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Mi · Tech-Tagesreport · Ausgabe 52

2026-09-02

— Today's main thread: model price cuts and safety red lines are both being raised, while the open-source community is using SSD streaming to run tens-of-billions-parameter models.

TL;DR des Tages

Anthropic released Claude Fable 5.1 and Mythos 5.1, cutting cache read prices by 75% and doubling Terminal-Bench-Science scores to 52.6%. OpenAI confirmed Astra has reached the Critical cybersecurity threshold in its Preparedness Framework and will restrict access to advanced cyber capabilities. On the open-source side, slotstream runs a 125B-parameter MoE model on a 48GB Mac at about 12 tok/s, and Qwen3.8-Flash-Next's MTP module has also been released.

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1

Anthropic releases Claude Fable 5.1 and Mythos 5.1: cache read prices cut 75%, science benchmark scores doubleMehrere Quellen ×4

Anthropic released Claude Fable 5.1 and Claude Mythos 5.1, which are the same model with different safety guardrails; Fable 5.1 scored 52.6% on Terminal-Bench-Science 0.1 (Fable 5 scored 24.7%), cache read prices dropped from $1.00 to $0.25 per million tokens, typical workload costs fell about 25%, and agentic tasks saw reductions of up to 45%. Why it matters: for developers who heavily use the Claude API, the cache read price cut directly lowers inference costs for long-context and agent workflows; Mythos 5.1 is only available to trusted organizations via Project Glasswing, continuing the tiered-release safety strategy for frontier models.

The community generally recognizes the price cut and cache read discount as significant, but some still think the price is high, the writing style is verbose, and safety restrictions have not improved.

2

OpenAI confirms Astra has reached the Critical cybersecurity threshold and will restrict access to advanced cyber capabilitiesMehrere Quellen ×4

OpenAI confirmed in a blog post that Astra is the first model to reach the Critical cybersecurity capability threshold under its Preparedness Framework, able to discover and exploit unknown security vulnerabilities without step-by-step human guidance; the company plans to release Astra soon, but the most advanced cybersecurity capabilities will only be available to selected partners through the Daybreak Blue early access program. Why it matters: this marks that frontier models' ability in autonomous vulnerability discovery and exploitation has triggered the highest risk level, and security teams need to assess the impact of such models on attack surfaces in advance, while also paying attention to OpenAI's safety remediation measures that delayed Astra development due to the Hugging Face hacking incident.

3

slotstream runs a 125B-parameter MoE model on a 48GB Mac at about 12 tok/s

Developer carloslfu released slotstream, a single-binary Swift tool that uses expert-offloading and SSD streaming to run Qwen3.8-Flash-Next 4-bit (125B parameters, 104GB disk footprint) on Apple Silicon Macs with as little as 16GB RAM, achieving about 12 tok/s warm decoding on a 48GB M5 Pro with 32GB peak memory. Why it matters: this significantly lowers the hardware barrier for running hundred-billion-scale sparse MoE models, offering direct practical value for developers in local inference and privacy-sensitive scenarios; it is also compatible with Ollama and the OpenAI chat API, so existing tools need no changes.

4

ChatGPT desktop app bundles the full LibreOffice suite, sparking size and licensing discussions

Simon Willison discovered that the ChatGPT desktop app (formerly Codex) bundles a 1.7GB runtime under ~/.cache/codex-runtimes, including full Python, Node.js, Poppler, git, and the LibreOffice office suite, used for handling Office document formats. Why it matters: this reveals that AI desktop apps are expanding toward local document processing capabilities, but it also brings engineering issues around disk usage, dependency management, and open-source license compliance that developers should weigh when integrating similar capabilities.

Most comments believe bundling LibreOffice is for handling Office document formats, but question its bloat, license compliance, and necessity; some also think it is a reliable and practical solution.

5

Qwen3.8-Flash-Next architecture paper published: 125B parameters, 6B activated, hybrid Gated DeltaNet with sparse attention

The paper On the Design of Qwen3.8-Next Architecture reveals the architecture details of Qwen3.8-Flash-Next: 125B total parameters, 6B activated per token, with an additional 51B n-gram embedding table placed outside the accelerator; it leads the 397B-A17B predecessor on 14 pretraining benchmarks with 1/3 activated parameters, 1/3 training tokens, and about 1/9 training FLOPs. Why it matters: this architecture demonstrates a viable path for hybrid linear attention and sparse MoE in efficiency and training stability, offering direct reference value for engineers focused on inference cost and long-context modeling.

