# KI/LLM-News – Montag, 15. Juni 2026

Recherchiert aus: HuggingFace Blog, HN Algolia API, Reddit r/LocalLLaMA, Ars Technica, offizielle Company Blogs (Anthropic, Google, OpenAI), heise online.

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## Story 1: Anthropic schaltet Fable 5 und Mythos 5 nach US-Regierungsdirektive komplett ab

**Teaser (DE):** Nur drei Tage nach dem Launch seiner neuen Frontier-Modelle Fable 5 und Mythos 5 musste Anthropic beide Modelle am Freitagabend abrupt für alle Kunden deaktivieren. Das US-Handelsministerium erließ eine Exportkontroll-Direktive, die den Zugriff für alle ausländischen Personen untersagt – faktisch ein Total-Shutdown. Hintergrund ist ein angeblicher Jailbreak, der Fable 5 dazu brachte, Softwareschwachstellen zu finden. Anthropic widerspricht: GPT-5.5 könne dasselbe, und ein solcher Standard würde alle Frontier-Modelle blockieren.

**Quelle:** https://arstechnica.com/ai/2026/06/anthropic-shuts-down-fable-mythos-models-following-trump-admin-directive/
**Offizielle Stellungnahme:** https://www.anthropic.com/news/fable-mythos-access
**Launch-Blog:** https://www.anthropic.com/news/claude-fable-5-mythos-5

**Open-Source / Lokal nutzbar:** NEIN – proprietäre Cloud-Modelle.

**Volltext (Ars Technica):**
Anthropic completely shut off access to its Mythos 5 and Fable 5 models Friday night, just days after they were launched. The move comes after Anthropic's receipt of a US Commerce Department directive Friday evening, subjecting the new models to export controls restricting their use anywhere outside the United States. In a message posted Friday night, Anthropic said the only way for it to ensure compliance with that government order in the immediate term "is that we must abruptly disable Fable 5 and Mythos 5 for all our customers." Access to other Anthropic models is not affected. An Axios report cited an administration official saying that the administration is concerned by reports of a jailbreak that reportedly gets around broad classifier-based safeguards meant to block Fable 5 prompts regarding cybersecurity, chemistry, and biology. The administration reportedly requested a pause in the release of these models to gain time for the "national security apparatus" to be "hardened" against this kind of threat. That hardening could be complete "in the next few weeks," Axios' source suggested. In its Friday night announcement post, Anthropic said the government has only provided it with "verbal evidence of a potential narrow, non-universal jailbreak" that involves getting Fable 5 to review a specific codebase for software flaws. The company says it has only seen evidence of this kind of jailbreak being used to find "minor" and "relatively simple" software vulnerabilities, and that other publicly available models like GPT-5.5 have similar capabilities on this score. "We are complying with the government's legal directive and are removing access to Fable 5 and Mythos 5 for all users," Anthropic writes. "However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people. If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers." Earlier this month, President Trump signed an executive order urging AI model makers to submit to voluntary government security testing. Anthropic apologized to customers for a "disruption" that it said is the result of a "misunderstanding," and said it will release more details about the situation in the next 24 hours.

**Volltext (Anthropic Statement):**
The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected. We received the directive from the government today at 5:21pm (ET). The letter did not provide specific details of its national security concern. Our understanding is that the government believes it has become aware of a method of bypassing, or "jailbreaking" Fable 5. We reviewed a demonstration of this specific technique being used to identify a small number of previously known, minor vulnerabilities. These vulnerabilities all appear relatively simple, and we have found that other publicly-available models are able to discover them as well without requiring a bypass. To date, the government has only given us verbal evidence of a potential narrow, non-universal jailbreak, which essentially consists of asking the model to read a specific codebase and fix any software flaws. Our understanding is that one potential jailbreak was shared with the government. We have reviewed a report that we believe is the basis of the government's directive and validated that the level of capability displayed there is widely available from other models (including OpenAI's GPT-5.5), and is used every day by the defenders who keep systems safe. We are complying with the government's legal directive and are removing access to Fable 5 and Mythos 5 for all users. However, we disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people. If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers. As we have stated publicly, we believe the government should have the ability to block unsafe deployments, as part of a statutory process that is transparent, fair, clear, and grounded in technical facts. This action does not adhere to those principles. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible.

