X Archive

@onusoz · /2026/07/20 · 05:55 PM View on

Speaking of graphs... here is something I wanted to build since 3 months, and finally had the chance to, thanks to @pidotdev I often have these sequence of prompts that emerge while I work. Not just sequences but conditionals that necessitate control flow For example, one workflow that resembles socratic questioning: 1. Discuss some problem 2. "What is the most elegant and long-term production ready solution for this?" -> Agent replies 3. "Is that the holy grail?" 4. Agent can reply "yes it is, basically" or "no, it is not, it is instead ..." 5. If yes, continue to "autoimplement". If no, think about it and decide what to do And "autoimplement" is a single prompt of 6-7 sequential steps, which I've been meaning to make more deterministic as well But I wasn't sure how to build it I had previously built acpx workflows to be a swiss army knife, "something like n8n, but can drive codex through deterministic steps, nodes in a graph. or claude code. or pi. it uses acp..." But it had one problem. It was run from outside the harness, like a CI orchestrator I wanted to integrate acpx into pi. Because pi was the only CLI that could enable building of such a thing. But I wasn't sure how to reconcile a general ACP-based tool into a single coding agent I was being too accommodative of all the other harnesses, claude code, codex. I was trying to be too general I have changed my mind since then ACP is great and lets you integrate a harness into other software in cool ways But maybe, if a harness is proprietary, does not accept outside contributions, or does not even *support ACP*, maybe, it does not deserve cool features 😤 (they know who they are) So I ripped out ACP, and built it natively, only for pi No need for a web viewer... Just view it in a native widget, right inside pi! I cannot put into words how awesome it is to be able to do this! I am still tinkering, discovering. It is at osolmaz/pi-workflows if you want to take a look

@onusoz · /2026/07/20 · 05:55 PM View on

Repo: github.com/osolmaz/pi-wor…

@onusoz · /2026/07/20 · 01:53 PM View on

It's interesting, a @pidotdev extension can be built with different attitudes: (a) To be installed as a package: You want other people to adopt, like @nicopreme /pi-web-access (a) is like "I want others to adopt it, so I will try to make it elegant, simple and make it have a good architecture" People install these by npm install ... or pi install ... (or rather, telling their agent to do it) (b) To be vendored: You build for yourself. It is too idiosyncratic of you, and you know others will likely not adopt. But you still put it out there, because it's easier to share them when you mention it to a friend. People (b) is like "This is mine, I don't care what others think about it. If they want to use it, they do whatever they want with it" People install these by pointing their agent and telling to copy it to their own extensions repo And then there is the maintenance dimension: - If an extension poses as (a), - but has not been maintained since 3 months, - and is the sort of package that has to be continuously maintained by its nature (not one-off), then you deem it unmaintained, and still vendor it in... Interestingly, most pi extensions I observe out in the wild appear to be of category (b), because creating (b) is easier than creating (a) This is nothing new, and I am kind of late to the game. Moreover, pi is not the first software of this kind, emacs for example did it decades before But code wasn't cheap then. So people still tried to coordinate effort When you don't have to write any code to extend---a lisp dialect, typescript or otherwise, when extending is 1 prompt away, when you can pump out 10 extensions in an evening, then we enter a new dimension of malleability Before, in the pre-AI era, most users of emacs were not producers of extensions but consumers of them, save for a few prolific authors After, now, every user can be a producer! This is significant! And a lot more messy! And a lot more fun! "A love letter to pi" indeed!

@onusoz · /2026/07/20 · 12:02 PM View on

I have a confession to make. I am Pi-curious I have spent the weekend to get the coding agent UX I have always wanted, with @pidotdev Here is one example: my turn-fold pi extension in action Brings codex desktop app UX into the CLI, where it collapses all the messages between the user message and the last assistant message It also lets me remove the pesky indentation so I don't have to do it manually every time I copy something. No need for an extension, just a config change My pi config is open source under osolmaz/onurpi

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@onusoz · /2026/07/20 · 12:56 PM View on

stealing this, thanks @tornikegomareli 😺 x.com/tornikegomarel…

@tornikegomareli · May 9, 2026

I’ve always loved nyan-mode in Emacs, so I built a pi-nyan-mode extension for @badlogicgames’s Pi. Instead of showing your position in the buffer, it moves left to right inside your footer as the active context fills.

@onusoz · /2026/07/19 · 05:28 AM View on

if you are below 30 and not old geezers like us, read about the history of gnu, linux and the pathetic struggle of microsoft trying to squash open source internet the flicker company is our day's microsoft, in its pathetic struggle to squash open source ai reading history will give you a potential map of what comes next

@qualiascript · Jul 18, 2026

it's 2001. open-source Linux is better than Windows on servers. Steve Ballmer calls Linux "cancer", "communist" and asks for it to be regulated away it's 2026. open-source LLMs are better than ChatGPT/Claude on costs. they are called "decelerationist" and "communist"

@onusoz · /2026/07/19 · 03:41 AM View on

Nothing accelerates progress as much as crony capitalist cartels and decelerates progress as much as open sharing of knowledge and science

@deanwball · Jul 17, 2026

Some observations on Kimi: 1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run. 2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China. 3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex. 4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business. 5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this. 6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.

@onusoz · /2026/07/19 · 02:50 AM View on

Inkling by @thinkymachines scores higher than GPT-5.6 Luna/Terra on AlmanBench. Around the same level as DeepSeek V4 Flash

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@onusoz · /2026/07/18 · 03:45 PM View on

I dared to post Alman to r/German, and it reminded me why I left Germany again Also, my post got removed, reddit being reddit reddit.com/r/German/comme…

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@onusoz · /2026/07/18 · 02:06 PM View on

Introducing AlmanBench A benchmark measuring how good an LLM can: simplify German 🇩🇪, thereby simplifying German thinking 🤔, thereby saving the EU from bureucracy and regulation 🇪🇺 GPT-5.5, GPT-5.6 Sol and Fable 5 are head to head. Interestingly, GPT-5.5 xhigh scores higher than 5.6 max. And I did not run Fable 5 maxxx thinking yet, that thing costs a ton. So the ranking will likely change Other interesting things: - Opus 4.8 max performs really bad, worse than Minimax M3 - DeepSeek v4 Flash performs better than Pro - GPT-5.6 Luna and Terra score almost the same The great thing about AlmanBench is that, big labs will not bother to benchmaxx this. So you know it will remain a truthful scorer of reasoning capabilities for some time And if labs *do* end up benchmaxxing AlmanBench, then that would mean Alman got in the weights and the EU won 😁

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@onusoz · /2026/07/18 · 01:38 PM View on

trying to hammer in some character into GoePT. now it speaks like it grew up in the streets of berlin 💀 "goethe meets gpt meets street" also, that feel when AI hits you with it's not X it's Y in German though 😩

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@onusoz · /2026/07/18 · 01:15 PM View on

I put my money where my mouth is What would be the point of training a German simplifier model, if I spoke english with my agent??? that would be dishonest here is GoePT being impressed by my dataset: "That is a serious dataset, 43k training pairs, reviewed by Fable, splitted leakage sage, with full provenance"

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@onusoz · /2026/07/18 · 12:59 PM View on

My ML Claw instance GoePT can read my private hugging face datasets using hf-broker (brokerkit) ML Claw connects to hf-broker through MCP. You can safely give read access behind the broker, and give write access only to the repos it needs to work with It cannot force push, unless you allow it to

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@onusoz · /2026/07/18 · 09:00 AM View on

hard to not like the reset company these days

@thsottiaux · Jul 18, 2026

Oops... I did it again. Enjoy reset usage limits for all paid users for Codex and ChatGPT Work. Super grateful for an incredible team who is iterating at lightspeed and keeping the infra up as we scale faster than ever. Enjoy the weekend!

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@onusoz · /2026/07/18 · 07:00 AM View on

so tired of LLMs pushing latin/greek GRE-cel words to me adjudication -> are you trying to be smart? just use "judging" provenance -> wow 🧠... why not just use "sourcing"??? judge and source already come from latin roots and are the everyday words we use big labs developing large LANGUAGE models have the biggest lever on language now if they wanted, they could finally put an end to the classist, anglosaxon-substitutionist insincerity that has plagued the english language for 500 years no english speaking country is ruled by a king now, the way it used to. there is no empire left to justify class language. these are vestigial remnants from the past maybe my next weekend project should be simplifying english...

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@onusoz · /2026/07/18 · 06:12 AM View on

acpx lets you build graphs though I have to admit I am not using it at scale these days...

