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Ornith-1.0-9B for agentic coding? It broke on tool calling mid-task and got stuck in a loop with no way to continue. Only real upside: it's less bad than Gemma-4-12B... but that's not saying much. TL;DR: Skip it.

Mia

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25,675 次观看 • 1 个月前 •via X (Twitter)

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🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

🔴 The Pain: Running local MLX models is incredibly fast and private. But let's be real - testing tool calling via terminal is clunky, and there's zero good UI for it. 🟢 The Fix: rapid-mlx share Just ONE command gives you a polished web chat + seamless tool calling (works beautifully with gemma-4-12b-qat). We are proud to be the ONLY MLX inference engine in the community shipping this. ⚡️ 👇 Try it now: brew install raullenchai/rapid-mlx/rapid-mlx

raullen

22,680 次观看 • 1 个月前

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

152,032 次观看 • 1 个月前

A train has its way designed specifically for a train only and it's called a railway line. And way it's built, it can be struck any, a railway can't be impassable cause it's only used by that train. But that's not the case in Uganda, trains in Uganda get stuck in a mud.

A train has its way designed specifically for a train only and it's called a railway line. And way it's built, it can be struck any, a railway can't be impassable cause it's only used by that train. But that's not the case in Uganda, trains in Uganda get stuck in a mud.

Brayo🇺🇬

38,487 次观看 • 16 天前

GEMMA 4 IS STARTING TO KILL A LOT OF AI SUBSCRIPTIONS. Local inference, multimodal support, strong coding, and no API bill is making “just run it on your own machine” feel way more real.

GEMMA 4 IS STARTING TO KILL A LOT OF AI SUBSCRIPTIONS. Local inference, multimodal support, strong coding, and no API bill is making “just run it on your own machine” feel way more real.

0xMarioNawfal

65,679 次观看 • 3 个月前

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 次观看 • 1 个月前

i gave a 9B model an agentic OS and told it to build Octopus Invaders from scratch. Qwen 3.5 9B running through Hermes Agent with full tool access on a single 3060 12GB. browser, file system, code execution, terminal. it reads the prompt, plans the architecture, writes every file, and serves the game. one prompt, zero steering, 11 minutes. i've been going back and forth with this model for days now. text, code, reasoning. it handles small chats, batching, bash scripting. at 9B it's fast enough to feel interactive and genuinely useful for local workflows where you'd normally reach for an API. but this is the real test. can it architect and build a full game autonomously without a single correction? it's coding right now. results coming next.

i gave a 9B model an agentic OS and told it to build Octopus Invaders from scratch. Qwen 3.5 9B running through Hermes Agent with full tool access on a single 3060 12GB. browser, file system, code execution, terminal. it reads the prompt, plans the architecture, writes every file, and serves the game. one prompt, zero steering, 11 minutes. i've been going back and forth with this model for days now. text, code, reasoning. it handles small chats, batching, bash scripting. at 9B it's fast enough to feel interactive and genuinely useful for local workflows where you'd normally reach for an API. but this is the real test. can it architect and build a full game autonomously without a single correction? it's coding right now. results coming next.

Sudo su

65,570 次观看 • 4 个月前

Claude Code + Opus 4 is the best agentic coding tool in the world and it’s not close. It’s a: - masterful full-stack dev - with great product sense - that’s a delight to work with - and easy to use If you’re not using it you are behind. Watch for a 72min build session & tips.

Claude Code + Opus 4 is the best agentic coding tool in the world and it’s not close. It’s a: - masterful full-stack dev - with great product sense - that’s a delight to work with - and easy to use If you’re not using it you are behind. Watch for a 72min build session & tips.

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319,736 次观看 • 1 年前

Google's new Gemma 4 is excellent, and the 26B MoE version is likely the best model to run on a 32GB Framework Desktop. It's fast, smart, and also great for tool calling if you use it with OpenClaw🦞 or other local agent platforms.

Google's new Gemma 4 is excellent, and the 26B MoE version is likely the best model to run on a 32GB Framework Desktop. It's fast, smart, and also great for tool calling if you use it with OpenClaw🦞 or other local agent platforms.