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## Story 2: Google DeepMind veröffentlicht DiffusionGemma – 4x schnellere lokale Textgenerierung durch Diffusion

**Teaser (DE):** Google DeepMind hat mit DiffusionGemma ein experimentelles Open-Source-Modell veröffentlicht, das Text nicht sequenziell, sondern per Diffusion in ganzen 256-Token-Blöcken generiert. Das 26B-MoE-Modell (3,8B aktiv) erreicht auf einer RTX 5090 ~700 Tokens/s – etwa 4x schneller als autoregressive Gemma-Modelle. Besonders geeignet für lokale, interaktive Workflows wie Inline-Editing und nicht-lineare Aufgaben (z.B. Sudoku). Apache-2.0-Lizenz, Gewichte auf HuggingFace.

**Quelle:** https://arstechnica.com/google/2026/06/googles-latest-diffusiongemma-open-ai-model-comes-with-a-4x-speed-boost/
**Google Blog:** https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/

**Open-Source / Lokal nutzbar:** JA – Apache 2.0, Gewichte auf HuggingFace, läuft auf Consumer-GPUs (RTX 5090, quantisiert).

**Volltext (Ars Technica):**
Another day, another AI model from Google. This time, Google DeepMind has released a new member of the Gemma 4 open model family, but it's fundamentally different from the rest of the lineup. DiffusionGemma doesn't generate outputs linearly like most AI models. Instead, it can produce an entire block of text in parallel. Google says this makes it faster and more efficient when running on local hardware like an Nvidia DGX or a humble gaming GPU. Most AI models are designed to be autoregressive—they generate text left to right one token at a time. DiffusionGemma has more in common with image generation models, which start with static and then denoise it to create the desired content. This model takes a field of placeholder tokens running over the canvas multiple times to generate likely tokens and using those to improve estimation of others. At the end of the process, the model finalizes its token outputs in one large block—the "denoised" text canvas. DiffusionGemma is fairly large in the realm of Google's open models. It's a Mixture of Experts (MoE) model with a total of 26 billion parameters, but only 3.8 billion are activated during inference. That means it should fit in the 18GB RAM allotment of a high-end GPU. In testing with an RTX 5090, DiffusionGemma spits out around 700 tokens per second. With a single Nvidia H100 AI accelerator, DiffusionGemma can produce 1,000+ tokens per second. That's about four times the output of the similarly sized autoregressive Gemma models. This approach to text generation shifts the bottleneck from memory bandwidth to compute, generating up to 256 tokens in parallel. Google says this offers a measurable boost in non-linear tasks like in-line editing, molecular sequencing, and mathematical graphing. DiffusionGemma was tuned to solve Sudoku puzzles, which is a notoriously challenging task for standard autoregressive AI models because each token depends on future tokens. DiffusionGemma's ability to continuously self-correct large sets of tokens makes that easier. If diffusion is so much faster, why isn't Google using it in big cloud-based Gemini models? Google has experimented with this, but there are a few drawbacks to text diffusion, including a higher error rate. In image diffusion models, a single badly predicted pixel doesn't make the image useless, but language is discrete. An equivalent error in text can make a block of tokens meaningless and force you to start over to get a better output. Diffusion models also waste resources when the desired output is only a few tokens long. The efficiency gain for local processing makes this an appealing avenue of experimentation, though. In the cloud, autoregressive models can batch large numbers of compute jobs from multiple users so they're always churning out tokens, and the high bandwidth memory (HBM) used in these systems can move data around much more efficiently. Conversely, local AI encounters wasted compute cycles due to lower memory bandwidth and idle time. Diffusion models can make more efficient use of available compute. Google stresses that DiffusionGemma is experimental, but it's available under the same Apache 2.0 license as all the other fourth-generation Gemma models. You can download the model weights today from Hugging Face. Google says it worked with Nvidia to ensure DiffusionGemma was optimized for a variety of setups, including high-end RTX GPUs (quantized) and enterprise systems like the H100 or DGX Spark platform.