@onusoz · Mar 30, 2026

acpx v0.4 ships Agentic Workflows, or as I like to call them "Agentic Graphs" It let's you create node-based workflows on top of ACP (Agent Client Protocol), to drive any coding agent (Codex, Claude Code, pi) through deterministic steps This let's you automate routine, mechanical legwork like triaging incoming PRs, bugs in error reporting, and so on... For example, OpenClaw receives 300~500 new PRs per day. A lot of them are low quality, but they still relate to real issues, so you have to address them somehow You need to: - extract the intent - cluster them based on intent - figure out if the proposed changes are legit, or whether they are slop local solutions, like trying to catch flies instead of drying out the swamp - if the PR is too low quality or the intent is not clear, close them - run AI review on them them and address any issues that come up - refactor them if the changes are half-baked - resolve conflicts - and so on... So that when the PR is presented to the attention of the maintainer, all the routine legwork is done and the only remaining thing is the decision to (a) merge, (b) give feedback to the PR author, or (c) take over the PR work yourself I wanted to build this feature since a couple months now, since Codex got so good. OpenAI models are now good at judging implementation quality, so I found myself repeating the same steps I wrote above over and over I also tried putting all this in a single prompt. But I believe there are workflows that should not be a single prompt, but a sequence of prompts in the same session That is because like humans, LLMs are prone to PRIMING. I claim that putting all steps in the same prompt at the beginning of the context will generally give suboptimal results, compared to revealing the intention to the model step by step Creating such a workflow also gives more OBSERVABILITY into the each step that an agent is supposed to take. Agent generates JSON at the end of each step, and that structured data can be used to monitor thousands of agents running at the same time in an easier way, on a dashboard Similar features have been introduced in e.g. n8n, langflow. But AFAIK they are not integrating ACP like the way I do I wanted to have a fresh approach, and to build an API that I can develop freely the way I want, so I created a new workflow API inside acpx The video is from the workflow run viewer, but that is not where you build the workflow. You build it by using the acpx flow typescript API. See examples/pr-triage in acpx repo Before building that, I started from a Markdown file with a Mermaid chart of the flow I had in mind. The Markdown file acts as a spec for the flow, and I have built the workflow through trial and error. I call this process "workflow tuning" I started working on acpx repo PRs one by one, tuning the flow, slowly scaling to more PRs. Finally, when I felt confident, I ran it in parallel over all external open PRs in the acpx repo. I believe it already saved me hours this week My next goal, if well received, is to set this up on a cloud agent so that it can process the 300~500 PRs the OpenClaw repo receives every day, in real time, as they come in I believe this will save all open source maintainers around the world countless hours and make it much easier to herd and absorb external contributions from everyone!

@onusoz · /2026/07/17 · 07:03 PM View on

Request for Review I spent a lot of time trying to profile local models, and am bothered by the fact that I don't have a mathematical framework to reason about the maximum token throughput I can expect from a model (to my knowledge) I did some exploration based on a toy model of a GPU. My main goal is to find guaranteed upper bounds for throughput and other performance, to act as a rule of thumb while trying to optimize inference Like, if I wanted to serve at 50 tok/s, how many parallel sessions can I do with this GPU, running a specific model's specific quantization with a specific architecture? Ideally, one should be able to plug in memory capacity and bandwidth of the GPU, and model specific parameters, like total model size, number of parameters or active parameters, etc. This is what I tried to do here and I think I have a good first try I am not sure if I am reinventing the wheel, so please tell me if this formulation already exists somewhere. The goal is to find an upper bound in terms of memory, so it makes the assumption that inference is bottlenecked by memory read/write and ignores the case where computation is a bottleneck I present 2 closed form upper bound formulas. The bigger one is to draw an absolute ceiling on token throughput from a GPU, which is: (memory capacity * memory bandwidth) / (model weight size * kv cache size per session) which is not practical and gives too high numbers due to ignoring overhead of KV operations, but it is a theoretical upper limit for the given numbers And then I introduce architecture specific formulas which take into account KV operations, and give much closer results. But I will not go into the details of that here, please refer to the post for that Speculative decoding speedup also appears as a simple multiplier rho If you are profiling local models, I would appreciate if you took time to look at this formulation and review it!🙏 Please let me know in the replies if you find any issues, because there most certainly are some! Post: Using the formulation, I calculated theoretical upper bounds for all the models on Hugging Face that have downloads over 100k, matched against a database of consumer GPUs. You can find the link to that in the tweet below 👇 The video here is a visualization of the 2 upper bounds I introduce, showing the bits of memory that is on the critical path of one cycle of inference, as if they are on a single pipeline

@onusoz · /2026/07/17 · 07:03 PM View on

Using the formulation, I calculated theoretical upper bounds for all the models on Hugging Face that have downloads over 100k, matched against a database of consumer GPUs. Here is an example of Qwen/Gemma NVFP4 quantizations and antirez/ds4 on the DGX Spark and some other 128 GB Mac configurations

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@onusoz · /2026/07/17 · 01:24 PM View on

"Laggard Models"

@arena · Jul 16, 2026

Kimi-K3 just topped the Frontend Code Arena with a 76% pairwise win rate. When its output was compared head-to-head against other models on the same task, it was picked as the better output 76% of the time on average. For reference: Claude Fable 5 (63%), GPT-5.6 Sol (58%). 50% is baseline, a model winning and losing equally often.

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@onusoz · /2026/07/17 · 01:26 PM View on

isn't AI amazing? you can just prompt things: --- download youtube.com/shorts/S72ZRBS… with yt dlp there is a sentence with "laggard models" download the video and cut out that sentence, the part with that sentence then it should be like Now, what I do worry about with these "laggard models" laggard models laggard models laggard models (slowed down) then also download some arena ai or artifical analysis leaderboard pictures or news article headlines showing kimi k3 beating all the other models including fable in the 2nd 3rd and 4th, it should alternate, and after that, it should stop in the most striking one and then it should play super mario loss sound do that video edit now

@onusoz · /2026/07/17 · 05:06 AM View on

fable is not the oracle. fable is the genie an oracle just talks a genie brings things into existence

@onusoz · /2026/07/17 · 04:04 AM View on

all I do now is wish for things from a genie every day

@onusoz · /2026/07/16 · 06:06 PM View on

I deleted my agent accounts. I don't need them anymore for secure access to GitHub and Hugging Face repos Instead, I am using brokerkit, a credential broker swiss army knife which can build an approval gate around any system My agents automatically get read access to all my repos, unless I specify certain ones to be excluded They then have to open PRs which only I can merge. Unless I allowlist them on certain repos to push to the main branch Best feature: Timed requests. e.g. "I want to be able to push to the main branch next 2 hours", "I want to release this package only once in the next 5 minutes" Which saves me from the hassle of changing a config for some short term change, but also protects me from the woes of long term prompt-injection risk My openclaw instance has its own linux user on my workstation, and is no longer root. It uses gh-broker and hf-broker from inside its account. Can also run sudo through sudo-broker, to install stuff. Secure access for free, without having to set up a separate server It ingests quite a bit of information from the internet every day, and the remaining lethal trifecta risk is leaking of some of my private repos, and nothing else Repo:

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@onusoz · /2026/07/16 · 05:32 PM View on

I deleted my agent accounts. I don't need them anymore for secure access to GitHub and Hugging Face Instead, I am using

@onusoz · /2026/07/16 · 05:18 PM View on

Your ML agent on Hugging Face infra: ML Claw 🦞🤗 Quickstart: npx mlclaw bootstrap If you have Hugging Face PRO, you can run your agent on a Space, with the highest possible download speed to Hugging Face models, datasets and buckets, for fast experimentation 🚀

@onusoz · /2026/07/16 · 05:18 PM View on

When I started building this, the docker space was actually free as well. But due to some bad actors exploiting the free tier at a mindblowing scale, free docker space had to be retracted from the free tier ☹️ However, you can still run ML Claw locally, but with your state backed up to a Hugging Face bucket for free, up to 100 GB of free private storage and 8 TB of public storage! Repo if you would like your agent to scan it before you run the command: github.com/osolmaz/mlclaw

@onusoz · /2026/07/16 · 01:51 PM View on

Codex and Claude code teams should learn something from Cursor team when it comes to queueing/steering

@onusoz · /2026/07/16 · 04:25 AM View on

I asked ML Claw to name itself. Of course in German/Alman Lame suggestions: Ulf, Strich, Fritz, Lex I intervened and called it GoePT Goethe rolling in his grave

