ask_know

33 posts

ask_know

ask_know

@Ydf_189

https://t.co/L8i21vFowx

Katılım Mayıs 2026
279 Takip Edilen5 Takipçiler
ask_know
ask_know@Ydf_189·
To my humble opinion, one of the rare contributions that shows real soul💪🦾, not today, but the day anthropic's behavior became public. "AI as civilizational infrastructure" is the vocabulary unique to its author.
Ahmad@TheAhmadOsman

Anthropic wants the public to see one thing: the careful lab, the safety lab, the grown-up in the room trying to keep frontier AI from running off a cliff. However, the pattern around Anthropic does not look like caution by itself. It looks like a company wrapping a business model in moral language, then using that language to justify opaque model behavior, anti-competitive access rules, regulatory pressure, and a future where builders, startups, researchers, and Opensource communities stay downstream of a few blessed frontier labs. Imagine a compiler that emits worse binaries when it thinks you are building a competing compiler. Imagine a microscope that blurs certain samples because the manufacturer dislikes the research direction. Imagine a debugger that lies only when your codebase resembles a future rival. Anthropic can learn from the internet, copyrighted books, code, public knowledge, user feedback if permitted, synthetic data, and its own models. But if a developer uses Claude to bootstrap a competitive open assistant, Anthropic calls foul. The company argues that safety controls may be lost and that competing models undermine the investment required to build frontier systems. The fight is whether intelligence becomes something people can own, inspect, modify, run locally, fine-tune, study, route, and improve, or whether it becomes a subscription permission layer run by companies that can refuse, degrade, surveil, retain, revoke, reroute, or lobby away your access. Anthropic's moat is being a permission regime. On daily basis, competitors and acquisition targets discover that access can disappear. The company asks governments to bless safety frameworks, deployment gates, incident reporting, evaluation regimes, and even future pauses that incumbents are best positioned to survive. If a coding or research model secretly changes the quality, direction, or reliability of an answer because it classified the user as doing disallowed frontier work, the tool is no longer merely "safe." It is untrustworthy. If Anthropic wants to be treated like a public-interest safety institution, it cannot behave like a hypersensitive platform monopolist whenever a customer gets too close to building alternatives. Yes, companies protect their IP. But Anthropic is not selling a normal SaaS widget. It is selling cognition as infrastructure. Once cognition becomes infrastructure, anti-competitive access control stops being a normal vendor dispute and becomes a social bottleneck. Anthropic repeatedly converts safety, security, and responsible deployment into mechanisms of control over who may build and what could be built. We cannot trust them.

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ask_know
ask_know@Ydf_189·
@TheAhmadOsman A nice move by openai and anthropic now would be opensourcing gpt 4.5, and Claude Sonnet 4.5.
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Ahmad
Ahmad@TheAhmadOsman·
Opensource AI should remain usable, understandable, reproducible, locally deployable, economically viable, and community-governed even if today's dominant labs, foreign labs, hardware vendors, cloud platforms, or open-weight model providers change direction or disappear. If intelligence becomes something people can only rent from a few closed institutions, the public does not just lose software freedom. It loses operational freedom. AI is a civilizational infrastructure for work, education, science, software, creativity, public services, and national capacity. This civilizational infrastructure must not become rented access through closed APIs, remote platforms, shifting terms, opaque moderation, and prices set by a handful of companies. The ability to study, build, repair, deploy, audit, adapt, teach, preserve, and run intelligence systems without asking permission is of EXISTENTIAL importance. With OpenAI, Anthropic, and a handful of other players controlling the models, this civilizational infrastructure risks becoming a subscription economy for cognition. The US should not fall behind on the freedom to run, inspect, modify, benchmark, teach, and preserve intelligence infrastructure. The practical posture is American capacity with global open standards.
Ahmad@TheAhmadOsman

We need to incentivize American Opensource AI not ban Opensource AI Both @MikeBradleyAI and I started @OsmanticAI because we believe that we need to make it the default The current administration needs to work with people like us to make American Opensource AI THE BEST there is