Framework

58,593 次观看 • 4 个月前

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 次观看 • 1 个月前

no prompt engineering, no agentic harness, no tool calls. just me being lazy in llama.cpp's web ui and gemma 4 31b dense taking the task seriously. i typed "create gpu marketplace cards with hardware specs and prices per hour" and the model went and coded this ui, one shot, navy bg, glassmorphism cards, neon accent buttons, realistic pricing tiers per architecture. it even wrote a "why this looks premium" explanation under the code. for context this is a q4 quant of google's 31b dense thinking model, running on a rtx 5090 mobile 24gb in the rog scar 18 at around 15 tok/s sustained, same vram tier as a 3090 or 4090 desktop, so whatever you see here translates directly to your card at home. the whole interaction was me not trying and the model reasoning harder than the prompt deserved. that tells me more about where local ai is at in april 2026 than any leaderboard score. next test drops gemma 4 into hermes agent, autonomous tool calling, multi step reasoning, real agentic loop instead of a chat window. let's see what the same model does when it gets the right environment. more experiments coming anon. octopus invaders queued. same hardware, different tasks, all published here on x and all translatable to your 24gb card. for now the video below shows it coding live, gpu going brrr.

no prompt engineering, no agentic harness, no tool calls. just me being lazy in llama.cpp's web ui and gemma 4 31b dense taking the task seriously. i typed "create gpu marketplace cards with hardware specs and prices per hour" and the model went and coded this ui, one shot, navy bg, glassmorphism cards, neon accent buttons, realistic pricing tiers per architecture. it even wrote a "why this looks premium" explanation under the code. for context this is a q4 quant of google's 31b dense thinking model, running on a rtx 5090 mobile 24gb in the rog scar 18 at around 15 tok/s sustained, same vram tier as a 3090 or 4090 desktop, so whatever you see here translates directly to your card at home. the whole interaction was me not trying and the model reasoning harder than the prompt deserved. that tells me more about where local ai is at in april 2026 than any leaderboard score. next test drops gemma 4 into hermes agent, autonomous tool calling, multi step reasoning, real agentic loop instead of a chat window. let's see what the same model does when it gets the right environment. more experiments coming anon. octopus invaders queued. same hardware, different tasks, all published here on x and all translatable to your 24gb card. for now the video below shows it coding live, gpu going brrr.

Sudo su

28,939 次观看 • 3 个月前

𝗞𝗶𝗺𝗶 𝗞𝟮.𝟲 𝗶𝘀 𝗖𝗵𝗶𝗻𝗮'𝘀 𝗻𝗲𝘄 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗻𝘀𝘄𝗲𝗿 𝘁𝗼 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲. It has 1 trillion parameters but only uses 32 billion at a time. Context window holds 256,000 tokens. It scored 85% on Live Code Bench. Claude hit 64%. It plugs into OpenClaw, Cursor, and Cline with no restrictions. Kimi Claw deploys in the cloud in 2 minutes. It's way cheaper to run than Claude day to day. Save this. Your coding stack just got a real rival.

𝗞𝗶𝗺𝗶 𝗞𝟮.𝟲 𝗶𝘀 𝗖𝗵𝗶𝗻𝗮'𝘀 𝗻𝗲𝘄 𝗼𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗻𝘀𝘄𝗲𝗿 𝘁𝗼 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲. It has 1 trillion parameters but only uses 32 billion at a time. Context window holds 256,000 tokens. It scored 85% on Live Code Bench. Claude hit 64%. It plugs into OpenClaw, Cursor, and Cline with no restrictions. Kimi Claw deploys in the cloud in 2 minutes. It's way cheaper to run than Claude day to day. Save this. Your coding stack just got a real rival.

Julian Goldie SEO

102,245 次观看 • 3 个月前

I took Grok 4 for a spin this weekend to build this game prototype. I used SuperGrok Chat to generate the initial game prototype and then brought it over to Cursor to continue coding with Grok 4 MAX. Grok 4 in Cursor is like a no-nonsense agent. Doesn't speak much, but delivers the goods. There were moments where I was rate-limited or stuck on a bug or two that I had to get other models to help, but otherwise it's a fast, reliable model to work with. This makes me incredibly excited for Grok Code to launch in August!

I took Grok 4 for a spin this weekend to build this game prototype. I used SuperGrok Chat to generate the initial game prototype and then brought it over to Cursor to continue coding with Grok 4 MAX. Grok 4 in Cursor is like a no-nonsense agent. Doesn't speak much, but delivers the goods. There were moments where I was rate-limited or stuck on a bug or two that I had to get other models to help, but otherwise it's a fast, reliable model to work with. This makes me incredibly excited for Grok Code to launch in August!