@onusoz · Jul 15, 2026

The EU 🇪🇺 is broken. It is drowning in regulation The reason for that is simple Germany 🇩🇪 is the dominant economy of the EU And German 🇩🇪 is the most rule-ridden language in the world Coincidence? I think not. The language people speak determines their thinking and their fate. The Sapir-Whorf hypothesis holds The solution is simple: Fix German, Save the EU 💪 I will SAVE EUROPE by simplifying German. With the help of AI. Once and for all 🫡 I will use OpenClaw 🦞 running on Hugging Face 🤗 to train a model that simplifies German The agent harness is called ML Claw. It has full access to GPUs, jobs, sandboxes and datasets on Hugging Face: github.com/osolmaz/mlclaw My new German dialect is called Alman: alman.ai I will be live-tweeting my Quixotic adventure as it unfolds, in this thread My goal is to show that you can run autoresearch loops on Hugging Face infra on free private Spaces, while getting access to SOTA open models like GLM 5.2. And even use your own Codex subscription! And it does not have to be ML. You can just use free Hugging Face Spaces for your OpenClaw agents. It's free compute! Bookmark to be able to find this thread later on 👇

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@onusoz · /2026/07/15 · 06:36 PM View on

Fable just seems to know what you want, and incredibly empathetic model AGI is definitely here This model can do anything the average human does and more, given the right context I don't like Anthropic's marketing team, but you've got to hand it to them. They reached there before OpenAI did, despite not having a head-start Now, I can't wait to be able to run a model of this caliber locally 🚀

@onusoz · /2026/07/15 · 06:17 PM View on

I have been working on this for 4 years Every time, I shelved it, because the models were not good enough I tried it with Claude 2 I tried with Gemini 2.5 Pro I tried it with Claude Opus 4 None of them were good enough Until Fable came along Fable achieved perfect score in my hand curated benchmark for Alman. It also one-shotted the landing page, features and translations you see on alman.ai. It is busy creating the golden dataset right now for training I am also happy to announce AlmanBench, a benchmark that measures how well models can translate from German to Alman. I will be posting about performance when a new model drops This idea should have died with me. But thanks to AI, I can unleash my madness onto the world 😈

@onusoz · Jul 15, 2026

The EU 🇪🇺 is broken. It is drowning in regulation The reason for that is simple Germany 🇩🇪 is the dominant economy of the EU And German 🇩🇪 is the most rule-ridden language in the world Coincidence? I think not. The language people speak determines their thinking and their fate. The Sapir-Whorf hypothesis holds The solution is simple: Fix German, Save the EU 💪 I will SAVE EUROPE by simplifying German. With the help of AI. Once and for all 🫡 I will use OpenClaw 🦞 running on Hugging Face 🤗 to train a model that simplifies German The agent harness is called ML Claw. It has full access to GPUs, jobs, sandboxes and datasets on Hugging Face: github.com/osolmaz/mlclaw My new German dialect is called Alman: alman.ai I will be live-tweeting my Quixotic adventure as it unfolds, in this thread My goal is to show that you can run autoresearch loops on Hugging Face infra on free private Spaces, while getting access to SOTA open models like GLM 5.2. And even use your own Codex subscription! And it does not have to be ML. You can just use free Hugging Face Spaces for your OpenClaw agents. It's free compute! Bookmark to be able to find this thread later on 👇

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@onusoz · /2026/07/15 · 06:27 PM View on

Fable one-shotted the scrolly thing that demonstrates the idea More like multiple messages back and forth where it does exactly what I picture in my head, and even better
@onusoz · /2026/07/15 · 06:31 PM View on

it actually one-shotted the checkbox showing the diff between German and Alman I wanted to do exactly this since 2 years, and it did it without even me asking for it in this session, when I purely asked for a German/Alman translation

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@onusoz · /2026/07/15 · 06:32 PM View on

Fable one-shotted the Institut seal of approval. I did not touch it The logo idea came from me though. I generated in chatgpt and vectorized in inkscape. But Fable still wrote the prompt for that

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@onusoz · /2026/07/15 · 06:36 PM View on

Fable just seems to know what you want, and incredibly empathetic model AGI is definitely here This model can do anything the average human does and more, given the right context I don't like Anthropic's marketing team, but you've got to hand it to them. They reached there before OpenAI did, despite not having a head-start Now, I can't wait to be able to run a model of this caliber locally 🚀

@onusoz · /2026/07/15 · 05:52 PM View on

The EU 🇪🇺 is broken. It is drowning in regulation The reason for that is simple Germany 🇩🇪 is the dominant economy of the EU And German 🇩🇪 is the most rule-ridden language in the world Coincidence? I think not. The language people speak determines their thinking and their fate. The Sapir-Whorf hypothesis holds The solution is simple: Fix German, Save the EU 💪 I will SAVE EUROPE by simplifying German. With the help of AI. Once and for all 🫡 I will use OpenClaw 🦞 running on Hugging Face 🤗 to train a model that simplifies German The agent harness is called ML Claw. It has full access to GPUs, jobs, sandboxes and datasets on Hugging Face: github.com/osolmaz/mlclaw My new German dialect is called Alman: alman.ai I will be live-tweeting my Quixotic adventure as it unfolds, in this thread My goal is to show that you can run autoresearch loops on Hugging Face infra on free private Spaces, while getting access to SOTA open models like GLM 5.2. And even use your own Codex subscription! And it does not have to be ML. You can just use free Hugging Face Spaces for your OpenClaw agents. It's free compute! Bookmark to be able to find this thread later on 👇

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@onusoz · /2026/07/15 · 12:13 PM View on

My friend Poli at @huggingface is the literal 1st to find out about new cool model drops in AI (he has his setup) He is the definition of *alpha* in AI When there is a new model drop, his agents autonomously create a demo around it, for better visibility and interactivity (with human curation of course) And now he created the @HuggingApps account to share these demos live, here on Twitter So if you follow this account, you will be getting the state-of-the-art directly from the publisher. Not weeks, but hours after! Give @HuggingApps and @multimodalart a follow!

@multimodalart · Jul 15, 2026

Kicking off @HuggingApps - a curated & high signal set of novel, cool & interesting demos and apps that you can play right now, for free, on Spaces Check it out 🤗

@onusoz · /2026/07/15 · 10:27 AM View on

A lot of criticism coming towards local models, and the money people are spending to run them Some of these criticisms are valid. No, the layperson will not buy a DGX station, nor spend $50k on a rig They will spend max $3k on a computer, and if their work really necessitates it, up to $10k But in some of these criticisms, I see a lack of first-principles thinking Local models might be shittier now compared to the ones you buy from the cloud The question is, do you WANT to LIVE in a world where you don't have a choice but to RENT your intelligence? From a duopoly that can extort you for the last cent in your wallet, in the long run? I personally do not like that idea. So open source AI HAS to win and keep on winning, continuously and permanently. There is no other choice There is also a preferential belief that AGI/ASI will be reached, but then we will NOT be able to use that to make local models work efficiently??? Like, can you believe the rate of optimization and compression that has been happening to models in the last 3 months? Would you have believed that a o4-mini level model could run in your phone and o4 in your desktop, if we had told you 1 year ago When we are finally there, we will probably look back at this moment and think, "wow, we had not even started to see the real gains from optimization" Local and open source AI HAS to win, and it is up to US So STOP complaining and get to work

@onusoz · /2026/07/15 · 11:42 AM View on

@TheAhmadOsman said it best opensourceaimustwin.com

@onusoz · /2026/07/15 · 07:07 AM View on

Fable 5 replaces GPT 5 Pro as the Oracle. And it is the go-to model for writing docs So I now have a $ use-fable skill to use whenever I write a README for example, inside codex, through acpx. Saves me from having to switch to another CLI github.com/osolmaz/tools/…

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@onusoz · /2026/07/14 · 05:00 PM View on

People report Codex deleting their home folder or production database? 🫪 Hasn't happened to me. But before someone reports their github or huggingface org being deleted: This is why you don't give your agent tokens with force-push or admin access Here is how to protect your hugging face account: (P.S. my local credential broker is almost finished and it works great on github, hf and sudo commands. Complete lockdown against agent deletion risk, without being bogged down with PRs, too many approval requests or configuration. Will launch here in a few days)

@onusoz · Jul 5, 2026

Worried that giving your 🦞 @openclaw agent write access to your 🤗 @huggingface account can risk deletion of your datasets/models/spaces/buckets, or cause irreversible damage? 😱😱😱 No need to be! I have created an agent login helper to run in your YOLO mode remote machine, which prevents any risk of irreversible deletion. Just run: uvx hf-auth-helper agent login There is a specific set of scopes you can choose while creating a fine-grained HF token. These include all read scopes + discussion.write, which let's your agent create PRs. Since you are not giving repo.write, your agent cannot force-push your main branch, change repo settings or delete them It is unfortunately not super straightforward to choose those on the web UI. This will hopefully change soon, and this functionality might even be natively in hf cli Until that happens, use hf-auth-helper to login worry free in your remote or local openclaw instance Your agents will be able to create PRs on datasets/models/spaces, which you will then be able to merge on your own browser 2 caveats: 1) This does not solve the data exfiltration attack vector—nothing does. Make sure to exclude any repos which absolutely must remain private while choosing your scopes. See for more info: 2) As buckets are not repos, your agent will not be able to modify a bucket (add/remove data). To help with that, I have another project on the way, a credential broker. Stay tuned, coming soon Source: Demo authentication flow:

@onusoz · /2026/07/14 · 11:38 AM View on

before fable is gone again... can't believe you can do this with a single prompt now

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@onusoz · /2026/07/14 · 05:55 AM View on

Germany's new Soofi model's website shows me cookie banner even though I am in Singapore 💀

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@onusoz · /2026/07/14 · 05:55 AM View on

Also, why gate the weights????