**Volltext (Google Blog):**
Today, we're introducing DiffusionGemma, an experimental open model that explores text diffusion, an exceptionally fast approach to text generation. Released under an Apache 2.0 license, this 26B Mixture of Experts (MoE) model moves beyond the sequential token-by-token processing of typical autoregressive Large Language Models (LLMs). Instead, it generates entire blocks of text simultaneously, delivering up to 4x faster text generation on GPUs. Built upon the industry-leading intelligence-per-parameter of our Gemma 4 family and cutting-edge Gemini Diffusion research, DiffusionGemma integrates a novel diffusion head designed to maximize generation speed. While autoregressive Gemma 4 models remain the standard for high-quality production outputs, DiffusionGemma is designed for researchers and developers exploring speed-critical, interactive local workflows such as in-line editing, rapid iteration, and generating non-linear text structures. Developers building real-time interactive AI applications often struggle with the latency bottlenecks of local inference. DiffusionGemma addresses these challenges directly, with some key trade-offs. You can improve DiffusionGemma's performance on specific tasks through fine-tuning. Unsloth fine-tuned DiffusionGemma to play Sudoku — a task autoregressive models struggle with because each token depends on future tokens. DiffusionGemma's bi-directional attention makes this much easier. While the AI research community has explored diffusion-based text generation for years, applying it to large models has remained a challenge. DiffusionGemma changes this by shifting how models use hardware. Most language models act like a typewriter, generating one token at a time from left to right. In the cloud, this is efficient because servers can batch thousands of user requests together to share the hardware load. But when run locally for a single user, this word-by-word process leaves your dedicated GPU or TPU underutilized — it spends most of its time simply waiting for the next "keystroke." DiffusionGemma reverses this inefficiency. Instead of predicting words sequentially, it drafts an entire 256-token paragraph simultaneously. By giving the computer's processor a larger chunk of work at once, DiffusionGemma utilizes your hardware to its full potential. It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously. This means DiffusionGemma's speedup is designed for local and low-concurrency inference. In high-QPS cloud serving, autoregressive models can be deployed to saturate compute efficiently, so DiffusionGemma's parallel decoding offers diminishing returns and can result in higher serving costs. The throughput advantage is strongest at low-to-medium batch sizes on a single accelerator. Similar to AI image generators that start with visual static and iteratively refine it into a clear picture, DiffusionGemma applies this to text. Because the model can process the whole paragraph while generating, it unlocks new patterns of model behavior, like perfectly closing complex markdown formatting or generating and rendering code in near real-time. Note: Because this speedup relies on exploiting the high arithmetic intensity of accelerators, unified-memory architectures like those in Apple Silicon Macs — which are often memory-bandwidth-bound rather than compute-bound during inference — may not see the same acceleration over autoregressive models like Gemma 4.

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## Story 3: OpenAI: „Chat ist tot" – Komplettumbau von ChatGPT zum Superapp-Agenten vor IPO

**Teaser (DE):** OpenAI bereitet den größten Umbau von ChatGPT seit dem Launch 2022 vor. Das $850-Mrd.-Unternehmen will den Chatbot in eine „Superapp" verwandeln, die Coding-Tools (Codex), KI-Agenten und Partner-Apps bündelt. Hintergrund ist der Druck, vor dem geplanten Börsengang profitable Geschäftskunden zu gewinnen. „Chat is dead", zitiert die Financial Times einen Senior-Mitarbeiter. Die Strategie konvergiert mit Anthropics Business-Fokus.