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Max For AI
Max For AI@MaxForAI·
‼️提醒一下:Qwen3.8-Max-Preview 版本已在生产环境中悄然升级🫡 我用同一个Prompt连续进行测试,但效果完全不一样👀 第一天:一只无手鸬鹚。 第一天:一只静态且无聊的鸬鹚。 第三天:这个。 当大家还在评判第一个版本的效果时,模型已经升级了 迫不及待 @Alibaba_Qwen 发布Qwen3.8正式版🔥
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merve
merve@mervenoyann·
tldr; I went to AI Engineer SF and met bunch of cool people who build the new gen of local AI software layer I asked them if we can chat on live and they said yes it's now on YT 🤗 @TheAhmadOsman @MikeBradleyAI @alexocheema linking their resources and 101 on the next one
Hugging Face@huggingface

Join us this Tuesday to learn more on Local AI, from software to hardware 🤗 We'll be joined by @TheAhmadOsman & @MikeBradleyAI covering hardware setups & local inference with live demo, and @alexocheema & @0xSero on picking model for your hardware, model compression and REAPs 🔥 Set your reminders to not miss out! 🔔

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ask_know
ask_know@Ydf_189·
@TheAhmadOsman @huggingface @mervenoyann @MikeBradleyAI Thanks, I loved it! I run ODS for a while on my single RTX 3090, and play with it often. It's still overwhelming for me as a beginner, but I'm happy that I've been able to use the chat, hermes & try a voice model. It doesn't allow me to use Qwen 3.6 35B, but I can live with it.
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Ahmad
Ahmad@TheAhmadOsman·
Had a great time today getting hosted by @huggingface to present and demo on Local AI, Inference Engines and ODS Huge shoutout to @mervenoyann for all her great work on educating people about Local and Opensource AI, and to my partner in crime @MikeBradleyAI
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Poolside
Poolside@poolsideai·
Today we're releasing Laguna S 2.1, our most capable model to date. It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes. Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark. Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface poolside.ai/blog/introduci…
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Mike Bradley
Mike Bradley@MikeBradleyAI·
@DaveShapi Open models : 1. Massively increase innovation 2. Decrease costs of experimentation 3. Create a much larger AI economy 4. Democratize intelligence 5. Improve competition 6. Accelerate hardware investments 7. Accelerate scientific progress 8. Reduce bottlenecks
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Marina Mogilko
Marina Mogilko@siliconvalleymm·
I think one of the most dangerous things happening right now is how extreme the AI conversation has become. On one side, people talk about it like it will solve everything. On the other, like it’s about to take everything away. But after this conversation with @drfeifei and @drogier , I kept thinking about how much nuance is missing. Fei-Fei built the data that made modern AI possible and just raised $1 billion for World Labs. David turned MasterClass into a $2.75 billion company. I had 30 minutes with both of them, and almost everything came back to one idea: the people who learn to work with AI are already pulling ahead, and that gap is growing fast. One of the strongest ideas from this interview was about agency. Fei-Fei said something I wrote down immediately: “Entrepreneurial is very much a synonym to agency. And that feels true far beyond startups.“ Because in a world where AI can do more and more, the people who will stay valuable are the ones who know how to move, adapt, test, build, and think for themselves. David shared something that honestly stuck with me too: “If you have an idea that everybody thinks is good, it’s probably not a good idea.” That’s such a simple sentence, but it says a lot about what building really looks like. We also talked about: -why AI isn’t “replacing intelligence” but expanding what one person can do -why schools should stop treating AI like cheating and start teaching people how to use it -how the future of work might split into two groups: true specialists and high-agency generalists -why product managers, designers, and operators are already working differently because of AI -what spatial intelligence is, and why it may be the missing piece in building much more capable systems -why the worst thing you can do right now is ignore this shift “Time of change could be a time of loss… but that’s also a time of opportunity.” That’s probably the most honest description of where we are right now. This conversation goes much deeper than AI itself. It’s really about how to stay relevant, curious, and useful in a world that’s moving faster than ever. Watch the full episode below. Worth your time: youtu.be/subu-xHrp1w?si…
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YouTube
Marina Mogilko tweet media
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ask_know
ask_know@Ydf_189·
@Alibaba_Qwen A huge number of researcher have limited number of GPU for local testing. Releasing a 27b version is much appreciated🫰🤝
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Qwen
Qwen@Alibaba_Qwen·
During Preview, Qwen3.8 is getting better by the day. Latest version is live now, with broad gains and a big step up on web frontend. Thank you all — the response to Qwen3.8-Max-Preview blew us away. 🫶🫶 Qwen3.8 is still evolving daily. Come test it, and tell us what breaks. We're looking forward to a more capable, official version — and to open-weight it for everyone.🚀🚀
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Hugging Face
Hugging Face@huggingface·
Join us this Tuesday to learn more on Local AI, from software to hardware 🤗 We'll be joined by @TheAhmadOsman & @MikeBradleyAI covering hardware setups & local inference with live demo, and @alexocheema & @0xSero on picking model for your hardware, model compression and REAPs 🔥 Set your reminders to not miss out! 🔔
Hugging Face tweet media
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Nader Khalil🍊
Nader Khalil🍊@NaderLikeLadder·
Open source creates the necessary environment for competition Without competition, only incumbents have power. We saw this first happen with Stable Diffusion. Open AI released Dall-E. In the name of safety it would not render faces and it was extremely limited via waitlist. I only got access because I was a YC founder. Then Stable Diffusion came out. It rendered faces. No wait list. Simply download the model and run it on your hardware. In response to this competitive pressure, open AI released DALL-E 2. No waitlist. Renders faces, added in painting, etc Same thing happened with reasoning models. OpenAI had o1, which at the time, seemed to give OpenAI an insurmountable advantage over everyone else. Deepseek released a reasoning model, open weights, which led to a proliferation of reasoning models coming from research labs. Same pattern again with Mythos and Fable. Closed model, extremely limited. This time worse, because of nonsensical fear-mongering. GLM 5.2, Kimi K3 both get released. Frontier Intelligence, open weights. But now the calculus is different: The fearmongering has inspired distrust in the closed-source model labs. If we can't trust continued access to the tools then why would we learn to use them? Closed source labs open sourcing harnesses is a great way to build trust. The ecosystem is only healthy when we have both. Competition is crucial to keep the ecosystem fair. Open source is necessary for competition.
martin_casado@martin_casado