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@onusoz · /2026/07/14 · 12:50 AM View on

No need to be offended, I'm actually a fan of your work! The metrics might have been wrong or just misfired. Looking at your recent posts, they don't have the smell Curious, as an example, was this a model, or written manually? I have the corpus here, and only some of them have the smell to me x.com/i/status/20559…

@sudoingX · May 17, 2026

anyone thinking about, learning, or already working with agentic systems, you should know this. the first few steps of your setup matter more than any model or framework you pick later. get them right and you never lose your flow. the foundation nobody posts about: > 1. tailscale. a private mesh network across every machine you own. laptop, desktop, rented node, all on one secure tailnet, reachable from anywhere. nothing else works well until this does. > 2. termius, over that tailnet. one SSH client that reaches every node, phone included. you are never away from your stack. > 3. tmux. persistent sessions. disconnect, close the laptop, come back, every session exactly where you left it. agentic work runs long, your terminal has to survive that. > 4. a private git repo. the one i am most glad i found. it is the memory layer across all my agents, they pull, they work, they merge back, the codebase stays alive between sessions. context that would die in a chat window lives in the repo instead. > 5. script everything from day one. ssh aliases for every node, setup scripts, the boring boilerplate automated. if you will do a thing more than twice, it is a script. everything past these five is decorative. know these cold. and the habit that ties it together: ask the AI itself. for the config, for the error, for any of it, let the agent do the lifting, then double check what it hands you. lock the five, build the habit, and you make it. skip it, anon, and you ngmi.

@onusoz · /2026/07/13 · 06:02 PM View on

I'm tired of AI writing 👃 smell 👃 here on X (and also the docs I generate) so I made a skill to 👃 de-smell 👃 Steal the skill, especially those who use GPT 5 to generate their long-form posts, for example 🤗 @analogalok @sudoingX @aijoey @ishaansehgal @stevibe and even @TheAhmadOsman 🤗 Surprisingly, @levelsio, a.k.a. AI reply guys' final boss, is the most *human* poster in the "sentence flow" metric that fable came up in an autoresearch loop. Very cool (though I haven't sampled everyone, just long-formish writers my xtap extension has picked up) I know I'm doing a favor to slop vendors out there. But there are people who are actually doing legit work, but then passing all their thoughts through GPT before putting it out here. I *have* to follow them due to my occupation. If this will save me from another "it's not X, it's Y", or "it is A, B and C", I will do it 😤 My de-smelled slop post has all the info, graphs and all: The skill (will keep updating it): This is not the end of this work, I am just beginning

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@onusoz · /2026/07/13 · 12:39 PM View on

don't tease us 😩

@appltrack · Jul 12, 2026

Apple's M7 Ultra chip coming in 2029 is rumored to support 1.5TB of RAM. This would make the processor much more capable for on-device AI.

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@onusoz · /2026/07/13 · 08:15 AM View on

I've started using Cursor since a few days now, and I have to say the experience is really pleasant. I had not touched it since Claude Code came out in May 2025, more than 1 year ago. I had even deleted it fully last year as an editor and replaced it with Zed, after it had become very bloated and unstable I can say it no longer feels bloated/unstable and that they have transitioned successfully to the agentic era Using it mostly for fable. Using it locally in the desktop app, and also through acpx, to make codex ask fable for review Thank you @cursor_ai for sponsoring my Ultra plan!

@onusoz · Jul 13, 2026

I told it only once before to do it on the session, and now codex habitually asks fable for review on data models and plans it creates through acpx (fable inside cursor) I then asked it what it thinks about the feedback from fable. gpt-5.6-sol is not impressed

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@onusoz · /2026/07/13 · 07:45 AM View on

I told it only once before to do it on the session, and now codex habitually asks fable for review on data models and plans it creates through acpx (fable inside cursor) I then asked it what it thinks about the feedback from fable. gpt-5.6-sol is not impressed

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@onusoz · /2026/07/13 · 07:15 AM View on

Reminder that this fever dream of a podcast exists with @dwarkesh_sp and @_sholtodouglas youtube.com/watch?v=3XDad4…

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@onusoz · /2026/07/13 · 07:11 AM View on

new satisfaction unlocked: wake up to an agent run that finishes seconds after you open the laptop

@onusoz · /2026/07/12 · 06:47 PM View on

gotta love competition

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@onusoz · /2026/07/12 · 01:54 PM View on

Anyone else notice that any non-codex harness is more token-efficient than codex on the same task?

@evalstate · Jul 12, 2026

I'm running Terminal Bench 2.1 with fast-agent (this is a subset while I try and get things working). Take this as raw, unchecked evalstate is normally wrong data but if we're talking about burning Codex quotas this might explain why I don't feel it so much?

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@onusoz · /2026/07/12 · 01:11 PM View on

I SEE BURNS... BURNS EVERYWHERE

@paulg · Jul 11, 2026

This is like someone who can bench press 30 pounds writing about what it takes to bench press 600.

@onusoz · /2026/07/12 · 11:26 AM View on

I vibed a TUI in just 13 codex messages, and it works great

@evalstate · Jul 12, 2026

AnnoTUI by @onusoz is next level🔥 Annotate assistant responses, return quotes and comments for your follow-up (video should explain). IMO one of the biggest productivity unlocks rn. Used here with fast-agent (I have it bound to CTRL+X a), easy to integrate anywhere.

@onusoz · /2026/07/12 · 11:44 AM View on

Here is the conversation where I built it. You can just prompt things @ratatui_rs highly recommended for TUIs. It just looks nice github.com/osolmaz/ann…

@onusoz · /2026/07/12 · 05:35 AM View on

No better feeling than waking up to the smell of 5 successfully finished agents

@onusoz · /2026/07/12 · 05:32 AM View on

protip: use fable to write your READMEs and other user facing docs thank god fable exists, now that gpt 4.5 is gone

@onusoz · /2026/07/11 · 12:54 PM View on

.@WisprFlow is much more convenient on android than iOS because the accessibility settings are more permissive It lets me overlay a button to record, anywhere on the screen, and lets me keep the regular keyboard at the same time

@onusoz · /2026/07/11 · 10:40 AM View on

most well-deserved ad in the world @Alibaba_Qwen

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@onusoz · /2026/07/10 · 08:04 PM View on

this is cool agentic behavior under /goal. in a separate session, I had created an implementation plan in the main branch. The agent picked it up without me explicitly telling it to. The goal was to "$autoimplement finish completely" Broad orders running in loops are useful

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@onusoz · /2026/07/07 · 11:41 AM View on

huge endorsement by geohot on GLM 5.2

@__tinygrad__ · Jul 6, 2026

I cannot believe how good GLM 5.2 is. Several weeks in now and it's mostly all I have been using. It doesn't have the alignment issue of cloud AI, it's much more clear what it can do and can't because it doesn't care about you taking another $$$ spin at the token slot machine.

@onusoz · /2026/07/06 · 10:29 PM View on

This is significant, not sure how it will play out. There is a chance it might be for the better

@mitsuhiko · Jul 4, 2026

I had some vibes that Opus 4.8 was performing worse than older ones for some of uses that are off distribution and now I have the receipts. Latest Opus/Sonnet are causing tool invocation failures on Pi's edit tool when older ones did not! I wrote about it. lucumr.pocoo.org/2026/7/4/bette…

@onusoz · /2026/07/06 · 08:35 PM View on

More on the way!!!