**Quelle:** https://arstechnica.com/ai/2026/06/chat-is-dead-openai-preps-overhaul-of-chatgpt/

**Open-Source / Lokal nutzbar:** NEIN – proprietäre Cloud-Plattform.

**Volltext (Ars Technica/Financial Times):**
OpenAI is preparing the biggest overhaul of ChatGPT since its launch kicked off the AI boom, as the $850 billion group hunts for new engines of growth ahead of a planned listing this year. The company intends to transform the chatbot into a "superapp" that combines coding tools and AI agents, adding products that executives believe will generate more revenue. The changes are part of a broader reorganization at OpenAI as the San Francisco-based company shifts resources into trying to win lucrative business customers and compete more fiercely with rival Anthropic, according to more than a dozen current and former employees. OpenAI faces growing pressure to drive revenues higher and forge a path to profitability, as it prepares for an initial public offering. The strategy marks a departure for a company, led by chief executive Sam Altman, that became the face of the AI boom and took the technology mainstream when it unveiled ChatGPT in 2022. The changes, which will give greater prominence and resources to OpenAI's coding product Codex, reflect a growing conviction within the company that the future of AI lies not in chatbots that answer questions but in agents that perform tasks for users. "Chat is dead," said one senior OpenAI employee. OpenAI executives increasingly view ChatGPT, which has attracted nearly 1 billion users since its launch, as a gateway to introduce users to higher-value products. The majority of consumers use the chatbot for free. The company is embarking on the changes amid a belief that the advent of AI agents, which can perform multiple tasks for users from booking travel to organizing calendars, will be a more valuable product than the chatbot. At the same time, products such as Codex are able to write code and create software based on simple instructions from users. The overhaul, which is set to begin rolling out in coming weeks, will initially appear as changes to ChatGPT's website and mobile apps, encouraging customers towards using coding, image-generation, and apps from external partners. The changes underline how OpenAI's strategy is moving closer to that of Anthropic, whose focus on developing products for businesses has stoked its blistering growth, and will be at the heart of its pitch to investors in an IPO this year. Outlining the changes, Thibault Sottiaux, who previously ran Codex and now leads all of OpenAI's core product and platform, told the FT: "It will transcend the actual surface… what we're building towards is where you have your own personal agent that is capable of helping you… across everything in your life, be it personally or at work." He added: "You can connect through it on your mobile, desktop, or web. When you're in the car, you can talk to it." The majority of Codex users pay for the service, according to people familiar with the matter, while the 2 million businesses that use OpenAI's products account for roughly 40 percent of its revenue. The company anticipates this will rise to 50 percent by the end of the year. OpenAI's Codex product has increased its user base sixfold to more than 5 million weekly active users since the launch of a desktop application in February. Its launch has intensified competition with Anthropic, whose Claude Code product has emerged as one of the start-up's fastest-growing businesses. "Approximately a year ago, OpenAI's strategy was swing for the fences, whereas Anthropic's strategy is make money first," said Jenny Xiao, partner at Leonis Capital and former researcher at OpenAI. "Now the two are converging, because both of them are trying to aim for an IPO and investors care more about money than dreams." To encourage users to adopt those services, OpenAI is redesigning ChatGPT's interface, adding new prompts and features that direct users towards coding tools, image generation, and applications built by partners such as Canva and Booking.com, according to people familiar with the plans. Over time, OpenAI intends to ditch the prompts and features, betting that its models will be able to automatically understand users' intentions when they are on the app or site. This year, the company has brought ChatGPT, Codex, and other product teams under a single leadership group led by Sottiaux, while several senior executives, including former product head Kevin Weil, have departed. In a sign of OpenAI's push to win more business customers, some consumer-focused initiatives have been sidelined, including a checkout feature that allowed purchases within ChatGPT. It also shut down Sora, its video-generation product, less than a year since its launch. Executives believe users will increasingly interact with a single AI assistant rather than a collection of separate applications. As agents become more capable, OpenAI expects the distinction between chatbots, coding tools, search products, and other software categories to blur. "When we have [artificial general intelligence], I don't think there will be a large number of distinct brands," said Alex Embiricos, OpenAI's head of enterprise product. "Probably there will be a single entity that I can talk to that can do whatever I need."

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## Story 4: Google kündigt Gemini 3.5 Live Translate für Echtzeit-Sprachübersetzung an

**Teaser (DE):** Google hat Gemini 3.5 Live Translate vorgestellt – ein Speech-to-Speech-Modell, das Konversationen in über 70 Sprachen mit nur wenigen Sekunden Verzögerung übersetzt. Es erhält Tonfall, Tempo und Tonhöhe des Sprechers. Alle Audio-Streams werden mit SynthID-Wasserzeichen versehen. Verfügbar über Gemini Live API, AI Studio, Google Meet (Enterprise) und bald in der Google Translate App für Android & iOS.