Do you know what open weights model actually decelerate? The formation and power of oligopolies. You know, those things which really do stifle innovation.

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ask_know
ask_know@Ydf_189·
@simonw There is no ChatGPT Work anymore. They rolled the name back to Codex.
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Simon Willison
Simon Willison@simonw·
OK, I think I get it "ChatGPT Work" is really "OpenAI Claw"
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Ahmad
Ahmad@TheAhmadOsman·
RTX 3090 owners tonight will be running Kimi_K3_3T_Q_0.001_K GGUF
Ahmad tweet media
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Google Gemma
Google Gemma@googlegemma·
Decoding centuries-old text can be a complex challenge. This project shows how to fine-tune Gemma 4 E2B on a single GPU. The model translates Classical Korean to modern text. The accuracy improved from 4% to 80%!
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ask_know
ask_know@Ydf_189·
@TheAhmadOsman Super happy that I now understand this sentence, thanks to your articles on various aspects of OS local AI.🙏💪🦾
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Ahmad
Ahmad@TheAhmadOsman·
Inference Engines have been stuck in loop of endless bad System Design choices.
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Ahmad
Ahmad@TheAhmadOsman·
Local AI was never this EASY > Install ODS > Let it detect your hardware > It will download the best model for your hardware > And then start local inference and Open WebUI for you With ODS, you can > Add voice, agents like Hermes, workflows, RAG, search, image generation, and more > Manage the whole stack from one dashboard Now your PC, Mac, or Linux box is a private AI server No cloud required No subscription required Your prompts and data stay on your machine unless you choose otherwise We're gonna make Local AI The Default
Ahmad tweet media
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ask_know
ask_know@Ydf_189·
@openclaw @huggingface What is the use of it, when the creator of openclaw himself does not believe in "running fully local. no cloud, no keys, no one watching"?!!
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OpenClaw🦞
OpenClaw🦞@openclaw·
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 🦞
OpenClaw🦞 tweet media
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Matt Wolfe
Matt Wolfe@mreflow·
The only benchmark I care about these days... lol
Matt Wolfe tweet media
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ask_know
ask_know@Ydf_189·
@mreflow OK, thanks! That is a very useful piece of information to have, because open weight models are getting more and more attention nowadays.
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Matt Wolfe
Matt Wolfe@mreflow·
@Ydf_189 They’re in there. They’re just nowhere near the top of the leaderboard.
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