@openclaw · Jul 6, 2026

OpenClaw landed on @huggingface local apps 🦞🤝🤗 1. Pick any GGUF/MLX model on hf 2. Copy the openclaw onboard setup 3. Volla you've got a tool-calling agent running fully local. no cloud, no keys, no one watching. Get your claw localmaxxing. resistance is futile 🦞

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@onusoz · /2026/07/06 · 07:33 PM View on

yo @huggingface paris hq has a SICK gym to celebrate, I invented some 🤗 exercises for you mandatory for every employee

@onusoz · /2026/07/06 · 05:35 PM View on

herdr is going places

@herdrdev · Jul 6, 2026

herdr is 105 days old. i started writing a 10,000 github stars celebration post. before i finished it, we passed 12,500! on june 30, herdr hit #1 on github trending. it has stayed in the top 10 since. i’ve been building it solo and full-time, in public from day one: roadmap, issues, feature requests, releases. so instead of just celebrating, i’m opening the stats too: stars, downloads, website traffic, releases, and where the numbers come from. thank you for being part of the herd 🐑

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@onusoz · /2026/07/06 · 04:06 PM View on

Experimenting with a credential broker, and a telegram based approval flow for giving your claw access to your main @huggingface account

@onusoz · Jul 5, 2026

Worried that giving your 🦞 @openclaw agent write access to your 🤗 @huggingface account can risk deletion of your datasets/models/spaces/buckets, or cause irreversible damage? 😱😱😱 No need to be! I have created an agent login helper to run in your YOLO mode remote machine, which prevents any risk of irreversible deletion. Just run: uvx hf-auth-helper agent login There is a specific set of scopes you can choose while creating a fine-grained HF token. These include all read scopes + discussion.write, which let's your agent create PRs. Since you are not giving repo.write, your agent cannot force-push your main branch, change repo settings or delete them It is unfortunately not super straightforward to choose those on the web UI. This will hopefully change soon, and this functionality might even be natively in hf cli Until that happens, use hf-auth-helper to login worry free in your remote or local openclaw instance Your agents will be able to create PRs on datasets/models/spaces, which you will then be able to merge on your own browser 2 caveats: 1) This does not solve the data exfiltration attack vector—nothing does. Make sure to exclude any repos which absolutely must remain private while choosing your scopes. See for more info: 2) As buckets are not repos, your agent will not be able to modify a bucket (add/remove data). To help with that, I have another project on the way, a credential broker. Stay tuned, coming soon Source: Demo authentication flow:

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@onusoz · /2026/07/06 · 03:56 PM View on

1.5 TB UNIFIED MEMORY MAC STUDIO PLEASE GOD

@Ambisphaeric · Jul 5, 2026

Went on the Apple refurb store and noticed something rather unusual. On their memory configuration page, they now have a 768gb (predicted to be expensive) option listed.. .. but I haven't seen anyone mention the potential for a 1.5tb ram configuration. apple.com/shop/refurbish…

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@onusoz · /2026/07/05 · 08:07 PM View on

OKF looks interesting

@hwchase17 · Jul 5, 2026

anyone looked into or using Open Knowledge Format (github.com/GoogleCloudPla…) for their wikis?

@onusoz · /2026/07/05 · 05:18 PM View on

Worried that giving your 🦞 @openclaw agent write access to your 🤗 @huggingface account can risk deletion of your datasets/models/spaces/buckets, or cause irreversible damage? 😱😱😱 No need to be! I have created an agent login helper to run in your YOLO mode remote machine, which prevents any risk of irreversible deletion. Just run: uvx hf-auth-helper agent login There is a specific set of scopes you can choose while creating a fine-grained HF token. These include all read scopes + discussion.write, which let's your agent create PRs. Since you are not giving repo.write, your agent cannot force-push your main branch, change repo settings or delete them It is unfortunately not super straightforward to choose those on the web UI. This will hopefully change soon, and this functionality might even be natively in hf cli Until that happens, use hf-auth-helper to login worry free in your remote or local openclaw instance Your agents will be able to create PRs on datasets/models/spaces, which you will then be able to merge on your own browser 2 caveats: 1) This does not solve the data exfiltration attack vector—nothing does. Make sure to exclude any repos which absolutely must remain private while choosing your scopes. See for more info: 2) As buckets are not repos, your agent will not be able to modify a bucket (add/remove data). To help with that, I have another project on the way, a credential broker. Stay tuned, coming soon Source: Demo authentication flow:

@onusoz · /2026/07/05 · 11:56 AM View on

does it still count as euromaxxing if I my agents are running?

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@onusoz · /2026/07/04 · 10:26 PM View on

OpenAI is following the ASPAVA strategy, with gamified subsidies ASPAVA is a type of kebab shop from turkey. These shops usually serve mid quality food But despite that, the majority loves them. That's because they serve free stuff throughout They serve free sides at the beginning. They serve free sides during the main course. They serve free dessert at the end. They serve free tea as many times as you like Some of them give you so much "free" food that you feel like you are stealing. A lot of people frequent these shops not because they like it a lot, but because they are addicted to getting things for free Main dish portions are made smaller in order to breakeven with so many side dishes, which I feel is analogous to what is happening with GPT these days---though I can't prove it NVIDIA is not a car, and OpenAI is not kebab. I don't know if codex resets cost OpenAI much, but if they are, then they might be a waste of resources Developers are the most disloyal customer group. Once the subsidies are gone, they can switch away in the blink of an eye

@onusoz · /2026/07/04 · 11:01 AM View on

The future of AI is neither fully local nor fully cloud. It is both, in an economic equilibrium

@onusoz · /2026/07/02 · 12:58 PM View on

AI folk I will be flying to Paris tomorrow to monitor the Le Chaton Fat situation Who should I meet there?

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@onusoz · /2026/07/02 · 05:57 AM View on

I am not worried about *Mythos-class* models What keeps me awake at night are GALACTUS and CTHULHU-class models like Le Chaton Fat *Those* will need to be export controlled

@onusoz · /2026/07/02 · 05:27 AM View on

Most people, including professionals, will likely NOT pay more than $10k capex for their home AI workstation, even in the long run (ignoring future inflation) If you stretch it, <= $20k They will also not want to pay more than $200~300/mo opex, on electricity for example Builders who are spending a lot more than that on hardware: keep that in mind, if you are building for the general public dogfood your product with that which everyone will have, not just 0.1%

@onusoz · /2026/07/01 · 04:59 PM View on

Is anyone able to run nvidia/Qwen3.6-35B-A3B-NVFP4 with the config suggested in the readme? It OOMs before it can start serving huggingface.co/nvidia/Qwen3.6…

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@onusoz · /2026/07/01 · 06:13 PM View on

(I am trying to run it on DGX spark)

@onusoz · /2026/07/01 · 12:31 PM View on

I know that my macbook local model benchmarks have started when my lap catches on fire Add to this list: "does not set the room on fire"

@onusoz · Jul 1, 2026

I keep seeing insanely expensive builds giving insanely impressive results These results don't matter What matters is, whether one can: - run a "SOTA level" model, whatever that is - with under 32gb VRAM or unified memory - in 5 parallel sessions - with 50~100 tok/s each - with enough leeway memory for other applications - in a system as cheap as $1000 That is our goalpost That is the threshold when open source AI will win

@onusoz · /2026/07/01 · 12:27 PM View on

I can't believe I'm asking GPT to use Claude to review It's almost as if there is a 9-month cornercutting cycle, a two-body problem between openai and anthropic

@onusoz · /2026/07/01 · 12:17 PM View on

I quite like this token speed simulator by @mikeveerman And I keep losing it, so hopefully I will remember to come back to this tweet:) Link: mikeveerman.github.io/tokenspeed

@onusoz · /2026/07/01 · 10:48 AM View on

I keep seeing insanely expensive builds giving insanely impressive results These results don't matter What matters is, whether one can: - run a "SOTA level" model, whatever that is - with under 32gb VRAM or unified memory - in 5 parallel sessions - with 50~100 tok/s each - with enough leeway memory for other applications - in a system as cheap as $1000 That is our goalpost That is the threshold when open source AI will win

@LottoLabs · Jun 30, 2026

That’s actually crazy A new frontier is found imo localmaxxing.com/en/runs/cmqznf…

@onusoz · /2026/06/30 · 04:30 PM View on

I hate to admit that Opus 4.8 is much, much better at prose and writing academic text, compared to GPT-5.5 GPT 4.5 was the last openai model that was good at writing, and it's gone now 😭

@onusoz · /2026/06/29 · 06:07 AM View on

Another annoying GPT-ism (circa June 2026): while describing something, it always describes what it *does*, but never what it *is* > "LocalPerf benchmarks local LLM inference servers and keeps the evidence in one portable run artifact." If I were a philosopher, I would say that "AI lacks ontology". Or that it is "anti-essentialist", believes that things cannot be things in themselves But all models literally have an ontology. They have it since word2vec days, you can plot it out. It's just an annoying tendency in GPT's writing So using philosophy to understand AI might be dumb sometimes If you don't want your README's to sound like slop, then you can steal my write-readme skill: After write-readme: "LocalPerf is a local LLM inference benchmark CLI. It runs benchmark plans against local inference servers and stores the evidence in one portable run artifact." Skill: github.com/osolmaz/tools/blob/main/agents/skill…