Danny Limanseta

20,483,563 次观看 • 1 年前

here's just the looping part on its own, real time, no edits. gemma 4 12b had already built the thing, the work was done, and instead of stopping it just kept going. the same fragments over and over, talking to itself while the token counter climbed and climbed. watching a model do this is its own special kind of pain, you can see it's stuck but it has absolutely no idea that it's stuck. genuine question for anyone running gemma 4 locally through an agent, are you hitting this too, or did i just pull a cursed seed and everyone else is fine. i can't tell yet if it's the model or me, so tell me what you're seeing.

here's just the looping part on its own, real time, no edits. gemma 4 12b had already built the thing, the work was done, and instead of stopping it just kept going. the same fragments over and over, talking to itself while the token counter climbed and climbed. watching a model do this is its own special kind of pain, you can see it's stuck but it has absolutely no idea that it's stuck. genuine question for anyone running gemma 4 locally through an agent, are you hitting this too, or did i just pull a cursed seed and everyone else is fine. i can't tell yet if it's the model or me, so tell me what you're seeing.

Sudo su

16,483 次观看 • 1 个月前

On "Södertälje should own Scania": Adam Kostyal recently told me why options, and equity participation shouldn't be only for "the young tech companies" I agree. But needs to be done in the right way It's NOT easy for Swedish companies today to share upside with employees. But it's EASY to look at best practice globally, and copy it

On "Södertälje should own Scania": Adam Kostyal recently told me why options, and equity participation shouldn't be only for "the young tech companies" I agree. But needs to be done in the right way It's NOT easy for Swedish companies today to share upside with employees. But it's EASY to look at best practice globally, and copy it

Fredrik Hjelm 🇸🇪

28,280 次观看 • 3 个月前

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Redes Sociales Twitter/X: Facebook: Telegram: Reddit: Discord: I'm on my way Can't settle down Stuck in ways of being an ass And I got a lot of nerve that I'm ready to pass I'm on my way Can't settle down Stuck in ways of sadistic joy My talent only goes as far as to annoy I'm on my way This is my main offender This is what I've got and it Got me saying Why me? I'm on my way I get around Not all too sure about what I do Feel I've got to stop a second just to think it through And so I stop I'm on my way Yeah, I get around Thought it all over and now I spit it out When I spit, I spit on those that I care less about I'm on my way This is my main offender This is what I've got and it Got me saying Why me? I'm on my way

HSP Chicas Azules

70,951 次观看 • 5 个月前

we plan to have 3d songs be imported into actual 3d Rather than use spritesheets since not only Does it help in optimization but Alsoe help open Massivies amounts of ideas with thise tool ! Songremix and Coding by Bromaster Bythewaye

we plan to have 3d songs be imported into actual 3d Rather than use spritesheets since not only Does it help in optimization but Alsoe help open Massivies amounts of ideas with thise tool ! Songremix and Coding by Bromaster Bythewaye

Mistful Crimson Morning

80,112 次观看 • 1 年前

We've partnered to bring more Gemma 3 quantized models to you! 🚀 We worked with Georgi Gerganov llama.cpp, LM Studio, MLX, ollama to make sure you can run it using your favorite tool! Gemma models optimized with QAT, reduce memory requirements while keeping quality! All models checkpoints are available on Hugging Face and Kaggle. 🤗 What does this mean? - Gemma 3 27B (int4): Fits on NVIDIA RTX 3090 (24GB VRAM) or similar. - Gemma 3 12B (int4): Only needs a NVIDIA RTX 4060 (8GB VRAM) or similar. - Gemma 3 4B, 1B (int4): Run anything with more than 2.5GB Memory. Want to see it in action? Video below shows how easy it is to get started using LMStudio:

We've partnered to bring more Gemma 3 quantized models to you! 🚀 We worked with Georgi Gerganov llama.cpp, LM Studio, MLX, ollama to make sure you can run it using your favorite tool! Gemma models optimized with QAT, reduce memory requirements while keeping quality! All models checkpoints are available on Hugging Face and Kaggle. 🤗 What does this mean? - Gemma 3 27B (int4): Fits on NVIDIA RTX 3090 (24GB VRAM) or similar. - Gemma 3 12B (int4): Only needs a NVIDIA RTX 4060 (8GB VRAM) or similar. - Gemma 3 4B, 1B (int4): Run anything with more than 2.5GB Memory. Want to see it in action? Video below shows how easy it is to get started using LMStudio:

Philipp Schmid

15,996 次观看 • 1 年前

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🚨 BREAKING: JD Vance says the US delegation will return to America WITHOUT a deal with Iran, but that this outcome is MUCH worse for Iran than the US “We've been at it for 21 HOURS. We've had substantive discussions...but the bad news is, we have NOT reached an agreement.” “That's bad news for Iran MUCH more than it is bad news for the USA.”

Nick Sortor

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naenia ¹ ⁶³

1,647,861 次观看 • 7 个月前