**Quelle:** https://arstechnica.com/ai/2026/06/google-announces-gemini-3-5-live-translate-for-instant-voice-to-voice-translation/

**Open-Source / Lokal nutzbar:** NEIN – Cloud-API (Gemini 3.5).

**Volltext (Ars Technica):**
Google has been chasing real-time translation for years, which it says has been one of its "pioneering machine learning experiments." We've seen numerous demos on stage at Google events in the past, but you needed Google phones, earbuds, or some other specific setup. Last year, Google brought real-time translation to more users in the Translate app, and now it's expanding availability more. With the release of Gemini 3.5 Live Translate, you'll have access to instant translation in more places and with lower latency than ever before. The new AI model is part of the version 3.5 family that launched at I/O. Before today, Google had only rolled out the Flash version, but we're expecting a Pro model to drop in the coming weeks. Gemini 3.5 Live Translate is a speech-to-speech model tuned to automatically detect and translate in more than 70 languages. Google says Gemini 3.5 Live Translate is fast enough to keep up with a normal conversation, following just a few seconds behind the speaker while also matching intonation, pacing, and pitch. In short, the voice sounds more like you than a generic robot. The demos, which are all being recorded under controlled conditions, do sound impressive. You won't have to wait long to verify the model's abilities for yourself, though. Gemini 3.5 Live Translate is rolling out across several parts of the Google ecosystem. Developers can begin building with a public preview in the Gemini Live API or AI Studio. The model processes speech continuously and handles all the multilingual inputs automatically, saving developers from manually configuring settings. It also filters out background noise in busy environments. Select enterprise customers will also get access to the new translation model in Google Meet starting this month in advance of a wider rollout. Google says it's tweaking the Meet interface to bring the live translate feature to the front, too. Most notably, 3.5 Live Translate will come to the Google Translate app on both Android and iOS soon. At the tail end of last year, Google began testing Gemini-based live translation in the app with any earbuds (and in the iOS app); previously, you needed to have the company's Pixel Buds with an Android phone. The pending update will expand further with the addition of the latest 3.5 model. Not only can you use any earbuds, you don't need earbuds at all. If you don't have any handy, you can hold the phone up to your ear like you're on a call to hear the spoken translation. However, this "listening mode" only works on Android at this time. The audio streams from Gemini 3.5 Live Translate are intended to sound lifelike even if they don't exactly mimic the user's voice. However, Google is still proceeding cautiously. All Gemini 3.5 Live Translate audio streams will have SynthID watermarks integrated into the waveform data. This will mark the speech as AI-generated, and there is (currently) no way to remove that.

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## Story 5: Allen AI veröffentlicht olmo-eval – Open-Source Evaluierungs-Workbench für den LLM-Entwicklungszyklus

**Teaser (DE):** Das Allen Institute for AI (Ai2) hat olmo-eval als Open-Source-Workbench für die iterative LLM-Entwicklung veröffentlicht. Aufbauend auf dem OLMES-Standard ermöglicht es modulare Benchmarks, agentische/multi-turn Evaluation, Sandboxing und paarweise Modellvergleiche. Entwickler können neue Benchmarks mit minimalem Code definieren. Code auf GitHub unter github.com/allenai/olmo-eval.