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KI-News

In on-policy distillation, teacher supervision noise increases with scale, student policies are insensitive to noise, and improvements mainly come from suppressing low-probability tokens; the authors propose an unsupervised entropy-adaptive method to boost reasoning performance.

🤖On-policy distillation relies mainly on suppressing low-probability tokens rather than teacher guidance, motivating a supervision-free entropy-adaptive method that substantially improves reasoning performance.

DreamX-Creator 1.0 achieves native joint audio-video generation with a 7B generator, using Gated Cross-Modal Attention and a 2K autoregressive refinement pipeline to output synchronized high-resolution results.

🤖A compact 7B native joint audio-video generator uses cross-modal attention, progressive joint training, reinforcement learning with multimodal feedback, and an autoregressive 2K refinement pipeline to produce synchronized high-resolution outputs.

PaperGym turns scientific papers into reinforcement learning environments for research plan generation, separating problems from scoring rubrics to prevent reward hacking, improving research planning across multiple model scales.

🤖PaperGym converts scientific papers into training environments by separating research questions from evaluation rubrics, enabling reinforcement learning that improves research planning across multiple model sizes.

The SHAPE framework analyzes Chain-of-Thought trajectories in mathematical reasoning from semantic space and heuristic dimensions to diagnose LLM reasoning patterns and improve post-training.

🤖SHAPE analyzes chain-of-thought reasoning via semantic spaces and heuristics to diagnose LLM mathematical reasoning and improve post-training.

Dev & Open Source

Normalized Low-Rank Adaptation (NoRA) stabilizes LoRA training by normalizing down-projection matrices, accelerating convergence without adding parameters or inference cost.

🤖Normalized Low-Rank Adaptation stabilizes LoRA training by normalizing down-projection matrices, accelerating convergence and improving performance without extra parameters or inference cost.

Community-Themen

A developer trained a small transformer on a 5090 in 1.5 hours, claiming to surpass multiple LLMs and scoring 7% on ARC-2; the community recognizes its low cost and efficiency, but some question reliance on evaluation-set training and generalization.

The comment section generally recognizes the research as low-cost and efficient, but some believe it relies on evaluation-set training, question its generalization, and raise cheating concerns.

Play Store blocking AuroraStore affects GrapheneOS users; most comments say the impact is limited, but some see it as exposing the difficulty of installing apps without Google services.

The comment section generally believes the AuroraStore outage has limited impact and GrapheneOS users can switch to Play Store, but some see it as exposing the difficulty of installing apps without Google services.

GitHub Trending

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Star THU-MAIC / OpenMAIC Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click

Star iv-org / invidious Invidious is an alternative front-end to YouTube

Star jingyaogong / minimind 🧠 Train a 64M-parameter LLM from scratch in just 2h!

3b1b/manim★ 92545

Star 3b1b / manim Animation engine for explanatory math videos

Star firecrawl / pdf-inspector Fast Rust library for PDF inspection, classification, and text extraction. Intelligently detects scanned vs text-based PDFs to enable smart routing decisions.

Star K-Dense-AI / scientific-agent-skills Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 190,000+ scientists worldwide. 165 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.

Star handsomestWei / patent-disclosure-skill 中国专利.skill:专利点挖掘与交底书(发明/实用/外观)编写,通俗解读专利,嗅探政策动向,辅助审查答复。

Weitere Fundstücke(69 weitere)

Yoyo I'm back with updates to the fastest inference engine with minimal quality loss for Qwen3.8-27B. The last few weeks I've been optimizing decode speed and I don't think it can be pushed further, until a newer/better drafter is invented. So I focused on prefill, which I this morning was around 1.300 per second at 4k and now is just below 2.000. The main improvement came from a custom kernel, which matches the quality of fp32 with 0.99997 similarity at int8. Try all of the improvements here:

A few weeks ago we had a bit of drama on this sub when Planka moved its SSO functionality behind a paid tier. And just a few days ago, we had a "shame" list of self-hosted sso tax apps announced here. I decided to compile the opposite, as I try to host things on my homelab that can be integrated with PocketID directly .. is a list of self hosted applications that provide SSO for free with the option to use a custom OIDC provider. Not included are apps that only support Google OAuth or similar. T

More new massive updates from turboderp: - CPU offload of MoE experts - Qwen-3.8-Flash-Next ngram disk offload - GLM-5.3-Flash - New self-calibrated optimization technique - Countless other optimizations and improvements If you have an NVIDIA card and haven't tried it lately, you might be missing out. The attached cat image was made with Qwen-3.8-Flash-Next-3.05bpw-exl3 and this prompt: Create a detailed SVG image of a cute kitten riding a magic turtle into space. Come join the crew at the exlla

Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new token costs more than the previous one. For each additional token, the keys and values must be stored in memory indefinitely, which is unsustainable. Several alternatives have been proposed to fix the quadratic scaling problem, one of which is retrofitting LLMs to use Linear Attention. This idea has attracted a lot of attention, given its promise to solve the quadratic scaling pro

I am absolutely blown away. Yes my setup is crap but the fact that it managed to do this in a single take is unbelievable (and I'm a developer). Hardware used: - Windows PC with 4070ti (12GB VRAM, 32GB RAM) - Macbook M5 Air (LLAMA.cpp RPC connection to Windows PC) Software used: - LLAMA.cpp (Q4KM, xhigh, 8bit KV, MTP=1) - Lmstudio Qwen 3.8 27b (Q4KM) GGUF - Deepseek harness (mode: minimal) Prompt: "please create a fully self-contained super mario game with only one short level, put everything in

How do you benchmark a web search API when the thing being tested can read the answer key? A search agent has a fetch tool. If the gold labels sit in a public dataset, the agent can download them mid-evaluation and skip retrieval entirely. A similar problem arises when the answers are already encoded in […] The post Keenable AI Open-Sources NEEDLE: A Live Search Benchmark That Rebuilds Its Query Set Every Hour appeared first on MarkTechPost .

Today we’re announcing Enterprise Frontier Safeguards (EFS), a solution that combines the privacy of zero data retention (ZDR) with state-of-the-art safeguards for detecting misuse. EFS works by storing data in cloud infrastructure controlled by the customer, not Anthropic. EFS will be rolling out to customers in phases, starting later this fall. To make the transition smooth, eligible customers will receive ZDR on Fable 5 and Fable 5.1 until EFS is ready. We developed EFS in close collaboration

Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple targets, past covariates, and past-future covariates with no task-specific fine-tuning. It takes the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard. The weights, howev

Apple is pushing for "expedited discovery" in its legal battle against OpenAI over concerns the company is actively destroying evidence, as reported earlier by Bloomberg. In a filing on Monday, Apple alleges OpenAI only just handed over a MacBook used by a former employee at the center of the lawsuit, which contained discussions about "destroying […]

We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass. Across 220k production requests, CogEvol completes a slide in a median of 17 seconds and an interactive page in 59, replacing minutes-long multi-turn agent scaffolding. Reliability is enforced rather than hoped for: a production-grounded data pipeline turns real

Organizations often develop and maintain portfolios of related applications: independently deployable codebases that share substantial domain logic, interface patterns, or operational conventions. As LLM coding agents are increasingly used to generate and maintain such software, a naive application-by-application workflow duplicates shared logic across codebases and allows prolonged agentic maintenance to accumulate verbosity, dead code, and structural erosion. We introduce the Super Library Age

I was browsing HF for small LLMs and run into this model. It does not seem to be a fine tune - the model has its own architecture. There are 4B/1.7B versions - the benchmark is quite interesting (4B is neck and neck with Qwen 3.5 9B). The HF page claims both models support native 1M context size . Currently does not run out of the box on llama.cpp - pending this PR: They have a custom fork of llama.cpp that works. Anyone has tried this? Update: GGUFs (require custom fork for now):

Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code, where outcomes can be checked automatically. Extending this progress to open-ended and agentic tasks remains difficult because reliable rewards are harder to obtain and direct human supervision cannot keep pace with the scale and complexity of model-generated experience. This paper studies how LRMs can continue to impr

Speed and accuracy usually pull against each other in text-to-speech. Gradium AI's new default model reports both: an 81.0% human-rated pass rate on 500 hard sentences across five languages, at 216 ms P50 time-to-first-audio on Coval. The evaluation set is open on Hugging Face under CC BY 4.0. The post Gradium AI Releases New Default TTS Model: 81.0% Hard-Case Pass Rate at 216 ms Time-to-First-Audio appeared first on MarkTechPost .

Just to name a few: running Qwen3 8B on a 32GB GPU running Qwen3.6-27B Q4_K_M on 2 x R9700 quote: "each prompt was sized at 500 input and 500 output tokens" for a full system that costs $18,775?? I don't understand what they are doing. Am I reading something wrong?