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@onusoz · /2026/06/27 · 04:28 AM View on

Give @LottoLabs a follow if you are not already He is building localmaxxing.com, crowdsourced LLM benchmark results and performance profilings Very very cool

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@onusoz · /2026/06/27 · 03:22 AM View on

This. Especially when the whole machine hangs instead of OOMing when my agent accidentally loads too many models into memory (lmk if there is a firmware update or sth that fixes this on the spark now, creating a cgroup doesn’t work) forums.developer.nvidia.com/t/dgx-spark-be…

@ekzhang1 · Jun 26, 2026

shower thought: AI inference engines like vLLM/SGLang might be the hardest “server”-shaped OSS to configure, ever made. Like it’s probably harder to configure them optimally than even Postgres, Nginx, Kubernetes, maybe even Kafka/Zookeeper … just in terms of all the issues, bugs, crashes, scheduler dynamics, kernels etc and it changes every month

@onusoz · /2026/06/26 · 02:48 AM View on

Apple hiked prices as I was posting this 💀

@onusoz · Jun 25, 2026

The Local Frontier is advancing The amount of AI memory for inference we can get for less than $3000 has been steadily increasing The memory crunch has slowed this down and even made it retrograde. However, once we bounce back from it, the progress will be glorious

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@onusoz · /2026/06/26 · 01:37 AM View on

Was great to be there, thanks for the invite @lionelsimai!

@lionelsimai · Jun 26, 2026

500 plus builders signed up for one night. Here is what that room taught us about agentic AI. Last night we hosted our first major OpenClaw Singapore Agentic Night, in partnership with the SMU Institute of Innovation and Entrepreneurship and the SMU AI Club. The room was full of people who do not just talk about AI, they build with it. David, who leads solution architecture at Alibaba Cloud, opened with Qwen 3.7 Plus. Built for agentic work, it can see, code and generalise across harnesses while handling long tasks by holding memory, planning in steps and correcting itself along the way. @vincent_koc, Chief Architect of OpenClaw, followed with The State of the Claw and showed what the team is building next. Mobile apps now launched, Microsoft Scout, a serverless gateway, agent profiles, agent identity and ShellBench. @onusoz from Hugging Face showed the power of pairing OpenClaw with local models, proving you can run powerful agents without sending your data to the cloud. Our fireside chat brought together Vincent Koc, Professor Sun Sun Lim and myself. We went deep on agentic AI, digital upskilling, and trust and collaboration in the age of AI. To close, Queenie Mengyun Wu from imToken demoed Sigil, a security plugin that makes running agents on the OpenClaw harness far safer. Three lessons stood out for any enterprise building with this. The model is not the product, the harness is. The real unlock is the layer around the model that decides what an agent can see, do and touch. You are not buying a model, you are building a system. Local models are ready for real work. You can run powerful agents without sending sensitive data to the cloud, which changes everything for finance, healthcare and legal. Safety is built in, not bolted on. The moment you let an agent act, you need a way to guide and contain it. The winners will treat trust and security as part of the build from day one. Thank you to the SMU Institute of Innovation and Entrepreneurship and the SMU AI Club for partnering with us, to every speaker who gave so generously, and to the 500 plus builders who showed up ready to build. Singapore is not just adopting agentic AI, it is ready to build it. The talent is here, the curiosity is here, and now the room is here too.

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@onusoz · /2026/06/25 · 05:01 PM View on

How I imagine @TheAhmadOsman I actually bought my GB10 thanks to him back in feb, at a discount Give him a follow if you are not already!

@TheAhmadOsman · Jun 24, 2026

We're eating good boys with a DGX Station tonight

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@onusoz · /2026/06/25 · 04:51 PM View on

The Local Frontier is advancing The amount of AI memory for inference we can get for less than $3000 has been steadily increasing The memory crunch has slowed this down and even made it retrograde. However, once we bounce back from it, the progress will be glorious

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@onusoz · /2026/06/25 · 04:21 PM View on

Was great to talk, thank you @ben_burtenshaw for inviting me!

@huggingface · Jun 25, 2026

Welcome to Open Source AI: Run Your Own Models Locally x.com/i/broadcasts/1…

@onusoz · /2026/06/24 · 04:22 PM View on

I knew it would find me, sooner or later 🫠

@victormustar · Jun 24, 2026

some genius invented a kebab benchmark for llms

@onusoz · /2026/06/24 · 05:11 AM View on

Besides being hot, this take is very correct and points out to a fundamental tradeoff in the storage layer being centralized versus distributed git is distributed and that makes total sense for code which takes small space by its nature. it is cheap for everyone to duplicate it locally. this proved to be very useful over e.g svn, when devs could develop independently from the centralized server AI artifacts, however, are 1 million times bigger than code. in that case, the bottleneck becomes storage and network. decentralization and version control become lower priority. they can be sacrificed the tradeoff tilts towards getting the cheapest possible storage and transfer. because you will need a LOT of that I regret to announce to competitors that Hugging Face has already won this game when they acquired Xet. the tech just works, and the network effects are immense

@ClementDelangue · Apr 2, 2026

Hot take: Git was the wrong abstraction for 90% of ML data. Checkpoints, optimizer states, training logs, agent traces - none of this needs version control. It needs fast, cheap, mutable storage. So we built Buckets. S3-like storage on the @huggingface Hub with Xet dedup and zero egress. Train in a bucket. Publish to a repo. One platform. 🤗🤗🤗

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@onusoz · /2026/06/24 · 03:48 AM View on

Awesome. A feat unimaginable before agents

@ishaan_jaff · Jun 23, 2026

LiteLLM is moving to Rust. 🦀 Sub-1ms overhead. A sub-100MB binary. The same Python SDK and AI gateway you already use. Here's why and what's changing 🧵 docs.litellm.ai/blog/litellm-r…

@onusoz · /2026/06/24 · 03:31 AM View on

My posts here on X now sync automatically to my blog, giving me full ownership of my content and zero effort SEO For free, no API costs My long-form posts are automatically featured and titled on the front page of solmaz [dot] io. Filtering is done by my claw running a sync-x skill daily, which then notifies me on Discord How do I scrape the posts? ALL the posts I view (including the ones I post) are saved locally using @kubmi's xTap and then synced to a private repo using my extension on it xtap-sync My claw has access to that private repo and can run programmatic tasks like sync-x, summarize what happened that day, notify me about any topics I want

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@onusoz · /2026/06/24 · 03:31 AM View on

My syncer: github.com/osolmaz/xta… xTap: github.com/mkubicek/xTap My blog: solmaz.io

@onusoz · /2026/06/24 · 02:08 AM View on

Excited!

@ClementDelangue · Jun 23, 2026

Shot a video this morning to announce a new collaboration with...

@onusoz · /2026/06/23 · 04:50 PM View on

If you are interested in running such demos, look into --demo mode in my local model swiss army knife localpi github.com/osolmaz/loc… Thank you @googlegemma for the shoutout

@googlegemma · Jun 23, 2026

16 parallel runs of Gemma 4 26B A4B on a single NVIDIA DGX Spark! Pushing 18 tok/s per instance and a 300 tok/s aggregate. It can even hit 32 parallel runs. This level of concurrency highlights how efficient the architecture is.

@onusoz · /2026/06/23 · 03:13 PM View on

Huge. Added my github TUI github.com/osolmaz/ghz…

@herdrdev · Jun 15, 2026

herdr 0.7.0 is out, and it's a major one: it introduces plugins! the idea is simple: herdr stays lean, and everything custom gets extended through plugins. shareable, scoped, built however you want, to fit your own flow. with this release we're also shipping a few examples of what the plugin system can do. first up: a telegram plugin. herdr already controls your agents and knows their status, so the plugin just hooks into agent events and pings telegram the moment one needs you. notification lands → `herdr --remote` or ssh from your phone → straight back to the agent that needs you.

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@onusoz · /2026/06/23 · 03:15 PM View on

i meant to qt this one x.com/herdrdev/statu…

@herdrdev · Jun 22, 2026

herdr plugin marketplace is live! herdr.dev/plugins/ if you'd like to share your plugins with other people, all you have to do is add "herdr-plugin" to your github repo's topic list, and it will be automatically added to the website!

