterprises will be broad model diversity, tighter governance frameworks and a serious reckoning with what it means to run mission-critical workflows through a third party that reserves the right to cut you off. Anthropic built a remarkable model and told the truth about how it works, the market's job now is to decide whether that transparency is enough to offset what the truth actually says.

Anthropic's new model is extraordinary and it just revealed a problem that most enterprise AI buyers have not fully reckoned with yet (Save this), The model is genuinely impressive, and Chamath Palihapitiya assessment is that Anthropic continues to push the frontier harder than almost anyone. But that same update also showed their hand on something that changes the risk calculus for every business using Claude. Anthropic's new architecture stores every prompt you send for 30 days, no exceptions, not even for enterprise customers with zero-data retention agreements. The mechanism works like this, Anthropic now evaluates your prompt before generating output, deciding what it will and will not respond to, which means your query gets filtered before you even see a response. For individual users, that introduces a meaningful risk of censorship. For companies, Chamath says it is almost a non starter, and the reason is not just the data retention itself, it is the exposure that comes from operating at scale inside a large organization. A downstream scientist using the Claude APIs could accidentally trip a filter without knowing it, a business executive inside your company could trip it, and a molecular biology researcher could trip it and all of a sudden the company gets silently cut off from a tool it has embedded into critical workflows, with no warning and no recourse. Chamath gives Anthropic credit for being honest about how the system works, saying they tell the truth but notes that in this case the truth is not good. What this moment actually signals is a structural shift in how serious companies need to think about AI governance, because the question is no longer just which model performs best on benchmarks. It is who controls the model, who is learning from your data, and whether you are comfortable with a single point of failure sitting at the center of your competitive advantage. The answer for most enterprises will be broad model diversity, tighter governance frameworks and a serious reckoning with what it means to run mission-critical workflows through a third party that reserves the right to cut you off. Anthropic built a remarkable model and told the truth about how it works, the market's job now is to decide whether that transparency is enough to offset what the truth actually says.

Milk Road AI

30,090 Aufrufe • vor 2 Monaten

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

60,091 Aufrufe • vor 3 Monaten

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

MTS

100,519 Aufrufe • vor 29 Tagen

The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

The robot is learning several novel tasks instantly, after just ONE demonstration each... Instant Policy makes it possible: no extra training, no weight updates, just pure in-context learning. It just got accepted at ICLR 2025, and it’s changing how robots learn. With just a single demo, a robot can pick up a new task and start performing it right away. Why this is a big deal: ✅ Learns tasks instantly with just one or a few demonstrations ✅ Improves over time as more demonstrations are given ✅ Uses simulation-based training with “pseudo-demonstrations” for scalability ✅ Can transfer skills across different robots and even follow language-defined tasks It brings in-context learning to robotics, opening up new possibilities for flexible, real-world automation. You can try it yourself: code and weights are available at • • • • Thank you to Edward Johns, Director of the Robot Learning Lab at Imperial College for sharing their work! 🙏

Ilir Aliu

46,285 Aufrufe • vor 1 Jahr

A NASA scientist built a device that produces thrust with no fuel, no propellant, and no exhaust! The force appears to come from nowhere. It runs on electric fields alone. Thousands of tests have been completed. Tests done in vacuum chambers with Ion wind being ruled out. A theoretical physicist who sat down with Dr. Buhler says he can’t debunk it. The data says the thrust is real even though our current model of physics says it shouldn’t be. And no one has explained where the force is coming from. This episode is WILD!

A NASA scientist built a device that produces thrust with no fuel, no propellant, and no exhaust! The force appears to come from nowhere. It runs on electric fields alone. Thousands of tests have been completed. Tests done in vacuum chambers with Ion wind being ruled out. A theoretical physicist who sat down with Dr. Buhler says he can’t debunk it. The data says the thrust is real even though our current model of physics says it shouldn’t be. And no one has explained where the force is coming from. This episode is WILD!

American Alchemy

19,075 Aufrufe • vor 4 Monaten

This is insane Just built a fully working AI Room Designer in minutes, no coding, no API keys, just chatting with OnSpace AI. I use Nano Banana Pro for generation and handles UI, logic, and integration automatically. Not a demo. A real app: • No API setup required – Just told OnSpace to use Nano Banana Pro for content generation and it worked instantly • Full app, not a prototype – Complete UI, logic, and AI integration handled automatically • Actually works – Nails my tone every single time The whole thing came together through conversation. No design work. No wrestling with code. Just described the app and OnSpace made it real. This is what building with AI should feel like. Check my app link in the thread and try it yourself