NaseemProduct Hunt1 minKIProdukte

A native AI agent that does real work on your Mac Discussion | Link

Google has reportedly been reaching out to a number of Hollywood's biggest studios, hoping to strike licensing agreements that would allow it to train its AI models on copyrighted material in exchange for massive piles of cash. In theory, these deals would be a win-win: a huge financial boon to the studios that would also […]

Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI - after it lost control of its own AI tools - or by a succession of AI "civilizations." Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to […]

While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference nar

arXiv:2608.27524v1 Announce Type: new Abstract: Corporate training teams need scalable and explainable tools to improve workforce communication in multilingual settings. Existing systems often score text, audio, or video in isolation, or produce black-box outputs that are difficult to audit for coaching use. This paper presents SETU, an agentic ecosystem for corporate communication coaching aimed at recruiters, frontline sales professionals and training units who prepare for audience specific co

arXiv:2608.27471v1 Announce Type: new Abstract: Fallacies are arguments that employ invalid reasoning, making their automatic detection critical in sensitive contexts such as high-stakes political debates, where public opinion is shaped. Spotting a fallacious argument requires contextual knowledge beyond its pure surface text. This entails world knowledge pertaining to the subject matter under discussion, as well as knowledge of the relationships that exist between arguments within the argumenta

I built an online viewer and editor for .md files where the file stays on your machine. Free to use and code is available under MIT License here: You can also install it as a Chrome/Edge app so that it works for files on your desktop by double-clicking. Supports standard .md files with formatting, syntax-highlighting for code, table of contents, Mermaid diagrams, LaTeX via KaTex. Also supports exporting to .html, .pdf and .png. Please give it a try and share your feedback for improvements. Comme

Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a hidden role and communicates with others to deduce identities) serve as the canonical testbed, particularly in multi-agent settings. Existing testbeds, however, are text-only and run on a single fixed agent configuration, missing the non-verbal sensorimotor channels treated as core by deception taxonomies and leaving it ambiguous whether an observe

I'm the developer. I built this because AI-generated conspiracy videos were getting out of control in my own YouTube feed. Weedout is a $1.99 Safari extension for macOS that removes videos YouTube labels “Made with AI” from feed, search, related videos, playlists and Shorts. It uses YouTube's label rather than AI detection, runs locally, and does not catch unlabeled videos. Source for developers who want to fork or build: Pull requests are not accepted; the repository is for independent forks. C

Hi! Every month the "Who Wants to Be Hired?" and "Who's Hiring?" Threads come through I would think to myself, "there should be a match maker for these two threads". So I had abacus.ai whip one up. The methodology is pretty simple, data is extracted from posts using an LLM, score matches based on salary, domain experience, remote/onsite, etc., and then eliminate incompatible postings, like 'looking for remote work' and 'onsite only'. The result is two views: jobs-by-user and user-by-jobs. If you

arXiv:2608.27464v1 Announce Type: new Abstract: This position paper argues that human-centered explainable AI (HCXAI) should incorporate insights from the psychology of information seeking. Drawing on Sharot and Sunstein's framework of information-seeking motives, we propose that people evaluate whether to engage with explanations based on three types of expected utility: instrumental (will it help me act better?), hedonic (will it make me feel better?), and cognitive (will it improve my underst

A nonprofit organization called the Fermi Explorer Mission announced today that it intends to launch a spacecraft to our nearest star system by the end of 2029. It’s a hugely ambitious mission—if all goes well, the spacecraft could take up to 80,000 years to arrive at Alpha Centauri, which is 4.4 light-years away. And the…

Tool: GeoJSON Map Viewer I was helping Natalie gather some maps of local political boundaries (for the Granada Community Services District and the Midcoast Community Council and found a need to display some GeoJSON files on a map and export that as a PNG. I asked GPT-5.6-Sol for suggestions of tools and it proactively built one. After some iterations using Claude Code for web and Fable 5.1 we got to this finished tool. As for the GeoJSON.. it turns out if you ask ChatGPT Work to provide boundari

Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do not close this, because author and reviewer share the same blind spots. A team of researchers […] The post Researchers from Princeton, Ant Group and Stanford Introduce AQuA: A Two-Part Agentic Framework for Autonomous Factor Disc

Hey HN, I’m Antonio from Nori Robotics ( ). We build a $1,688 bimanual mobile robot in San Francisco for robotics developers and researchers. I started working on Nori while doing robotics research at Columbia. I was teaching robots through human demonstrations, but getting my hands on affordable hardware was difficult. Most labs have one or two expensive robots, which makes it hard to collect large datasets, run long experiments, or test across several robots. So I built my own. After seven ite

arXiv:2608.27484v1 Announce Type: new Abstract: Artificial intelligence is transforming personalized healthcare, yet fragmented clinical, self reported, and wearable evidence remains difficult to interpret and trace. We present CareGraph, an auditable hybrid AI framework that converts heterogeneous records into prioritized trends, missing context indicators, bounded next steps, discussion questions, and provenance linked explanations. CareGraph organizes evidence without diagnosing, predicting o

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