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@onusoz · /2026/06/23 · 02:29 PM View on

Will talk about my recent adventures running local models on the Spark, Pi and OpenClaw Click Notify Me on youtube to stay tuned

@ben_burtenshaw · Jun 23, 2026

the livestream will be here: youtube.com/watch?v=wRcByx… and on @huggingface x profile.

@onusoz · /2026/06/23 · 01:34 PM View on

New blog post: Using local models for agentic zero-shot classification, in real-time, high frequency triage If you have a 128gb of memory for models (a DGX spark like I do for example), you can create a real time classifier and notifier for yourself that can classify more than >20 items per minute, using mid-sized @googlegemma and @Alibaba_Qwen models, with over 200-300 output tok/s aggregate throughput Like processing new tweets on twitter, issues/prs on github, messages on telegram and discord, in real-time Over the past few weeks, I have built one for myself, to filter and get notified about local model related issues on the OpenClaw repo I initially thought gemma-4-e4b would give me the best tradeoff I was wrong. I learned that if one has enough memory already, one should not bother with <10b models like gemma4 e4b or e2b. Precision and recall were much higher zero-shot with gemma-4-26b-a4b, whereas the smaller e4b needed significant prompt optimization to eventually not perform nearly as good To provide more context to the model, I created a restricted bash-like shell, called reposhell. In that shell, it can run read-only commands to ls/find/grep/cat openclaw source code, but only that. When the PR description/diffs are not clear enough as to categorize it, the agent reads the code to figure it out Because small models can get prompt injected, and I need to make sure that someone can't harm my setup by creating a malicious issue or PR in the openclaw repo I found that for specific systems like this, it is very convenient to extend and bundle Pi. You can create agentic CLI tools that work fully locally and for free, and keep that separate from your main pi coding setup. localpager-agent has its own session dir and tools, and I ensure that it will run local models in a secure way by isolating it from my main pi setup Once localpager-agent categorizes a PR/issue as local_models and related labels, I automatically receive it as a notification on Discord The whole implementation is fully open source and MIT licensed, alongside the dataset we used to benchmark the performance I believe zero-shot agentic classification running on local hardware will find many use cases across a wide variety of business applications, like news gathering, open source software development, customer support, content moderation, sales and so on Agents increase the amount of information produced in a lot of systems, and hence we will need to set up cheap ways to wrangle all that information In times where governments can cut off access to SOTA models on a whim, it is more important than ever to build your business on open models and if possible, run them on your own hardware! Big thanks to @evalstate and @ben_burtenshaw for their valuable feedback, especially with helping me evaluate this more rigorously! One take-away is that categorizing contributions in an open source repo is a *hard* problem, and that it is not trivial to reliably create a golden dataset with LLMs, for evaluation purposes Read more here: huggingface.co/blog/local-models-pr-triage

@onusoz · /2026/06/22 · 04:49 PM View on

gpt 5.5 is not naturally good at modeling and cannot create simplified nice mathematical models completely autonomously I did a parameter sweep with gemma-4-31b-a4b on memory usage, output tok/s etc. while varying context window, concurrency and other parameters. It took quite a few tries, and I still do not trust the model that gpt5 fit to the data besides, it measured linux cgroup memory and not the actual gpu memory used, so the whole sweep is wasted... output tok/s looks more accurate though, soon I will have a model that can give the optimal parameters over the space of context window <> concurrency <> tok/s <> memory usage off to do another run

@onusoz · /2026/06/23 · 11:26 AM View on

One sweep over 100 samples takes around 4 hours. Next up: cross reference ground truth with predictions from hf-mem by @alvarobartt github.com/alvarobartt/hf…

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@onusoz · /2026/06/23 · 03:01 AM View on

gpt5.5 and most other models are very bad at one-shotting nice data models gpt5.5 also has this annoying property that once it decides for a schema (or any design), it's very hard to trigger thinking again. and if you ask to "rewrite from scratch", it will write create something even more ridiculous To solve this problem, I have built a meta-harness over codex just for simplifying slop data models called schemator (work in progress) Basic idea: it mimics what I myself do while I am designing a schema: scrutinize and question each field one by one It starts a fresh codex session for each field with a fixed prompt like "Try to come up with the most Lindy data model" + a prompt for side notes It does that with a fresh context for each field, so that they are independent from each other. At the end of a review run over a field, the reviewer can propose to keep, rename or remove the field When all fields are reviewed once, that makes one iteration. Then this is looped over until the review results stabilize, and do not propose any further changes I get better results by just asking my agent to "use schemator on this" after it creates a JSON schema or SQL table Give it a try if you have codex! It has a skill, so should be easy for an agent to figure out how to use github.com/osolmaz/schemator

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@onusoz · /2026/06/22 · 07:10 AM View on

For my recent LLM leaderboard osolmaz-leaderboard.hf.space, I sum up all time total downloads (or likes) across model variants, and then divide it by the age of that model. I.e. "time decay" for popularity This gives a more time-agnostic metric for the popularity of that model. In an ideal ranking, older models that are not popular anymore should be demoted, like 2 year old Llama 3 models. If you don't do that, they might still occupy top 10 needlessly, despite having been replaced by e.g. qwen in practice Thanks to that, qwen-3-6b which came up 1 year ago and has 150m downloads can surpass llama-3-1-8b which came up 2 years ago and has 200m downloads More notes on my post: solmaz.io/popularity-ranking

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@onusoz · /2026/06/22 · 06:58 AM View on

if you take the Most Downloaded Models of All Time, Llama 3.1 makes it to Top 10 with around 200 million total downloads (ranking is done w.r. to time-averaged downloads) RIP Llama, you walked so @googlegemma and @Alibaba_Qwen can run Also a reminder that if you build your branding on top of open weight models developed by big corps, you might eventually be the de facto owner of that brand if they pull the plug on it. Like llama.cpp @ggml_org Huge fumble by Meta

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@onusoz · /2026/06/22 · 05:19 AM View on

My LLM leaderboard osolmaz-leaderboard.hf.space auto discovers different variants of model releases, even if they are not linked by base_model From this, I found out that @RedHat_AI was the first to release NVFP4 quantization for qwen3-6-35b-a3b Nice to see everything in one place

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@onusoz · /2026/06/22 · 02:24 AM View on

"big token will bless me with free tokens today inshallah" is not a healthy mindset to nurture don't get me wrong, I love the subsidies and the memes

@thsottiaux · Jun 21, 2026

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@onusoz · /2026/06/21 · 03:18 AM View on

If you are in AI, just don’t be anon here I see a bunch of anon accounts posting great local model content… what’s the point of being anon? To seem cool? Most of those accounts are not doing anything illegal, so there is no point. It would add so much more legitimacy to your work if you just put your real face and not a slop or anime girl pfp it only makes sense for those who are abliterating models. otherwise, it makes you seem sus just put your real face anon

@onusoz · /2026/06/20 · 05:07 PM View on

I created an LLM leaderboard based on Hugging Face download and like counts, grouped, filtered and time-averaged. Top 5 downloads is shared by @Alibaba_Qwen and @googlegemma 👑🤝👑 Top 5 likes, on the other hand also includes @deepseek_ai V4 Pro 👑 Even @OpenAI makes it to #8 top downloads with gpt-oss-20b 👑 qwen3-6-35b-a3b is the second most CIRCULATED LLM of this year, with an average of 21 million downloads per month, since the day it was released 2 months ago 📈📈📈 Despite first place belonging to 8mo old qwen3-vl-2b-instruct, the highlight belongs to the mid-sized MoE model, which has hit a size/performance sweet spot so hard that it absolutely 💥 SHATTERED 💥 Hugging Face leaderboards in the 2 months since it has launched qwen3-6-35b-a3b is followed closely by its dense sibling 27b --- and then the mid-sized gemma 4 models 26b-a4b and 31b Note that a model's distribution is inversely proportional to its size, but not strictly! Usefulness plays a factor as well, since gemma 4 26b-a4b is being downloaded more than the smaller gemma 4 e4b I created this leaderboard because Hugging Face's all time highest downloads and likes did not give me enough information about what is really popular, neither today, nor all-time. I wanted something in between How do I calculate this ranking? - Get models that with n_downloads >= 100k - Exclude models older than 1 year - Deduplicate and group quantizations and variants of the same model based on slug prefix heuristics - For each group, sum up total downloads of all time - Sort by descending total_downloads / age = average_downloads_per_day (can also sort w.r. to likes per month) - Repeat every day to get the most up to date ranking More info and source on the leaderboard page, hosted on a Hugging Face space: osolmaz-leaderboard.hf.space This is a work in progress, please reply below if you see a model that should be there is missing, or any other mistakes

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@onusoz · /2026/06/20 · 10:26 AM View on