This is insane Just built a fully working AI Room Designer in minutes, no coding, no API keys, just chatting with OnSpace AI. I use Nano Banana Pro for generation and handles UI, logic, and integration automatically. Not a demo. A real app: • No API setup required – Just told OnSpace to use Nano Banana Pro for content generation and it worked instantly • Full app, not a prototype – Complete UI, logic, and AI integration handled automatically • Actually works – Nails my tone every single time The whole thing came together through conversation. No design work. No wrestling with code. Just described the app and OnSpace made it real. This is what building with AI should feel like. Check my app link in the thread and try it yourself

AI Frontliner

70,013 Aufrufe • vor 8 Monaten

For me, this moment from the scene works so well because it feels very real... There’s no big horror music or over-the-top acting, just a woman in a vulnerable state confronting someone she saw lurking.... The towel adds to the helplessness. It makes the scene feel intimate and uncomfortable, like you’re witnessing something you shouldn’t... It’s a small scene, but it stuck with me because it builds quiet dread really effectively. It makes you wonder what's coming after her and why she’s so shaken...

For me, this moment from the scene works so well because it feels very real... There’s no big horror music or over-the-top acting, just a woman in a vulnerable state confronting someone she saw lurking.... The towel adds to the helplessness. It makes the scene feel intimate and uncomfortable, like you’re witnessing something you shouldn’t... It’s a small scene, but it stuck with me because it builds quiet dread really effectively. It makes you wonder what's coming after her and why she’s so shaken...

Every movie 🎬🍿

82,858 Aufrufe • vor 2 Monaten

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

small local model that falls apart in bloated agents like openclaw just runs like a wild horse in hermes agent. and that's not even my line, someone else called it that, i've just been quietly pointing people at this harness for months because it held up on everything i threw at it, 3b models all the way to one trillion params. watch this happen on my own machine. i pointed hermes agent at a local http endpoint, gemma 4 12b on my 3090 llama.cpp server, and it auto-detected the model and started working immediately. no config wrestling, no broken tool calls, no babysitting the output format, i typed in a url and it just went. the whole clip is exactly that, start to finish, no errors, no retries, butter smooth. and the tool calling, the one thing that quietly breaks most local setups, works here like it's nothing. it's not the model that's flaky, it's the harness around it. hermes agent is the first agent i've run that actually gets that right. one url, one local model on one card, and it runs like a wild horse.

Sudo su

27,339 Aufrufe • vor 2 Monaten

🇬🇧THIS HELICOPTER FLIES ITSELF AND NEEDS ZERO PILOTS No glass. No cockpit. No pilot. Just pure robot vibes. It’s not a drone because no one is flying it. Not even from a base. This chopper thinks for itself and can fly for 8 hours without snacks, sleep or bathroom breaks. Built in the UK, it is the world’s first fully autonomous helicopter and it is kinda terrifying. Source: BBC

🇬🇧THIS HELICOPTER FLIES ITSELF AND NEEDS ZERO PILOTS No glass. No cockpit. No pilot. Just pure robot vibes. It’s not a drone because no one is flying it. Not even from a base. This chopper thinks for itself and can fly for 8 hours without snacks, sleep or bathroom breaks. Built in the UK, it is the world’s first fully autonomous helicopter and it is kinda terrifying. Source: BBC

Mario Nawfal

52,852 Aufrufe • vor 1 Jahr

Q: Is there a special reason you decided to complete 200 with 106 and 94? 🐯: Actually, there is no special reason. It just sounded right phonetically, so in a way, that’s where the idea of ‘200’ came from. Unfortunately, there’s no special fantastical reason behind it. *Mark’s official statement* There’s no particular reason! Full :

Q: Is there a special reason you decided to complete 200 with 106 and 94? 🐯: Actually, there is no special reason. It just sounded right phonetically, so in a way, that’s where the idea of ‘200’ came from. Unfortunately, there’s no special fantastical reason behind it. Mark’s official statement There’s no particular reason! Full :

MARK LEE BASE

265,779 Aufrufe • vor 2 Monaten

Forget the headlines and the chaos for a second. In 1984, Somali model Iman just opened her home to You magazine and said, “Come, let me show you something real.” No glamour, no pose; just her, in something comfy, sitting cross legged, making Somali tea the way it’s been made for generations. The spices hit the milk, the smell fills the room, she pours it high so it foams just right… and for those few quiet pages, everything feels gentle and right. That is it. That’s the moment. 🫖💛

Forget the headlines and the chaos for a second. In 1984, Somali model Iman just opened her home to You magazine and said, “Come, let me show you something real.” No glamour, no pose; just her, in something comfy, sitting cross legged, making Somali tea the way it’s been made for generations. The spices hit the milk, the smell fills the room, she pours it high so it foams just right… and for those few quiet pages, everything feels gentle and right. That is it. That’s the moment. 🫖💛

Abla عبلة

156,408 Aufrufe • vor 8 Monaten