**Quelle:** https://huggingface.co/blog/allenai/olmo-eval
**GitHub:** https://github.com/allenai/olmo-eval

**Open-Source / Lokal nutzbar:** JA – Open Source (Code auf GitHub), lokal ausführbar.

**Volltext (HuggingFace Blog):**
While you're building an LLM, you evaluate it over and over across many interventions. Every adjustment to its data, architecture, or hyperparameters — and every step up in scale — sends you back through the same loop: adding or reconfiguring benchmarks, re-running them on each new model checkpoint, noting the results, and checking whether something that helped in a small experiment still holds up on the full training run. Most evaluation tools aren't designed for this—they're either built to run established benchmarks across finished models or run a model through multi-step, tool-using problems in a sandbox. They don't keep up with a model that's constantly changing, nor do they reflect how a model might behave under specific real-world conditions. Our last project to address this evaluation challenge was OLMES, the Open Language Model Evaluation Standard. Introduced in 2024, it was meant to make LLM benchmark scores easier to compare across releases. OLMES pinned benchmarking choices down in an open, documented standard, and it became the basis for evaluating our open models from Olmo to Tulu. But a model's final score is only part of the evaluation process—which is why we're releasing olmo-eval, a new workbench that builds on OLMES and extends it across the rest of LLM development. Compared to OLMES, olmo-eval cuts down the work of implementing new evaluations, offers more flexibility in defining where and how they run, and makes it easier to compose individual components into larger workflows. Agentic and multi-turn evaluation is supported as a first-class use case, and stronger analysis tools help you judge whether an intervention actually improved on the baseline or the difference amounts to noise. olmo-eval overlaps in some ways with Harbor, an open framework for evaluating AI agents inside containerized, sandboxed environments. But the two tools differ in their scope. Harbor is aimed mainly at running and publishing agent benchmarks; olmo-eval was built for the everyday work of developing a model—adding and configuring benchmarks, running them across checkpoints, and analyzing the results prompt by prompt instead of as a single overall score. Harbor runs everything the same way—inside sealed, reproducible containers. Because containers can be resource-intensive, olmo-eval lets you choose how each benchmark runs instead. A benchmark that just needs a model to answer questions can run directly, which is faster and cheaper; a benchmark that needs a locked-down environment — say, one that runs code the model wrote — gets an isolated container setup. The lightweight path is the default, and olmo-eval only opts for the heavy setup when a benchmark actually requires it. Both Harbor and olmo-eval keep benchmarks separate from the runtime policy (how the model is run to produce its answers) so you can change one without rewriting the other, but olmo-eval is designed for greater modularity. In olmo-eval, the model being evaluated, the tools it can use, the containerized environment, and any helper models – like an LLM-as-a-judge – are all swappable components. You can reuse a tool across many harnesses, or plug a grading model into one benchmark without perturbing the others, and adjust small settings (e.g., the exact wording of the prompt) without extensive effort. olmo-eval reports scores with a standard error and a minimum detectable effect (the smallest difference that can be reliably distinguished from noise). But the more useful view lines the same questions up across two model checkpoints and compares them one by one, with all else held fixed. This helps you to see whether a tiny change in an overall average might indicate a real improvement or simply noise. olmo-eval is composed of four components: A task/suite/harness abstraction that decouples benchmark logic from runtime policy; A sandbox and capability-routing layer, including an asynchronous sandbox planner; A normalized experiment schema that records every run, its configuration, and the results in the same structured format; A results viewer for pairwise model comparison. In most model evaluation setups, adding a benchmark is a sizeable integration project. In olmo-eval, all that's needed is a task—tasks define the benchmark dataset, how evaluation requests are built, and how model answers are scored (all code in Python).

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## Zusammenfassung der Quellenlage

| # | Story | Quellen | Volltext gescrapt? |
|---|-------|---------|-------------------|
| 1 | Anthropic Fable/Mythos Shutdown | Ars Technica + Anthropic Blog | ✅ Ja, beide |
| 2 | DiffusionGemma Release | Ars Technica + Google Blog | ✅ Ja, beide |
| 3 | OpenAI ChatGPT Superapp-Umbau | Ars Technica/FT | ✅ Ja |
| 4 | Gemini 3.5 Live Translate | Ars Technica | ✅ Ja |
| 5 | olmo-eval Workbench | HuggingFace Blog | ✅ Ja |

**Nicht erreichbare Quellen:**
- Reddit r/LocalLLaMA: Blockiert (Netzwerk-Policy), keine API-Credentials
- HN Algolia API: Keine relevanten aktuellen Treffer für die letzten 3 Tage (die großen Stories laufen über andere Kanäle)
- heise online: RSS-Feed war nicht parsebar (Encoding-Probleme), Website-Scraping ergab keine aktuellen KI-News
- Mistral/OpenAI/Meta Blogs: Keine neuen Releases in den letzten Tagen