I just deleted an earlier post about ranking of HF models based on their total downloads because I made an error Will post with updated values soon

@onusoz · /2026/06/20 · 10:01 AM View on

gemma-4-26b-a4b is the most CIRCULATED LLM of recent history, with an average of 126k downloads per day, since the day it was released 3 months ago Top 10 is shared by Qwen and Gemma, with DeepSeek V4 Pro coming in close 🤝 Note that a model's distribution is inversely proportional to its size, but not strictly! Usefulness plays a factor as well, since gemma 4 26b-a4b is being downloaded more than the smaller gemma 4 e4b I created this leaderboard because Hugging Face's all time highest downloads and likes did not give me enough information about what is really popular *these last few months* How do I calculate this ranking? - Get models that with n_downloads >= 100k - Exclude models older than 1 year - Sort by descending total_downloads / age = average_downloads_per_day (can also sort w.r. to likes per month) - Deduplicate quantizations etc. of the same model based on slug prefix heuristics More info and source on the leaderboard page, hosted on a Hugging Face space: osolmaz-leaderboard.hf.space

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@onusoz · /2026/06/20 · 02:17 AM View on

I need better UI/UX on queueing messages to agents. I want to be able to: switch the order of queued messages pause the queue edit any message that are still in the queue undo steer messages in the few seconds they are being sent I want more visual emphasis on the queue, like a Queue View I can toggle, that puts the queue at the center I want this in all the UIs and coding agents, codex CLI, desktop, moshi... especially while on the phone

@onusoz · /2026/06/20 · 02:06 AM View on

saving the world from AI cartelization for fun and profit

@mervenoyann · Jun 19, 2026

you don't know but @huggingface is bunch of hobbyists asked to do what they like full-time you can't beat someone who's having fun

@onusoz · /2026/06/19 · 01:44 AM View on

herdr is my terminal now, both local and remote. highly recommend

@herdrdev · Jun 18, 2026

still leaving your laptop open so the agent doesn't die? still hand-rolling tmux + ssh + notifications? still can't check on it from your phone? you don't have to. try herdr.dev

@onusoz · /2026/06/18 · 06:09 AM View on

16x parallel Gemma-4-26B-A4B-NVFP4 runs 🤯🤯🤯 18 output tokens/s, aggregate 300 tok/s 🫪 1 DGX Spark with 128 GB unified memory Concurrency so high I had to demo it programmatically It can go up to 32 even! 🤯 But then my screen would not have been readable for you And this is not even using flashinfer yet! Please reply if you know whether support is on the way Note that this is not dumb e4b or e2b that you can run on the average laptop. This is the big Gemma MoE Model link: huggingface.co/nvidia/Gemma-4-26B-A4B-NVFP4

@onusoz · /2026/06/18 · 03:44 AM View on

accurate

@RhysSullivan · Jun 17, 2026

there's this valley of despair when programming with agents you either have to be an inference maximalist or minimalist, but anywhere in between you just get slop and pain

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@onusoz · /2026/06/17 · 04:06 PM View on

I did some math, and running my Nvidia GB10 workstation (Asus GX10) costs me maximum: 12~13 USD / month or 150~160 USD / year It is a little bit above half the price of ChatGPT plus subscription. For that, I get to run models that can fit in 128 GB of memory How I calculated: You can see how much power your apartment uses in Singapore in half-hourly resolution. We turned off all devices and A/C while we sleep, and got only the fridge and the GB10 remaining From that, we see it uses around 80-100 Watt while I was running an inference workload overnight. So this is like an upper bound I take it as 90 Watt. Electricity here costs 0.25 SGD / kWh 0.09 * 0.25 * 24 * 30 * (SGD/USD conversion rate) = 12~13 USD / month = 150~160 USD / year Local models are getting very good now, small ones roughly around GPT 5.x-mini level. This workstation makes all sorts of workloads possible for me that would otherwise cost a ton on the API It is also my always on workstation that works overnight. I use Codex for my work, and my workstation is always running agents. It never sleeps. I never have to worry about keeping my laptop lid open. I connect and monitor the agents anytime on my phone using mosh and herdr We have crossed a threshold. Running local models is cheaper than a big token sub for quite a few workloads already. If you are running a business, that makes a difference The localening is here

@onusoz · /2026/06/17 · 04:52 AM View on

THE LOCALENING IS HERE

@mitchellh · Jun 16, 2026

We've gone really quickly from "local models are dogshit" to "local models are good actually" (like, a 12 month window from A to B). I don't think they're actually good ENOUGH yet. We need an Opus 4.5 quality local model. When that happens, I think the world will spill over. Opus 4.5 is/was amazing, and is more than good enough for almost all tasks still as long as you pair with a frontier-level planner/judge. It'll still require a hugely expensive machine to run it, I'm sure, like a $5K or more laptop or mac studio. But, that's going to be pennies compared to the API costs plus all the benefits of guaranteed privacy and so on.

@onusoz · /2026/06/17 · 03:55 AM View on

New agent benchmark alert: SkillsBench

@xdotli · Jun 16, 2026

A big pain point in using AI benchmarks is encountering errors after its first release. Today, we're releasing SkillsBench 1.1, the first benchmark for how well AI agents use skills, now audited end to end and verified error-free. Prof. @dawnsongtweets joins 1.1 as advising author. We worked through every task with several frontier labs to eliminate the errors in the previous version. We also added new tasks, moved the ones with external dependencies into a separate set so the core suite runs clean, and expanded coverage to more models. Capability is climbing fast. The best with-skills resolution rate rose from ~36% (Claude Sonnet 4.5, Sep 2025) to 67% (GPT-5.5, May 2026), about +1.9 points per month. The frontier is hill-climbing SkillsBench fast. The right skills still matter. Across the fleet, curated skills lift resolution rate by +16.6 points on average (33.9% → 50.5%), and by as much as +25.7 points for a single model. The top configuration is GPT-5.5 on OpenHands at 67.3%. By popular demand (thx Nate @cursor_ai), we're now tracking skills invocation: how often an agent actually uses the skills it's given. Recent flagship configurations invoke them 90–99% of the time (Codex 99%, OpenHands + GPT-5.5 92%, Gemini CLI 90%), versus roughly 50% for older setups. Also new in 1.1: @OpenHands joins as a fourth harness, alongside Claude Code, Codex, and Gemini CLI; a rebuilt leaderboard with refined categories, subdomain skill rankings, and Skill Lift; and native task . md on BenchFlow, with multi-scene environments and rollout branching. We also partnered with @k_dense_ai to add scientific skills to some science tasks. One implication for deployment: skills can substitute for scale. GLM 5.1 with skills (58.4%) outperforms Opus 4.8 without (45.7%). A smaller model with the right procedural knowledge can beat a larger one running without it. Huge thanks to @nick_kango @ivanleomk @kaggle @GoogleDeepMind for hosting a launch event with us. Thanks for everyone who's come on May 27! Also thanks to our partners @gneubig @OpenHandsDev @ivanburazin @daytonaio @jackminong @johannes_hage @PrimeIntellect @TimothyKassis @k_dense_ai for providing support in credits, compute, and skills. SkillsBench live leaderboard will also come to @ValsAI. Many people have told us they use SkillsBench as an index to measure models' agentic capability over diverse and high GDP value domains. Great work on Valkyrie as well! @ Jarett @nikilravi @langstonnashold @RayanKrishnan SkillsBench is fully open-source. Explore the leaderboard and tasks, read the docs, or contribute your own skill set or harness and join the leaderboard. 🧵

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@onusoz · /2026/06/16 · 09:11 AM View on

nvidia/Qwen3.6-35B-A3B-NVFP4 running in vLLM nightly on my Nvidia GB10 is actually insane 50 tok/s, 4 concurrent generations. total 200 tok/s. ideal for spawning subagents or working in parallel its tool calling behavior is very good as well. I will be giving it test drive on an openclaw instance, and keep you posted More details on NVIDIA forum: forums.developer.nvidia.com/t/benchmark-report-…

@onusoz · /2026/06/16 · 05:19 PM View on

Link to model: huggingface.co/nvidia/Qwen3.6…

@onusoz · /2026/06/16 · 05:06 PM View on

Click open GitHub PRs and issues directly in the side pane in @herdrdev, instead of having to go to the browser. As many issues and PRs as you want, WITH TABS! Install ghzinga herdr plugin and just ctrl+click the link: github.com/osolmaz/ghzinga Thanks @lumendriada for sneaking in the ability to capture link clicks 2 days after I requested it! God I love open source...

@onusoz · /2026/06/16 · 04:05 PM View on

Hugging Face buckets are very literally, actually, 100%, a game changer note that I never ever use that phrase